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is-link">Go</button> </div> </div> </form> </div> </div> <nav class="pagination is-small is-centered breathe-horizontal" role="navigation" aria-label="pagination"> <a href="" class="pagination-previous is-invisible">Previous </a> <a href="/search/?searchtype=author&query=Shin%2C+W&start=50" class="pagination-next" >Next </a> <ul class="pagination-list"> <li> <a href="/search/?searchtype=author&query=Shin%2C+W&start=0" class="pagination-link is-current" aria-label="Goto page 1">1 </a> </li> <li> <a href="/search/?searchtype=author&query=Shin%2C+W&start=50" class="pagination-link " aria-label="Page 2" aria-current="page">2 </a> </li> <li> <a href="/search/?searchtype=author&query=Shin%2C+W&start=100" class="pagination-link " aria-label="Page 3" aria-current="page">3 </a> </li> </ul> </nav> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.05547">arXiv:2411.05547</a> <span> [<a href="https://arxiv.org/pdf/2411.05547">pdf</a>, <a href="https://arxiv.org/format/2411.05547">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Assessing the Answerability of Queries in Retrieval-Augmented Code Generation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kim%2C+G">Geonmin Kim</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+J">Jaeyeon Kim</a>, <a href="/search/cs?searchtype=author&query=Park%2C+H">Hancheol Park</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Wooksu Shin</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+T">Tae-Ho Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.05547v2-abstract-short" style="display: inline;"> Thanks to unprecedented language understanding and generation capabilities of large language model (LLM), Retrieval-augmented Code Generation (RaCG) has recently been widely utilized among software developers. While this has increased productivity, there are still frequent instances of incorrect codes being provided. In particular, there are cases where plausible yet incorrect codes are generated… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.05547v2-abstract-full').style.display = 'inline'; document.getElementById('2411.05547v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.05547v2-abstract-full" style="display: none;"> Thanks to unprecedented language understanding and generation capabilities of large language model (LLM), Retrieval-augmented Code Generation (RaCG) has recently been widely utilized among software developers. While this has increased productivity, there are still frequent instances of incorrect codes being provided. In particular, there are cases where plausible yet incorrect codes are generated for queries from users that cannot be answered with the given queries and API descriptions. This study proposes a task for evaluating answerability, which assesses whether valid answers can be generated based on users' queries and retrieved APIs in RaCG. Additionally, we build a benchmark dataset called Retrieval-augmented Code Generability Evaluation (RaCGEval) to evaluate the performance of models performing this task. Experimental results show that this task remains at a very challenging level, with baseline models exhibiting a low performance of 46.7%. Furthermore, this study discusses methods that could significantly improve performance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.05547v2-abstract-full').style.display = 'none'; document.getElementById('2411.05547v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 8 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.20350">arXiv:2410.20350</a> <span> [<a href="https://arxiv.org/pdf/2410.20350">pdf</a>, <a href="https://arxiv.org/format/2410.20350">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> </div> </div> <p class="title is-5 mathjax"> Beyond Trivial Edges: A Fractional Approach to Cohesive Subgraph Detection in Hypergraphs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kim%2C+H">Hyewon Kim</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Woocheol Shin</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+D">Dahee Kim</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+J">Junghoon Kim</a>, <a href="/search/cs?searchtype=author&query=Lim%2C+S">Sungsu Lim</a>, <a href="/search/cs?searchtype=author&query=Jeong%2C+H">Hyunji Jeong</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.20350v1-abstract-short" style="display: inline;"> Hypergraphs serve as a powerful tool for modeling complex relationships across domains like social networks, transactions, and recommendation systems. The (k,g)-core model effectively identifies cohesive subgraphs by assessing internal connections and co-occurrence patterns, but it is susceptible to inflated cohesiveness due to trivial hyperedges. To address this, we propose the $(k,g,p)$-core mod… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.20350v1-abstract-full').style.display = 'inline'; document.getElementById('2410.20350v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.20350v1-abstract-full" style="display: none;"> Hypergraphs serve as a powerful tool for modeling complex relationships across domains like social networks, transactions, and recommendation systems. The (k,g)-core model effectively identifies cohesive subgraphs by assessing internal connections and co-occurrence patterns, but it is susceptible to inflated cohesiveness due to trivial hyperedges. To address this, we propose the $(k,g,p)$-core model, which incorporates the relative importance of hyperedges for more accurate subgraph detection. We develop both Na茂ve and Advanced pruning algorithms, demonstrating through extensive experiments that our approach reduces the execution frequency of costly operations by 51.9% on real-world datasets. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.20350v1-abstract-full').style.display = 'none'; document.getElementById('2410.20350v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.15008">arXiv:2410.15008</a> <span> [<a href="https://arxiv.org/pdf/2410.15008">pdf</a>, <a href="https://arxiv.org/format/2410.15008">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Hardware Architecture">cs.AR</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1145/3620666.3651324">10.1145/3620666.3651324 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> IANUS: Integrated Accelerator based on NPU-PIM Unified Memory System </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Seo%2C+M">Minseok Seo</a>, <a href="/search/cs?searchtype=author&query=Nguyen%2C+X+T">Xuan Truong Nguyen</a>, <a href="/search/cs?searchtype=author&query=Hwang%2C+S+J">Seok Joong Hwang</a>, <a href="/search/cs?searchtype=author&query=Kwon%2C+Y">Yongkee Kwon</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+G">Guhyun Kim</a>, <a href="/search/cs?searchtype=author&query=Park%2C+C">Chanwook Park</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+I">Ilkon Kim</a>, <a href="/search/cs?searchtype=author&query=Park%2C+J">Jaehan Park</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+J">Jeongbin Kim</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Woojae Shin</a>, <a href="/search/cs?searchtype=author&query=Won%2C+J">Jongsoon Won</a>, <a href="/search/cs?searchtype=author&query=Choi%2C+H">Haerang Choi</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+K">Kyuyoung Kim</a>, <a href="/search/cs?searchtype=author&query=Kwon%2C+D">Daehan Kwon</a>, <a href="/search/cs?searchtype=author&query=Jeong%2C+C">Chunseok Jeong</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+S">Sangheon Lee</a>, <a href="/search/cs?searchtype=author&query=Choi%2C+Y">Yongseok Choi</a>, <a href="/search/cs?searchtype=author&query=Byun%2C+W">Wooseok Byun</a>, <a href="/search/cs?searchtype=author&query=Baek%2C+S">Seungcheol Baek</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+H">Hyuk-Jae Lee</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+J">John Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.15008v1-abstract-short" style="display: inline;"> Accelerating end-to-end inference of transformer-based large language models (LLMs) is a critical component of AI services in datacenters. However, diverse compute characteristics of end-to-end LLM inference present challenges as previously proposed accelerators only address certain operations or stages (e.g., self-attention, generation stage, etc.). To address the unique challenges of acceleratin… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.15008v1-abstract-full').style.display = 'inline'; document.getElementById('2410.15008v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.15008v1-abstract-full" style="display: none;"> Accelerating end-to-end inference of transformer-based large language models (LLMs) is a critical component of AI services in datacenters. However, diverse compute characteristics of end-to-end LLM inference present challenges as previously proposed accelerators only address certain operations or stages (e.g., self-attention, generation stage, etc.). To address the unique challenges of accelerating end-to-end inference, we propose IANUS -- Integrated Accelerator based on NPU-PIM Unified Memory System. IANUS is a domain-specific system architecture that combines a Neural Processing Unit (NPU) with a Processing-in-Memory (PIM) to leverage both the NPU's high computation throughput and the PIM's high effective memory bandwidth. In particular, IANUS employs a unified main memory system where the PIM memory is used both for PIM operations and for NPU's main memory. The unified main memory system ensures that memory capacity is efficiently utilized and the movement of shared data between NPU and PIM is minimized. However, it introduces new challenges since normal memory accesses and PIM computations cannot be performed simultaneously. Thus, we propose novel PIM Access Scheduling that manages normal memory accesses and PIM computations through workload mapping and scheduling across the PIM and the NPU. Our detailed simulation evaluations show that IANUS improves the performance of GPT-2 by 6.2$\times$ and 3.2$\times$, on average, compared to the NVIDIA A100 GPU and the state-of-the-art accelerator. As a proof-of-concept, we develop a prototype of IANUS with a commercial PIM, NPU, and an FPGA-based PIM controller to demonstrate the feasibility of IANUS. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.15008v1-abstract-full').style.display = 'none'; document.getElementById('2410.15008v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Updated version of the paper accepted to ASPLOS 2024</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> ASPLOS 2024 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.14943">arXiv:2410.14943</a> <span> [<a href="https://arxiv.org/pdf/2410.14943">pdf</a>, <a href="https://arxiv.org/format/2410.14943">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.5281/zenodo.13844758">10.5281/zenodo.13844758 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Workflows Community Summit 2024: Future Trends and Challenges in Scientific Workflows </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=da+Silva%2C+R+F">Rafael Ferreira da Silva</a>, <a href="/search/cs?searchtype=author&query=Bard%2C+D">Deborah Bard</a>, <a href="/search/cs?searchtype=author&query=Chard%2C+K">Kyle Chard</a>, <a href="/search/cs?searchtype=author&query=de+Witt%2C+S">Shaun de Witt</a>, <a href="/search/cs?searchtype=author&query=Foster%2C+I+T">Ian T. Foster</a>, <a href="/search/cs?searchtype=author&query=Gibbs%2C+T">Tom Gibbs</a>, <a href="/search/cs?searchtype=author&query=Goble%2C+C">Carole Goble</a>, <a href="/search/cs?searchtype=author&query=Godoy%2C+W">William Godoy</a>, <a href="/search/cs?searchtype=author&query=Gustafsson%2C+J">Johan Gustafsson</a>, <a href="/search/cs?searchtype=author&query=Haus%2C+U">Utz-Uwe Haus</a>, <a href="/search/cs?searchtype=author&query=Hudson%2C+S">Stephen Hudson</a>, <a href="/search/cs?searchtype=author&query=Jha%2C+S">Shantenu Jha</a>, <a href="/search/cs?searchtype=author&query=Los%2C+L">Laila Los</a>, <a href="/search/cs?searchtype=author&query=Paine%2C+D">Drew Paine</a>, <a href="/search/cs?searchtype=author&query=Suter%2C+F">Fr茅d茅ric Suter</a>, <a href="/search/cs?searchtype=author&query=Ward%2C+L">Logan Ward</a>, <a href="/search/cs?searchtype=author&query=Wilkinson%2C+S">Sean Wilkinson</a>, <a href="/search/cs?searchtype=author&query=Amaris%2C+M">Marcos Amaris</a>, <a href="/search/cs?searchtype=author&query=Babuji%2C+Y">Yadu Babuji</a>, <a href="/search/cs?searchtype=author&query=Bader%2C+J">Jonathan Bader</a>, <a href="/search/cs?searchtype=author&query=Balin%2C+R">Riccardo Balin</a>, <a href="/search/cs?searchtype=author&query=Balouek%2C+D">Daniel Balouek</a>, <a href="/search/cs?searchtype=author&query=Beecroft%2C+S">Sarah Beecroft</a>, <a href="/search/cs?searchtype=author&query=Belhajjame%2C+K">Khalid Belhajjame</a>, <a href="/search/cs?searchtype=author&query=Bhattarai%2C+R">Rajat Bhattarai</a> , et al. (86 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.14943v1-abstract-short" style="display: inline;"> The Workflows Community Summit gathered 111 participants from 18 countries to discuss emerging trends and challenges in scientific workflows, focusing on six key areas: time-sensitive workflows, AI-HPC convergence, multi-facility workflows, heterogeneous HPC environments, user experience, and FAIR computational workflows. The integration of AI and exascale computing has revolutionized scientific w… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.14943v1-abstract-full').style.display = 'inline'; document.getElementById('2410.14943v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.14943v1-abstract-full" style="display: none;"> The Workflows Community Summit gathered 111 participants from 18 countries to discuss emerging trends and challenges in scientific workflows, focusing on six key areas: time-sensitive workflows, AI-HPC convergence, multi-facility workflows, heterogeneous HPC environments, user experience, and FAIR computational workflows. The integration of AI and exascale computing has revolutionized scientific workflows, enabling higher-fidelity models and complex, time-sensitive processes, while introducing challenges in managing heterogeneous environments and multi-facility data dependencies. The rise of large language models is driving computational demands to zettaflop scales, necessitating modular, adaptable systems and cloud-service models to optimize resource utilization and ensure reproducibility. Multi-facility workflows present challenges in data movement, curation, and overcoming institutional silos, while diverse hardware architectures require integrating workflow considerations into early system design and developing standardized resource management tools. The summit emphasized improving user experience in workflow systems and ensuring FAIR workflows to enhance collaboration and accelerate scientific discovery. Key recommendations include developing standardized metrics for time-sensitive workflows, creating frameworks for cloud-HPC integration, implementing distributed-by-design workflow modeling, establishing multi-facility authentication protocols, and accelerating AI integration in HPC workflow management. The summit also called for comprehensive workflow benchmarks, workflow-specific UX principles, and a FAIR workflow maturity model, highlighting the need for continued collaboration in addressing the complex challenges posed by the convergence of AI, HPC, and multi-facility research environments. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.14943v1-abstract-full').style.display = 'none'; document.getElementById('2410.14943v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> ORNL/TM-2024/3573 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.05133">arXiv:2410.05133</a> <span> [<a href="https://arxiv.org/pdf/2410.05133">pdf</a>, <a href="https://arxiv.org/format/2410.05133">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/SC41406.2024.00029">10.1109/SC41406.2024.00029 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> A Digital Twin Framework for Liquid-cooled Supercomputers as Demonstrated at Exascale </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Brewer%2C+W">Wesley Brewer</a>, <a href="/search/cs?searchtype=author&query=Maiterth%2C+M">Matthias Maiterth</a>, <a href="/search/cs?searchtype=author&query=Kumar%2C+V">Vineet Kumar</a>, <a href="/search/cs?searchtype=author&query=Wojda%2C+R">Rafal Wojda</a>, <a href="/search/cs?searchtype=author&query=Bouknight%2C+S">Sedrick Bouknight</a>, <a href="/search/cs?searchtype=author&query=Hines%2C+J">Jesse Hines</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Woong Shin</a>, <a href="/search/cs?searchtype=author&query=Greenwood%2C+S">Scott Greenwood</a>, <a href="/search/cs?searchtype=author&query=Grant%2C+D">David Grant</a>, <a href="/search/cs?searchtype=author&query=Williams%2C+W">Wesley Williams</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+F">Feiyi Wang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.05133v1-abstract-short" style="display: inline;"> We present ExaDigiT, an open-source framework for developing comprehensive digital twins of liquid-cooled supercomputers. It integrates three main modules: (1) a resource allocator and power simulator, (2) a transient thermo-fluidic cooling model, and (3) an augmented reality model of the supercomputer and central energy plant. The framework enables the study of "what-if" scenarios, system optimiz… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.05133v1-abstract-full').style.display = 'inline'; document.getElementById('2410.05133v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.05133v1-abstract-full" style="display: none;"> We present ExaDigiT, an open-source framework for developing comprehensive digital twins of liquid-cooled supercomputers. It integrates three main modules: (1) a resource allocator and power simulator, (2) a transient thermo-fluidic cooling model, and (3) an augmented reality model of the supercomputer and central energy plant. The framework enables the study of "what-if" scenarios, system optimizations, and virtual prototyping of future systems. Using Frontier as a case study, we demonstrate the framework's capabilities by replaying six months of system telemetry for systematic verification and validation. Such a comprehensive analysis of a liquid-cooled exascale supercomputer is the first of its kind. ExaDigiT elucidates complex transient cooling system dynamics, runs synthetic or real workloads, and predicts energy losses due to rectification and voltage conversion. Throughout our paper, we present lessons learned to benefit HPC practitioners developing similar digital twins. We envision the digital twin will be a key enabler for sustainable, energy-efficient supercomputing. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.05133v1-abstract-full').style.display = 'none'; document.getElementById('2410.05133v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">14 pages, 9 figures, To be published in the Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.07770">arXiv:2409.07770</a> <span> [<a href="https://arxiv.org/pdf/2409.07770">pdf</a>, <a href="https://arxiv.org/format/2409.07770">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Universal Pooling Method of Multi-layer Features from Pretrained Models for Speaker Verification </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kim%2C+J+S">Jin Sob Kim</a>, <a href="/search/cs?searchtype=author&query=Park%2C+H+J">Hyun Joon Park</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Wooseok Shin</a>, <a href="/search/cs?searchtype=author&query=Han%2C+S+W">Sung Won Han</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.07770v1-abstract-short" style="display: inline;"> Recent advancements in automatic speaker verification (ASV) studies have been achieved by leveraging large-scale pretrained networks. In this study, we analyze the approaches toward such a paradigm and underline the significance of interlayer information processing as a result. Accordingly, we present a novel approach for exploiting the multilayered nature of pretrained models for ASV, which compr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.07770v1-abstract-full').style.display = 'inline'; document.getElementById('2409.07770v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.07770v1-abstract-full" style="display: none;"> Recent advancements in automatic speaker verification (ASV) studies have been achieved by leveraging large-scale pretrained networks. In this study, we analyze the approaches toward such a paradigm and underline the significance of interlayer information processing as a result. Accordingly, we present a novel approach for exploiting the multilayered nature of pretrained models for ASV, which comprises a layer/frame-level network and two steps of pooling architectures for each layer and frame axis. Specifically, we let convolutional architecture directly processes a stack of layer outputs.Then, we present a channel attention-based scheme of gauging layer significance and squeeze the layer level with the most representative value. Finally, attentive statistics over frame-level representations yield a single vector speaker embedding. Comparative experiments are designed using versatile data environments and diverse pretraining models to validate the proposed approach. The experimental results demonstrate the stability of the approach using multi-layer outputs in leveraging pretrained architectures. Then, we verify the superiority of the proposed ASV backend structure, which involves layer-wise operations, in terms of performance improvement along with cost efficiency compared to the conventional method. The ablation study shows how the proposed interlayer processing aids in maximizing the advantage of utilizing pretrained models. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.07770v1-abstract-full').style.display = 'none'; document.getElementById('2409.07770v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Preprint</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.06323">arXiv:2409.06323</a> <span> [<a href="https://arxiv.org/pdf/2409.06323">pdf</a>, <a href="https://arxiv.org/format/2409.06323">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> </div> </div> <p class="title is-5 mathjax"> LAMP: Learnable Meta-Path Guided Adversarial Contrastive Learning for Heterogeneous Graphs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Li%2C+S">Siqing Li</a>, <a href="/search/cs?searchtype=author&query=Park%2C+J">Jin-Duk Park</a>, <a href="/search/cs?searchtype=author&query=Huang%2C+W">Wei Huang</a>, <a href="/search/cs?searchtype=author&query=Cao%2C+X">Xin Cao</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Won-Yong Shin</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+Z">Zhiqiang Xu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.06323v1-abstract-short" style="display: inline;"> Heterogeneous graph neural networks (HGNNs) have significantly propelled the information retrieval (IR) field. Still, the effectiveness of HGNNs heavily relies on high-quality labels, which are often expensive to acquire. This challenge has shifted attention towards Heterogeneous Graph Contrastive Learning (HGCL), which usually requires pre-defined meta-paths. However, our findings reveal that met… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.06323v1-abstract-full').style.display = 'inline'; document.getElementById('2409.06323v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.06323v1-abstract-full" style="display: none;"> Heterogeneous graph neural networks (HGNNs) have significantly propelled the information retrieval (IR) field. Still, the effectiveness of HGNNs heavily relies on high-quality labels, which are often expensive to acquire. This challenge has shifted attention towards Heterogeneous Graph Contrastive Learning (HGCL), which usually requires pre-defined meta-paths. However, our findings reveal that meta-path combinations significantly affect performance in unsupervised settings, an aspect often overlooked in current literature. Existing HGCL methods have considerable variability in outcomes across different meta-path combinations, thereby challenging the optimization process to achieve consistent and high performance. In response, we introduce \textsf{LAMP} (\underline{\textbf{L}}earn\underline{\textbf{A}}ble \underline{\textbf{M}}eta-\underline{\textbf{P}}ath), a novel adversarial contrastive learning approach that integrates various meta-path sub-graphs into a unified and stable structure, leveraging the overlap among these sub-graphs. To address the denseness of this integrated sub-graph, we propose an adversarial training strategy for edge pruning, maintaining sparsity to enhance model performance and robustness. \textsf{LAMP} aims to maximize the difference between meta-path and network schema views for guiding contrastive learning to capture the most meaningful information. Our extensive experimental study conducted on four diverse datasets from the Heterogeneous Graph Benchmark (HGB) demonstrates that \textsf{LAMP} significantly outperforms existing state-of-the-art unsupervised models in terms of accuracy and robustness. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.06323v1-abstract-full').style.display = 'none'; document.getElementById('2409.06323v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">19 pages, 7 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.05878">arXiv:2409.05878</a> <span> [<a href="https://arxiv.org/pdf/2409.05878">pdf</a>, <a href="https://arxiv.org/format/2409.05878">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> CF-KAN: Kolmogorov-Arnold Network-based Collaborative Filtering to Mitigate Catastrophic Forgetting in Recommender Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Park%2C+J">Jin-Duk Park</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+K">Kyung-Min Kim</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Won-Yong Shin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.05878v2-abstract-short" style="display: inline;"> Collaborative filtering (CF) remains essential in recommender systems, leveraging user--item interactions to provide personalized recommendations. Meanwhile, a number of CF techniques have evolved into sophisticated model architectures based on multi-layer perceptrons (MLPs). However, MLPs often suffer from catastrophic forgetting, and thus lose previously acquired knowledge when new information i… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.05878v2-abstract-full').style.display = 'inline'; document.getElementById('2409.05878v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.05878v2-abstract-full" style="display: none;"> Collaborative filtering (CF) remains essential in recommender systems, leveraging user--item interactions to provide personalized recommendations. Meanwhile, a number of CF techniques have evolved into sophisticated model architectures based on multi-layer perceptrons (MLPs). However, MLPs often suffer from catastrophic forgetting, and thus lose previously acquired knowledge when new information is learned, particularly in dynamic environments requiring continual learning. To tackle this problem, we propose CF-KAN, a new CF method utilizing Kolmogorov-Arnold networks (KANs). By learning nonlinear functions on the edge level, KANs are more robust to the catastrophic forgetting problem than MLPs. Built upon a KAN-based autoencoder, CF-KAN is designed in the sense of effectively capturing the intricacies of sparse user--item interactions and retaining information from previous data instances. Despite its simplicity, our extensive experiments demonstrate 1) CF-KAN's superiority over state-of-the-art methods in recommendation accuracy, 2) CF-KAN's resilience to catastrophic forgetting, underscoring its effectiveness in both static and dynamic recommendation scenarios, and 3) CF-KAN's edge-level interpretation facilitating the explainability of recommendations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.05878v2-abstract-full').style.display = 'none'; document.getElementById('2409.05878v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 25 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">9 pages, 7 figures, 4 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.05026">arXiv:2409.05026</a> <span> [<a href="https://arxiv.org/pdf/2409.05026">pdf</a>, <a href="https://arxiv.org/format/2409.05026">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> A Double-Difference Doppler Shift-Based Positioning Framework with Ephemeris Error Correction of LEO Satellites </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Hasan%2C+M+A">Md. Ali Hasan</a>, <a href="/search/cs?searchtype=author&query=Kabir%2C+M+H">M. Humayun Kabir</a>, <a href="/search/cs?searchtype=author&query=Islam%2C+M+S">Md. Shafiqul Islam</a>, <a href="/search/cs?searchtype=author&query=Han%2C+S">Sangmin Han</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Wonjae Shin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.05026v1-abstract-short" style="display: inline;"> In signals of opportunity (SOPs)-based positioning utilizing low Earth orbit (LEO) satellites, ephemeris data derived from two-line element files can introduce increasing error over time. To handle the erroneous measurement, an additional base receiver with a known position is often used to compensate for the effect of ephemeris error when positioning the user terminal (UT). However, this approach… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.05026v1-abstract-full').style.display = 'inline'; document.getElementById('2409.05026v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.05026v1-abstract-full" style="display: none;"> In signals of opportunity (SOPs)-based positioning utilizing low Earth orbit (LEO) satellites, ephemeris data derived from two-line element files can introduce increasing error over time. To handle the erroneous measurement, an additional base receiver with a known position is often used to compensate for the effect of ephemeris error when positioning the user terminal (UT). However, this approach is insufficient for the long baseline (the distance between the base receiver and UT) as it fails to adequately correct Doppler shift measurement errors caused by ephemeris inaccuracies, resulting in degraded positioning performance. Moreover, the lack of clock synchronization between the base receiver and UT exacerbates erroneous Doppler shift measurements. To address these challenges, we put forth a robust double-difference Doppler shift-based positioning framework, coined 3DPose, to handle the clock synchronization issue between the base receiver and UT, and positioning degradation due to the long baseline. The proposed 3DPose framework leverages double-difference Doppler shift measurements to eliminate the clock synchronization issue and incorporates a novel ephemeris error correction algorithm to enhance UT positioning accuracy in case of the long baseline. The algorithm specifically characterizes and corrects the Doppler shift measurement errors arising from erroneous ephemeris data, focusing on satellite position errors in the tangential direction. To validate the effectiveness of the proposed framework, we conduct comparative analyses across three different scenarios, contrasting its performance with the existing differential Doppler positioning method. The results demonstrate that the proposed 3DPose framework achieves an average reduction of 90% in 3-dimensional positioning errors compared to the existing differential Doppler approach. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.05026v1-abstract-full').style.display = 'none'; document.getElementById('2409.05026v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">32 pages, 8 figures, 2 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.05025">arXiv:2409.05025</a> <span> [<a href="https://arxiv.org/pdf/2409.05025">pdf</a>, <a href="https://arxiv.org/format/2409.05025">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Cooperative Learning-Based Framework for VNF Caching and Placement Optimization over Low Earth Orbit Satellite Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Doan%2C+K">Khai Doan</a>, <a href="/search/cs?searchtype=author&query=Avgeris%2C+M">Marios Avgeris</a>, <a href="/search/cs?searchtype=author&query=Leivadeas%2C+A">Aris Leivadeas</a>, <a href="/search/cs?searchtype=author&query=Lambadaris%2C+I">Ioannis Lambadaris</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Wonjae Shin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.05025v1-abstract-short" style="display: inline;"> Low Earth Orbit Satellite Networks (LSNs) are integral to supporting a broad range of modern applications, which are typically modeled as Service Function Chains (SFCs). Each SFC is composed of Virtual Network Functions (VNFs), where each VNF performs a specific task. In this work, we tackle two key challenges in deploying SFCs across an LSN. Firstly, we aim to optimize the long-term system perfor… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.05025v1-abstract-full').style.display = 'inline'; document.getElementById('2409.05025v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.05025v1-abstract-full" style="display: none;"> Low Earth Orbit Satellite Networks (LSNs) are integral to supporting a broad range of modern applications, which are typically modeled as Service Function Chains (SFCs). Each SFC is composed of Virtual Network Functions (VNFs), where each VNF performs a specific task. In this work, we tackle two key challenges in deploying SFCs across an LSN. Firstly, we aim to optimize the long-term system performance by minimizing the average end-to-end SFC execution delay, given that each satellite comes with a pre-installed/cached subset of VNFs. To achieve optimal SFC placement, we formulate an offline Dynamic Programming (DP) equation. To overcome the challenges associated with DP, such as its complexity, the need for probability knowledge, and centralized decision-making, we put forth an online Multi-Agent Q-Learning (MAQL) solution. Our MAQL approach addresses convergence issues in the non-stationary LSN environment by enabling satellites to share learning parameters and update their Q-tables based on distinct rules for their selected actions. Secondly, to determine the optimal VNF subsets for satellite caching, we develop a Bayesian Optimization (BO)-based learning mechanism that operates both offline and continuously in the background during runtime. Extensive experiments demonstrate that our MAQL approach achieves near-optimal performance comparable to the DP model and significantly outperforms existing baselines. Moreover, the BO-based approach effectively enhances the request serving rate over time. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.05025v1-abstract-full').style.display = 'none'; document.getElementById('2409.05025v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">40 pages, 11 figure, 3 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.12727">arXiv:2408.12727</a> <span> [<a href="https://arxiv.org/pdf/2408.12727">pdf</a>, <a href="https://arxiv.org/format/2408.12727">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> BankTweak: Adversarial Attack against Multi-Object Trackers by Manipulating Feature Banks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Shin%2C+W">Woojin Shin</a>, <a href="/search/cs?searchtype=author&query=Kang%2C+D">Donghwa Kang</a>, <a href="/search/cs?searchtype=author&query=Choi%2C+D">Daejin Choi</a>, <a href="/search/cs?searchtype=author&query=Kang%2C+B">Brent Kang</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+J">Jinkyu Lee</a>, <a href="/search/cs?searchtype=author&query=Baek%2C+H">Hyeongboo Baek</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.12727v1-abstract-short" style="display: inline;"> Multi-object tracking (MOT) aims to construct moving trajectories for objects, and modern multi-object trackers mainly utilize the tracking-by-detection methodology. Initial approaches to MOT attacks primarily aimed to degrade the detection quality of the frames under attack, thereby reducing accuracy only in those specific frames, highlighting a lack of \textit{efficiency}. To improve efficiency,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.12727v1-abstract-full').style.display = 'inline'; document.getElementById('2408.12727v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.12727v1-abstract-full" style="display: none;"> Multi-object tracking (MOT) aims to construct moving trajectories for objects, and modern multi-object trackers mainly utilize the tracking-by-detection methodology. Initial approaches to MOT attacks primarily aimed to degrade the detection quality of the frames under attack, thereby reducing accuracy only in those specific frames, highlighting a lack of \textit{efficiency}. To improve efficiency, recent advancements manipulate object positions to cause persistent identity (ID) switches during the association phase, even after the attack ends within a few frames. However, these position-manipulating attacks have inherent limitations, as they can be easily counteracted by adjusting distance-related parameters in the association phase, revealing a lack of \textit{robustness}. In this paper, we present \textsf{BankTweak}, a novel adversarial attack designed for MOT trackers, which features efficiency and robustness. \textsf{BankTweak} focuses on the feature extractor in the association phase and reveals vulnerability in the Hungarian matching method used by feature-based MOT systems. Exploiting the vulnerability, \textsf{BankTweak} induces persistent ID switches (addressing \textit{efficiency}) even after the attack ends by strategically injecting altered features into the feature banks without modifying object positions (addressing \textit{robustness}). To demonstrate the applicability, we apply \textsf{BankTweak} to three multi-object trackers (DeepSORT, StrongSORT, and MOTDT) with one-stage, two-stage, anchor-free, and transformer detectors. Extensive experiments on the MOT17 and MOT20 datasets show that our method substantially surpasses existing attacks, exposing the vulnerability of the tracking-by-detection framework to \textsf{BankTweak}. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.12727v1-abstract-full').style.display = 'none'; document.getElementById('2408.12727v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.02872">arXiv:2408.02872</a> <span> [<a href="https://arxiv.org/pdf/2408.02872">pdf</a>, <a href="https://arxiv.org/format/2408.02872">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> Rate-Splitting for Joint Unicast and Multicast Transmission in LEO Satellite Networks with Non-Uniform Traffic Demand </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Seong%2C+J">Jaehyup Seong</a>, <a href="/search/cs?searchtype=author&query=Park%2C+J">Juha Park</a>, <a href="/search/cs?searchtype=author&query=Jung%2C+D">Dong-Hyun Jung</a>, <a href="/search/cs?searchtype=author&query=Park%2C+J">Jeonghun Park</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Wonjae Shin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.02872v1-abstract-short" style="display: inline;"> Low Earth orbit (LEO) satellite communications (SATCOM) with ubiquitous global connectivity is deemed a pivotal catalyst in advancing wireless communication systems for 5G and beyond. LEO SATCOM excels in delivering versatile information services across expansive areas, facilitating both unicast and multicast transmissions via high-speed broadband capability. Nonetheless, given the broadband cover… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.02872v1-abstract-full').style.display = 'inline'; document.getElementById('2408.02872v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.02872v1-abstract-full" style="display: none;"> Low Earth orbit (LEO) satellite communications (SATCOM) with ubiquitous global connectivity is deemed a pivotal catalyst in advancing wireless communication systems for 5G and beyond. LEO SATCOM excels in delivering versatile information services across expansive areas, facilitating both unicast and multicast transmissions via high-speed broadband capability. Nonetheless, given the broadband coverage of LEO SATCOM, traffic demand distribution within the service area is non-uniform, and the time/frequency/power resources available at LEO satellites remain significantly limited. Motivated by these challenges, we propose a rate-matching framework for non-orthogonal unicast and multicast (NOUM) transmission. Our approach aims to minimize the difference between offered rates and traffic demands for both unicast and multicast messages. By multiplexing unicast and multicast transmissions over the same radio resource, rate-splitting multiple access (RSMA) is employed to manage interference between unicast and multicast streams, as well as inter-user interference under imperfect channel state information at the LEO satellite. To address the formulated problems non-smoothness and non-convexity, the common rate is approximated using the LogSumExp technique. Thereafter, we represent the common rate portion as the ratio of the approximated function, converting the problem into an unconstrained form. A generalized power iteration (GPI)-based algorithm, coined GPI-RS-NOUM, is proposed upon this reformulation. Through comprehensive numerical analysis across diverse simulation setups, we demonstrate that the proposed framework outperforms various benchmarks for LEO SATCOM with uneven traffic demands. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.02872v1-abstract-full').style.display = 'none'; document.getElementById('2408.02872v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">39 pages, 9 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.01997">arXiv:2408.01997</a> <span> [<a href="https://arxiv.org/pdf/2408.01997">pdf</a>, <a href="https://arxiv.org/format/2408.01997">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Rate-Splitting Multiple Access for GEO-LEO Coexisting Satellite Systems: A Traffic-Aware Throughput Maximization Precoder Design </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ryu%2C+J">Jaehak Ryu</a>, <a href="/search/cs?searchtype=author&query=Kaushik%2C+A">Aryan Kaushik</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+B">Byungju Lee</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Wonjae Shin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.01997v1-abstract-short" style="display: inline;"> The frequency coexistence between geostationary orbit (GEO) and low earth orbit (LEO) satellite systems is expected to be a promising approach for relieving spectrum scarcity. However, it is essential to manage mutual interference between GEO and LEO satellite systems for frequency coexistence. Specifically, \emph{in-line interference}, caused by LEO satellites moving near the line-of-sight path b… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.01997v1-abstract-full').style.display = 'inline'; document.getElementById('2408.01997v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.01997v1-abstract-full" style="display: none;"> The frequency coexistence between geostationary orbit (GEO) and low earth orbit (LEO) satellite systems is expected to be a promising approach for relieving spectrum scarcity. However, it is essential to manage mutual interference between GEO and LEO satellite systems for frequency coexistence. Specifically, \emph{in-line interference}, caused by LEO satellites moving near the line-of-sight path between GEO satellite and GEO users (GUs), can significantly degrade GEO system throughput. This paper put forth a novel rate-splitting multiple access (RSMA) with a super-common message for GEO-LEO coexisting satellite systems (CSS). By employing a super-common message that GUs can decode, GUs can mitigate the in-line interference by successive interference cancellation (SIC). Moreover, we formulate a traffic-aware throughput maximization (TTM) problem to satisfy the heterogeneous traffic demands of users by minimizing total unmet throughput demands (or user dissatisfaction). By doing so, the TTM precoder can be flexibly adjusted according to the interference leakage from LEO satellites to GUs and target traffic demands. Numerical results confirm that our proposed method ensures seamless connectivity even in the GEO-LEO in-line interference regime under imperfect channel state information (CSI) at both the transmitter and receiver. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.01997v1-abstract-full').style.display = 'none'; document.getElementById('2408.01997v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">17 pages, 4 figures, 1 table</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.01552">arXiv:2408.01552</a> <span> [<a href="https://arxiv.org/pdf/2408.01552">pdf</a>, <a href="https://arxiv.org/format/2408.01552">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> </div> <p class="title is-5 mathjax"> Exploring the Frontiers of Energy Efficiency using Power Management at System Scale </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Karimi%2C+A+M">Ahmad Maroof Karimi</a>, <a href="/search/cs?searchtype=author&query=Maiterth%2C+M">Matthias Maiterth</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Woong Shin</a>, <a href="/search/cs?searchtype=author&query=Sattar%2C+N+S">Naw Safrin Sattar</a>, <a href="/search/cs?searchtype=author&query=Lu%2C+H">Hao Lu</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+F">Feiyi Wang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.01552v1-abstract-short" style="display: inline;"> In the face of surging power demands for exascale HPC systems, this work tackles the critical challenge of understanding the impact of software-driven power management techniques like Dynamic Voltage and Frequency Scaling (DVFS) and Power Capping. These techniques have been actively developed over the past few decades. By combining insights from GPU benchmarking to understand application power pro… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.01552v1-abstract-full').style.display = 'inline'; document.getElementById('2408.01552v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.01552v1-abstract-full" style="display: none;"> In the face of surging power demands for exascale HPC systems, this work tackles the critical challenge of understanding the impact of software-driven power management techniques like Dynamic Voltage and Frequency Scaling (DVFS) and Power Capping. These techniques have been actively developed over the past few decades. By combining insights from GPU benchmarking to understand application power profiles, we present a telemetry data-driven approach for deriving energy savings projections. This approach has been demonstrably applied to the Frontier supercomputer at scale. Our findings based on three months of telemetry data indicate that, for certain resource-constrained jobs, significant energy savings (up to 8.5%) can be achieved without compromising performance. This translates to a substantial cost reduction, equivalent to 1438 MWh of energy saved. The key contribution of this work lies in the methodology for establishing an upper limit for these best-case scenarios and its successful application. This work sheds light on potential energy savings and empowers HPC professionals to optimize the power-performance trade-off within constrained power budgets, not only for the exascale era but also beyond. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.01552v1-abstract-full').style.display = 'none'; document.getElementById('2408.01552v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.12374">arXiv:2407.12374</a> <span> [<a href="https://arxiv.org/pdf/2407.12374">pdf</a>, <a href="https://arxiv.org/format/2407.12374">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Graph Signal Processing for Cross-Domain Recommendation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Lee%2C+J">Jeongeun Lee</a>, <a href="/search/cs?searchtype=author&query=Kang%2C+S">Seongku Kang</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Won-Yong Shin</a>, <a href="/search/cs?searchtype=author&query=Choi%2C+J">Jeongwhan Choi</a>, <a href="/search/cs?searchtype=author&query=Park%2C+N">Noseong Park</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+D">Dongha Lee</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.12374v2-abstract-short" style="display: inline;"> Cross-domain recommendation (CDR) extends conventional recommender systems by leveraging user-item interactions from dense domains to mitigate data sparsity and the cold start problem. While CDR offers substantial potential for enhancing recommendation performance, most existing CDR methods suffer from sensitivity to the ratio of overlapping users and intrinsic discrepancy between source and targe… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.12374v2-abstract-full').style.display = 'inline'; document.getElementById('2407.12374v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.12374v2-abstract-full" style="display: none;"> Cross-domain recommendation (CDR) extends conventional recommender systems by leveraging user-item interactions from dense domains to mitigate data sparsity and the cold start problem. While CDR offers substantial potential for enhancing recommendation performance, most existing CDR methods suffer from sensitivity to the ratio of overlapping users and intrinsic discrepancy between source and target domains. To overcome these limitations, in this work, we explore the application of graph signal processing (GSP) in CDR scenarios. We propose CGSP, a unified CDR framework based on GSP, which employs a cross-domain similarity graph constructed by flexibly combining target-only similarity and source-bridged similarity. By processing personalized graph signals computed for users from either the source or target domain, our framework effectively supports both inter-domain and intra-domain recommendations. Our empirical evaluation demonstrates that CGSP consistently outperforms various encoder-based CDR approaches in both intra-domain and inter-domain recommendation scenarios, especially when the ratio of overlapping users is low, highlighting its significant practical implication in real-world applications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.12374v2-abstract-full').style.display = 'none'; document.getElementById('2407.12374v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 17 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.10461">arXiv:2407.10461</a> <span> [<a href="https://arxiv.org/pdf/2407.10461">pdf</a>, <a href="https://arxiv.org/ps/2407.10461">ps</a>, <a href="https://arxiv.org/format/2407.10461">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Multibeam Satellite Communications with Massive MIMO: Asymptotic Performance Analysis and Design Insights </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kim%2C+S">Seyong Kim</a>, <a href="/search/cs?searchtype=author&query=Choi%2C+J">Jinseok Choi</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Wonjae Shin</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+N">Namyoon Lee</a>, <a href="/search/cs?searchtype=author&query=Park%2C+J">Jeonghun Park</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.10461v1-abstract-short" style="display: inline;"> To achieve high performance without substantial overheads associated with channel state information (CSI) of ground users, we consider a fixed-beam precoding approach, where a satellite forms multiple fixed-beams without relying on CSI, then select a suitable user set for each beam. Upon this precoding method, we put forth a satellite equipped with massive multiple-input multiple-output (MIMO), by… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.10461v1-abstract-full').style.display = 'inline'; document.getElementById('2407.10461v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.10461v1-abstract-full" style="display: none;"> To achieve high performance without substantial overheads associated with channel state information (CSI) of ground users, we consider a fixed-beam precoding approach, where a satellite forms multiple fixed-beams without relying on CSI, then select a suitable user set for each beam. Upon this precoding method, we put forth a satellite equipped with massive multiple-input multiple-output (MIMO), by which inter-beam interference is efficiently mitigated by narrowing corresponding beam width. By modeling the ground users' locations via a Poisson point process, we rigorously analyze the achievable performance of the presented multibeam satellite system. In particular, we investigate the asymptotic scaling laws that reveal the interplay between the user density, the number of beams, and the number of antennas. Our analysis offers critical design insights for the multibeam satellite with massive MIMO: i) If the user density scales in power with the number of antennas, the considered precoding can achieve a linear fraction of the optimal rate in the asymptotic regime. ii) A certain additional scaling factor for the user density is needed as the number of beams increases to maintain the asymptotic optimality. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.10461v1-abstract-full').style.display = 'none'; document.getElementById('2407.10461v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.08923">arXiv:2407.08923</a> <span> [<a href="https://arxiv.org/pdf/2407.08923">pdf</a>, <a href="https://arxiv.org/format/2407.08923">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> A Bistatic ISAC Framework for LEO Satellite Systems: A Rate-Splitting Approach </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Park%2C+J">Juha Park</a>, <a href="/search/cs?searchtype=author&query=Seong%2C+J">Jaehyup Seong</a>, <a href="/search/cs?searchtype=author&query=Ryu%2C+J">Jaehak Ryu</a>, <a href="/search/cs?searchtype=author&query=Mao%2C+Y">Yijie Mao</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Wonjae Shin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.08923v1-abstract-short" style="display: inline;"> Aiming to achieve ubiquitous global connectivity and target detection on the same platform with improved spectral/energy efficiency and reduced onboard hardware cost, low Earth orbit (LEO) satellite systems capable of simultaneously performing communications and radar have attracted significant attention. Designing such a joint system should address not only the challenges of integrating two funct… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.08923v1-abstract-full').style.display = 'inline'; document.getElementById('2407.08923v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.08923v1-abstract-full" style="display: none;"> Aiming to achieve ubiquitous global connectivity and target detection on the same platform with improved spectral/energy efficiency and reduced onboard hardware cost, low Earth orbit (LEO) satellite systems capable of simultaneously performing communications and radar have attracted significant attention. Designing such a joint system should address not only the challenges of integrating two functions but also the unique propagation characteristics of the satellites. To overcome severe echo signal path loss due to the high altitude of the satellite, we put forth a bistatic integrated sensing and communication (ISAC) framework with a radar receiver separated from the satellite. For robust and effective interference management, we employ rate-splitting multiple access (RSMA), which splits and encodes users messages into private and common streams. We optimize the dual-functional precoders to maximize the minimum rate among all users while satisfying the Cramer-Rao bound (CRB) constraints. Given the challenge of acquiring instantaneous channel state information (iCSI) for LEO satellites, we exploit the geometrical and statistical characteristics of the satellite channel. To develop an efficient optimization algorithm, semidefinite relaxation (SDR), sequential rank-1 constraint relaxation (SROCR), and successive convex approximation (SCA) are utilized. Numerical results show that the proposed framework efficiently performs both communication and radar, demonstrating superior interference control capabilities. Furthermore, it is validated that the common stream plays three vital roles: i) beamforming towards the radar target, ii) interference management between communications and radar, and iii) interference management among communication users. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.08923v1-abstract-full').style.display = 'none'; document.getElementById('2407.08923v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">33 pages, 8 figures, 2 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.19135">arXiv:2406.19135</a> <span> [<a href="https://arxiv.org/pdf/2406.19135">pdf</a>, <a href="https://arxiv.org/format/2406.19135">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> DEX-TTS: Diffusion-based EXpressive Text-to-Speech with Style Modeling on Time Variability </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Park%2C+H+J">Hyun Joon Park</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+J+S">Jin Sob Kim</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Wooseok Shin</a>, <a href="/search/cs?searchtype=author&query=Han%2C+S+W">Sung Won Han</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.19135v1-abstract-short" style="display: inline;"> Expressive Text-to-Speech (TTS) using reference speech has been studied extensively to synthesize natural speech, but there are limitations to obtaining well-represented styles and improving model generalization ability. In this study, we present Diffusion-based EXpressive TTS (DEX-TTS), an acoustic model designed for reference-based speech synthesis with enhanced style representations. Based on a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.19135v1-abstract-full').style.display = 'inline'; document.getElementById('2406.19135v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.19135v1-abstract-full" style="display: none;"> Expressive Text-to-Speech (TTS) using reference speech has been studied extensively to synthesize natural speech, but there are limitations to obtaining well-represented styles and improving model generalization ability. In this study, we present Diffusion-based EXpressive TTS (DEX-TTS), an acoustic model designed for reference-based speech synthesis with enhanced style representations. Based on a general diffusion TTS framework, DEX-TTS includes encoders and adapters to handle styles extracted from reference speech. Key innovations contain the differentiation of styles into time-invariant and time-variant categories for effective style extraction, as well as the design of encoders and adapters with high generalization ability. In addition, we introduce overlapping patchify and convolution-frequency patch embedding strategies to improve DiT-based diffusion networks for TTS. DEX-TTS yields outstanding performance in terms of objective and subjective evaluation in English multi-speaker and emotional multi-speaker datasets, without relying on pre-training strategies. Lastly, the comparison results for the general TTS on a single-speaker dataset verify the effectiveness of our enhanced diffusion backbone. Demos are available here. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.19135v1-abstract-full').style.display = 'none'; document.getElementById('2406.19135v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Preprint</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.11504">arXiv:2406.11504</a> <span> [<a href="https://arxiv.org/pdf/2406.11504">pdf</a>, <a href="https://arxiv.org/format/2406.11504">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Neural and Evolutionary Computing">cs.NE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> </div> </div> <p class="title is-5 mathjax"> On the Feasibility of Fidelity$^-$ for Graph Pruning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Shin%2C+Y">Yong-Min Shin</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Won-Yong Shin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.11504v1-abstract-short" style="display: inline;"> As one of popular quantitative metrics to assess the quality of explanation of graph neural networks (GNNs), fidelity measures the output difference after removing unimportant parts of the input graph. Fidelity has been widely used due to its straightforward interpretation that the underlying model should produce similar predictions when features deemed unimportant from the explanation are removed… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.11504v1-abstract-full').style.display = 'inline'; document.getElementById('2406.11504v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.11504v1-abstract-full" style="display: none;"> As one of popular quantitative metrics to assess the quality of explanation of graph neural networks (GNNs), fidelity measures the output difference after removing unimportant parts of the input graph. Fidelity has been widely used due to its straightforward interpretation that the underlying model should produce similar predictions when features deemed unimportant from the explanation are removed. This raises a natural question: "Does fidelity induce a global (soft) mask for graph pruning?" To solve this, we aim to explore the potential of the fidelity measure to be used for graph pruning, eventually enhancing the GNN models for better efficiency. To this end, we propose Fidelity$^-$-inspired Pruning (FiP), an effective framework to construct global edge masks from local explanations. Our empirical observations using 7 edge attribution methods demonstrate that, surprisingly, general eXplainable AI methods outperform methods tailored to GNNs in terms of graph pruning performance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.11504v1-abstract-full').style.display = 'none'; document.getElementById('2406.11504v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">6 pages, 3 figures, 2 tables; IJCAI Workshop on Explainable AI (XAI 2024) (to appear) (Please cite our workshop version.)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.05602">arXiv:2406.05602</a> <span> [<a href="https://arxiv.org/pdf/2406.05602">pdf</a>, <a href="https://arxiv.org/format/2406.05602">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Can Prompt Modifiers Control Bias? A Comparative Analysis of Text-to-Image Generative Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Shin%2C+P+W">Philip Wootaek Shin</a>, <a href="/search/cs?searchtype=author&query=Ahn%2C+J+J">Jihyun Janice Ahn</a>, <a href="/search/cs?searchtype=author&query=Yin%2C+W">Wenpeng Yin</a>, <a href="/search/cs?searchtype=author&query=Sampson%2C+J">Jack Sampson</a>, <a href="/search/cs?searchtype=author&query=Narayanan%2C+V">Vijaykrishnan Narayanan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.05602v1-abstract-short" style="display: inline;"> It has been shown that many generative models inherit and amplify societal biases. To date, there is no uniform/systematic agreed standard to control/adjust for these biases. This study examines the presence and manipulation of societal biases in leading text-to-image models: Stable Diffusion, DALL-E 3, and Adobe Firefly. Through a comprehensive analysis combining base prompts with modifiers and t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.05602v1-abstract-full').style.display = 'inline'; document.getElementById('2406.05602v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.05602v1-abstract-full" style="display: none;"> It has been shown that many generative models inherit and amplify societal biases. To date, there is no uniform/systematic agreed standard to control/adjust for these biases. This study examines the presence and manipulation of societal biases in leading text-to-image models: Stable Diffusion, DALL-E 3, and Adobe Firefly. Through a comprehensive analysis combining base prompts with modifiers and their sequencing, we uncover the nuanced ways these AI technologies encode biases across gender, race, geography, and region/culture. Our findings reveal the challenges and potential of prompt engineering in controlling biases, highlighting the critical need for ethical AI development promoting diversity and inclusivity. This work advances AI ethics by not only revealing the nuanced dynamics of bias in text-to-image generation models but also by offering a novel framework for future research in controlling bias. Our contributions-panning comparative analyses, the strategic use of prompt modifiers, the exploration of prompt sequencing effects, and the introduction of a bias sensitivity taxonomy-lay the groundwork for the development of common metrics and standard analyses for evaluating whether and how future AI models exhibit and respond to requests to adjust for inherent biases. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.05602v1-abstract-full').style.display = 'none'; document.getElementById('2406.05602v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.04612">arXiv:2406.04612</a> <span> [<a href="https://arxiv.org/pdf/2406.04612">pdf</a>, <a href="https://arxiv.org/format/2406.04612">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Neural and Evolutionary Computing">cs.NE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> </div> </div> <p class="title is-5 mathjax"> Revisiting Attention Weights as Interpretations of Message-Passing Neural Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Shin%2C+Y">Yong-Min Shin</a>, <a href="/search/cs?searchtype=author&query=Li%2C+S">Siqing Li</a>, <a href="/search/cs?searchtype=author&query=Cao%2C+X">Xin Cao</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Won-Yong Shin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.04612v1-abstract-short" style="display: inline;"> The self-attention mechanism has been adopted in several widely-used message-passing neural networks (MPNNs) (e.g., GATs), which adaptively controls the amount of information that flows along the edges of the underlying graph. This usage of attention has made such models a baseline for studies on explainable AI (XAI) since interpretations via attention have been popularized in various domains (e.g… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.04612v1-abstract-full').style.display = 'inline'; document.getElementById('2406.04612v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.04612v1-abstract-full" style="display: none;"> The self-attention mechanism has been adopted in several widely-used message-passing neural networks (MPNNs) (e.g., GATs), which adaptively controls the amount of information that flows along the edges of the underlying graph. This usage of attention has made such models a baseline for studies on explainable AI (XAI) since interpretations via attention have been popularized in various domains (e.g., natural language processing and computer vision). However, existing studies often use naive calculations to derive attribution scores from attention, and do not take the precise and careful calculation of edge attribution into consideration. In our study, we aim to fill the gap between the widespread usage of attention-enabled MPNNs and their potential in largely under-explored explainability, a topic that has been actively investigated in other areas. To this end, as the first attempt, we formalize the problem of edge attribution from attention weights in GNNs. Then, we propose GATT, an edge attribution calculation method built upon the computation tree. Through comprehensive experiments, we demonstrate the effectiveness of our proposed method when evaluating attributions from GATs. Conversely, we empirically validate that simply averaging attention weights over graph attention layers is insufficient to interpret the GAT model's behavior. Code is publicly available at https://github.com/jordan7186/GAtt/tree/main. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.04612v1-abstract-full').style.display = 'none'; document.getElementById('2406.04612v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">11 pages, 3 figures, 5 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.20610">arXiv:2405.20610</a> <span> [<a href="https://arxiv.org/pdf/2405.20610">pdf</a>, <a href="https://arxiv.org/format/2405.20610">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Revisiting and Maximizing Temporal Knowledge in Semi-supervised Semantic Segmentation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Shin%2C+W">Wooseok Shin</a>, <a href="/search/cs?searchtype=author&query=Park%2C+H+J">Hyun Joon Park</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+J+S">Jin Sob Kim</a>, <a href="/search/cs?searchtype=author&query=Han%2C+S+W">Sung Won Han</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.20610v1-abstract-short" style="display: inline;"> In semi-supervised semantic segmentation, the Mean Teacher- and co-training-based approaches are employed to mitigate confirmation bias and coupling problems. However, despite their high performance, these approaches frequently involve complex training pipelines and a substantial computational burden, limiting the scalability and compatibility of these methods. In this paper, we propose a PrevMatc… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.20610v1-abstract-full').style.display = 'inline'; document.getElementById('2405.20610v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.20610v1-abstract-full" style="display: none;"> In semi-supervised semantic segmentation, the Mean Teacher- and co-training-based approaches are employed to mitigate confirmation bias and coupling problems. However, despite their high performance, these approaches frequently involve complex training pipelines and a substantial computational burden, limiting the scalability and compatibility of these methods. In this paper, we propose a PrevMatch framework that effectively mitigates the aforementioned limitations by maximizing the utilization of the temporal knowledge obtained during the training process. The PrevMatch framework relies on two core strategies: (1) we reconsider the use of temporal knowledge and thus directly utilize previous models obtained during training to generate additional pseudo-label guidance, referred to as previous guidance. (2) we design a highly randomized ensemble strategy to maximize the effectiveness of the previous guidance. Experimental results on four benchmark semantic segmentation datasets confirm that the proposed method consistently outperforms existing methods across various evaluation protocols. In particular, with DeepLabV3+ and ResNet-101 network settings, PrevMatch outperforms the existing state-of-the-art method, Diverse Co-training, by +1.6 mIoU on Pascal VOC with only 92 annotated images, while achieving 2.4 times faster training. Furthermore, the results indicate that PrevMatch induces stable optimization, particularly in benefiting classes that exhibit poor performance. Code is available at https://github.com/wooseok-shin/PrevMatch <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.20610v1-abstract-full').style.display = 'none'; document.getElementById('2405.20610v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">14 pages, 5 figures, submitted to IEEE TPAMI. This work has been submitted to the IEEE for possible publication</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.14243">arXiv:2404.14243</a> <span> [<a href="https://arxiv.org/pdf/2404.14243">pdf</a>, <a href="https://arxiv.org/format/2404.14243">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> </div> </div> <p class="title is-5 mathjax"> Turbo-CF: Matrix Decomposition-Free Graph Filtering for Fast Recommendation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Park%2C+J">Jin-Duk Park</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+Y">Yong-Min Shin</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Won-Yong Shin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.14243v1-abstract-short" style="display: inline;"> A series of graph filtering (GF)-based collaborative filtering (CF) showcases state-of-the-art performance on the recommendation accuracy by using a low-pass filter (LPF) without a training process. However, conventional GF-based CF approaches mostly perform matrix decomposition on the item-item similarity graph to realize the ideal LPF, which results in a non-trivial computational cost and thus m… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.14243v1-abstract-full').style.display = 'inline'; document.getElementById('2404.14243v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.14243v1-abstract-full" style="display: none;"> A series of graph filtering (GF)-based collaborative filtering (CF) showcases state-of-the-art performance on the recommendation accuracy by using a low-pass filter (LPF) without a training process. However, conventional GF-based CF approaches mostly perform matrix decomposition on the item-item similarity graph to realize the ideal LPF, which results in a non-trivial computational cost and thus makes them less practical in scenarios where rapid recommendations are essential. In this paper, we propose Turbo-CF, a GF-based CF method that is both training-free and matrix decomposition-free. Turbo-CF employs a polynomial graph filter to circumvent the issue of expensive matrix decompositions, enabling us to make full use of modern computer hardware components (i.e., GPU). Specifically, Turbo-CF first constructs an item-item similarity graph whose edge weights are effectively regulated. Then, our own polynomial LPFs are designed to retain only low-frequency signals without explicit matrix decompositions. We demonstrate that Turbo-CF is extremely fast yet accurate, achieving a runtime of less than 1 second on real-world benchmark datasets while achieving recommendation accuracies comparable to best competitors. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.14243v1-abstract-full').style.display = 'none'; document.getElementById('2404.14243v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">5 pages, 4 figures, 4 tables; 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2024) (to appear) (Please cite our conference version.)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.14240">arXiv:2404.14240</a> <span> [<a href="https://arxiv.org/pdf/2404.14240">pdf</a>, <a href="https://arxiv.org/format/2404.14240">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> </div> </div> <p class="title is-5 mathjax"> Collaborative Filtering Based on Diffusion Models: Unveiling the Potential of High-Order Connectivity </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Hou%2C+Y">Yu Hou</a>, <a href="/search/cs?searchtype=author&query=Park%2C+J">Jin-Duk Park</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Won-Yong Shin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.14240v1-abstract-short" style="display: inline;"> A recent study has shown that diffusion models are well-suited for modeling the generative process of user-item interactions in recommender systems due to their denoising nature. However, existing diffusion model-based recommender systems do not explicitly leverage high-order connectivities that contain crucial collaborative signals for accurate recommendations. Addressing this gap, we propose CF-… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.14240v1-abstract-full').style.display = 'inline'; document.getElementById('2404.14240v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.14240v1-abstract-full" style="display: none;"> A recent study has shown that diffusion models are well-suited for modeling the generative process of user-item interactions in recommender systems due to their denoising nature. However, existing diffusion model-based recommender systems do not explicitly leverage high-order connectivities that contain crucial collaborative signals for accurate recommendations. Addressing this gap, we propose CF-Diff, a new diffusion model-based collaborative filtering (CF) method, which is capable of making full use of collaborative signals along with multi-hop neighbors. Specifically, the forward-diffusion process adds random noise to user-item interactions, while the reverse-denoising process accommodates our own learning model, named cross-attention-guided multi-hop autoencoder (CAM-AE), to gradually recover the original user-item interactions. CAM-AE consists of two core modules: 1) the attention-aided AE module, responsible for precisely learning latent representations of user-item interactions while preserving the model's complexity at manageable levels, and 2) the multi-hop cross-attention module, which judiciously harnesses high-order connectivity information to capture enhanced collaborative signals. Through comprehensive experiments on three real-world datasets, we demonstrate that CF-Diff is (a) Superior: outperforming benchmark recommendation methods, achieving remarkable gains up to 7.29% compared to the best competitor, (b) Theoretically-validated: reducing computations while ensuring that the embeddings generated by our model closely approximate those from the original cross-attention, and (c) Scalable: proving the computational efficiency that scales linearly with the number of users or items. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.14240v1-abstract-full').style.display = 'none'; document.getElementById('2404.14240v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">10 pages, 6 figures, 4 tables; 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2024) (to appear) (Please cite our conference version.)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.01954">arXiv:2404.01954</a> <span> [<a href="https://arxiv.org/pdf/2404.01954">pdf</a>, <a href="https://arxiv.org/format/2404.01954">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> HyperCLOVA X Technical Report </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Yoo%2C+K+M">Kang Min Yoo</a>, <a href="/search/cs?searchtype=author&query=Han%2C+J">Jaegeun Han</a>, <a href="/search/cs?searchtype=author&query=In%2C+S">Sookyo In</a>, <a href="/search/cs?searchtype=author&query=Jeon%2C+H">Heewon Jeon</a>, <a href="/search/cs?searchtype=author&query=Jeong%2C+J">Jisu Jeong</a>, <a href="/search/cs?searchtype=author&query=Kang%2C+J">Jaewook Kang</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H">Hyunwook Kim</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+K">Kyung-Min Kim</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+M">Munhyong Kim</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+S">Sungju Kim</a>, <a href="/search/cs?searchtype=author&query=Kwak%2C+D">Donghyun Kwak</a>, <a href="/search/cs?searchtype=author&query=Kwak%2C+H">Hanock Kwak</a>, <a href="/search/cs?searchtype=author&query=Kwon%2C+S+J">Se Jung Kwon</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+B">Bado Lee</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+D">Dongsoo Lee</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+G">Gichang Lee</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+J">Jooho Lee</a>, <a href="/search/cs?searchtype=author&query=Park%2C+B">Baeseong Park</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+S">Seongjin Shin</a>, <a href="/search/cs?searchtype=author&query=Yu%2C+J">Joonsang Yu</a>, <a href="/search/cs?searchtype=author&query=Baek%2C+S">Seolki Baek</a>, <a href="/search/cs?searchtype=author&query=Byeon%2C+S">Sumin Byeon</a>, <a href="/search/cs?searchtype=author&query=Cho%2C+E">Eungsup Cho</a>, <a href="/search/cs?searchtype=author&query=Choe%2C+D">Dooseok Choe</a>, <a href="/search/cs?searchtype=author&query=Han%2C+J">Jeesung Han</a> , et al. (371 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.01954v2-abstract-short" style="display: inline;"> We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding. HyperCLOVA X was trained on a balanced mix of Korean, English, and code data, followed by instruction-tuning with high-quality human-annotated datasets while abiding by strict safety guidelines reflecting our commitment t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.01954v2-abstract-full').style.display = 'inline'; document.getElementById('2404.01954v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.01954v2-abstract-full" style="display: none;"> We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding. HyperCLOVA X was trained on a balanced mix of Korean, English, and code data, followed by instruction-tuning with high-quality human-annotated datasets while abiding by strict safety guidelines reflecting our commitment to responsible AI. The model is evaluated across various benchmarks, including comprehensive reasoning, knowledge, commonsense, factuality, coding, math, chatting, instruction-following, and harmlessness, in both Korean and English. HyperCLOVA X exhibits strong reasoning capabilities in Korean backed by a deep understanding of the language and cultural nuances. Further analysis of the inherent bilingual nature and its extension to multilingualism highlights the model's cross-lingual proficiency and strong generalization ability to untargeted languages, including machine translation between several language pairs and cross-lingual inference tasks. We believe that HyperCLOVA X can provide helpful guidance for regions or countries in developing their sovereign LLMs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.01954v2-abstract-full').style.display = 'none'; document.getElementById('2404.01954v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 2 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">44 pages; updated authors list and fixed author names</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.15048">arXiv:2403.15048</a> <span> [<a href="https://arxiv.org/pdf/2403.15048">pdf</a>, <a href="https://arxiv.org/format/2403.15048">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Multimedia">cs.MM</span> </div> </div> <p class="title is-5 mathjax"> Cartoon Hallucinations Detection: Pose-aware In Context Visual Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kim%2C+B">Bumsoo Kim</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Wonseop Shin</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+K">Kyuchul Lee</a>, <a href="/search/cs?searchtype=author&query=Seo%2C+S">Sanghyun Seo</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.15048v2-abstract-short" style="display: inline;"> Large-scale Text-to-Image (TTI) models have become a common approach for generating training data in various generative fields. However, visual hallucinations, which contain perceptually critical defects, remain a concern, especially in non-photorealistic styles like cartoon characters. We propose a novel visual hallucination detection system for cartoon character images generated by TTI models. O… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.15048v2-abstract-full').style.display = 'inline'; document.getElementById('2403.15048v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.15048v2-abstract-full" style="display: none;"> Large-scale Text-to-Image (TTI) models have become a common approach for generating training data in various generative fields. However, visual hallucinations, which contain perceptually critical defects, remain a concern, especially in non-photorealistic styles like cartoon characters. We propose a novel visual hallucination detection system for cartoon character images generated by TTI models. Our approach leverages pose-aware in-context visual learning (PA-ICVL) with Vision-Language Models (VLMs), utilizing both RGB images and pose information. By incorporating pose guidance from a fine-tuned pose estimator, we enable VLMs to make more accurate decisions. Experimental results demonstrate significant improvements in identifying visual hallucinations compared to baseline methods relying solely on RGB images. This research advances TTI models by mitigating visual hallucinations, expanding their potential in non-photorealistic domains. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.15048v2-abstract-full').style.display = 'none'; document.getElementById('2403.15048v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 22 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">11 pages, 12 figures, 1 table, Project page: https://gh-bumsookim.github.io/Cartoon-Hallucinations-Detection/</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.14155">arXiv:2403.14155</a> <span> [<a href="https://arxiv.org/pdf/2403.14155">pdf</a>, <a href="https://arxiv.org/format/2403.14155">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Harmonizing Visual and Textual Embeddings for Zero-Shot Text-to-Image Customization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Song%2C+Y">Yeji Song</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+J">Jimyeong Kim</a>, <a href="/search/cs?searchtype=author&query=Park%2C+W">Wonhark Park</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Wonsik Shin</a>, <a href="/search/cs?searchtype=author&query=Rhee%2C+W">Wonjong Rhee</a>, <a href="/search/cs?searchtype=author&query=Kwak%2C+N">Nojun Kwak</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.14155v1-abstract-short" style="display: inline;"> In a surge of text-to-image (T2I) models and their customization methods that generate new images of a user-provided subject, current works focus on alleviating the costs incurred by a lengthy per-subject optimization. These zero-shot customization methods encode the image of a specified subject into a visual embedding which is then utilized alongside the textual embedding for diffusion guidance.… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.14155v1-abstract-full').style.display = 'inline'; document.getElementById('2403.14155v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.14155v1-abstract-full" style="display: none;"> In a surge of text-to-image (T2I) models and their customization methods that generate new images of a user-provided subject, current works focus on alleviating the costs incurred by a lengthy per-subject optimization. These zero-shot customization methods encode the image of a specified subject into a visual embedding which is then utilized alongside the textual embedding for diffusion guidance. The visual embedding incorporates intrinsic information about the subject, while the textual embedding provides a new, transient context. However, the existing methods often 1) are significantly affected by the input images, eg., generating images with the same pose, and 2) exhibit deterioration in the subject's identity. We first pin down the problem and show that redundant pose information in the visual embedding interferes with the textual embedding containing the desired pose information. To address this issue, we propose orthogonal visual embedding which effectively harmonizes with the given textual embedding. We also adopt the visual-only embedding and inject the subject's clear features utilizing a self-attention swap. Our results demonstrate the effectiveness and robustness of our method, which offers highly flexible zero-shot generation while effectively maintaining the subject's identity. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.14155v1-abstract-full').style.display = 'none'; document.getElementById('2403.14155v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Project page: https://ldynx.github.io/harmony-zero-t2i/</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.11925">arXiv:2402.11925</a> <span> [<a href="https://arxiv.org/pdf/2402.11925">pdf</a>, <a href="https://arxiv.org/format/2402.11925">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Energy-Efficient Edge Learning via Joint Data Deepening-and-Prefetching </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kook%2C+S">Sujin Kook</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Won-Yong Shin</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+S">Seong-Lyun Kim</a>, <a href="/search/cs?searchtype=author&query=Ko%2C+S">Seung-Woo Ko</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2402.11925v1-abstract-short" style="display: inline;"> The vision of pervasive artificial intelligence (AI) services can be realized by training an AI model on time using real-time data collected by internet of things (IoT) devices. To this end, IoT devices require offloading their data to an edge server in proximity. However, transmitting high-dimensional and voluminous data from energy-constrained IoT devices poses a significant challenge. To addres… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.11925v1-abstract-full').style.display = 'inline'; document.getElementById('2402.11925v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.11925v1-abstract-full" style="display: none;"> The vision of pervasive artificial intelligence (AI) services can be realized by training an AI model on time using real-time data collected by internet of things (IoT) devices. To this end, IoT devices require offloading their data to an edge server in proximity. However, transmitting high-dimensional and voluminous data from energy-constrained IoT devices poses a significant challenge. To address this limitation, we propose a novel offloading architecture, called joint data deepening-and-prefetching (JD2P), which is feature-by-feature offloading comprising two key techniques. The first one is data deepening, where each data sample's features are sequentially offloaded in the order of importance determined by the data embedding technique such as principle component analysis (PCA). Offloading is terminated once the already transmitted features are sufficient for accurate data classification, resulting in a reduction in the amount of transmitted data. The criteria to offload data are derived for binary and multi-class classifiers, which are designed based on support vector machine (SVM) and deep neural network (DNN), respectively. The second one is data prefetching, where some features potentially required in the future are offloaded in advance, thus achieving high efficiency via precise prediction and parameter optimization. We evaluate the effectiveness of JD2P through experiments using the MNIST dataset, and the results demonstrate its significant reduction in expected energy consumption compared to several benchmarks without degrading learning accuracy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.11925v1-abstract-full').style.display = 'none'; document.getElementById('2402.11925v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">accepted for publication in IEEE Transactions on Wireless Communications. arXiv admin note: text overlap with arXiv:2211.07146</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.10781">arXiv:2402.10781</a> <span> [<a href="https://arxiv.org/pdf/2402.10781">pdf</a>, <a href="https://arxiv.org/format/2402.10781">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Towards 6G Evolution: Three Enhancements, Three Innovations, and Three Major Challenges </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Singh%2C+R">Rohit Singh</a>, <a href="/search/cs?searchtype=author&query=Kaushik%2C+A">Aryan Kaushik</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Wonjae Shin</a>, <a href="/search/cs?searchtype=author&query=Di+Renzo%2C+M">Marco Di Renzo</a>, <a href="/search/cs?searchtype=author&query=Sciancalepore%2C+V">Vincenzo Sciancalepore</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+D">Doohwan Lee</a>, <a href="/search/cs?searchtype=author&query=Sasaki%2C+H">Hirofumi Sasaki</a>, <a href="/search/cs?searchtype=author&query=Shojaeifard%2C+A">Arman Shojaeifard</a>, <a href="/search/cs?searchtype=author&query=Dobre%2C+O+A">Octavia A. Dobre</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2402.10781v1-abstract-short" style="display: inline;"> Over the past few decades, wireless communication has witnessed remarkable growth, experiencing several transformative changes. This article aims to provide a comprehensive overview of wireless communication technologies, from the foundations to the recent wireless advances. Specifically, we take a neutral look at the state-of-the-art technologies for 5G and the ongoing evolutions towards 6G, revi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.10781v1-abstract-full').style.display = 'inline'; document.getElementById('2402.10781v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.10781v1-abstract-full" style="display: none;"> Over the past few decades, wireless communication has witnessed remarkable growth, experiencing several transformative changes. This article aims to provide a comprehensive overview of wireless communication technologies, from the foundations to the recent wireless advances. Specifically, we take a neutral look at the state-of-the-art technologies for 5G and the ongoing evolutions towards 6G, reviewing the recommendations of the International Mobile Communication vision for 2030 (IMT-2030). We first highlight specific features of IMT 2030, including three IMT-2020 extensions (URLLC+, eMBB+, and mMTC+) and three new innovations (Ubiquitous connectivity and integrating the new capabilities of sensing & AI with communication functionality). Then, we delve into three major challenges in implementing 6G, along with global standardization efforts. Besides, a proof of concept is provided by demonstrating terahertz (THz) signal transmission using Orbital Angular Momentum (OAM) multiplexing, which is one of the potential candidates for 6G and beyond. To inspire further potential research, we conclude by identifying research opportunities and future visions on IMT-2030 recommendations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.10781v1-abstract-full').style.display = 'none'; document.getElementById('2402.10781v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">8 pages, 4 figures, 1 table</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.07381">arXiv:2402.07381</a> <span> [<a href="https://arxiv.org/pdf/2402.07381">pdf</a>, <a href="https://arxiv.org/format/2402.07381">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> RIS-Empowered LEO Satellite Networks for 6G: Promising Usage Scenarios and Future Directions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Toka%2C+M">Mesut Toka</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+B">Byungju Lee</a>, <a href="/search/cs?searchtype=author&query=Seong%2C+J">Jaehyup Seong</a>, <a href="/search/cs?searchtype=author&query=Kaushik%2C+A">Aryan Kaushik</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+J">Juhwan Lee</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+J">Jungwoo Lee</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+N">Namyoon Lee</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Wonjae Shin</a>, <a href="/search/cs?searchtype=author&query=Poor%2C+H+V">H. Vincent Poor</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2402.07381v1-abstract-short" style="display: inline;"> Low-Earth orbit (LEO) satellite systems have been deemed a promising key enabler for current 5G and the forthcoming 6G wireless networks. Such LEO satellite constellations can provide worldwide three-dimensional coverage, high data rate, and scalability, thus enabling truly ubiquitous connectivity. On the other hand, another promising technology, reconfigurable intelligent surfaces (RISs), has eme… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.07381v1-abstract-full').style.display = 'inline'; document.getElementById('2402.07381v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.07381v1-abstract-full" style="display: none;"> Low-Earth orbit (LEO) satellite systems have been deemed a promising key enabler for current 5G and the forthcoming 6G wireless networks. Such LEO satellite constellations can provide worldwide three-dimensional coverage, high data rate, and scalability, thus enabling truly ubiquitous connectivity. On the other hand, another promising technology, reconfigurable intelligent surfaces (RISs), has emerged with favorable features, such as flexible deployment, cost & power efficiency, less transmission delay, noise-free nature, and in-band full-duplex structure. LEO satellite networks have many practical imperfections and limitations; however, exploiting RISs has been shown to be a potential solution to overcome these challenges. Particularly, RISs can enhance link quality, reduce the Doppler shift effect, and mitigate inter-/intra beam interference. In this article, we delve into exploiting RISs in LEO satellite networks. First, we present a holistic overview of LEO satellite communication and RIS technology, highlighting potential benefits and challenges. Second, we describe promising usage scenarios and applications in detail. Finally, we discuss potential future directions and challenges on RIS-empowered LEO networks, offering futuristic visions of the upcoming 6G era. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.07381v1-abstract-full').style.display = 'none'; document.getElementById('2402.07381v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">18 pages, 5 figures, Paper accepted by IEEE Communications Magazine</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.05448">arXiv:2402.05448</a> <span> [<a href="https://arxiv.org/pdf/2402.05448">pdf</a>, <a href="https://arxiv.org/format/2402.05448">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Graphics">cs.GR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Multimedia">cs.MM</span> </div> </div> <p class="title is-5 mathjax"> Minecraft-ify: Minecraft Style Image Generation with Text-guided Image Editing for In-Game Application </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kim%2C+B">Bumsoo Kim</a>, <a href="/search/cs?searchtype=author&query=Byun%2C+S">Sanghyun Byun</a>, <a href="/search/cs?searchtype=author&query=Jung%2C+Y">Yonghoon Jung</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Wonseop Shin</a>, <a href="/search/cs?searchtype=author&query=Amin%2C+S+U">Sareer UI Amin</a>, <a href="/search/cs?searchtype=author&query=Seo%2C+S">Sanghyun Seo</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2402.05448v2-abstract-short" style="display: inline;"> In this paper, we first present the character texture generation system \textit{Minecraft-ify}, specified to Minecraft video game toward in-game application. Ours can generate face-focused image for texture mapping tailored to 3D virtual character having cube manifold. While existing projects or works only generate texture, proposed system can inverse the user-provided real image, or generate aver… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.05448v2-abstract-full').style.display = 'inline'; document.getElementById('2402.05448v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.05448v2-abstract-full" style="display: none;"> In this paper, we first present the character texture generation system \textit{Minecraft-ify}, specified to Minecraft video game toward in-game application. Ours can generate face-focused image for texture mapping tailored to 3D virtual character having cube manifold. While existing projects or works only generate texture, proposed system can inverse the user-provided real image, or generate average/random appearance from learned distribution. Moreover, it can be manipulated with text-guidance using StyleGAN and StyleCLIP. These features provide a more extended user experience with enlarged freedom as a user-friendly AI-tool. Project page can be found at https://gh-bumsookim.github.io/Minecraft-ify/ <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.05448v2-abstract-full').style.display = 'none'; document.getElementById('2402.05448v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 8 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">2 pages, 2 figures. Accepted as Spotlight to NeurIPS 2023 Workshop on Machine Learning for Creativity and Design</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.00729">arXiv:2402.00729</a> <span> [<a href="https://arxiv.org/pdf/2402.00729">pdf</a>, <a href="https://arxiv.org/format/2402.00729">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> </div> <p class="title is-5 mathjax"> Profiling and Modeling of Power Characteristics of Leadership-Scale HPC System Workloads </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Karimi%2C+A+M">Ahmad Maroof Karimi</a>, <a href="/search/cs?searchtype=author&query=Sattar%2C+N+S">Naw Safrin Sattar</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Woong Shin</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+F">Feiyi Wang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2402.00729v1-abstract-short" style="display: inline;"> In the exascale era in which application behavior has large power & energy footprints, per-application job-level awareness of such impression is crucial in taking steps towards achieving efficiency goals beyond performance, such as energy efficiency, and sustainability. To achieve these goals, we have developed a novel low-latency job power profiling machine learning pipeline that can group job-… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.00729v1-abstract-full').style.display = 'inline'; document.getElementById('2402.00729v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.00729v1-abstract-full" style="display: none;"> In the exascale era in which application behavior has large power & energy footprints, per-application job-level awareness of such impression is crucial in taking steps towards achieving efficiency goals beyond performance, such as energy efficiency, and sustainability. To achieve these goals, we have developed a novel low-latency job power profiling machine learning pipeline that can group job-level power profiles based on their shapes as they complete. This pipeline leverages a comprehensive feature extraction and clustering pipeline powered by a generative adversarial network (GAN) model to handle the feature-rich time series of job-level power measurements. The output is then used to train a classification model that can predict whether an incoming job power profile is similar to a known group of profiles or is completely new. With extensive evaluations, we demonstrate the effectiveness of each component in our pipeline. Also, we provide a preliminary analysis of the resulting clusters that depict the power profile landscape of the Summit supercomputer from more than 60K jobs sampled from the year 2021. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.00729v1-abstract-full').style.display = 'none'; document.getElementById('2402.00729v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.09511">arXiv:2312.09511</a> <span> [<a href="https://arxiv.org/pdf/2312.09511">pdf</a>, <a href="https://arxiv.org/format/2312.09511">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> MONET: Modality-Embracing Graph Convolutional Network and Target-Aware Attention for Multimedia Recommendation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kim%2C+Y">Yungi Kim</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+T">Taeri Kim</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Won-Yong Shin</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+S">Sang-Wook Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2312.09511v1-abstract-short" style="display: inline;"> In this paper, we focus on multimedia recommender systems using graph convolutional networks (GCNs) where the multimodal features as well as user-item interactions are employed together. Our study aims to exploit multimodal features more effectively in order to accurately capture users' preferences for items. To this end, we point out following two limitations of existing GCN-based multimedia reco… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.09511v1-abstract-full').style.display = 'inline'; document.getElementById('2312.09511v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.09511v1-abstract-full" style="display: none;"> In this paper, we focus on multimedia recommender systems using graph convolutional networks (GCNs) where the multimodal features as well as user-item interactions are employed together. Our study aims to exploit multimodal features more effectively in order to accurately capture users' preferences for items. To this end, we point out following two limitations of existing GCN-based multimedia recommender systems: (L1) although multimodal features of interacted items by a user can reveal her preferences on items, existing methods utilize GCN designed to focus only on capturing collaborative signals, resulting in insufficient reflection of the multimodal features in the final user/item embeddings; (L2) although a user decides whether to prefer the target item by considering its multimodal features, existing methods represent her as only a single embedding regardless of the target item's multimodal features and then utilize her embedding to predict her preference for the target item. To address the above issues, we propose a novel multimedia recommender system, named MONET, composed of following two core ideas: modality-embracing GCN (MeGCN) and target-aware attention. Through extensive experiments using four real-world datasets, we demonstrate i) the significant superiority of MONET over seven state-of-the-art competitors (up to 30.32% higher accuracy in terms of recall@20, compared to the best competitor) and ii) the effectiveness of the two core ideas in MONET. All MONET codes are available at https://github.com/Kimyungi/MONET. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.09511v1-abstract-full').style.display = 'none'; document.getElementById('2312.09511v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted by WSDM 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.02503">arXiv:2312.02503</a> <span> [<a href="https://arxiv.org/pdf/2312.02503">pdf</a>, <a href="https://arxiv.org/format/2312.02503">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> SAVE: Protagonist Diversification with Structure Agnostic Video Editing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Song%2C+Y">Yeji Song</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Wonsik Shin</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+J">Junsoo Lee</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+J">Jeesoo Kim</a>, <a href="/search/cs?searchtype=author&query=Kwak%2C+N">Nojun Kwak</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2312.02503v1-abstract-short" style="display: inline;"> Driven by the upsurge progress in text-to-image (T2I) generation models, text-to-video (T2V) generation has experienced a significant advance as well. Accordingly, tasks such as modifying the object or changing the style in a video have been possible. However, previous works usually work well on trivial and consistent shapes, and easily collapse on a difficult target that has a largely different b… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.02503v1-abstract-full').style.display = 'inline'; document.getElementById('2312.02503v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.02503v1-abstract-full" style="display: none;"> Driven by the upsurge progress in text-to-image (T2I) generation models, text-to-video (T2V) generation has experienced a significant advance as well. Accordingly, tasks such as modifying the object or changing the style in a video have been possible. However, previous works usually work well on trivial and consistent shapes, and easily collapse on a difficult target that has a largely different body shape from the original one. In this paper, we spot the bias problem in the existing video editing method that restricts the range of choices for the new protagonist and attempt to address this issue using the conventional image-level personalization method. We adopt motion personalization that isolates the motion from a single source video and then modifies the protagonist accordingly. To deal with the natural discrepancy between image and video, we propose a motion word with an inflated textual embedding to properly represent the motion in a source video. We also regulate the motion word to attend to proper motion-related areas by introducing a novel pseudo optical flow, efficiently computed from the pre-calculated attention maps. Finally, we decouple the motion from the appearance of the source video with an additional pseudo word. Extensive experiments demonstrate the editing capability of our method, taking a step toward more diverse and extensive video editing. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.02503v1-abstract-full').style.display = 'none'; document.getElementById('2312.02503v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Project website: https://ldynx.github.io/SAVE/</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.17781">arXiv:2311.17781</a> <span> [<a href="https://arxiv.org/pdf/2311.17781">pdf</a>, <a href="https://arxiv.org/format/2311.17781">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Neural and Evolutionary Computing">cs.NE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> </div> </div> <p class="title is-5 mathjax"> Propagate & Distill: Towards Effective Graph Learners Using Propagation-Embracing MLPs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Shin%2C+Y">Yong-Min Shin</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Won-Yong Shin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2311.17781v1-abstract-short" style="display: inline;"> Recent studies attempted to utilize multilayer perceptrons (MLPs) to solve semisupervised node classification on graphs, by training a student MLP by knowledge distillation from a teacher graph neural network (GNN). While previous studies have focused mostly on training the student MLP by matching the output probability distributions between the teacher and student models during distillation, it h… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.17781v1-abstract-full').style.display = 'inline'; document.getElementById('2311.17781v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.17781v1-abstract-full" style="display: none;"> Recent studies attempted to utilize multilayer perceptrons (MLPs) to solve semisupervised node classification on graphs, by training a student MLP by knowledge distillation from a teacher graph neural network (GNN). While previous studies have focused mostly on training the student MLP by matching the output probability distributions between the teacher and student models during distillation, it has not been systematically studied how to inject the structural information in an explicit and interpretable manner. Inspired by GNNs that separate feature transformation $T$ and propagation $螤$, we re-frame the distillation process as making the student MLP learn both $T$ and $螤$. Although this can be achieved by applying the inverse propagation $螤^{-1}$ before distillation from the teacher, it still comes with a high computational cost from large matrix multiplications during training. To solve this problem, we propose Propagate & Distill (P&D), which propagates the output of the teacher before distillation, which can be interpreted as an approximate process of the inverse propagation. We demonstrate that P&D can readily improve the performance of the student MLP. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.17781v1-abstract-full').style.display = 'none'; document.getElementById('2311.17781v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">17 pages, 2 figures, 8 tables; 2nd Learning on Graphs Conference (LoG 2023) (Please cite our conference version.). arXiv admin note: substantial text overlap with arXiv:2311.11759</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.11759">arXiv:2311.11759</a> <span> [<a href="https://arxiv.org/pdf/2311.11759">pdf</a>, <a href="https://arxiv.org/format/2311.11759">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Neural and Evolutionary Computing">cs.NE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> </div> </div> <p class="title is-5 mathjax"> Unveiling the Unseen Potential of Graph Learning through MLPs: Effective Graph Learners Using Propagation-Embracing MLPs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Shin%2C+Y">Yong-Min Shin</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Won-Yong Shin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2311.11759v1-abstract-short" style="display: inline;"> Recent studies attempted to utilize multilayer perceptrons (MLPs) to solve semi-supervised node classification on graphs, by training a student MLP by knowledge distillation (KD) from a teacher graph neural network (GNN). While previous studies have focused mostly on training the student MLP by matching the output probability distributions between the teacher and student models during KD, it has n… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.11759v1-abstract-full').style.display = 'inline'; document.getElementById('2311.11759v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.11759v1-abstract-full" style="display: none;"> Recent studies attempted to utilize multilayer perceptrons (MLPs) to solve semi-supervised node classification on graphs, by training a student MLP by knowledge distillation (KD) from a teacher graph neural network (GNN). While previous studies have focused mostly on training the student MLP by matching the output probability distributions between the teacher and student models during KD, it has not been systematically studied how to inject the structural information in an explicit and interpretable manner. Inspired by GNNs that separate feature transformation $T$ and propagation $螤$, we re-frame the KD process as enabling the student MLP to explicitly learn both $T$ and $螤$. Although this can be achieved by applying the inverse propagation $螤^{-1}$ before distillation from the teacher GNN, it still comes with a high computational cost from large matrix multiplications during training. To solve this problem, we propose Propagate & Distill (P&D), which propagates the output of the teacher GNN before KD and can be interpreted as an approximate process of the inverse propagation $螤^{-1}$. Through comprehensive evaluations using real-world benchmark datasets, we demonstrate the effectiveness of P&D by showing further performance boost of the student MLP. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.11759v1-abstract-full').style.display = 'none'; document.getElementById('2311.11759v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">35 pages, 5 figures, 8 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.07223">arXiv:2311.07223</a> <span> [<a href="https://arxiv.org/pdf/2311.07223">pdf</a>, <a href="https://arxiv.org/format/2311.07223">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Programming Languages">cs.PL</span> </div> </div> <p class="title is-5 mathjax"> Wasm SpecTec: Engineering a Formal Language Standard </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Breitner%2C+J">Joachim Breitner</a>, <a href="/search/cs?searchtype=author&query=Gardner%2C+P">Philippa Gardner</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+J">Jaehyun Lee</a>, <a href="/search/cs?searchtype=author&query=Lindley%2C+S">Sam Lindley</a>, <a href="/search/cs?searchtype=author&query=Pretnar%2C+M">Matija Pretnar</a>, <a href="/search/cs?searchtype=author&query=Rao%2C+X">Xiaojia Rao</a>, <a href="/search/cs?searchtype=author&query=Rossberg%2C+A">Andreas Rossberg</a>, <a href="/search/cs?searchtype=author&query=Ryu%2C+S">Sukyoung Ryu</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Wonho Shin</a>, <a href="/search/cs?searchtype=author&query=Watt%2C+C">Conrad Watt</a>, <a href="/search/cs?searchtype=author&query=Youn%2C+D">Dongjun Youn</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2311.07223v1-abstract-short" style="display: inline;"> WebAssembly (Wasm) is a low-level bytecode language and virtual machine, intended as a compilation target for a wide range of programming languages, which is seeing increasing adoption across diverse ecosystems. As a young technology, Wasm continues to evolve -- it reached version 2.0 last year and another major update is expected soon. For a new feature to be standardised in Wasm, four key arte… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.07223v1-abstract-full').style.display = 'inline'; document.getElementById('2311.07223v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.07223v1-abstract-full" style="display: none;"> WebAssembly (Wasm) is a low-level bytecode language and virtual machine, intended as a compilation target for a wide range of programming languages, which is seeing increasing adoption across diverse ecosystems. As a young technology, Wasm continues to evolve -- it reached version 2.0 last year and another major update is expected soon. For a new feature to be standardised in Wasm, four key artefacts must be presented: a formal (mathematical) specification of the feature, an accompanying prose pseudocode description, an implementation in the official reference interpreter, and a suite of unit tests. This rigorous process helps to avoid errors in the design and implementation of new Wasm features, and Wasm's distinctive formal specification in particular has facilitated machine-checked proofs of various correctness properties for the language. However, manually crafting all of these artefacts requires expert knowledge combined with repetitive and tedious labor, which is a burden on the language's standardization process and authoring of the specification. This paper presents Wasm SpecTec, a technology to express the formal specification of Wasm through a domain-specific language. This DSL allows all of Wasm's currently handwritten specification artefacts to be error-checked and generated automatically from a single source of truth, and is designed to be easy to write, read, compare, and review. We believe that Wasm SpecTec's automation and meta-level error checking will significantly ease the current burden of the language's specification authors. We demonstrate the current capabilities of Wasm SpecTec by showcasing its proficiency in generating various artefacts, and describe our work towards replacing the manually written official Wasm specification document with specifications generated by Wasm SpecTec. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.07223v1-abstract-full').style.display = 'none'; document.getElementById('2311.07223v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">5 pages, 7 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.16371">arXiv:2310.16371</a> <span> [<a href="https://arxiv.org/pdf/2310.16371">pdf</a>, <a href="https://arxiv.org/format/2310.16371">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> Synergizing Airborne Non-Terrestrial Networks and Reconfigurable Intelligent Surfaces-Aided 6G IoT </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jamshed%2C+M+A">Muhammad Ali Jamshed</a>, <a href="/search/cs?searchtype=author&query=Kaushik%2C+A">Aryan Kaushik</a>, <a href="/search/cs?searchtype=author&query=Toka%2C+M">Mesut Toka</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Wonjae Shin</a>, <a href="/search/cs?searchtype=author&query=Shakir%2C+M+Z">Muhammad Zeeshan Shakir</a>, <a href="/search/cs?searchtype=author&query=Dash%2C+S+P">Soumya P. Dash</a>, <a href="/search/cs?searchtype=author&query=Dardari%2C+D">Davide Dardari</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.16371v1-abstract-short" style="display: inline;"> On the one hand, Reconfigurable Intelligent Surfaces (RISs) emerge as a promising solution to meet the demand for higher data rates, improved coverage, and efficient spectrum utilization. On the other hand, Non-Terrestrial Networks (NTNs) offer unprecedented possibilities for global connectivity. Moreover, the NTN can also support the upsurge in the number of Internet of Things (IoT) devices by pr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.16371v1-abstract-full').style.display = 'inline'; document.getElementById('2310.16371v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.16371v1-abstract-full" style="display: none;"> On the one hand, Reconfigurable Intelligent Surfaces (RISs) emerge as a promising solution to meet the demand for higher data rates, improved coverage, and efficient spectrum utilization. On the other hand, Non-Terrestrial Networks (NTNs) offer unprecedented possibilities for global connectivity. Moreover, the NTN can also support the upsurge in the number of Internet of Things (IoT) devices by providing reliable and ubiquitous connectivity. Although NTNs have shown promising results, there are several challenges associated with their usage, such as signal propagation delays, interference, security, etc. In this article, we have discussed the possibilities of integrating RIS with an NTN platform to overcome the issues associated with NTN. Furthermore, through experimental validation, we have demonstrated that the RIS-assisted NTN can play a pivotal role in improving the performance of the entire communication system. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.16371v1-abstract-full').style.display = 'none'; document.getElementById('2310.16371v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">15 pages, 5 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.00244">arXiv:2310.00244</a> <span> [<a href="https://arxiv.org/pdf/2310.00244">pdf</a>, <a href="https://arxiv.org/format/2310.00244">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> Coordinated Rate-Splitting Multiple Access for Integrated Satellite-Terrestrial Networks with Super-Common Message </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Lee%2C+J">Juhwan Lee</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+J">Jungwoo Lee</a>, <a href="/search/cs?searchtype=author&query=Yin%2C+L">Longfei Yin</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Wonjae Shin</a>, <a href="/search/cs?searchtype=author&query=Clerckx%2C+B">Bruno Clerckx</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.00244v1-abstract-short" style="display: inline;"> Rate-splitting multiple access (RSMA) is an emerging multiple access technique for multi-antenna networks that splits messages into common and private parts for flexible interference mitigation. Motivated by its robustness and scalability, it is promising to employ RSMA in integrated satellite-terrestrial networks (ISTN), where a satellite serves satellite users (SUs) broadly with a multibeam mult… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.00244v1-abstract-full').style.display = 'inline'; document.getElementById('2310.00244v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.00244v1-abstract-full" style="display: none;"> Rate-splitting multiple access (RSMA) is an emerging multiple access technique for multi-antenna networks that splits messages into common and private parts for flexible interference mitigation. Motivated by its robustness and scalability, it is promising to employ RSMA in integrated satellite-terrestrial networks (ISTN), where a satellite serves satellite users (SUs) broadly with a multibeam multicast transmission while terrestrial base station (BS) serves cellular users (CUs) with a unicast transmission, operating in the same frequency band. To avoid the data exchange between satellite/cellular networks via backhaul, we assume a coordinated ISTN relying on imperfect channel state information. We put forth a coordinated RSMA framework tailored to the coordinated ISTN by applying inter-network rate-splitting (RS) with a super-common message on top of intra-network RS with common/private messages. With the unified RS design for inter- and intra-networks, we jointly optimize the precoding and power allocation of the private/common/super-common messages to achieve max-min fairness among all SUs and CUs through successive convex approximation. By doing so, the power of the super-common message can be adjusted according to interference levels of the satellite towards CUs, thereby potentially mitigating inter-network interference. Simulation results demonstrate the superiority and robustness of our approach to cope with various interference and propagation conditions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.00244v1-abstract-full').style.display = 'none'; document.getElementById('2310.00244v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">16 pages, 3 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.15433">arXiv:2309.15433</a> <span> [<a href="https://arxiv.org/pdf/2309.15433">pdf</a>, <a href="https://arxiv.org/format/2309.15433">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Databases">cs.DB</span> </div> </div> <p class="title is-5 mathjax"> Cardinality Estimation of Subgraph Matching: A Filtering-Sampling Approach </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Shin%2C+W">Wonseok Shin</a>, <a href="/search/cs?searchtype=author&query=Song%2C+S">Siwoo Song</a>, <a href="/search/cs?searchtype=author&query=Park%2C+K">Kunsoo Park</a>, <a href="/search/cs?searchtype=author&query=Han%2C+W">Wook-Shin Han</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2309.15433v2-abstract-short" style="display: inline;"> Subgraph counting is a fundamental problem in understanding and analyzing graph structured data, yet computationally challenging. This calls for an accurate and efficient algorithm for Subgraph Cardinality Estimation, which is to estimate the number of all isomorphic embeddings of a query graph in a data graph. We present FaSTest, a novel algorithm that combines (1) a powerful filtering technique… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.15433v2-abstract-full').style.display = 'inline'; document.getElementById('2309.15433v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.15433v2-abstract-full" style="display: none;"> Subgraph counting is a fundamental problem in understanding and analyzing graph structured data, yet computationally challenging. This calls for an accurate and efficient algorithm for Subgraph Cardinality Estimation, which is to estimate the number of all isomorphic embeddings of a query graph in a data graph. We present FaSTest, a novel algorithm that combines (1) a powerful filtering technique to significantly reduce the sample space, (2) an adaptive tree sampling algorithm for accurate and efficient estimation, and (3) a worst-case optimal stratified graph sampling algorithm for difficult instances. Extensive experiments on real-world datasets show that FaSTest outperforms state-of-the-art sampling-based methods by up to two orders of magnitude and GNN-based methods by up to three orders of magnitude in terms of accuracy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.15433v2-abstract-full').style.display = 'none'; document.getElementById('2309.15433v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 27 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.13542">arXiv:2309.13542</a> <span> [<a href="https://arxiv.org/pdf/2309.13542">pdf</a>, <a href="https://arxiv.org/format/2309.13542">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Integrated Sensing and Communications for IoT: Synergies with Key 6G Technology Enablers </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kaushik%2C+A">Aryan Kaushik</a>, <a href="/search/cs?searchtype=author&query=Singh%2C+R">Rohit Singh</a>, <a href="/search/cs?searchtype=author&query=Li%2C+M">Ming Li</a>, <a href="/search/cs?searchtype=author&query=Luo%2C+H">Honghao Luo</a>, <a href="/search/cs?searchtype=author&query=Dayarathna%2C+S">Shalanika Dayarathna</a>, <a href="/search/cs?searchtype=author&query=Senanayake%2C+R">Rajitha Senanayake</a>, <a href="/search/cs?searchtype=author&query=An%2C+X">Xueli An</a>, <a href="/search/cs?searchtype=author&query=Stirling-Gallacher%2C+R+A">Richard A. Stirling-Gallacher</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Wonjae Shin</a>, <a href="/search/cs?searchtype=author&query=Di+Renzo%2C+M">Marco Di Renzo</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2309.13542v1-abstract-short" style="display: inline;"> The Internet of Things (IoT) and wireless generations have been evolving simultaneously for the past few decades. Built upon wireless communication and sensing technologies, IoT networks are usually evaluated based on metrics that measure the device ability to sense information and effectively share it with the network, which makes Integrated Sensing and Communication (ISAC) a pivotal candidate fo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.13542v1-abstract-full').style.display = 'inline'; document.getElementById('2309.13542v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.13542v1-abstract-full" style="display: none;"> The Internet of Things (IoT) and wireless generations have been evolving simultaneously for the past few decades. Built upon wireless communication and sensing technologies, IoT networks are usually evaluated based on metrics that measure the device ability to sense information and effectively share it with the network, which makes Integrated Sensing and Communication (ISAC) a pivotal candidate for the sixth-generation (6G) IoT standards. This paper reveals several innovative aspects of ISAC from an IoT perspective in 6G, empowering various modern IoT use cases and key technology enablers. Moreover, we address the challenges and future potential of ISAC-enabled IoT, including synergies with Reconfigurable Intelligent Surfaces (RIS), Artificial Intelligence (AI), and key updates of ISAC-IoT in 6G standardization. Furthermore, several evolutionary concepts are introduced to open future research in 6G ISAC-IoT, including the interplay with Non-Terrestrial Networks (NTN) and Orthogonal Time-Frequency Space (OTFS) modulation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.13542v1-abstract-full').style.display = 'none'; document.getElementById('2309.13542v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">7 pages, 6 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.06325">arXiv:2309.06325</a> <span> [<a href="https://arxiv.org/pdf/2309.06325">pdf</a>, <a href="https://arxiv.org/format/2309.06325">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Distributed Precoding for Satellite-Terrestrial Integrated Networks Without Sharing CSIT: A Rate-Splitting Approach </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kim%2C+D">Doseon Kim</a>, <a href="/search/cs?searchtype=author&query=Cho%2C+S">Sungyoon Cho</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Wonjae Shin</a>, <a href="/search/cs?searchtype=author&query=Park%2C+J">Jeonghun Park</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+D+K">Dong Ku Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2309.06325v4-abstract-short" style="display: inline;"> Satellite-terrestrial integrated networks (STINs) are promising architecture for providing global coverage. In STINs, full frequency reuse between a satellite and a terrestrial base station (BS) is encouraged for aggressive spectrum reuse, which induces non-negligible amount of interference. To address the interference management problem in STINs, this paper proposes a novel distributed precoding… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.06325v4-abstract-full').style.display = 'inline'; document.getElementById('2309.06325v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.06325v4-abstract-full" style="display: none;"> Satellite-terrestrial integrated networks (STINs) are promising architecture for providing global coverage. In STINs, full frequency reuse between a satellite and a terrestrial base station (BS) is encouraged for aggressive spectrum reuse, which induces non-negligible amount of interference. To address the interference management problem in STINs, this paper proposes a novel distributed precoding method. Key features of our method are: i) a rate-splitting (RS) strategy is incorporated for efficient interference management and ii) the precoders are designed in a distributed way without sharing channel state information between a satellite and a terrestrial BS. Specifically, to design the precoders in a distributed fashion, we put forth a spectral efficiency decoupling technique, that disentangles the total spectral efficiency function into two distinct terms, each of which is dependent solely on the satellite's precoder and the terrestrial BS's precoder, respectively. Then, to resolve the non-smoothness raised by the RS strategy, we approximate the spectral efficiency expression as a smooth function by using the LogSumExp technique; thereafter we develop a generalized power iteration inspired optimization algorithm built based on the first-order optimality condition. Simulation results demonstrate that the proposed method offers considerable spectral efficiency gains compared to the existing methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.06325v4-abstract-full').style.display = 'none'; document.getElementById('2309.06325v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 12 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">16 pages, 7 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.01961">arXiv:2309.01961</a> <span> [<a href="https://arxiv.org/pdf/2309.01961">pdf</a>, <a href="https://arxiv.org/format/2309.01961">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> NICE: CVPR 2023 Challenge on Zero-shot Image Captioning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kim%2C+T">Taehoon Kim</a>, <a href="/search/cs?searchtype=author&query=Ahn%2C+P">Pyunghwan Ahn</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+S">Sangyun Kim</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+S">Sihaeng Lee</a>, <a href="/search/cs?searchtype=author&query=Marsden%2C+M">Mark Marsden</a>, <a href="/search/cs?searchtype=author&query=Sala%2C+A">Alessandra Sala</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+S+H">Seung Hwan Kim</a>, <a href="/search/cs?searchtype=author&query=Han%2C+B">Bohyung Han</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+K+M">Kyoung Mu Lee</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+H">Honglak Lee</a>, <a href="/search/cs?searchtype=author&query=Bae%2C+K">Kyounghoon Bae</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+X">Xiangyu Wu</a>, <a href="/search/cs?searchtype=author&query=Gao%2C+Y">Yi Gao</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+H">Hailiang Zhang</a>, <a href="/search/cs?searchtype=author&query=Yang%2C+Y">Yang Yang</a>, <a href="/search/cs?searchtype=author&query=Guo%2C+W">Weili Guo</a>, <a href="/search/cs?searchtype=author&query=Lu%2C+J">Jianfeng Lu</a>, <a href="/search/cs?searchtype=author&query=Oh%2C+Y">Youngtaek Oh</a>, <a href="/search/cs?searchtype=author&query=Cho%2C+J+W">Jae Won Cho</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+D">Dong-jin Kim</a>, <a href="/search/cs?searchtype=author&query=Kweon%2C+I+S">In So Kweon</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+J">Junmo Kim</a>, <a href="/search/cs?searchtype=author&query=Kang%2C+W">Wooyoung Kang</a>, <a href="/search/cs?searchtype=author&query=Jhoo%2C+W+Y">Won Young Jhoo</a>, <a href="/search/cs?searchtype=author&query=Roh%2C+B">Byungseok Roh</a> , et al. (17 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2309.01961v3-abstract-short" style="display: inline;"> In this report, we introduce NICE (New frontiers for zero-shot Image Captioning Evaluation) project and share the results and outcomes of 2023 challenge. This project is designed to challenge the computer vision community to develop robust image captioning models that advance the state-of-the-art both in terms of accuracy and fairness. Through the challenge, the image captioning models were tested… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.01961v3-abstract-full').style.display = 'inline'; document.getElementById('2309.01961v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.01961v3-abstract-full" style="display: none;"> In this report, we introduce NICE (New frontiers for zero-shot Image Captioning Evaluation) project and share the results and outcomes of 2023 challenge. This project is designed to challenge the computer vision community to develop robust image captioning models that advance the state-of-the-art both in terms of accuracy and fairness. Through the challenge, the image captioning models were tested using a new evaluation dataset that includes a large variety of visual concepts from many domains. There was no specific training data provided for the challenge, and therefore the challenge entries were required to adapt to new types of image descriptions that had not been seen during training. This report includes information on the newly proposed NICE dataset, evaluation methods, challenge results, and technical details of top-ranking entries. We expect that the outcomes of the challenge will contribute to the improvement of AI models on various vision-language tasks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.01961v3-abstract-full').style.display = 'none'; document.getElementById('2309.01961v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 5 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Tech report, project page https://nice.lgresearch.ai/</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2308.01227">arXiv:2308.01227</a> <span> [<a href="https://arxiv.org/pdf/2308.01227">pdf</a>, <a href="https://arxiv.org/format/2308.01227">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Towards Integrated Sensing and Communications for 6G: A Standardization Perspective </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kaushik%2C+A">Aryan Kaushik</a>, <a href="/search/cs?searchtype=author&query=Singh%2C+R">Rohit Singh</a>, <a href="/search/cs?searchtype=author&query=Dayarathna%2C+S">Shalanika Dayarathna</a>, <a href="/search/cs?searchtype=author&query=Senanayake%2C+R">Rajitha Senanayake</a>, <a href="/search/cs?searchtype=author&query=Di+Renzo%2C+M">Marco Di Renzo</a>, <a href="/search/cs?searchtype=author&query=Dajer%2C+M">Miguel Dajer</a>, <a href="/search/cs?searchtype=author&query=Ji%2C+H">Hyoungju Ji</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+Y">Younsun Kim</a>, <a href="/search/cs?searchtype=author&query=Sciancalepore%2C+V">Vincenzo Sciancalepore</a>, <a href="/search/cs?searchtype=author&query=Zappone%2C+A">Alessio Zappone</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Wonjae Shin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2308.01227v1-abstract-short" style="display: inline;"> The radio communication division of the International Telecommunication Union (ITU-R) has recently adopted Integrated Sensing and Communication (ISAC) among the key usage scenarios for IMT-2030/6G. ISAC is envisioned to play a vital role in the upcoming wireless generation standards. In this work, we bring together several paramount and innovative aspects of ISAC technology from a global 6G standa… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.01227v1-abstract-full').style.display = 'inline'; document.getElementById('2308.01227v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2308.01227v1-abstract-full" style="display: none;"> The radio communication division of the International Telecommunication Union (ITU-R) has recently adopted Integrated Sensing and Communication (ISAC) among the key usage scenarios for IMT-2030/6G. ISAC is envisioned to play a vital role in the upcoming wireless generation standards. In this work, we bring together several paramount and innovative aspects of ISAC technology from a global 6G standardization perspective, including both industrial and academic progress. Specifically, this article provides 6G requirements and ISAC-enabled vision, including various aspects of 6G standardization, benefits of ISAC co-existence, and integration challenges. Moreover, we present key enabling technologies, including intelligent metasurface-aided ISAC, as well as Orthogonal Time Frequency Space (OTFS) waveform design and interference management for ISAC. Finally, future aspects are discussed to open various research opportunities and challenges on the ISAC technology towards 6G wireless communications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.01227v1-abstract-full').style.display = 'none'; document.getElementById('2308.01227v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">7 pages, 5 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2307.07382">arXiv:2307.07382</a> <span> [<a href="https://arxiv.org/pdf/2307.07382">pdf</a>, <a href="https://arxiv.org/format/2307.07382">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Distributed Rate-Splitting Multiple Access for Multilayer Satellite Communications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Xu%2C+Y">Yunnuo Xu</a>, <a href="/search/cs?searchtype=author&query=Yin%2C+L">Longfei Yin</a>, <a href="/search/cs?searchtype=author&query=Mao%2C+Y">Yijie Mao</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Wonjae Shin</a>, <a href="/search/cs?searchtype=author&query=Clerckx%2C+B">Bruno Clerckx</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2307.07382v3-abstract-short" style="display: inline;"> Future wireless networks, in particular, 5G and beyond, are anticipated to deploy dense Low Earth Orbit (LEO) satellites to provide global coverage and broadband connectivity. However, the limited frequency band and the coexistence of multiple constellations bring new challenges for interference management. In this paper, we propose a robust multilayer interference management scheme for spectrum s… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.07382v3-abstract-full').style.display = 'inline'; document.getElementById('2307.07382v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2307.07382v3-abstract-full" style="display: none;"> Future wireless networks, in particular, 5G and beyond, are anticipated to deploy dense Low Earth Orbit (LEO) satellites to provide global coverage and broadband connectivity. However, the limited frequency band and the coexistence of multiple constellations bring new challenges for interference management. In this paper, we propose a robust multilayer interference management scheme for spectrum sharing in heterogeneous satellite networks with statistical channel state information (CSI) at the transmitter (CSIT) and receivers (CSIR). In the proposed scheme, Rate-Splitting Multiple Access (RSMA), as a general and powerful framework for interference management and multiple access strategies, is implemented distributedly at GEO and LEO satellites, coined Distributed-RSMA (D-RSMA). By doing so, D-RSMA aims to mitigate the interference and boost the user fairness of the overall multilayer satellite system. Specifically, we study the problem of jointly optimizing the GEO/LEO precoders and message splits to maximize the minimum rate among User Terminals (UTs) subject to a transmit power constraint at all satellites. A robust algorithm is proposed to solve the original non-convex optimization problem. Numerical results demonstrate the effectiveness and robustness towards network load and CSI uncertainty of our proposed D-RSMA scheme. Benefiting from the interference management capability, D-RSMA provides significant max-min fairness performance gains compared to several benchmark schemes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.07382v3-abstract-full').style.display = 'none'; document.getElementById('2307.07382v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 14 July, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.12978">arXiv:2306.12978</a> <span> [<a href="https://arxiv.org/pdf/2306.12978">pdf</a>, <a href="https://arxiv.org/format/2306.12978">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Rate-Splitting Multiple Access for 6G Networks: Ten Promising Scenarios and Applications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Park%2C+J">Jeonghun Park</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+B">Byungju Lee</a>, <a href="/search/cs?searchtype=author&query=Choi%2C+J">Jinseok Choi</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+H">Hoon Lee</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+N">Namyoon Lee</a>, <a href="/search/cs?searchtype=author&query=Park%2C+S">Seok-Hwan Park</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+K">Kyoung-Jae Lee</a>, <a href="/search/cs?searchtype=author&query=Choi%2C+J">Junil Choi</a>, <a href="/search/cs?searchtype=author&query=Chae%2C+S+H">Sung Ho Chae</a>, <a href="/search/cs?searchtype=author&query=Jeon%2C+S">Sang-Woon Jeon</a>, <a href="/search/cs?searchtype=author&query=Kwak%2C+K+S">Kyung Sup Kwak</a>, <a href="/search/cs?searchtype=author&query=Clerckx%2C+B">Bruno Clerckx</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Wonjae Shin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2306.12978v1-abstract-short" style="display: inline;"> In the upcoming 6G era, multiple access (MA) will play an essential role in achieving high throughput performances required in a wide range of wireless applications. Since MA and interference management are closely related issues, the conventional MA techniques are limited in that they cannot provide near-optimal performance in universal interference regimes. Recently, rate-splitting multiple acce… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.12978v1-abstract-full').style.display = 'inline'; document.getElementById('2306.12978v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.12978v1-abstract-full" style="display: none;"> In the upcoming 6G era, multiple access (MA) will play an essential role in achieving high throughput performances required in a wide range of wireless applications. Since MA and interference management are closely related issues, the conventional MA techniques are limited in that they cannot provide near-optimal performance in universal interference regimes. Recently, rate-splitting multiple access (RSMA) has been gaining much attention. RSMA splits an individual message into two parts: a common part, decodable by every user, and a private part, decodable only by the intended user. Each user first decodes the common message and then decodes its private message by applying successive interference cancellation (SIC). By doing so, RSMA not only embraces the existing MA techniques as special cases but also provides significant performance gains by efficiently mitigating inter-user interference in a broad range of interference regimes. In this article, we first present the theoretical foundation of RSMA. Subsequently, we put forth four key benefits of RSMA: spectral efficiency, robustness, scalability, and flexibility. Upon this, we describe how RSMA can enable ten promising scenarios and applications along with future research directions to pave the way for 6G. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.12978v1-abstract-full').style.display = 'none'; document.getElementById('2306.12978v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">17 pages, 6 figures, submitted to IEEE Network Magazine</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.12977">arXiv:2306.12977</a> <span> [<a href="https://arxiv.org/pdf/2306.12977">pdf</a>, <a href="https://arxiv.org/format/2306.12977">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Sum-Rate Maximization of RSMA-based Aerial Communications with Energy Harvesting: A Reinforcement Learning Approach </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Seong%2C+J">Jaehyup Seong</a>, <a href="/search/cs?searchtype=author&query=Toka%2C+M">Mesut Toka</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Wonjae Shin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2306.12977v1-abstract-short" style="display: inline;"> In this letter, we investigate a joint power and beamforming design problem for rate-splitting multiple access (RSMA)-based aerial communications with energy harvesting, where a self-sustainable aerial base station serves multiple users by utilizing the harvested energy. Considering maximizing the sum-rate from the long-term perspective, we utilize a deep reinforcement learning (DRL) approach, nam… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.12977v1-abstract-full').style.display = 'inline'; document.getElementById('2306.12977v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.12977v1-abstract-full" style="display: none;"> In this letter, we investigate a joint power and beamforming design problem for rate-splitting multiple access (RSMA)-based aerial communications with energy harvesting, where a self-sustainable aerial base station serves multiple users by utilizing the harvested energy. Considering maximizing the sum-rate from the long-term perspective, we utilize a deep reinforcement learning (DRL) approach, namely the soft actor-critic algorithm, to restrict the maximum transmission power at each time based on the stochastic property of the channel environment, harvested energy, and battery power information. Moreover, for designing precoders and power allocation among all the private/common streams of the RSMA, we employ sequential least squares programming (SLSQP) using the Han-Powell quasi-Newton method to maximize the sum-rate for the given transmission power via DRL. Numerical results show the superiority of the proposed scheme over several baseline methods in terms of the average sum-rate performance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.12977v1-abstract-full').style.display = 'none'; document.getElementById('2306.12977v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">13 pages, 4 figures, submitted to IEEE Wireless Communications Letters</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.18885">arXiv:2305.18885</a> <span> [<a href="https://arxiv.org/pdf/2305.18885">pdf</a>, <a href="https://arxiv.org/format/2305.18885">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Criteria Tell You More than Ratings: Criteria Preference-Aware Light Graph Convolution for Effective Multi-Criteria Recommendation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Park%2C+J">Jin-Duk Park</a>, <a href="/search/cs?searchtype=author&query=Li%2C+S">Siqing Li</a>, <a href="/search/cs?searchtype=author&query=Cao%2C+X">Xin Cao</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Won-Yong Shin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2305.18885v4-abstract-short" style="display: inline;"> The multi-criteria (MC) recommender system, which leverages MC rating information in a wide range of e-commerce areas, is ubiquitous nowadays. Surprisingly, although graph neural networks (GNNs) have been widely applied to develop various recommender systems due to GNN's high expressive capability in learning graph representations, it has been still unexplored how to design MC recommender systems… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.18885v4-abstract-full').style.display = 'inline'; document.getElementById('2305.18885v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.18885v4-abstract-full" style="display: none;"> The multi-criteria (MC) recommender system, which leverages MC rating information in a wide range of e-commerce areas, is ubiquitous nowadays. Surprisingly, although graph neural networks (GNNs) have been widely applied to develop various recommender systems due to GNN's high expressive capability in learning graph representations, it has been still unexplored how to design MC recommender systems with GNNs. In light of this, we make the first attempt towards designing a GNN-aided MC recommender system. Specifically, rather than straightforwardly adopting existing GNN-based recommendation methods, we devise a novel criteria preference-aware light graph convolution CPA-LGC method, which is capable of precisely capturing the criteria preference of users as well as the collaborative signal in complex high-order connectivities. To this end, we first construct an MC expansion graph that transforms user--item MC ratings into an expanded bipartite graph to potentially learn from the collaborative signal in MC ratings. Next, to strengthen the capability of criteria preference awareness, CPA-LGC incorporates newly characterized embeddings, including user-specific criteria-preference embeddings and item-specific criterion embeddings, into our graph convolution model. Through comprehensive evaluations using four real-world datasets, we demonstrate (a) the superiority over benchmark MC recommendation methods and benchmark recommendation methods using GNNs with tremendous gains, (b) the effectiveness of core components in CPA-LGC, and (c) the computational efficiency. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.18885v4-abstract-full').style.display = 'none'; document.getElementById('2305.18885v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 30 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">12 pages, 10 figures, 5 tables; 29th ACM SIGKDD Conference on Knowledge Discovery & Data (KDD 2023) (to appear) (Please cite our conference version.)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2304.12751">arXiv:2304.12751</a> <span> [<a href="https://arxiv.org/pdf/2304.12751">pdf</a>, <a href="https://arxiv.org/format/2304.12751">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Neural and Evolutionary Computing">cs.NE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> Centrality-Based Node Feature Augmentation for Robust Network Alignment </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Park%2C+J">Jin-Duk Park</a>, <a href="/search/cs?searchtype=author&query=Tran%2C+C">Cong Tran</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Won-Yong Shin</a>, <a href="/search/cs?searchtype=author&query=Cao%2C+X">Xin Cao</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2304.12751v5-abstract-short" style="display: inline;"> Network alignment (NA) is the task of discovering node correspondences across multiple networks. Although NA methods have achieved remarkable success in a myriad of scenarios, their effectiveness is not without additional information such as prior anchor links and/or node features, which may not always be available due to privacy concerns or access restrictions. To tackle this challenge, we propos… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.12751v5-abstract-full').style.display = 'inline'; document.getElementById('2304.12751v5-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2304.12751v5-abstract-full" style="display: none;"> Network alignment (NA) is the task of discovering node correspondences across multiple networks. Although NA methods have achieved remarkable success in a myriad of scenarios, their effectiveness is not without additional information such as prior anchor links and/or node features, which may not always be available due to privacy concerns or access restrictions. To tackle this challenge, we propose Grad-Align+, a novel NA method built upon a recent state-of-the-art NA method, the so-called Grad-Align, that gradually discovers a part of node pairs until all node pairs are found. In designing Grad-Align+, we account for how to augment node features in the sense of performing the NA task and how to design our NA method by maximally exploiting the augmented node features. To achieve this goal, Grad-Align+ consists of three key components: 1) centrality-based node feature augmentation (CNFA), 2) graph neural network (GNN)-aided embedding similarity calculation alongside the augmented node features, and 3) gradual NA with similarity calculation using aligned cross-network neighbor-pairs (ACNs). Through comprehensive experiments, we demonstrate that Grad-Align+ exhibits (a) the superiority over benchmark NA methods, (b) empirical validations as well as our theoretical findings to see the effectiveness of CNFA, (c) the influence of each component, (d) the robustness to network noises, and (e) the computational efficiency. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.12751v5-abstract-full').style.display = 'none'; document.getElementById('2304.12751v5-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 25 April, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">19 pages, 12 figures, 5 tables; its conference version was presented at the ACM International Conference on Information and Knowledge Management (CIKM 2022)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2304.04497">arXiv:2304.04497</a> <span> [<a href="https://arxiv.org/pdf/2304.04497">pdf</a>, <a href="https://arxiv.org/format/2304.04497">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Neural and Evolutionary Computing">cs.NE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> A Unified Framework for Exploratory Learning-Aided Community Detection Under Topological Uncertainty </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Hou%2C+Y">Yu Hou</a>, <a href="/search/cs?searchtype=author&query=Tran%2C+C">Cong Tran</a>, <a href="/search/cs?searchtype=author&query=Li%2C+M">Ming Li</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+W">Won-Yong Shin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2304.04497v3-abstract-short" style="display: inline;"> In social networks, the discovery of community structures has received considerable attention as a fundamental problem in various network analysis tasks. However, due to privacy concerns or access restrictions, the network structure is often uncertain, thereby rendering established community detection approaches ineffective without costly network topology acquisition. To tackle this challenge, we… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.04497v3-abstract-full').style.display = 'inline'; document.getElementById('2304.04497v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2304.04497v3-abstract-full" style="display: none;"> In social networks, the discovery of community structures has received considerable attention as a fundamental problem in various network analysis tasks. However, due to privacy concerns or access restrictions, the network structure is often uncertain, thereby rendering established community detection approaches ineffective without costly network topology acquisition. To tackle this challenge, we present META-CODE, a unified framework for detecting overlapping communities via exploratory learning aided by easy-to-collect node metadata when networks are topologically unknown (or only partially known). Specifically, META-CODE consists of three iterative steps in addition to the initial network inference step: 1) node-level community-affiliation embeddings based on graph neural networks (GNNs) trained by our new reconstruction loss, 2) network exploration via community-affiliation-based node queries, and 3) network inference using an edge connectivity-based Siamese neural network model from the explored network. Through extensive experiments on five real-world datasets including two large networks, we demonstrated: (a) the superiority of META-CODE over benchmark community detection methods, achieving remarkable gains up to 151.27% compared to the best existing competitor, (b) the impact of each module in META-CODE, (c) the effectiveness of node queries in META-CODE based on empirical evaluations and theoretical findings, (d) the convergence of the inferred network, and (e) the computational efficiency of META-CODE. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.04497v3-abstract-full').style.display = 'none'; document.getElementById('2304.04497v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 April, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">17 pages, 9 figures, 6 tables; its conference version was presented at the ACM International Conference on Information and Knowledge Management (CIKM 2022)</span> </p> </li> </ol> <nav class="pagination is-small is-centered breathe-horizontal" role="navigation" aria-label="pagination"> <a href="" class="pagination-previous is-invisible">Previous </a> <a href="/search/?searchtype=author&query=Shin%2C+W&start=50" class="pagination-next" >Next </a> <ul class="pagination-list"> <li> <a href="/search/?searchtype=author&query=Shin%2C+W&start=0" class="pagination-link is-current" aria-label="Goto page 1">1 </a> </li> <li> <a href="/search/?searchtype=author&query=Shin%2C+W&start=50" class="pagination-link " aria-label="Page 2" aria-current="page">2 </a> </li> <li> <a 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