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breathe-horizontal" role="navigation" aria-label="pagination"> <a href="" class="pagination-previous is-invisible">Previous </a> <a href="/search/?searchtype=author&query=Ning%2C+H&start=50" class="pagination-next" >Next </a> <ul class="pagination-list"> <li> <a href="/search/?searchtype=author&query=Ning%2C+H&start=0" class="pagination-link is-current" aria-label="Goto page 1">1 </a> </li> <li> <a href="/search/?searchtype=author&query=Ning%2C+H&start=50" class="pagination-link " aria-label="Page 2" aria-current="page">2 </a> </li> <li> <a href="/search/?searchtype=author&query=Ning%2C+H&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/2411.08724">arXiv:2411.08724</a> <span> [<a href="https://arxiv.org/pdf/2411.08724">pdf</a>, <a href="https://arxiv.org/format/2411.08724">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"> QCG-Rerank: Chunks Graph Rerank with Query Expansion in Retrieval-Augmented LLMs for Tourism Domain </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Wei%2C+Q">Qikai Wei</a>, <a href="/search/?searchtype=author&query=Yang%2C+M">Mingzhi Yang</a>, <a href="/search/?searchtype=author&query=Han%2C+C">Chunlong Han</a>, <a href="/search/?searchtype=author&query=Wei%2C+J">Jingfu Wei</a>, <a href="/search/?searchtype=author&query=Zhang%2C+M">Minghao Zhang</a>, <a href="/search/?searchtype=author&query=Shi%2C+F">Feifei Shi</a>, <a href="/search/?searchtype=author&query=Ning%2C+H">Huansheng Ning</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="2411.08724v1-abstract-short" style="display: inline;"> Retrieval-Augmented Generation (RAG) mitigates the issue of hallucination in Large Language Models (LLMs) by integrating information retrieval techniques. However, in the tourism domain, since the query is usually brief and the content in the database is diverse, existing RAG may contain a significant amount of irrelevant or contradictory information contents after retrieval. To address this chall… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.08724v1-abstract-full').style.display = 'inline'; document.getElementById('2411.08724v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.08724v1-abstract-full" style="display: none;"> Retrieval-Augmented Generation (RAG) mitigates the issue of hallucination in Large Language Models (LLMs) by integrating information retrieval techniques. However, in the tourism domain, since the query is usually brief and the content in the database is diverse, existing RAG may contain a significant amount of irrelevant or contradictory information contents after retrieval. To address this challenge, we propose the QCG-Rerank model. This model first performs an initial retrieval to obtain candidate chunks and then enhances semantics by extracting critical information to expand the original query. Next, we utilize the expanded query and candidate chunks to calculate similarity scores as the initial transition probability and construct the chunks graph. Subsequently, We iteratively compute the transition probabilities based on an initial estimate until convergence. The chunks with the highest score are selected and input into the LLMs to generate responses. We evaluate the model on Cultour, IIRC, StrategyQA, HotpotQA, SQuAD, and MuSiQue datasets. The experimental results demonstrate the effectiveness and superiority of the QCG-Rerank method. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.08724v1-abstract-full').style.display = 'none'; document.getElementById('2411.08724v1-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 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/2411.03205">arXiv:2411.03205</a> <span> [<a href="https://arxiv.org/pdf/2411.03205">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Emerging Technologies">cs.ET</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Software Engineering">cs.SE</span> </div> </div> <p class="title is-5 mathjax"> GIS Copilot: Towards an Autonomous GIS Agent for Spatial Analysis </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Akinboyewa%2C+T">Temitope Akinboyewa</a>, <a href="/search/?searchtype=author&query=Li%2C+Z">Zhenlong Li</a>, <a href="/search/?searchtype=author&query=Ning%2C+H">Huan Ning</a>, <a href="/search/?searchtype=author&query=Lessani%2C+M+N">M. Naser Lessani</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="2411.03205v4-abstract-short" style="display: inline;"> Recent advancements in Generative AI offer promising capabilities for spatial analysis. Despite their potential, the integration of generative AI with established GIS platforms remains underexplored. In this study, we propose a framework for integrating LLMs directly into existing GIS platforms, using QGIS as an example. Our approach leverages the reasoning and programming capabilities of LLMs to… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.03205v4-abstract-full').style.display = 'inline'; document.getElementById('2411.03205v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.03205v4-abstract-full" style="display: none;"> Recent advancements in Generative AI offer promising capabilities for spatial analysis. Despite their potential, the integration of generative AI with established GIS platforms remains underexplored. In this study, we propose a framework for integrating LLMs directly into existing GIS platforms, using QGIS as an example. Our approach leverages the reasoning and programming capabilities of LLMs to autonomously generate spatial analysis workflows and code through an informed agent that has comprehensive documentation of key GIS tools and parameters. The implementation of this framework resulted in the development of a "GIS Copilot" that allows GIS users to interact with QGIS using natural language commands for spatial analysis. The GIS Copilot was evaluated with over 100 spatial analysis tasks with three complexity levels: basic tasks that require one GIS tool and typically involve one data layer to perform simple operations; intermediate tasks involving multi-step processes with multiple tools, guided by user instructions; and advanced tasks which involve multi-step processes that require multiple tools but not guided by user instructions, necessitating the agent to independently decide on and executes the necessary steps. The evaluation reveals that the GIS Copilot demonstrates strong potential in automating foundational GIS operations, with a high success rate in tool selection and code generation for basic and intermediate tasks, while challenges remain in achieving full autonomy for more complex tasks. This study contributes to the emerging vision of Autonomous GIS, providing a pathway for non-experts to engage with geospatial analysis with minimal prior expertise. While full autonomy is yet to be achieved, the GIS Copilot demonstrates significant potential for simplifying GIS workflows and enhancing decision-making processes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.03205v4-abstract-full').style.display = 'none'; document.getElementById('2411.03205v4-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 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 5 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/2410.16462">arXiv:2410.16462</a> <span> [<a href="https://arxiv.org/pdf/2410.16462">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> </div> </div> <p class="title is-5 mathjax"> Comparative Analysis of Human Mobility Patterns: Utilizing Taxi and Mobile (SafeGraph) Data to Investigate Neighborhood-Scale Mobility in New York City </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Jiang%2C+Y">Yuqin Jiang</a>, <a href="/search/?searchtype=author&query=Li%2C+Z">Zhenlong Li</a>, <a href="/search/?searchtype=author&query=Kim%2C+J">Joon-Seok Kim</a>, <a href="/search/?searchtype=author&query=Ning%2C+H">Huan Ning</a>, <a href="/search/?searchtype=author&query=Han%2C+S+Y">Su Yeon 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="2410.16462v1-abstract-short" style="display: inline;"> Numerous researchers have utilized GPS-enabled vehicle data and SafeGraph mobility data to analyze human movements. However, the comparison of their ability to capture human mobility remains unexplored. This study investigates differences in human mobility using taxi trip records and the SafeGraph dataset in New York City neighborhoods. The analysis includes neighborhood clustering to identify pop… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.16462v1-abstract-full').style.display = 'inline'; document.getElementById('2410.16462v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.16462v1-abstract-full" style="display: none;"> Numerous researchers have utilized GPS-enabled vehicle data and SafeGraph mobility data to analyze human movements. However, the comparison of their ability to capture human mobility remains unexplored. This study investigates differences in human mobility using taxi trip records and the SafeGraph dataset in New York City neighborhoods. The analysis includes neighborhood clustering to identify population characteristics and a comparative analysis of mobility patterns. Our findings show that taxi data tends to capture human mobility to and from locations such as Lower Manhattan, where taxi demand is consistently high, while often underestimating the volume of trips originating from areas with lower taxi demand, particularly in the suburbs of NYC. In contrast, SafeGraph data excels in capturing trips to and from areas where commuting by driving one's own car is common, but underestimates trips in pedestrian-heavy areas. The comparative analysis also sheds new light on transportation mode choices for trips across various neighborhoods. The results of this study underscore the importance of understanding the representativeness of human mobility big data and highlight the necessity for careful consideration when selecting the most suitable dataset for human mobility research. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.16462v1-abstract-full').style.display = 'none'; document.getElementById('2410.16462v1-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 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/2410.10989">arXiv:2410.10989</a> <span> [<a href="https://arxiv.org/pdf/2410.10989">pdf</a>, <a href="https://arxiv.org/format/2410.10989">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="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> </div> <p class="title is-5 mathjax"> Liger Kernel: Efficient Triton Kernels for LLM Training </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Hsu%2C+P">Pin-Lun Hsu</a>, <a href="/search/?searchtype=author&query=Dai%2C+Y">Yun Dai</a>, <a href="/search/?searchtype=author&query=Kothapalli%2C+V">Vignesh Kothapalli</a>, <a href="/search/?searchtype=author&query=Song%2C+Q">Qingquan Song</a>, <a href="/search/?searchtype=author&query=Tang%2C+S">Shao Tang</a>, <a href="/search/?searchtype=author&query=Zhu%2C+S">Siyu Zhu</a>, <a href="/search/?searchtype=author&query=Shimizu%2C+S">Steven Shimizu</a>, <a href="/search/?searchtype=author&query=Sahni%2C+S">Shivam Sahni</a>, <a href="/search/?searchtype=author&query=Ning%2C+H">Haowen Ning</a>, <a href="/search/?searchtype=author&query=Chen%2C+Y">Yanning Chen</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="2410.10989v2-abstract-short" style="display: inline;"> Training Large Language Models (LLMs) efficiently at scale presents a formidable challenge, driven by their ever-increasing computational demands and the need for enhanced performance. In this work, we introduce Liger-Kernel, an open-sourced set of Triton kernels developed specifically for LLM training. With kernel optimization techniques like kernel operation fusing and input chunking, our kernel… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.10989v2-abstract-full').style.display = 'inline'; document.getElementById('2410.10989v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.10989v2-abstract-full" style="display: none;"> Training Large Language Models (LLMs) efficiently at scale presents a formidable challenge, driven by their ever-increasing computational demands and the need for enhanced performance. In this work, we introduce Liger-Kernel, an open-sourced set of Triton kernels developed specifically for LLM training. With kernel optimization techniques like kernel operation fusing and input chunking, our kernels achieve on average a 20% increase in training throughput and a 60% reduction in GPU memory usage for popular LLMs compared to HuggingFace implementations. In addition, Liger-Kernel is designed with modularity, accessibility, and adaptability in mind, catering to both casual and expert users. Comprehensive benchmarks and integration tests are built in to ensure compatibility, performance, correctness, and convergence across diverse computing environments and model architectures. The source code is available under a permissive license at: github.com/linkedin/Liger-Kernel. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.10989v2-abstract-full').style.display = 'none'; document.getElementById('2410.10989v2-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">v1</span> submitted 14 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">17 pages, 12 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/2410.10693">arXiv:2410.10693</a> <span> [<a href="https://arxiv.org/pdf/2410.10693">pdf</a>, <a href="https://arxiv.org/format/2410.10693">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optics">physics.optics</span> </div> </div> <p class="title is-5 mathjax"> Spontaneous emergence of phonon angular momentum through hybridization with magnons </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Ning%2C+H">Honglie Ning</a>, <a href="/search/?searchtype=author&query=Luo%2C+T">Tianchuang Luo</a>, <a href="/search/?searchtype=author&query=Ilyas%2C+B">Batyr Ilyas</a>, <a href="/search/?searchtype=author&query=Bostr%C3%B6m%2C+E+V">Emil Vi帽as Bostr枚m</a>, <a href="/search/?searchtype=author&query=Park%2C+J">Jaena Park</a>, <a href="/search/?searchtype=author&query=Kim%2C+J">Junghyun Kim</a>, <a href="/search/?searchtype=author&query=Park%2C+J">Je-Geun Park</a>, <a href="/search/?searchtype=author&query=Juraschek%2C+D+M">Dominik M. Juraschek</a>, <a href="/search/?searchtype=author&query=Rubio%2C+A">Angel Rubio</a>, <a href="/search/?searchtype=author&query=Gedik%2C+N">Nuh Gedik</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="2410.10693v1-abstract-short" style="display: inline;"> Chirality, the breaking of improper rotational symmetry, is a fundamental concept spanning diverse scientific domains. In condensed matter physics, chiral phonons, originating from circular atomic motions that carry angular momentum, have sparked intense interest due to their coupling to magnetic degrees of freedom, enabling potential phonon-controlled spintronics. However, modes and their counter… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.10693v1-abstract-full').style.display = 'inline'; document.getElementById('2410.10693v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.10693v1-abstract-full" style="display: none;"> Chirality, the breaking of improper rotational symmetry, is a fundamental concept spanning diverse scientific domains. In condensed matter physics, chiral phonons, originating from circular atomic motions that carry angular momentum, have sparked intense interest due to their coupling to magnetic degrees of freedom, enabling potential phonon-controlled spintronics. However, modes and their counter-rotating counterparts are typically degenerate at the Brillouin zone center. Selective excitation of a single-handed circulating phonon requires external stimuli that break the degeneracy. Whether energetically nondegenerate circularly polarized phonons can appear spontaneously without structural or external symmetry breaking remains an open question. Here, we demonstrate that nondegenerate elliptically polarized phonon pairs can be induced by coupling to magnons with same helicity in the van der Waals antiferromagnet $\mathrm{FePSe_3}$. We confirm the presence of magnon-phonon hybrids, also known as magnon polarons, which exhibit inherent elliptical polarization with opposite helicities and distinct energies. This nondegeneracy enables their coherent excitation with linearly polarized terahertz pulses, which also endows these rotating modes with chirality. By tuning the polarization of the terahertz drive and measuring phase-resolved polarimetry of the resulting coherent oscillations, we determine the ellipticity and map the trajectory of these hybrid quasiparticles. Our findings establish a general approach to search for intrinsically nondegenerate phonons with angular momentum at the center of the Brillouin zone and introduce a new methodology for characterizing their ellipticity, outlining a roadmap towards chiral-phonon-controlled spintronic functionalities. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.10693v1-abstract-full').style.display = 'none'; document.getElementById('2410.10693v1-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 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, 4 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/2409.14669">arXiv:2409.14669</a> <span> [<a href="https://arxiv.org/pdf/2409.14669">pdf</a>, <a href="https://arxiv.org/format/2409.14669">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optics">physics.optics</span> </div> </div> <p class="title is-5 mathjax"> Terahertz Control of Linear and Nonlinear Magno-Phononics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Luo%2C+T">Tianchuang Luo</a>, <a href="/search/?searchtype=author&query=Ning%2C+H">Honglie Ning</a>, <a href="/search/?searchtype=author&query=Ilyas%2C+B">Batyr Ilyas</a>, <a href="/search/?searchtype=author&query=von+Hoegen%2C+A">Alexander von Hoegen</a>, <a href="/search/?searchtype=author&query=Bostr%C3%B6m%2C+E+V">Emil Vi帽as Bostr枚m</a>, <a href="/search/?searchtype=author&query=Park%2C+J">Jaena Park</a>, <a href="/search/?searchtype=author&query=Kim%2C+J">Junghyun Kim</a>, <a href="/search/?searchtype=author&query=Park%2C+J">Je-Geun Park</a>, <a href="/search/?searchtype=author&query=Juraschek%2C+D+M">Dominik M. Juraschek</a>, <a href="/search/?searchtype=author&query=Rubio%2C+A">Angel Rubio</a>, <a href="/search/?searchtype=author&query=Gedik%2C+N">Nuh Gedik</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="2409.14669v1-abstract-short" style="display: inline;"> Coherent manipulation of magnetism through the lattice provides unprecedented opportunities for controlling spintronic functionalities on the ultrafast timescale. Such nonthermal control conventionally involves nonlinear excitation of Raman-active phonons which are coupled to the magnetic order. Linear excitation, in contrast, holds potential for more efficient and selective modulation of magnetic… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.14669v1-abstract-full').style.display = 'inline'; document.getElementById('2409.14669v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.14669v1-abstract-full" style="display: none;"> Coherent manipulation of magnetism through the lattice provides unprecedented opportunities for controlling spintronic functionalities on the ultrafast timescale. Such nonthermal control conventionally involves nonlinear excitation of Raman-active phonons which are coupled to the magnetic order. Linear excitation, in contrast, holds potential for more efficient and selective modulation of magnetic properties. However, the linear channel remains uncharted, since it is conventionally considered forbidden in inversion symmetric quantum materials. Here, we harness strong coupling between magnons and Raman-active phonons to achieve both linear and quadratic excitation regimes of magnon-polarons, magnon-phonon hybrid quasiparticles. We demonstrate this by driving magnon-polarons with an intense terahertz pulse in the van der Waals antiferromagnet $\mathrm{FePS_3}$. Such excitation behavior enables a unique way to coherently control the amplitude of magnon-polaron oscillations by tuning the terahertz field strength and its polarization. The polarimetry of the resulting coherent oscillation amplitude breaks the crystallographic $C_2$ symmetry due to strong interference between different excitation channels. Our findings unlock a wide range of possibilities to manipulate material properties, including modulation of exchange interactions by phonon-Floquet engineering. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.14669v1-abstract-full').style.display = 'none'; document.getElementById('2409.14669v1-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 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">15 pages, 4 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/2409.09286">arXiv:2409.09286</a> <span> [<a href="https://arxiv.org/pdf/2409.09286">pdf</a>, <a href="https://arxiv.org/format/2409.09286">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"> SAM-OCTA2: Layer Sequence OCTA Segmentation with Fine-tuned Segment Anything Model 2 </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Chen%2C+X">Xinrun Chen</a>, <a href="/search/?searchtype=author&query=Wang%2C+C">Chengliang Wang</a>, <a href="/search/?searchtype=author&query=Ning%2C+H">Haojian Ning</a>, <a href="/search/?searchtype=author&query=Zhang%2C+M">Mengzhan Zhang</a>, <a href="/search/?searchtype=author&query=Shen%2C+M">Mei Shen</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Shiying Li</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="2409.09286v1-abstract-short" style="display: inline;"> Segmentation of indicated targets aids in the precise analysis of optical coherence tomography angiography (OCTA) samples. Existing segmentation methods typically perform on 2D projection targets, making it challenging to capture the variance of segmented objects through the 3D volume. To address this limitation, the low-rank adaptation technique is adopted to fine-tune the Segment Anything Model… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.09286v1-abstract-full').style.display = 'inline'; document.getElementById('2409.09286v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.09286v1-abstract-full" style="display: none;"> Segmentation of indicated targets aids in the precise analysis of optical coherence tomography angiography (OCTA) samples. Existing segmentation methods typically perform on 2D projection targets, making it challenging to capture the variance of segmented objects through the 3D volume. To address this limitation, the low-rank adaptation technique is adopted to fine-tune the Segment Anything Model (SAM) version 2, enabling the tracking and segmentation of specified objects across the OCTA scanning layer sequence. To further this work, a prompt point generation strategy in frame sequence and a sparse annotation method to acquire retinal vessel (RV) layer masks are proposed. This method is named SAM-OCTA2 and has been experimented on the OCTA-500 dataset. It achieves state-of-the-art performance in segmenting the foveal avascular zone (FAZ) on regular 2D en-face and effectively tracks local vessels across scanning layer sequences. The code is available at: https://github.com/ShellRedia/SAM-OCTA2. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.09286v1-abstract-full').style.display = 'none'; document.getElementById('2409.09286v1-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 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.21024">arXiv:2407.21024</a> <span> [<a href="https://arxiv.org/pdf/2407.21024">pdf</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="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Emerging Technologies">cs.ET</span> </div> </div> <p class="title is-5 mathjax"> An Autonomous GIS Agent Framework for Geospatial Data Retrieval </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Ning%2C+H">Huan Ning</a>, <a href="/search/?searchtype=author&query=Li%2C+Z">Zhenlong Li</a>, <a href="/search/?searchtype=author&query=Akinboyewa%2C+T">Temitope Akinboyewa</a>, <a href="/search/?searchtype=author&query=Lessani%2C+M+N">M. Naser Lessani</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="2407.21024v2-abstract-short" style="display: inline;"> Powered by the emerging large language models (LLMs), autonomous geographic information systems (GIS) agents have the potential to accomplish spatial analyses and cartographic tasks. However, a research gap exists to support fully autonomous GIS agents: how to enable agents to discover and download the necessary data for geospatial analyses. This study proposes an autonomous GIS agent framework ca… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.21024v2-abstract-full').style.display = 'inline'; document.getElementById('2407.21024v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.21024v2-abstract-full" style="display: none;"> Powered by the emerging large language models (LLMs), autonomous geographic information systems (GIS) agents have the potential to accomplish spatial analyses and cartographic tasks. However, a research gap exists to support fully autonomous GIS agents: how to enable agents to discover and download the necessary data for geospatial analyses. This study proposes an autonomous GIS agent framework capable of retrieving required geospatial data by generating, executing, and debugging programs. The framework utilizes the LLM as the decision-maker, selects the appropriate data source (s) from a pre-defined source list, and fetches the data from the chosen source. Each data source has a handbook that records the metadata and technical details for data retrieval. The proposed framework is designed in a plug-and-play style to ensure flexibility and extensibility. Human users or autonomous data scrawlers can add new data sources by adding new handbooks. We developed a prototype agent based on the framework, released as a QGIS plugin (GeoData Retrieve Agent) and a Python program. Experiment results demonstrate its capability of retrieving data from various sources including OpenStreetMap, administrative boundaries and demographic data from the US Census Bureau, satellite basemaps from ESRI World Imagery, global digital elevation model (DEM) from OpenTopography.org, weather data from a commercial provider, the COVID-19 cases from the NYTimes GitHub. Our study is among the first attempts to develop an autonomous geospatial data retrieval agent. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.21024v2-abstract-full').style.display = 'none'; document.getElementById('2407.21024v2-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 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 13 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/2407.15253">arXiv:2407.15253</a> <span> [<a href="https://arxiv.org/pdf/2407.15253">pdf</a>, <a href="https://arxiv.org/format/2407.15253">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</span> </div> </div> <p class="title is-5 mathjax"> Keldysh tuning of photoluminescence in a lead halide perovskite crystal </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zhang%2C+Z">Zhuquan Zhang</a>, <a href="/search/?searchtype=author&query=Ning%2C+H">Honglie Ning</a>, <a href="/search/?searchtype=author&query=Liu%2C+Z">Zi-Jie Liu</a>, <a href="/search/?searchtype=author&query=Hou%2C+J">Jin Hou</a>, <a href="/search/?searchtype=author&query=Mohite%2C+A+D">Aditya D. Mohite</a>, <a href="/search/?searchtype=author&query=Baldini%2C+E">Edoardo Baldini</a>, <a href="/search/?searchtype=author&query=Gedik%2C+N">Nuh Gedik</a>, <a href="/search/?searchtype=author&query=Nelson%2C+K+A">Keith A. Nelson</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="2407.15253v1-abstract-short" style="display: inline;"> In 1964, Keldysh laid the groundwork for strong-field physics in atomic, molecular, and solid-state systems by delineating a ubiquitous transition from multiphoton absorption to quantum electron tunneling under intense AC driving forces. While both processes in semiconductors can generate carriers and result in photon emission through electron-hole recombination, the low quantum yields in most mat… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.15253v1-abstract-full').style.display = 'inline'; document.getElementById('2407.15253v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.15253v1-abstract-full" style="display: none;"> In 1964, Keldysh laid the groundwork for strong-field physics in atomic, molecular, and solid-state systems by delineating a ubiquitous transition from multiphoton absorption to quantum electron tunneling under intense AC driving forces. While both processes in semiconductors can generate carriers and result in photon emission through electron-hole recombination, the low quantum yields in most materials have hindered direct observation of the Keldysh crossover. Leveraging the large quantum yields of photoluminescence in lead halide perovskites, we show that we can not only induce bright light emission from extreme sub-bandgap light excitation but also distinguish between photon-induced and electric-field-induced processes. Our results are rationalized by the Landau-Dykhne formalism, providing insights into the non-equilibrium dynamics of strong-field light-matter interactions. These findings open new avenues for light upconversion and sub-bandgap photon detection, highlighting the potential of lead halide perovskites in advanced optoelectronic applications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.15253v1-abstract-full').style.display = 'none'; document.getElementById('2407.15253v1-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 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/2407.12791">arXiv:2407.12791</a> <span> [<a href="https://arxiv.org/pdf/2407.12791">pdf</a>, <a href="https://arxiv.org/format/2407.12791">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"> TourLLM: Enhancing LLMs with Tourism Knowledge </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Wei%2C+Q">Qikai Wei</a>, <a href="/search/?searchtype=author&query=Yang%2C+M">Mingzhi Yang</a>, <a href="/search/?searchtype=author&query=Wang%2C+J">Jinqiang Wang</a>, <a href="/search/?searchtype=author&query=Mao%2C+W">Wenwei Mao</a>, <a href="/search/?searchtype=author&query=Xu%2C+J">Jiabo Xu</a>, <a href="/search/?searchtype=author&query=Ning%2C+H">Huansheng Ning</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="2407.12791v1-abstract-short" style="display: inline;"> Recently, large language models (LLMs) have demonstrated their effectiveness in various natural language processing (NLP) tasks. However, the lack of tourism knowledge limits the performance of LLMs in tourist attraction presentations and travel planning. To address this challenge, we constructed a supervised fine-tuning dataset for the culture and tourism domain, named Cultour. This dataset consi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.12791v1-abstract-full').style.display = 'inline'; document.getElementById('2407.12791v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.12791v1-abstract-full" style="display: none;"> Recently, large language models (LLMs) have demonstrated their effectiveness in various natural language processing (NLP) tasks. However, the lack of tourism knowledge limits the performance of LLMs in tourist attraction presentations and travel planning. To address this challenge, we constructed a supervised fine-tuning dataset for the culture and tourism domain, named Cultour. This dataset consists of three parts: tourism knowledge base QA data, travelogues data, and tourism diversity QA data. Additionally, we propose TourLLM, a Qwen-based model supervised fine-tuned with Cultour, to improve the quality of the information provided about attractions and travel planning. To evaluate the performance of TourLLM, we employed both automatic and human evaluation, and we proposed a human evaluation criterion named CRA (Consistency, Readability, Availability). The experimental results demonstrate the effectiveness of the responses generated by the TourLLM. Our proposed Cultour is accessible at https://github.com/mrweiqk/Cultour. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.12791v1-abstract-full').style.display = 'none'; document.getElementById('2407.12791v1-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 June, 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/2407.12154">arXiv:2407.12154</a> <span> [<a href="https://arxiv.org/pdf/2407.12154">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> </div> </div> <p class="title is-5 mathjax"> Cyberbullying Detection: Exploring Datasets, Technologies, and Approaches on Social Media Platforms </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Philipo%2C+A+G">Adamu Gaston Philipo</a>, <a href="/search/?searchtype=author&query=Sarwatt%2C+D+S">Doreen Sebastian Sarwatt</a>, <a href="/search/?searchtype=author&query=Ding%2C+J">Jianguo Ding</a>, <a href="/search/?searchtype=author&query=Daneshmand%2C+M">Mahmoud Daneshmand</a>, <a href="/search/?searchtype=author&query=Ning%2C+H">Huansheng Ning</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="2407.12154v1-abstract-short" style="display: inline;"> Cyberbullying has been a significant challenge in the digital era world, given the huge number of people, especially adolescents, who use social media platforms to communicate and share information. Some individuals exploit these platforms to embarrass others through direct messages, electronic mail, speech, and public posts. This behavior has direct psychological and physical impacts on victims o… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.12154v1-abstract-full').style.display = 'inline'; document.getElementById('2407.12154v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.12154v1-abstract-full" style="display: none;"> Cyberbullying has been a significant challenge in the digital era world, given the huge number of people, especially adolescents, who use social media platforms to communicate and share information. Some individuals exploit these platforms to embarrass others through direct messages, electronic mail, speech, and public posts. This behavior has direct psychological and physical impacts on victims of bullying. While several studies have been conducted in this field and various solutions proposed to detect, prevent, and monitor cyberbullying instances on social media platforms, the problem continues. Therefore, it is necessary to conduct intensive studies and provide effective solutions to address the situation. These solutions should be based on detection, prevention, and prediction criteria methods. This paper presents a comprehensive systematic review of studies conducted on cyberbullying detection. It explores existing studies, proposed solutions, identified gaps, datasets, technologies, approaches, challenges, and recommendations, and then proposes effective solutions to address research gaps in future studies. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.12154v1-abstract-full').style.display = 'none'; document.getElementById('2407.12154v1-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 May, 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">34 pages, 7 figures, 11 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/2406.10303">arXiv:2406.10303</a> <span> [<a href="https://arxiv.org/pdf/2406.10303">pdf</a>, <a href="https://arxiv.org/format/2406.10303">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"> A Survey on Large Language Models from General Purpose to Medical Applications: Datasets, Methodologies, and Evaluations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Wang%2C+J">Jinqiang Wang</a>, <a href="/search/?searchtype=author&query=Ning%2C+H">Huansheng Ning</a>, <a href="/search/?searchtype=author&query=Peng%2C+Y">Yi Peng</a>, <a href="/search/?searchtype=author&query=Wei%2C+Q">Qikai Wei</a>, <a href="/search/?searchtype=author&query=Tesfai%2C+D">Daniel Tesfai</a>, <a href="/search/?searchtype=author&query=Mao%2C+W">Wenwei Mao</a>, <a href="/search/?searchtype=author&query=Zhu%2C+T">Tao Zhu</a>, <a href="/search/?searchtype=author&query=Huang%2C+R">Runhe Huang</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="2406.10303v2-abstract-short" style="display: inline;"> Large Language Models (LLMs) have demonstrated surprising performance across various natural language processing tasks. Recently, medical LLMs enhanced with domain-specific knowledge have exhibited excellent capabilities in medical consultation and diagnosis. These models can smoothly simulate doctor-patient dialogues and provide professional medical advice. Most medical LLMs are developed through… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.10303v2-abstract-full').style.display = 'inline'; document.getElementById('2406.10303v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.10303v2-abstract-full" style="display: none;"> Large Language Models (LLMs) have demonstrated surprising performance across various natural language processing tasks. Recently, medical LLMs enhanced with domain-specific knowledge have exhibited excellent capabilities in medical consultation and diagnosis. These models can smoothly simulate doctor-patient dialogues and provide professional medical advice. Most medical LLMs are developed through continued training of open-source general LLMs, which require significantly fewer computational resources than training LLMs from scratch. Additionally, this approach offers better patient privacy protection than API-based solutions. Given the above advantages, this survey systematically summarizes how to train medical LLMs based on open-source general LLMs from a more fine-grained perspective. It covers (a) how to acquire training corpus and construct customized medical training sets, (b) how to choose an appropriate training paradigm, (c) how to choose a suitable evaluation benchmark, and (d) existing challenges and promising research directions are discussed. This survey can provide guidance for the development of LLMs focused on various medical applications, such as medical education, diagnostic planning, and clinical assistants. Related resources and supplemental information can be found on the GitHub repository. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.10303v2-abstract-full').style.display = 'none'; document.getElementById('2406.10303v2-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 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 13 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">25 pages,4 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/2406.05467">arXiv:2406.05467</a> <span> [<a href="https://arxiv.org/pdf/2406.05467">pdf</a>, <a href="https://arxiv.org/format/2406.05467">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Solar and Stellar Astrophysics">astro-ph.SR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Plasma Physics">physics.plasm-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Space Physics">physics.space-ph</span> </div> </div> <p class="title is-5 mathjax"> Prevalence of non-standard collapsing of strong Langmuir turbulence in solar corona plasmas </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+Y">Yaokun Li</a>, <a href="/search/?searchtype=author&query=Sun%2C+H">Haomin Sun</a>, <a href="/search/?searchtype=author&query=Ning%2C+H">Hao Ning</a>, <a href="/search/?searchtype=author&query=Ni%2C+S">Sulan Ni</a>, <a href="/search/?searchtype=author&query=Kong%2C+X">Xiangliang Kong</a>, <a href="/search/?searchtype=author&query=He%2C+J">Jiansen He</a>, <a href="/search/?searchtype=author&query=Chen%2C+Y">Yao Chen</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="2406.05467v1-abstract-short" style="display: inline;"> We present a fully-kinetic simulation of the full life cycle of strong Langmuir turbulence (SLT) excited by electron beams that are accelerated under the solar corona conditions. We find that (1) most packets ($\sim$80%) are affected by their neighbors during their collapse, as a result, their spatial scale variations present non-standard evolutionary features, i.e., deviating away from what was p… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.05467v1-abstract-full').style.display = 'inline'; document.getElementById('2406.05467v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.05467v1-abstract-full" style="display: none;"> We present a fully-kinetic simulation of the full life cycle of strong Langmuir turbulence (SLT) excited by electron beams that are accelerated under the solar corona conditions. We find that (1) most packets ($\sim$80%) are affected by their neighbors during their collapse, as a result, their spatial scale variations present non-standard evolutionary features, i.e., deviating away from what was predicted by the Zakharov model; (2) the collapsing cavity is too shallow to trap the wave packet due to the growth of the Coulomb force, as a result a majority ($\sim$70%) of the packet energy runs away and a secondary localization may occur. The study indicates that the non-standard Langmuir collapse may play an important role in coronal plasmas interacting with an intense electron beam, that may be eventually confirmed by humanity's first mission to fly through the corona. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.05467v1-abstract-full').style.display = 'none'; document.getElementById('2406.05467v1-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/2406.02075">arXiv:2406.02075</a> <span> [<a href="https://arxiv.org/pdf/2406.02075">pdf</a>, <a href="https://arxiv.org/format/2406.02075">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="Neural and Evolutionary Computing">cs.NE</span> </div> </div> <p class="title is-5 mathjax"> ReLU-KAN: New Kolmogorov-Arnold Networks that Only Need Matrix Addition, Dot Multiplication, and ReLU </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Qiu%2C+Q">Qi Qiu</a>, <a href="/search/?searchtype=author&query=Zhu%2C+T">Tao Zhu</a>, <a href="/search/?searchtype=author&query=Gong%2C+H">Helin Gong</a>, <a href="/search/?searchtype=author&query=Chen%2C+L">Liming Chen</a>, <a href="/search/?searchtype=author&query=Ning%2C+H">Huansheng Ning</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="2406.02075v2-abstract-short" style="display: inline;"> Limited by the complexity of basis function (B-spline) calculations, Kolmogorov-Arnold Networks (KAN) suffer from restricted parallel computing capability on GPUs. This paper proposes a novel ReLU-KAN implementation that inherits the core idea of KAN. By adopting ReLU (Rectified Linear Unit) and point-wise multiplication, we simplify the design of KAN's basis function and optimize the computation… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.02075v2-abstract-full').style.display = 'inline'; document.getElementById('2406.02075v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.02075v2-abstract-full" style="display: none;"> Limited by the complexity of basis function (B-spline) calculations, Kolmogorov-Arnold Networks (KAN) suffer from restricted parallel computing capability on GPUs. This paper proposes a novel ReLU-KAN implementation that inherits the core idea of KAN. By adopting ReLU (Rectified Linear Unit) and point-wise multiplication, we simplify the design of KAN's basis function and optimize the computation process for efficient CUDA computing. The proposed ReLU-KAN architecture can be readily implemented on existing deep learning frameworks (e.g., PyTorch) for both inference and training. Experimental results demonstrate that ReLU-KAN achieves a 20x speedup compared to traditional KAN with 4-layer networks. Furthermore, ReLU-KAN exhibits a more stable training process with superior fitting ability while preserving the "catastrophic forgetting avoidance" property of KAN. You can get the code in https://github.com/quiqi/relu_kan <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.02075v2-abstract-full').style.display = 'none'; document.getElementById('2406.02075v2-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 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 4 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/2405.12851">arXiv:2405.12851</a> <span> [<a href="https://arxiv.org/pdf/2405.12851">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Optics">physics.optics</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-ph</span> </div> </div> <p class="title is-5 mathjax"> Ultrafast Broadband Strong-Field Tunnelling in Asymmetric Nanogaps for Time-Resolved Nanoscopy </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Ning%2C+H">Haoqing Ning</a>, <a href="/search/?searchtype=author&query=Maimaris%2C+M">Marios Maimaris</a>, <a href="/search/?searchtype=author&query=Wei%2C+J">Jiewen Wei</a>, <a href="/search/?searchtype=author&query=G%C3%A9rouville%2C+E">Emilie G茅rouville</a>, <a href="/search/?searchtype=author&query=Moutoulas%2C+E">Evangelos Moutoulas</a>, <a href="/search/?searchtype=author&query=Meng%2C+Z">Zhu Meng</a>, <a href="/search/?searchtype=author&query=Ferchaud%2C+C">Clement Ferchaud</a>, <a href="/search/?searchtype=author&query=Maslennikov%2C+D">Dmitry Maslennikov</a>, <a href="/search/?searchtype=author&query=Mondal%2C+N">Navendu Mondal</a>, <a href="/search/?searchtype=author&query=Wang%2C+T">Tong Wang</a>, <a href="/search/?searchtype=author&query=Chow%2C+C">Colin Chow</a>, <a href="/search/?searchtype=author&query=Ivanov%2C+A+P">Aleksandar P. Ivanov</a>, <a href="/search/?searchtype=author&query=Edel%2C+J+B">Joshua B. Edel</a>, <a href="/search/?searchtype=author&query=Haque%2C+S+A">Saif A. Haque</a>, <a href="/search/?searchtype=author&query=Ivanov%2C+M">Misha Ivanov</a>, <a href="/search/?searchtype=author&query=Marangos%2C+J+P">Jon P. Marangos</a>, <a href="/search/?searchtype=author&query=Georgiadou%2C+D+G">Dimitra G. Georgiadou</a>, <a href="/search/?searchtype=author&query=Bakulin%2C+A+A">Artem A. Bakulin</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="2405.12851v1-abstract-short" style="display: inline;"> Femtosecond-fast and nanometre-size pulses of electrons are emerging as unique probes for ultrafast dynamics at the nanoscale. Presently, such pulses are achievable only in highly sophisticated ultrafast electron microscopes or equally complex setups involving few-cycle-pulsed lasers with stable carrier-envelope phase (CEP) and nanotip probes. Here, we show that the generation of femtosecond pulse… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.12851v1-abstract-full').style.display = 'inline'; document.getElementById('2405.12851v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.12851v1-abstract-full" style="display: none;"> Femtosecond-fast and nanometre-size pulses of electrons are emerging as unique probes for ultrafast dynamics at the nanoscale. Presently, such pulses are achievable only in highly sophisticated ultrafast electron microscopes or equally complex setups involving few-cycle-pulsed lasers with stable carrier-envelope phase (CEP) and nanotip probes. Here, we show that the generation of femtosecond pulses of nanoscale tunnelling electrons can be achieved in any ultrafast optical laboratory, using any (deep-UV to mid-IR) femtosecond laser in combination with photosensitive asymmetric nanogap (PAN) diodes fabricated via easy-to-scale adhesion lithography. The dominant mechanism producing tunnelling electrons in PANs is strong-field emission, which is easily achievable without CEP locking or external bias voltage. We employ PANs to demonstrate ultrafast nanoscopy of metal-halide perovskite quantum dots immobilised inside a 10-nm Al/Au nanogap and to characterise laser pulses across the entire optical region (266-6700 nm). Short electron pulses in PANs open the way towards scalable on-chip femtosecond electron measurements and novel design approaches for integrated ultrafast sensing nanodevices. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.12851v1-abstract-full').style.display = 'none'; document.getElementById('2405.12851v1-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 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.18096">arXiv:2404.18096</a> <span> [<a href="https://arxiv.org/pdf/2404.18096">pdf</a>, <a href="https://arxiv.org/format/2404.18096">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Snake with Shifted Window: Learning to Adapt Vessel Pattern for OCTA Segmentation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Chen%2C+X">Xinrun Chen</a>, <a href="/search/?searchtype=author&query=Shen%2C+M">Mei Shen</a>, <a href="/search/?searchtype=author&query=Ning%2C+H">Haojian Ning</a>, <a href="/search/?searchtype=author&query=Zhang%2C+M">Mengzhan Zhang</a>, <a href="/search/?searchtype=author&query=Wang%2C+C">Chengliang Wang</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Shiying Li</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="2404.18096v1-abstract-short" style="display: inline;"> Segmenting specific targets or structures in optical coherence tomography angiography (OCTA) images is fundamental for conducting further pathological studies. The retinal vascular layers are rich and intricate, and such vascular with complex shapes can be captured by the widely-studied OCTA images. In this paper, we thus study how to use OCTA images with projection vascular layers to segment reti… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.18096v1-abstract-full').style.display = 'inline'; document.getElementById('2404.18096v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.18096v1-abstract-full" style="display: none;"> Segmenting specific targets or structures in optical coherence tomography angiography (OCTA) images is fundamental for conducting further pathological studies. The retinal vascular layers are rich and intricate, and such vascular with complex shapes can be captured by the widely-studied OCTA images. In this paper, we thus study how to use OCTA images with projection vascular layers to segment retinal structures. To this end, we propose the SSW-OCTA model, which integrates the advantages of deformable convolutions suited for tubular structures and the swin-transformer for global feature extraction, adapting to the characteristics of OCTA modality images. Our model underwent testing and comparison on the OCTA-500 dataset, achieving state-of-the-art performance. The code is available at: https://github.com/ShellRedia/Snake-SWin-OCTA. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.18096v1-abstract-full').style.display = 'none'; document.getElementById('2404.18096v1-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> 28 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.14443">arXiv:2404.14443</a> <span> [<a href="https://arxiv.org/pdf/2404.14443">pdf</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"> Evaluation of Machine Translation Based on Semantic Dependencies and Keywords </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Yuan%2C+K">Kewei Yuan</a>, <a href="/search/?searchtype=author&query=Zhao%2C+Q">Qiurong Zhao</a>, <a href="/search/?searchtype=author&query=Xu%2C+Y">Yang Xu</a>, <a href="/search/?searchtype=author&query=Zhang%2C+X">Xiao Zhang</a>, <a href="/search/?searchtype=author&query=Ning%2C+H">Huansheng Ning</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="2404.14443v1-abstract-short" style="display: inline;"> In view of the fact that most of the existing machine translation evaluation algorithms only consider the lexical and syntactic information, but ignore the deep semantic information contained in the sentence, this paper proposes a computational method for evaluating the semantic correctness of machine translations based on reference translations and incorporating semantic dependencies and sentence… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.14443v1-abstract-full').style.display = 'inline'; document.getElementById('2404.14443v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.14443v1-abstract-full" style="display: none;"> In view of the fact that most of the existing machine translation evaluation algorithms only consider the lexical and syntactic information, but ignore the deep semantic information contained in the sentence, this paper proposes a computational method for evaluating the semantic correctness of machine translations based on reference translations and incorporating semantic dependencies and sentence keyword information. Use the language technology platform developed by the Social Computing and Information Retrieval Research Center of Harbin Institute of Technology to conduct semantic dependency analysis and keyword analysis on sentences, and obtain semantic dependency graphs, keywords, and weight information corresponding to keywords. It includes all word information with semantic dependencies in the sentence and keyword information that affects semantic information. Construct semantic association pairs including word and dependency multi-features. The key semantics of the sentence cannot be highlighted in the semantic information extracted through semantic dependence, resulting in vague semantics analysis. Therefore, the sentence keyword information is also included in the scope of machine translation semantic evaluation. To achieve a comprehensive and in-depth evaluation of the semantic correctness of sentences, the experimental results show that the accuracy of the evaluation algorithm has been improved compared with similar methods, and it can more accurately measure the semantic correctness of machine translation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.14443v1-abstract-full').style.display = 'none'; document.getElementById('2404.14443v1-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 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.13378">arXiv:2404.13378</a> <span> [<a href="https://arxiv.org/pdf/2404.13378">pdf</a>, <a href="https://arxiv.org/format/2404.13378">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</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/TIV.2024.3352180">10.1109/TIV.2024.3352180 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Social Force Embedded Mixed Graph Convolutional Network for Multi-class Trajectory Prediction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Du%2C+Q">Quancheng Du</a>, <a href="/search/?searchtype=author&query=Wang%2C+X">Xiao Wang</a>, <a href="/search/?searchtype=author&query=Yin%2C+S">Shouguo Yin</a>, <a href="/search/?searchtype=author&query=Li%2C+L">Lingxi Li</a>, <a href="/search/?searchtype=author&query=Ning%2C+H">Huansheng Ning</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="2404.13378v1-abstract-short" style="display: inline;"> Accurate prediction of agent motion trajectories is crucial for autonomous driving, contributing to the reduction of collision risks in human-vehicle interactions and ensuring ample response time for other traffic participants. Current research predominantly focuses on traditional deep learning methods, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). These meth… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.13378v1-abstract-full').style.display = 'inline'; document.getElementById('2404.13378v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.13378v1-abstract-full" style="display: none;"> Accurate prediction of agent motion trajectories is crucial for autonomous driving, contributing to the reduction of collision risks in human-vehicle interactions and ensuring ample response time for other traffic participants. Current research predominantly focuses on traditional deep learning methods, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). These methods leverage relative distances to forecast the motion trajectories of a single class of agents. However, in complex traffic scenarios, the motion patterns of various types of traffic participants exhibit inherent randomness and uncertainty. Relying solely on relative distances may not adequately capture the nuanced interaction patterns between different classes of road users. In this paper, we propose a novel multi-class trajectory prediction method named the social force embedded mixed graph convolutional network (SFEM-GCN). SFEM-GCN comprises three graph topologies: the semantic graph (SG), position graph (PG), and velocity graph (VG). These graphs encode various of social force relationships among different classes of agents in complex scenes. Specifically, SG utilizes one-hot encoding of agent-class information to guide the construction of graph adjacency matrices based on semantic information. PG and VG create adjacency matrices to capture motion interaction relationships between different classes agents. These graph structures are then integrated into a mixed graph, where learning is conducted using a spatiotemporal graph convolutional neural network (ST-GCNN). To further enhance prediction performance, we adopt temporal convolutional networks (TCNs) to generate the predicted trajectory with fewer parameters. Experimental results on publicly available datasets demonstrate that SFEM-GCN surpasses state-of-the-art methods in terms of accuracy and robustness. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.13378v1-abstract-full').style.display = 'none'; document.getElementById('2404.13378v1-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 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">11 pages,3 figures, published to IEEE Transactions on Intelligent vehicles</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.09045">arXiv:2404.09045</a> <span> [<a href="https://arxiv.org/pdf/2404.09045">pdf</a>, <a href="https://arxiv.org/format/2404.09045">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> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</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"> Adapting Mental Health Prediction Tasks for Cross-lingual Learning via Meta-Training and In-context Learning with Large Language Model </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Lifelo%2C+Z">Zita Lifelo</a>, <a href="/search/?searchtype=author&query=Ning%2C+H">Huansheng Ning</a>, <a href="/search/?searchtype=author&query=Dhelim%2C+S">Sahraoui Dhelim</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="2404.09045v1-abstract-short" style="display: inline;"> Timely identification is essential for the efficient handling of mental health illnesses such as depression. However, the current research fails to adequately address the prediction of mental health conditions from social media data in low-resource African languages like Swahili. This study introduces two distinct approaches utilising model-agnostic meta-learning and leveraging large language mode… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.09045v1-abstract-full').style.display = 'inline'; document.getElementById('2404.09045v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.09045v1-abstract-full" style="display: none;"> Timely identification is essential for the efficient handling of mental health illnesses such as depression. However, the current research fails to adequately address the prediction of mental health conditions from social media data in low-resource African languages like Swahili. This study introduces two distinct approaches utilising model-agnostic meta-learning and leveraging large language models (LLMs) to address this gap. Experiments are conducted on three datasets translated to low-resource language and applied to four mental health tasks, which include stress, depression, depression severity and suicidal ideation prediction. we first apply a meta-learning model with self-supervision, which results in improved model initialisation for rapid adaptation and cross-lingual transfer. The results show that our meta-trained model performs significantly better than standard fine-tuning methods, outperforming the baseline fine-tuning in macro F1 score with 18\% and 0.8\% over XLM-R and mBERT. In parallel, we use LLMs' in-context learning capabilities to assess their performance accuracy across the Swahili mental health prediction tasks by analysing different cross-lingual prompting approaches. Our analysis showed that Swahili prompts performed better than cross-lingual prompts but less than English prompts. Our findings show that in-context learning can be achieved through cross-lingual transfer through carefully crafted prompt templates with examples and instructions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.09045v1-abstract-full').style.display = 'none'; document.getElementById('2404.09045v1-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">originally announced</span> April 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.20276">arXiv:2403.20276</a> <span> [<a href="https://arxiv.org/pdf/2403.20276">pdf</a>, <a href="https://arxiv.org/format/2403.20276">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Phenomenology">hep-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> </div> </div> <p class="title is-5 mathjax"> Constraints on the Blazar-Boosted Dark Matter from the CDEX-10 Experiment </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Xu%2C+R">R. Xu</a>, <a href="/search/?searchtype=author&query=Yang%2C+L+T">L. T. Yang</a>, <a href="/search/?searchtype=author&query=Yue%2C+Q">Q. Yue</a>, <a href="/search/?searchtype=author&query=Kang%2C+K+J">K. J. Kang</a>, <a href="/search/?searchtype=author&query=Li%2C+Y+J">Y. J. Li</a>, <a href="/search/?searchtype=author&query=An%2C+H+P">H. P. An</a>, <a href="/search/?searchtype=author&query=C.%2C+G">Greeshma C.</a>, <a href="/search/?searchtype=author&query=Chang%2C+J+P">J. P. Chang</a>, <a href="/search/?searchtype=author&query=Chen%2C+Y+H">Y. H. Chen</a>, <a href="/search/?searchtype=author&query=Cheng%2C+J+P">J. P. Cheng</a>, <a href="/search/?searchtype=author&query=Dai%2C+W+H">W. H. Dai</a>, <a href="/search/?searchtype=author&query=Deng%2C+Z">Z. Deng</a>, <a href="/search/?searchtype=author&query=Fang%2C+C+H">C. H. Fang</a>, <a href="/search/?searchtype=author&query=Geng%2C+X+P">X. P. Geng</a>, <a href="/search/?searchtype=author&query=Gong%2C+H">H. Gong</a>, <a href="/search/?searchtype=author&query=Guo%2C+Q+J">Q. J. Guo</a>, <a href="/search/?searchtype=author&query=Guo%2C+T">T. Guo</a>, <a href="/search/?searchtype=author&query=Guo%2C+X+Y">X. Y. Guo</a>, <a href="/search/?searchtype=author&query=He%2C+L">L. He</a>, <a href="/search/?searchtype=author&query=He%2C+S+M">S. M. He</a>, <a href="/search/?searchtype=author&query=Hu%2C+J+W">J. W. Hu</a>, <a href="/search/?searchtype=author&query=Huang%2C+H+X">H. X. Huang</a>, <a href="/search/?searchtype=author&query=Huang%2C+T+C">T. C. Huang</a>, <a href="/search/?searchtype=author&query=Jiang%2C+L">L. Jiang</a>, <a href="/search/?searchtype=author&query=Karmakar%2C+S">S. Karmakar</a> , et al. (59 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="2403.20276v1-abstract-short" style="display: inline;"> We report new constraints on light dark matter (DM) boosted by blazars using the 205.4 kg day data from the CDEX-10 experiment located at the China Jinping Underground Laboratory. Two representative blazars, TXS 0506+56 and BL Lacertae are studied. The results derived from TXS 0506+56 exclude DM-nucleon elastic scattering cross sections from $4.6\times 10^{-33}\ \rm cm^2$ to… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.20276v1-abstract-full').style.display = 'inline'; document.getElementById('2403.20276v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.20276v1-abstract-full" style="display: none;"> We report new constraints on light dark matter (DM) boosted by blazars using the 205.4 kg day data from the CDEX-10 experiment located at the China Jinping Underground Laboratory. Two representative blazars, TXS 0506+56 and BL Lacertae are studied. The results derived from TXS 0506+56 exclude DM-nucleon elastic scattering cross sections from $4.6\times 10^{-33}\ \rm cm^2$ to $1\times10^{-26}\ \rm cm^2$ for DM masses between 10 keV and 1 GeV, and the results derived from BL Lacertae exclude DM-nucleon elastic scattering cross sections from $2.4\times 10^{-34}\ \rm cm^2$ to $1\times10^{-26}\ \rm cm^2$ for the same range of DM masses. The constraints correspond to the best sensitivities among solid-state detector experiments in the sub-MeV mass range. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.20276v1-abstract-full').style.display = 'none'; document.getElementById('2403.20276v1-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 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">7 pages, 4 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/2403.20263">arXiv:2403.20263</a> <span> [<a href="https://arxiv.org/pdf/2403.20263">pdf</a>, <a href="https://arxiv.org/format/2403.20263">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Phenomenology">hep-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</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/10.1007/s11433-024-2446-2">10.1007/s11433-024-2446-2 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Probing Dark Matter Particles from Evaporating Primordial Black Holes via Electron Scattering in the CDEX-10 Experiment </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zhang%2C+Z+H">Z. H. Zhang</a>, <a href="/search/?searchtype=author&query=Yang%2C+L+T">L. T. Yang</a>, <a href="/search/?searchtype=author&query=Yue%2C+Q">Q. Yue</a>, <a href="/search/?searchtype=author&query=Kang%2C+K+J">K. J. Kang</a>, <a href="/search/?searchtype=author&query=Li%2C+Y+J">Y. J. Li</a>, <a href="/search/?searchtype=author&query=An%2C+H+P">H. P. An</a>, <a href="/search/?searchtype=author&query=C.%2C+G">Greeshma C.</a>, <a href="/search/?searchtype=author&query=Chang%2C+J+P">J. P. Chang</a>, <a href="/search/?searchtype=author&query=Chen%2C+Y+H">Y. H. Chen</a>, <a href="/search/?searchtype=author&query=Cheng%2C+J+P">J. P. Cheng</a>, <a href="/search/?searchtype=author&query=Dai%2C+W+H">W. H. Dai</a>, <a href="/search/?searchtype=author&query=Deng%2C+Z">Z. Deng</a>, <a href="/search/?searchtype=author&query=Fang%2C+C+H">C. H. Fang</a>, <a href="/search/?searchtype=author&query=Geng%2C+X+P">X. P. Geng</a>, <a href="/search/?searchtype=author&query=Gong%2C+H">H. Gong</a>, <a href="/search/?searchtype=author&query=Guo%2C+Q+J">Q. J. Guo</a>, <a href="/search/?searchtype=author&query=Guo%2C+T">T. Guo</a>, <a href="/search/?searchtype=author&query=Guo%2C+X+Y">X. Y. Guo</a>, <a href="/search/?searchtype=author&query=He%2C+L">L. He</a>, <a href="/search/?searchtype=author&query=He%2C+S+M">S. M. He</a>, <a href="/search/?searchtype=author&query=Hu%2C+J+W">J. W. Hu</a>, <a href="/search/?searchtype=author&query=Huang%2C+H+X">H. X. Huang</a>, <a href="/search/?searchtype=author&query=Huang%2C+T+C">T. C. Huang</a>, <a href="/search/?searchtype=author&query=Jiang%2C+L">L. Jiang</a>, <a href="/search/?searchtype=author&query=Karmakar%2C+S">S. Karmakar</a> , et al. (59 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="2403.20263v2-abstract-short" style="display: inline;"> Dark matter (DM) is a major constituent of the Universe. However, no definite evidence of DM particles (denoted as ``$蠂$") has been found in DM direct detection (DD) experiments to date. There is a novel concept of detecting $蠂$ from evaporating primordial black holes (PBHs). We search for $蠂$ emitted from PBHs by investigating their interaction with target electrons. The examined PBH masses range… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.20263v2-abstract-full').style.display = 'inline'; document.getElementById('2403.20263v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.20263v2-abstract-full" style="display: none;"> Dark matter (DM) is a major constituent of the Universe. However, no definite evidence of DM particles (denoted as ``$蠂$") has been found in DM direct detection (DD) experiments to date. There is a novel concept of detecting $蠂$ from evaporating primordial black holes (PBHs). We search for $蠂$ emitted from PBHs by investigating their interaction with target electrons. The examined PBH masses range from 1$\times$10$^{15}$ to 7$\times$10$^{16}$ g under the current limits of PBH abundance $f_{PBH}$. Using 205.4 kg$\cdot$day data obtained from the CDEX-10 experiment conducted in the China Jinping Underground Laboratory, we exclude the $蠂$--electron ($蠂$--$e$) elastic-scattering cross section $蟽_{蠂e} \sim 5\times10^{-29}$ cm$^2$ for $蠂$ with a mass $m_蠂\lesssim$ 0.1 keV from our results. With the higher radiation background but lower energy threshold (160 eV), CDEX-10 fill a part of the gap in the previous work. If ($m_蠂$, $蟽_{蠂e}$) can be determined in the future, DD experiments are expected to impose strong constraints on $f_{PBH}$ for large $M_{PBH}$s. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.20263v2-abstract-full').style.display = 'none'; document.getElementById('2403.20263v2-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 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 29 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">9 pages, 6 figures, 3 tables. Version updated to match SCPMA version</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Sci. China Phys. Mech. Astron. 67, 101011 (2024) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.20183">arXiv:2403.20183</a> <span> [<a href="https://arxiv.org/pdf/2403.20183">pdf</a>, <a href="https://arxiv.org/format/2403.20183">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> </div> </div> <p class="title is-5 mathjax"> HARMamba: Efficient and Lightweight Wearable Sensor Human Activity Recognition Based on Bidirectional Mamba </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+S">Shuangjian Li</a>, <a href="/search/?searchtype=author&query=Zhu%2C+T">Tao Zhu</a>, <a href="/search/?searchtype=author&query=Duan%2C+F">Furong Duan</a>, <a href="/search/?searchtype=author&query=Chen%2C+L">Liming Chen</a>, <a href="/search/?searchtype=author&query=Ning%2C+H">Huansheng Ning</a>, <a href="/search/?searchtype=author&query=Nugent%2C+C">Christopher Nugent</a>, <a href="/search/?searchtype=author&query=Wan%2C+Y">Yaping Wan</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="2403.20183v3-abstract-short" style="display: inline;"> Wearable sensor-based human activity recognition (HAR) is a critical research domain in activity perception. However, achieving high efficiency and long sequence recognition remains a challenge. Despite the extensive investigation of temporal deep learning models, such as CNNs, RNNs, and transformers, their extensive parameters often pose significant computational and memory constraints, rendering… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.20183v3-abstract-full').style.display = 'inline'; document.getElementById('2403.20183v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.20183v3-abstract-full" style="display: none;"> Wearable sensor-based human activity recognition (HAR) is a critical research domain in activity perception. However, achieving high efficiency and long sequence recognition remains a challenge. Despite the extensive investigation of temporal deep learning models, such as CNNs, RNNs, and transformers, their extensive parameters often pose significant computational and memory constraints, rendering them less suitable for resource-constrained mobile health applications. This study introduces HARMamba, an innovative light-weight and versatile HAR architecture that combines selective bidirectional State Spaces Model and hardware-aware design. To optimize real-time resource consumption in practical scenarios, HARMamba employs linear recursive mechanisms and parameter discretization, allowing it to selectively focus on relevant input sequences while efficiently fusing scan and recompute operations. The model employs independent channels to process sensor data streams, dividing each channel into patches and appending classification tokens to the end of the sequence. It utilizes position embedding to represent the sequence order. The patch sequence is subsequently processed by HARMamba Block, and the classification head finally outputs the activity category. The HARMamba Block serves as the fundamental component of the HARMamba architecture, enabling the effective capture of more discriminative activity sequence features. HARMamba outperforms contemporary state-of-the-art frameworks, delivering comparable or better accuracy with significantly reducing computational and memory demands. It's effectiveness has been extensively validated on 4 publically available datasets namely PAMAP2, WISDM, UNIMIB SHAR and UCI. The F1 scores of HARMamba on the four datasets are 99.74%, 99.20%, 88.23% and 97.01%, respectively. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.20183v3-abstract-full').style.display = 'none'; document.getElementById('2403.20183v3-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 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 29 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.09223">arXiv:2403.09223</a> <span> [<a href="https://arxiv.org/pdf/2403.09223">pdf</a>, <a href="https://arxiv.org/format/2403.09223">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="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> MCformer: Multivariate Time Series Forecasting with Mixed-Channels Transformer </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Han%2C+W">Wenyong Han</a>, <a href="/search/?searchtype=author&query=Member%2C+T+Z">Tao Zhu Member</a>, <a href="/search/?searchtype=author&query=Chen%2C+L">Liming Chen</a>, <a href="/search/?searchtype=author&query=Ning%2C+H">Huansheng Ning</a>, <a href="/search/?searchtype=author&query=Luo%2C+Y">Yang Luo</a>, <a href="/search/?searchtype=author&query=Wan%2C+Y">Yaping Wan</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="2403.09223v1-abstract-short" style="display: inline;"> The massive generation of time-series data by largescale Internet of Things (IoT) devices necessitates the exploration of more effective models for multivariate time-series forecasting. In previous models, there was a predominant use of the Channel Dependence (CD) strategy (where each channel represents a univariate sequence). Current state-of-the-art (SOTA) models primarily rely on the Channel In… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.09223v1-abstract-full').style.display = 'inline'; document.getElementById('2403.09223v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.09223v1-abstract-full" style="display: none;"> The massive generation of time-series data by largescale Internet of Things (IoT) devices necessitates the exploration of more effective models for multivariate time-series forecasting. In previous models, there was a predominant use of the Channel Dependence (CD) strategy (where each channel represents a univariate sequence). Current state-of-the-art (SOTA) models primarily rely on the Channel Independence (CI) strategy. The CI strategy treats all channels as a single channel, expanding the dataset to improve generalization performance and avoiding inter-channel correlation that disrupts long-term features. However, the CI strategy faces the challenge of interchannel correlation forgetting. To address this issue, we propose an innovative Mixed Channels strategy, combining the data expansion advantages of the CI strategy with the ability to counteract inter-channel correlation forgetting. Based on this strategy, we introduce MCformer, a multivariate time-series forecasting model with mixed channel features. The model blends a specific number of channels, leveraging an attention mechanism to effectively capture inter-channel correlation information when modeling long-term features. Experimental results demonstrate that the Mixed Channels strategy outperforms pure CI strategy in multivariate time-series forecasting tasks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.09223v1-abstract-full').style.display = 'none'; document.getElementById('2403.09223v1-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 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.08214">arXiv:2403.08214</a> <span> [<a href="https://arxiv.org/pdf/2403.08214">pdf</a>, <a href="https://arxiv.org/format/2403.08214">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> </div> </div> <p class="title is-5 mathjax"> P2LHAP:Wearable sensor-based human activity recognition, segmentation and forecast through Patch-to-Label Seq2Seq Transformer </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+S">Shuangjian Li</a>, <a href="/search/?searchtype=author&query=Zhu%2C+T">Tao Zhu</a>, <a href="/search/?searchtype=author&query=Nie%2C+M">Mingxing Nie</a>, <a href="/search/?searchtype=author&query=Ning%2C+H">Huansheng Ning</a>, <a href="/search/?searchtype=author&query=Liu%2C+Z">Zhenyu Liu</a>, <a href="/search/?searchtype=author&query=Chen%2C+L">Liming Chen</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="2403.08214v3-abstract-short" style="display: inline;"> Traditional deep learning methods struggle to simultaneously segment, recognize, and forecast human activities from sensor data. This limits their usefulness in many fields such as healthcare and assisted living, where real-time understanding of ongoing and upcoming activities is crucial. This paper introduces P2LHAP, a novel Patch-to-Label Seq2Seq framework that tackles all three tasks in a effic… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.08214v3-abstract-full').style.display = 'inline'; document.getElementById('2403.08214v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.08214v3-abstract-full" style="display: none;"> Traditional deep learning methods struggle to simultaneously segment, recognize, and forecast human activities from sensor data. This limits their usefulness in many fields such as healthcare and assisted living, where real-time understanding of ongoing and upcoming activities is crucial. This paper introduces P2LHAP, a novel Patch-to-Label Seq2Seq framework that tackles all three tasks in a efficient single-task model. P2LHAP divides sensor data streams into a sequence of "patches", served as input tokens, and outputs a sequence of patch-level activity labels including the predicted future activities. A unique smoothing technique based on surrounding patch labels, is proposed to identify activity boundaries accurately. Additionally, P2LHAP learns patch-level representation by sensor signal channel-independent Transformer encoders and decoders. All channels share embedding and Transformer weights across all sequences. Evaluated on three public datasets, P2LHAP significantly outperforms the state-of-the-art in all three tasks, demonstrating its effectiveness and potential for real-world applications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.08214v3-abstract-full').style.display = 'none'; document.getElementById('2403.08214v3-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 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 12 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.03169">arXiv:2403.03169</a> <span> [<a href="https://arxiv.org/pdf/2403.03169">pdf</a>, <a href="https://arxiv.org/format/2403.03169">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Strongly Correlated Electrons">cond-mat.str-el</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Superconductivity">cond-mat.supr-con</span> </div> </div> <p class="title is-5 mathjax"> Dynamical decoding of the competition between charge density waves in a kagome superconductor </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Ning%2C+H">Honglie Ning</a>, <a href="/search/?searchtype=author&query=Oh%2C+K+H">Kyoung Hun Oh</a>, <a href="/search/?searchtype=author&query=Su%2C+Y">Yifan Su</a>, <a href="/search/?searchtype=author&query=von+Hoegen%2C+A">Alexander von Hoegen</a>, <a href="/search/?searchtype=author&query=Porter%2C+Z">Zach Porter</a>, <a href="/search/?searchtype=author&query=Salinas%2C+A+C">Andrea Capa Salinas</a>, <a href="/search/?searchtype=author&query=Nguyen%2C+Q+L">Quynh L Nguyen</a>, <a href="/search/?searchtype=author&query=Chollet%2C+M">Matthieu Chollet</a>, <a href="/search/?searchtype=author&query=Sato%2C+T">Takahiro Sato</a>, <a href="/search/?searchtype=author&query=Esposito%2C+V">Vincent Esposito</a>, <a href="/search/?searchtype=author&query=Hoffmann%2C+M+C">Matthias C Hoffmann</a>, <a href="/search/?searchtype=author&query=White%2C+A">Adam White</a>, <a href="/search/?searchtype=author&query=Melendrez%2C+C">Cynthia Melendrez</a>, <a href="/search/?searchtype=author&query=Zhu%2C+D">Diling Zhu</a>, <a href="/search/?searchtype=author&query=Wilson%2C+S+D">Stephen D Wilson</a>, <a href="/search/?searchtype=author&query=Gedik%2C+N">Nuh Gedik</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="2403.03169v1-abstract-short" style="display: inline;"> The kagome superconductor CsV$_3$Sb$_5$ hosts a variety of charge density wave (CDW) phases, which play a fundamental role in the formation of other exotic electronic instabilities. However, identifying the precise structure of these CDW phases and their intricate relationships remain the subject of intense debate, due to the lack of static probes that can distinguish the CDW phases with identical… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.03169v1-abstract-full').style.display = 'inline'; document.getElementById('2403.03169v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.03169v1-abstract-full" style="display: none;"> The kagome superconductor CsV$_3$Sb$_5$ hosts a variety of charge density wave (CDW) phases, which play a fundamental role in the formation of other exotic electronic instabilities. However, identifying the precise structure of these CDW phases and their intricate relationships remain the subject of intense debate, due to the lack of static probes that can distinguish the CDW phases with identical spatial periodicity. Here, we unveil the competition between two coexisting $2\times2\times2$ CDWs in CsV$_3$Sb$_5$ harnessing time-resolved X-ray diffraction. By analyzing the light-induced changes in the intensity of CDW superlattice peaks, we demonstrate the presence of both phases, each displaying a significantly different amount of melting upon excitation. The anomalous light-induced sharpening of peak width further shows that the phase that is more resistant to photo-excitation exhibits an increase in domain size at the expense of the other, thereby showcasing a hallmark of phase competition. Our results not only shed light on the interplay between the multiple CDW phases in CsV$_3$Sb$_5$, but also establish a non-equilibrium framework for comprehending complex phase relationships that are challenging to disentangle using static techniques. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.03169v1-abstract-full').style.display = 'none'; document.getElementById('2403.03169v1-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">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">10 pages, 4 figures with supplemental Material</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.16904">arXiv:2402.16904</a> <span> [<a href="https://arxiv.org/pdf/2402.16904">pdf</a>, <a href="https://arxiv.org/format/2402.16904">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="Neural and Evolutionary Computing">cs.NE</span> </div> </div> <p class="title is-5 mathjax"> Selective Task offloading for Maximum Inference Accuracy and Energy efficient Real-Time IoT Sensing Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Sada%2C+A+B">Abdelkarim Ben Sada</a>, <a href="/search/?searchtype=author&query=Khelloufi%2C+A">Amar Khelloufi</a>, <a href="/search/?searchtype=author&query=Naouri%2C+A">Abdenacer Naouri</a>, <a href="/search/?searchtype=author&query=Ning%2C+H">Huansheng Ning</a>, <a href="/search/?searchtype=author&query=Dhelim%2C+S">Sahraoui Dhelim</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="2402.16904v1-abstract-short" style="display: inline;"> The recent advancements in small-size inference models facilitated AI deployment on the edge. However, the limited resource nature of edge devices poses new challenges especially for real-time applications. Deploying multiple inference models (or a single tunable model) varying in size and therefore accuracy and power consumption, in addition to an edge server inference model, can offer a dynamic… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.16904v1-abstract-full').style.display = 'inline'; document.getElementById('2402.16904v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.16904v1-abstract-full" style="display: none;"> The recent advancements in small-size inference models facilitated AI deployment on the edge. However, the limited resource nature of edge devices poses new challenges especially for real-time applications. Deploying multiple inference models (or a single tunable model) varying in size and therefore accuracy and power consumption, in addition to an edge server inference model, can offer a dynamic system in which the allocation of inference models to inference jobs is performed according to the current resource conditions. Therefore, in this work, we tackle the problem of selectively allocating inference models to jobs or offloading them to the edge server to maximize inference accuracy under time and energy constraints. This problem is shown to be an instance of the unbounded multidimensional knapsack problem which is considered a strongly NP-hard problem. We propose a lightweight hybrid genetic algorithm (LGSTO) to solve this problem. We introduce a termination condition and neighborhood exploration techniques for faster evolution of populations. We compare LGSTO with the Naive and Dynamic programming solutions. In addition to classic genetic algorithms using different reproduction methods including NSGA-II, and finally we compare to other evolutionary methods such as Particle swarm optimization (PSO) and Ant colony optimization (ACO). Experiment results show that LGSTO performed 3 times faster than the fastest comparable schemes while producing schedules with higher average accuracy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.16904v1-abstract-full').style.display = 'none'; document.getElementById('2402.16904v1-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 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/2402.16684">arXiv:2402.16684</a> <span> [<a href="https://arxiv.org/pdf/2402.16684">pdf</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> </div> </div> <p class="title is-5 mathjax"> Automated Floodwater Depth Estimation Using Large Multimodal Model for Rapid Flood Mapping </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Akinboyewa%2C+T">Temitope Akinboyewa</a>, <a href="/search/?searchtype=author&query=Ning%2C+H">Huan Ning</a>, <a href="/search/?searchtype=author&query=Lessani%2C+M+N">M. Naser Lessani</a>, <a href="/search/?searchtype=author&query=Li%2C+Z">Zhenlong Li</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="2402.16684v1-abstract-short" style="display: inline;"> Information on the depth of floodwater is crucial for rapid mapping of areas affected by floods. However, previous approaches for estimating floodwater depth, including field surveys, remote sensing, and machine learning techniques, can be time-consuming and resource-intensive. This paper presents an automated and fast approach for estimating floodwater depth from on-site flood photos. A pre-train… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.16684v1-abstract-full').style.display = 'inline'; document.getElementById('2402.16684v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.16684v1-abstract-full" style="display: none;"> Information on the depth of floodwater is crucial for rapid mapping of areas affected by floods. However, previous approaches for estimating floodwater depth, including field surveys, remote sensing, and machine learning techniques, can be time-consuming and resource-intensive. This paper presents an automated and fast approach for estimating floodwater depth from on-site flood photos. A pre-trained large multimodal model, GPT-4 Vision, was used specifically for estimating floodwater. The input data were flooding photos that contained referenced objects, such as street signs, cars, people, and buildings. Using the heights of the common objects as references, the model returned the floodwater depth as the output. Results show that the proposed approach can rapidly provide a consistent and reliable estimation of floodwater depth from flood photos. Such rapid estimation is transformative in flood inundation mapping and assessing the severity of the flood in near-real time, which is essential for effective flood response strategies. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.16684v1-abstract-full').style.display = 'none'; document.getElementById('2402.16684v1-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> 26 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/2402.16052">arXiv:2402.16052</a> <span> [<a href="https://arxiv.org/pdf/2402.16052">pdf</a>, <a href="https://arxiv.org/format/2402.16052">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</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="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Maximizing UAV Fog Deployment Efficiency for Critical Rescue Operations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Naouri%2C+A">Abdenacer Naouri</a>, <a href="/search/?searchtype=author&query=Ning%2C+H">Huansheng Ning</a>, <a href="/search/?searchtype=author&query=Nouri%2C+N+A">Nabil Abdelkader Nouri</a>, <a href="/search/?searchtype=author&query=Khelloufi%2C+A">Amar Khelloufi</a>, <a href="/search/?searchtype=author&query=Sada%2C+A+B">Abdelkarim Ben Sada</a>, <a href="/search/?searchtype=author&query=Naouri%2C+S">Salim Naouri</a>, <a href="/search/?searchtype=author&query=Qammar%2C+A">Attia Qammar</a>, <a href="/search/?searchtype=author&query=Dhelim%2C+S">Sahraoui Dhelim</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="2402.16052v1-abstract-short" style="display: inline;"> In disaster scenarios and high-stakes rescue operations, integrating Unmanned Aerial Vehicles (UAVs) as fog nodes has become crucial. This integration ensures a smooth connection between affected populations and essential health monitoring devices, supported by the Internet of Things (IoT). Integrating UAVs in such environments is inherently challenging, where the primary objectives involve maximi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.16052v1-abstract-full').style.display = 'inline'; document.getElementById('2402.16052v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.16052v1-abstract-full" style="display: none;"> In disaster scenarios and high-stakes rescue operations, integrating Unmanned Aerial Vehicles (UAVs) as fog nodes has become crucial. This integration ensures a smooth connection between affected populations and essential health monitoring devices, supported by the Internet of Things (IoT). Integrating UAVs in such environments is inherently challenging, where the primary objectives involve maximizing network connectivity and coverage while extending the network's lifetime through energy-efficient strategies to serve the maximum number of affected individuals. In this paper, We propose a novel model centred around dynamic UAV-based fog deployment that optimizes the system's adaptability and operational efficacy within the afflicted areas. First, we decomposed the problem into two subproblems. Connectivity and coverage subproblem, and network lifespan optimization subproblem. We shape our UAV fog deployment problem as a uni-objective optimization and introduce a specialized UAV fog deployment algorithm tailored specifically for UAV fog nodes deployed in rescue missions. While the network lifespan optimization subproblem is efficiently solved via a one-dimensional swapping method. Following that, We introduce a novel optimization strategy for UAV fog node placement in dynamic networks during evacuation scenarios, with a primary focus on ensuring robust connectivity and maximal coverage for mobile users, while extending the network's lifespan. Finally, we introduce Adaptive Whale Optimization Algorithm (WOA) for fog node deployment in a dynamic network. Its agility, rapid convergence, and low computational demands make it an ideal fit for high-pressure environments. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.16052v1-abstract-full').style.display = 'none'; document.getElementById('2402.16052v1-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 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/2402.05650">arXiv:2402.05650</a> <span> [<a href="https://arxiv.org/pdf/2402.05650">pdf</a>, <a href="https://arxiv.org/format/2402.05650">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Software Engineering">cs.SE</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"> Rocks Coding, Not Development--A Human-Centric, Experimental Evaluation of LLM-Supported SE Tasks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Wang%2C+W">Wei Wang</a>, <a href="/search/?searchtype=author&query=Ning%2C+H">Huilong Ning</a>, <a href="/search/?searchtype=author&query=Zhang%2C+G">Gaowei Zhang</a>, <a href="/search/?searchtype=author&query=Liu%2C+L">Libo Liu</a>, <a href="/search/?searchtype=author&query=Wang%2C+Y">Yi 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="2402.05650v3-abstract-short" style="display: inline;"> Recently, large language models (LLM) based generative AI has been gaining momentum for their impressive high-quality performances in multiple domains, particularly after the release of the ChatGPT. Many believe that they have the potential to perform general-purpose problem-solving in software development and replace human software developers. Nevertheless, there are in a lack of serious investig… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.05650v3-abstract-full').style.display = 'inline'; document.getElementById('2402.05650v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.05650v3-abstract-full" style="display: none;"> Recently, large language models (LLM) based generative AI has been gaining momentum for their impressive high-quality performances in multiple domains, particularly after the release of the ChatGPT. Many believe that they have the potential to perform general-purpose problem-solving in software development and replace human software developers. Nevertheless, there are in a lack of serious investigation into the capability of these LLM techniques in fulfilling software development tasks. In a controlled 2 x 2 between-subject experiment with 109 participants, we examined whether and to what degree working with ChatGPT was helpful in the coding task and typical software development task and how people work with ChatGPT. We found that while ChatGPT performed well in solving simple coding problems, its performance in supporting typical software development tasks was not that good. We also observed the interactions between participants and ChatGPT and found the relations between the interactions and the outcomes. Our study thus provides first-hand insights into using ChatGPT to fulfill software engineering tasks with real-world developers and motivates the need for novel interaction mechanisms that help developers effectively work with large language models to achieve desired outcomes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.05650v3-abstract-full').style.display = 'none'; document.getElementById('2402.05650v3-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 February, 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">The paper has been accepted by FSE</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 65-XX <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> D.2; I.2 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.13617">arXiv:2312.13617</a> <span> [<a href="https://arxiv.org/pdf/2312.13617">pdf</a>, <a href="https://arxiv.org/ps/2312.13617">ps</a>, <a href="https://arxiv.org/format/2312.13617">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Solar and Stellar Astrophysics">astro-ph.SR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Astrophysical Phenomena">astro-ph.HE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Plasma Physics">physics.plasm-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Space Physics">physics.space-ph</span> </div> </div> <p class="title is-5 mathjax"> High-harmonic Plasma Emission Induced by Electron Beams in Weakly Magnetized Plasmas </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+C">Chuanyang Li</a>, <a href="/search/?searchtype=author&query=Chen%2C+Y">Yao Chen</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Z">Zilong Zhang</a>, <a href="/search/?searchtype=author&query=Ning%2C+H">Hao Ning</a>, <a href="/search/?searchtype=author&query=Li%2C+T">TangMu Li</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="2312.13617v1-abstract-short" style="display: inline;"> Electromagnetic radiation at higher harmonics of the plasma frequency ($蠅\sim n蠅_{pe}, n > 2$) has been occasionally observed in type II and type III solar radio bursts, yet the underlying mechanism remains undetermined. Here we present two-dimensional fully kinetic electromagnetic particle-in-cell simulations with high spectral resolution to investigate the beam-driven plasma emission process in… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.13617v1-abstract-full').style.display = 'inline'; document.getElementById('2312.13617v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.13617v1-abstract-full" style="display: none;"> Electromagnetic radiation at higher harmonics of the plasma frequency ($蠅\sim n蠅_{pe}, n > 2$) has been occasionally observed in type II and type III solar radio bursts, yet the underlying mechanism remains undetermined. Here we present two-dimensional fully kinetic electromagnetic particle-in-cell simulations with high spectral resolution to investigate the beam-driven plasma emission process in weakly magnetized plasmas of typical coronal conditions. We focused on the generation mechanisms of high-harmonic emission. We found that a larger beam velocity ($u_d$) favors the generation of the higher-harmonic emission. The emissions grow later for higher harmonics and decrease in intensity by $\sim$2 orders of magnitude for each jump of the harmonic number. The second and third harmonic ($\rm H_2$ and $\rm H_3$) emissions get closer in intensity with larger $u_d$. We also show that (1) the $\rm H_3$ emission is mainly generated via the coalescence of the $\rm H_2$ emission with the Langmuir waves, i.e., $\rm H_2 + L \rightarrow H_3$, wherein the coalescence with the forward-propagating beam-Langmuir wave leads to the forward-propagating $\rm H_3$, and coalescence with the backward-propagating Langmuir wave leads to the backward-propagating $\rm H_3$; and (2) the $\rm H_4$ emission mainly arises from the coalescence of the $\rm H_3$ emission with the forward- (backward-) propagating Langmuir wave, in terms of $\rm H_3 + L \rightarrow H_4$. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.13617v1-abstract-full').style.display = 'none'; document.getElementById('2312.13617v1-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 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.09484">arXiv:2312.09484</a> <span> [<a href="https://arxiv.org/pdf/2312.09484">pdf</a>, <a href="https://arxiv.org/format/2312.09484">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Strongly Correlated Electrons">cond-mat.str-el</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/10.1038/s41467-023-44021-4">10.1038/s41467-023-44021-4 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> A coherent phonon-induced hidden quadrupolar ordered state in Ca$_2$RuO$_4$ </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Ning%2C+H">H. Ning</a>, <a href="/search/?searchtype=author&query=Mehio%2C+O">O. Mehio</a>, <a href="/search/?searchtype=author&query=Li%2C+X">X. Li</a>, <a href="/search/?searchtype=author&query=Buchhold%2C+M">M. Buchhold</a>, <a href="/search/?searchtype=author&query=Driesse%2C+M">M. Driesse</a>, <a href="/search/?searchtype=author&query=Zhao%2C+H">H. Zhao</a>, <a href="/search/?searchtype=author&query=Cao%2C+G">G. Cao</a>, <a href="/search/?searchtype=author&query=Hsieh%2C+D">D. Hsieh</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="2312.09484v1-abstract-short" style="display: inline;"> Ultrafast laser excitation provides a means to transiently realize long-range ordered electronic states of matter that are hidden in thermal equilibrium. Recently, this approach has unveiled a variety of thermally inaccessible ordered states in strongly correlated materials, including charge density wave, ferroelectric, magnetic, and intertwined charge-orbital ordered states. However, more exotic… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.09484v1-abstract-full').style.display = 'inline'; document.getElementById('2312.09484v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.09484v1-abstract-full" style="display: none;"> Ultrafast laser excitation provides a means to transiently realize long-range ordered electronic states of matter that are hidden in thermal equilibrium. Recently, this approach has unveiled a variety of thermally inaccessible ordered states in strongly correlated materials, including charge density wave, ferroelectric, magnetic, and intertwined charge-orbital ordered states. However, more exotic hidden states exhibiting higher multipolar ordering remain elusive owing to the challenge of directly manipulating and detecting them with light. Here we demonstrate a method to induce a dynamical transition from a thermally allowed to a thermally forbidden spin-orbit entangled quadrupolar ordered state in Ca$_2$RuO$_4$ by coherently exciting a phonon that is strongly coupled to the order parameter. Combining probe photon energy-resolved coherent phonon spectroscopy measurements with model Hamiltonian calculations, we show that the dynamical transition is manifested through anomalies in the temperature, pump excitation fluence, and probe photon energy dependence of the strongly coupled phonon. With this procedure, we introduce a general pathway to uncover hidden multipolar ordered states and to control their re-orientation on ultrashort timescales. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.09484v1-abstract-full').style.display = 'none'; document.getElementById('2312.09484v1-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">8 pages, 4 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Nature Communications 14, 8258 (2023) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.04147">arXiv:2312.04147</a> <span> [<a href="https://arxiv.org/pdf/2312.04147">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> </div> </div> <p class="title is-5 mathjax"> An Improved Masking Strategy for Self-supervised Masked Reconstruction in Human Activity Recognition </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Wang%2C+J">Jinqiang Wang</a>, <a href="/search/?searchtype=author&query=Zhu%2C+T">Tao Zhu</a>, <a href="/search/?searchtype=author&query=Ning%2C+H">Huansheng Ning</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="2312.04147v1-abstract-short" style="display: inline;"> Masked reconstruction serves as a fundamental pretext task for self-supervised learning, enabling the model to enhance its feature extraction capabilities by reconstructing the masked segments from extensive unlabeled data. In human activity recognition, this pretext task employed a masking strategy centered on the time dimension. However, this masking strategy fails to fully exploit the inherent… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.04147v1-abstract-full').style.display = 'inline'; document.getElementById('2312.04147v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.04147v1-abstract-full" style="display: none;"> Masked reconstruction serves as a fundamental pretext task for self-supervised learning, enabling the model to enhance its feature extraction capabilities by reconstructing the masked segments from extensive unlabeled data. In human activity recognition, this pretext task employed a masking strategy centered on the time dimension. However, this masking strategy fails to fully exploit the inherent characteristics of wearable sensor data and overlooks the inter-channel information coupling, thereby limiting its potential as a powerful pretext task. To address these limitations, we propose a novel masking strategy called Channel Masking. It involves masking the sensor data along the channel dimension, thereby compelling the encoder to extract channel-related features while performing the masked reconstruction task. Moreover, Channel Masking can be seamlessly integrated with masking strategies along the time dimension, thereby motivating the self-supervised model to undertake the masked reconstruction task in both the time and channel dimensions. Integrated masking strategies are named Time-Channel Masking and Span-Channel Masking. Finally, we optimize the reconstruction loss function to incorporate the reconstruction loss in both the time and channel dimensions. We evaluate proposed masking strategies on three public datasets, and experimental results show that the proposed strategies outperform prior strategies in both self-supervised and semi-supervised scenarios. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.04147v1-abstract-full').style.display = 'none'; document.getElementById('2312.04147v1-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 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.07183">arXiv:2310.07183</a> <span> [<a href="https://arxiv.org/pdf/2310.07183">pdf</a>, <a href="https://arxiv.org/format/2310.07183">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> </div> </div> <p class="title is-5 mathjax"> SAM-OCTA: Prompting Segment-Anything for OCTA Image Segmentation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Chen%2C+X">Xinrun Chen</a>, <a href="/search/?searchtype=author&query=Wang%2C+C">Chengliang Wang</a>, <a href="/search/?searchtype=author&query=Ning%2C+H">Haojian Ning</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Shiying Li</a>, <a href="/search/?searchtype=author&query=Shen%2C+M">Mei Shen</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="2310.07183v2-abstract-short" style="display: inline;"> Segmenting specific targets or biomarkers is necessary to analyze optical coherence tomography angiography (OCTA) images. Previous methods typically segment all the targets in an OCTA sample, such as retinal vessels (RVs). Although these methods perform well in accuracy and precision, OCTA analyses often focusing local information within the images which has not been fulfilled. In this paper, we p… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.07183v2-abstract-full').style.display = 'inline'; document.getElementById('2310.07183v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.07183v2-abstract-full" style="display: none;"> Segmenting specific targets or biomarkers is necessary to analyze optical coherence tomography angiography (OCTA) images. Previous methods typically segment all the targets in an OCTA sample, such as retinal vessels (RVs). Although these methods perform well in accuracy and precision, OCTA analyses often focusing local information within the images which has not been fulfilled. In this paper, we propose a method called SAM-OCTA for local segmentation in OCTA images. The method fine-tunes a pre-trained segment anything model (SAM) using low-rank adaptation (LoRA) and utilizes prompt points for local RVs, arteries, and veins segmentation in OCTA. To explore the effect and mechanism of prompt points, we set up global and local segmentation modes with two prompt point generation strategies, namely random selection and special annotation. Considering practical usage, we conducted extended experiments with different model scales and analyzed the model performance before and after fine-tuning besides the general segmentation task. From comprehensive experimental results with the OCTA-500 dataset, our SAM-OCTA method has achieved state-of-the-art performance in common OCTA segmentation tasks related to RV and FAZ, and it also performs accurate segmentation of artery-vein and local vessels. The code is available at https://github.com/ShellRedia/SAM-OCTA-extend. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.07183v2-abstract-full').style.display = 'none'; document.getElementById('2310.07183v2-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 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 11 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">arXiv admin note: text overlap with arXiv:2309.11758</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.14982">arXiv:2309.14982</a> <span> [<a href="https://arxiv.org/pdf/2309.14982">pdf</a>, <a href="https://arxiv.org/format/2309.14982">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Phenomenology">hep-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</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/10.1103/PhysRevLett.132.171001">10.1103/PhysRevLett.132.171001 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Experimental Limits on Solar Reflected Dark Matter with a New Approach on Accelerated-Dark-Matter-Electron Analysis in Semiconductors </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zhang%2C+Z+Y">Z. Y. Zhang</a>, <a href="/search/?searchtype=author&query=Yang%2C+L+T">L. T. Yang</a>, <a href="/search/?searchtype=author&query=Yue%2C+Q">Q. Yue</a>, <a href="/search/?searchtype=author&query=Kang%2C+K+J">K. J. Kang</a>, <a href="/search/?searchtype=author&query=Li%2C+Y+J">Y. J. Li</a>, <a href="/search/?searchtype=author&query=An%2C+H+P">H. P. An</a>, <a href="/search/?searchtype=author&query=C.%2C+G">Greeshma C.</a>, <a href="/search/?searchtype=author&query=Chang%2C+J+P">J. P. Chang</a>, <a href="/search/?searchtype=author&query=Chen%2C+Y+H">Y. H. Chen</a>, <a href="/search/?searchtype=author&query=Cheng%2C+J+P">J. P. Cheng</a>, <a href="/search/?searchtype=author&query=Dai%2C+W+H">W. H. Dai</a>, <a href="/search/?searchtype=author&query=Deng%2C+Z">Z. Deng</a>, <a href="/search/?searchtype=author&query=Fang%2C+C+H">C. H. Fang</a>, <a href="/search/?searchtype=author&query=Geng%2C+X+P">X. P. Geng</a>, <a href="/search/?searchtype=author&query=Gong%2C+H">H. Gong</a>, <a href="/search/?searchtype=author&query=Guo%2C+Q+J">Q. J. Guo</a>, <a href="/search/?searchtype=author&query=Guo%2C+T">T. Guo</a>, <a href="/search/?searchtype=author&query=Guo%2C+X+Y">X. Y. Guo</a>, <a href="/search/?searchtype=author&query=He%2C+L">L. He</a>, <a href="/search/?searchtype=author&query=He%2C+S+M">S. M. He</a>, <a href="/search/?searchtype=author&query=Hu%2C+J+W">J. W. Hu</a>, <a href="/search/?searchtype=author&query=Huang%2C+H+X">H. X. Huang</a>, <a href="/search/?searchtype=author&query=Huang%2C+T+C">T. C. Huang</a>, <a href="/search/?searchtype=author&query=Jiang%2C+L">L. Jiang</a>, <a href="/search/?searchtype=author&query=Karmakar%2C+S">S. Karmakar</a> , et al. (59 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="2309.14982v3-abstract-short" style="display: inline;"> Recently a dark matter-electron (DM-electron) paradigm has drawn much attention. Models beyond the standard halo model describing DM accelerated by high energy celestial bodies are under intense examination as well. In this Letter, a velocity components analysis (VCA) method dedicated to swift analysis of accelerated DM-electron interactions via semiconductor detectors is proposed and the first HP… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.14982v3-abstract-full').style.display = 'inline'; document.getElementById('2309.14982v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.14982v3-abstract-full" style="display: none;"> Recently a dark matter-electron (DM-electron) paradigm has drawn much attention. Models beyond the standard halo model describing DM accelerated by high energy celestial bodies are under intense examination as well. In this Letter, a velocity components analysis (VCA) method dedicated to swift analysis of accelerated DM-electron interactions via semiconductor detectors is proposed and the first HPGe detector-based accelerated DM-electron analysis is realized. Utilizing the method, the first germanium based constraint on sub-GeV solar reflected DM-electron interaction is presented with the 205.4 kg$\cdot$day dataset from the CDEX-10 experiment. In the heavy mediator scenario, our result excels in the mass range of 5$-$15 keV/$c^2$, achieving a 3 orders of magnitude improvement comparing with previous semiconductor experiments. In the light mediator scenario, the strongest laboratory constraint for DM lighter than 0.1 MeV/$c^2$ is presented. The result proves the feasibility and demonstrates the vast potential of the VCA technique in future accelerated DM-electron analyses with semiconductor detectors. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.14982v3-abstract-full').style.display = 'none'; document.getElementById('2309.14982v3-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 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 26 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, 4 figures. Version updated to match PRL version</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Phys. Rev. Lett. 132, 171001 (2024) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.11758">arXiv:2309.11758</a> <span> [<a href="https://arxiv.org/pdf/2309.11758">pdf</a>, <a href="https://arxiv.org/format/2309.11758">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="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> SAM-OCTA: A Fine-Tuning Strategy for Applying Foundation Model to OCTA Image Segmentation Tasks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Wang%2C+C">Chengliang Wang</a>, <a href="/search/?searchtype=author&query=Chen%2C+X">Xinrun Chen</a>, <a href="/search/?searchtype=author&query=Ning%2C+H">Haojian Ning</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Shiying Li</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="2309.11758v1-abstract-short" style="display: inline;"> In the analysis of optical coherence tomography angiography (OCTA) images, the operation of segmenting specific targets is necessary. Existing methods typically train on supervised datasets with limited samples (approximately a few hundred), which can lead to overfitting. To address this, the low-rank adaptation technique is adopted for foundation model fine-tuning and proposed corresponding promp… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.11758v1-abstract-full').style.display = 'inline'; document.getElementById('2309.11758v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.11758v1-abstract-full" style="display: none;"> In the analysis of optical coherence tomography angiography (OCTA) images, the operation of segmenting specific targets is necessary. Existing methods typically train on supervised datasets with limited samples (approximately a few hundred), which can lead to overfitting. To address this, the low-rank adaptation technique is adopted for foundation model fine-tuning and proposed corresponding prompt point generation strategies to process various segmentation tasks on OCTA datasets. This method is named SAM-OCTA and has been experimented on the publicly available OCTA-500 dataset. While achieving state-of-the-art performance metrics, this method accomplishes local vessel segmentation as well as effective artery-vein segmentation, which was not well-solved in previous works. The code is available at: https://github.com/ShellRedia/SAM-OCTA. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.11758v1-abstract-full').style.display = 'none'; document.getElementById('2309.11758v1-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 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">ICASSP conference is in submission</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.09483">arXiv:2309.09483</a> <span> [<a href="https://arxiv.org/pdf/2309.09483">pdf</a>, <a href="https://arxiv.org/format/2309.09483">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> An Accurate and Efficient Neural Network for OCTA Vessel Segmentation and a New Dataset </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Ning%2C+H">Haojian Ning</a>, <a href="/search/?searchtype=author&query=Wang%2C+C">Chengliang Wang</a>, <a href="/search/?searchtype=author&query=Chen%2C+X">Xinrun Chen</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Shiying Li</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="2309.09483v1-abstract-short" style="display: inline;"> Optical coherence tomography angiography (OCTA) is a noninvasive imaging technique that can reveal high-resolution retinal vessels. In this work, we propose an accurate and efficient neural network for retinal vessel segmentation in OCTA images. The proposed network achieves accuracy comparable to other SOTA methods, while having fewer parameters and faster inference speed (e.g. 110x lighter and 1… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.09483v1-abstract-full').style.display = 'inline'; document.getElementById('2309.09483v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.09483v1-abstract-full" style="display: none;"> Optical coherence tomography angiography (OCTA) is a noninvasive imaging technique that can reveal high-resolution retinal vessels. In this work, we propose an accurate and efficient neural network for retinal vessel segmentation in OCTA images. The proposed network achieves accuracy comparable to other SOTA methods, while having fewer parameters and faster inference speed (e.g. 110x lighter and 1.3x faster than U-Net), which is very friendly for industrial applications. This is achieved by applying the modified Recurrent ConvNeXt Block to a full resolution convolutional network. In addition, we create a new dataset containing 918 OCTA images and their corresponding vessel annotations. The data set is semi-automatically annotated with the help of Segment Anything Model (SAM), which greatly improves the annotation speed. For the benefit of the community, our code and dataset can be obtained from https://github.com/nhjydywd/OCTA-FRNet. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.09483v1-abstract-full').style.display = 'none'; document.getElementById('2309.09483v1-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 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/2309.01843">arXiv:2309.01843</a> <span> [<a href="https://arxiv.org/pdf/2309.01843">pdf</a>, <a href="https://arxiv.org/format/2309.01843">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</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/10.1088/1475-7516/2024/07/009">10.1088/1475-7516/2024/07/009 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Projected WIMP sensitivity of the CDEX-50 dark matter experiment </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Geng%2C+X+P">X. P. Geng</a>, <a href="/search/?searchtype=author&query=Yang%2C+L+T">L. T. Yang</a>, <a href="/search/?searchtype=author&query=Yue%2C+Q">Q. Yue</a>, <a href="/search/?searchtype=author&query=Kang%2C+K+J">K. J. Kang</a>, <a href="/search/?searchtype=author&query=Li%2C+Y+J">Y. J. Li</a>, <a href="/search/?searchtype=author&query=An%2C+H+P">H. P. An</a>, <a href="/search/?searchtype=author&query=C.%2C+G">Greeshma C.</a>, <a href="/search/?searchtype=author&query=Chang%2C+J+P">J. P. Chang</a>, <a href="/search/?searchtype=author&query=Chen%2C+Y+H">Y. H. Chen</a>, <a href="/search/?searchtype=author&query=Cheng%2C+J+P">J. P. Cheng</a>, <a href="/search/?searchtype=author&query=Dai%2C+W+H">W. H. Dai</a>, <a href="/search/?searchtype=author&query=Deng%2C+Z">Z. Deng</a>, <a href="/search/?searchtype=author&query=Fang%2C+C+H">C. H. Fang</a>, <a href="/search/?searchtype=author&query=Gong%2C+H">H. Gong</a>, <a href="/search/?searchtype=author&query=Guo%2C+Q+J">Q. J. Guo</a>, <a href="/search/?searchtype=author&query=Guo%2C+T">T. Guo</a>, <a href="/search/?searchtype=author&query=Guo%2C+X+Y">X. Y. Guo</a>, <a href="/search/?searchtype=author&query=He%2C+L">L. He</a>, <a href="/search/?searchtype=author&query=He%2C+S+M">S. M. He</a>, <a href="/search/?searchtype=author&query=Hu%2C+J+W">J. W. Hu</a>, <a href="/search/?searchtype=author&query=Huang%2C+H+X">H. X. Huang</a>, <a href="/search/?searchtype=author&query=Huang%2C+T+C">T. C. Huang</a>, <a href="/search/?searchtype=author&query=Jiang%2C+L">L. Jiang</a>, <a href="/search/?searchtype=author&query=Karmakar%2C+S">S. Karmakar</a>, <a href="/search/?searchtype=author&query=Li%2C+H+B">H. B. Li</a> , et al. (59 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="2309.01843v2-abstract-short" style="display: inline;"> CDEX-50 is a next-generation project of the China Dark Matter Experiment (CDEX) that aims to search for dark matter using a 50-kg germanium detector array. This paper comprises a thorough summary of the CDEX-50 dark matter experiment, including an investigation of potential background sources and the development of a background model. Based on the baseline model, the projected sensitivity of weakl… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.01843v2-abstract-full').style.display = 'inline'; document.getElementById('2309.01843v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.01843v2-abstract-full" style="display: none;"> CDEX-50 is a next-generation project of the China Dark Matter Experiment (CDEX) that aims to search for dark matter using a 50-kg germanium detector array. This paper comprises a thorough summary of the CDEX-50 dark matter experiment, including an investigation of potential background sources and the development of a background model. Based on the baseline model, the projected sensitivity of weakly interacting massive particle (WIMP) is also presented. The expected background level within the energy region of interest, set to 2--2.5 keVee, is $\sim$0.01 counts keVee$^{-1}$ kg$^{-1}$ day$^{-1}$. At 90\% confidence level, the expected sensitivity to spin-independent WIMP-nucleon couplings is estimated to reach a cross-section of 5.1 $\times$ 10$^{-45}$ cm$^{2}$ for a WIMP mass of 5 GeV/c$^{2}$ with an exposure objective of 150 kg$\cdot$year and an analysis threshold of 160 eVee. This science goal will correspond to the most sensitive results for WIMPs with a mass of 2.2--8 GeV/c$^{2}$. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.01843v2-abstract-full').style.display = 'none'; document.getElementById('2309.01843v2-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 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 4 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">12 pages, 11 figures. Version updated to match JCAP version</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> JCAP 07 (2024) 009 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.00884">arXiv:2309.00884</a> <span> [<a href="https://arxiv.org/pdf/2309.00884">pdf</a>, <a href="https://arxiv.org/format/2309.00884">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Earth and Planetary Astrophysics">astro-ph.EP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Plasma Physics">physics.plasm-ph</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/10.1051/0004-6361/202347149">10.1051/0004-6361/202347149 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Excitation of extraordinary modes inside the source of Saturn's kilometric radiation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Ning%2C+H">Hao Ning</a>, <a href="/search/?searchtype=author&query=Chen%2C+Y">Yao Chen</a>, <a href="/search/?searchtype=author&query=Li%2C+C">Chuanyang Li</a>, <a href="/search/?searchtype=author&query=Ye%2C+S">Shengyi Ye</a>, <a href="/search/?searchtype=author&query=Kuznetsov%2C+A">Alexey Kuznetsov</a>, <a href="/search/?searchtype=author&query=Wu%2C+S">Siyuan Wu</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="2309.00884v1-abstract-short" style="display: inline;"> The electron cyclotron maser instability (ECMI) of extraordinary mode waves was investigated with the parameters observed in Saturn's kilometric radiation (SKR) sources. Previous studies employed simplified dispersion relations, and did not consider the excitation of the relativistic (R) mode. This mode is introduced by considering the relativistic effect in plasmas consisting of both cold and hot… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.00884v1-abstract-full').style.display = 'inline'; document.getElementById('2309.00884v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.00884v1-abstract-full" style="display: none;"> The electron cyclotron maser instability (ECMI) of extraordinary mode waves was investigated with the parameters observed in Saturn's kilometric radiation (SKR) sources. Previous studies employed simplified dispersion relations, and did not consider the excitation of the relativistic (R) mode. This mode is introduced by considering the relativistic effect in plasmas consisting of both cold and hot electrons. Using particle-in-cell simulations, we investigated the excitation of R and X modes based on the measured data. Using the reported value of the density ratio of energetic to total electrons $n_e/n_0=24\%$, the most unstable mode is the R mode. The escaping X-mode emissions are amplified only if the energetic electrons are dominant with $n_e/n_0 \ge 90\%$. For these cases, only the X mode is excited and the R mode disappears due to its strong coupling. The results are well in line with the linear kinetic theory of ECMI. The properties of both the R and X modes are consistent with the observed SKR emissions. This raises questions about the nature of the measured electric field fluctuations within ``presumed'' SKR sources. The study provides new insights into the ECMI process relevant to SKR emission mechanisms. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.00884v1-abstract-full').style.display = 'none'; document.getElementById('2309.00884v1-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 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">Journal ref:</span> A&A 678, A94 (2023) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2308.10593">arXiv:2308.10593</a> <span> [<a href="https://arxiv.org/pdf/2308.10593">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</span> </div> </div> <p class="title is-5 mathjax"> Interplay Between Mixed and Pure Exciton States Controls Singlet Fission in Rubrene Single Crystals </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Maslennikov%2C+D+R">Dmitry R. Maslennikov</a>, <a href="/search/?searchtype=author&query=Maimaris%2C+M">Marios Maimaris</a>, <a href="/search/?searchtype=author&query=Ning%2C+H">Haoqing Ning</a>, <a href="/search/?searchtype=author&query=Zheng%2C+X">Xijia Zheng</a>, <a href="/search/?searchtype=author&query=Mondal%2C+N">Navendu Mondal</a>, <a href="/search/?searchtype=author&query=Bruevich%2C+V+V">Vladimir V. Bruevich</a>, <a href="/search/?searchtype=author&query=Pratik%2C+S+M">Saied Md Pratik</a>, <a href="/search/?searchtype=author&query=Musser%2C+A+J">Andrew J. Musser</a>, <a href="/search/?searchtype=author&query=Podzorov%2C+V">Vitaly Podzorov</a>, <a href="/search/?searchtype=author&query=Bredas%2C+J">Jean-Luc Bredas</a>, <a href="/search/?searchtype=author&query=Coropceanu%2C+V">Veaceslav Coropceanu</a>, <a href="/search/?searchtype=author&query=Bakulin%2C+A+A">Artem A. Bakulin</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="2308.10593v1-abstract-short" style="display: inline;"> Singlet fission (SF) is a multielectron process in which one singlet exciton S converts into a pair of triplet excitons T+T. SF is widely studied as it may help overcome the Shockley-Queisser efficiency limit for semiconductor photovoltaic cells. To elucidate and control the SF mechanism, great attention has been given to the identification of intermediate states in SF materials, which often appea… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.10593v1-abstract-full').style.display = 'inline'; document.getElementById('2308.10593v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2308.10593v1-abstract-full" style="display: none;"> Singlet fission (SF) is a multielectron process in which one singlet exciton S converts into a pair of triplet excitons T+T. SF is widely studied as it may help overcome the Shockley-Queisser efficiency limit for semiconductor photovoltaic cells. To elucidate and control the SF mechanism, great attention has been given to the identification of intermediate states in SF materials, which often appear elusive due to the complexity and fast timescales of the SF process. Here, we apply 10fs-1ms transient absorption techniques to high-purity rubrene single crystals to disentangle the intrinsic fission dynamics from the effects of defects and grain boundaries and to identify reliably the fission intermediates. We show that above-gap excitation directly generates a hybrid vibronically assisted mixture of singlet state and triplet-pair multiexciton [S:TT], which rapidly (<100fs) and coherently branches into pure singlet or triplet excitations. The relaxation of [S:TT] to S is followed by a relatively slow and temperature-activated (48 meV activation energy) incoherent fission process. The SF competing pathways and intermediates revealed here unify the observations and models presented in previous studies of SF in rubrene and propose alternative strategies for the development of SF-enhanced photovoltaic materials. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.10593v1-abstract-full').style.display = 'none'; document.getElementById('2308.10593v1-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 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2308.08499">arXiv:2308.08499</a> <span> [<a href="https://arxiv.org/pdf/2308.08499">pdf</a>, <a href="https://arxiv.org/format/2308.08499">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="Networking and Internet Architecture">cs.NI</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"> Context-Aware Service Recommendation System for the Social Internet of Things </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Khelloufi%2C+A">Amar Khelloufi</a>, <a href="/search/?searchtype=author&query=Ning%2C+H">Huansheng Ning</a>, <a href="/search/?searchtype=author&query=Sada%2C+A+B">Abdelkarim Ben Sada</a>, <a href="/search/?searchtype=author&query=Naouri%2C+A">Abdenacer Naouri</a>, <a href="/search/?searchtype=author&query=Dhelim%2C+S">Sahraoui Dhelim</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="2308.08499v1-abstract-short" style="display: inline;"> The Social Internet of Things (SIoT) enables interconnected smart devices to share data and services, opening up opportunities for personalized service recommendations. However, existing research often overlooks crucial aspects that can enhance the accuracy and relevance of recommendations in the SIoT context. Specifically, existing techniques tend to consider the extraction of social relationship… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.08499v1-abstract-full').style.display = 'inline'; document.getElementById('2308.08499v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2308.08499v1-abstract-full" style="display: none;"> The Social Internet of Things (SIoT) enables interconnected smart devices to share data and services, opening up opportunities for personalized service recommendations. However, existing research often overlooks crucial aspects that can enhance the accuracy and relevance of recommendations in the SIoT context. Specifically, existing techniques tend to consider the extraction of social relationships between devices and neglect the contextual presentation of service reviews. This study aims to address these gaps by exploring the contextual representation of each device-service pair. Firstly, we propose a latent features combination technique that can capture latent feature interactions, by aggregating the device-device relationships within the SIoT. Then, we leverage Factorization Machines to model higher-order feature interactions specific to each SIoT device-service pair to accomplish accurate rating prediction. Finally, we propose a service recommendation framework for SIoT based on review aggregation and feature learning processes. The experimental evaluation demonstrates the framework's effectiveness in improving service recommendation accuracy and relevance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.08499v1-abstract-full').style.display = 'none'; document.getElementById('2308.08499v1-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 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2308.07193">arXiv:2308.07193</a> <span> [<a href="https://arxiv.org/pdf/2308.07193">pdf</a>, <a href="https://arxiv.org/format/2308.07193">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</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="Computers and Society">cs.CY</span> </div> </div> <p class="title is-5 mathjax"> Task Offloading for Smart Glasses in Healthcare: Enhancing Detection of Elevated Body Temperature </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Naouri%2C+A">Abdenacer Naouri</a>, <a href="/search/?searchtype=author&query=Nouri%2C+N+A">Nabil Abdelkader Nouri</a>, <a href="/search/?searchtype=author&query=Qammar%2C+A">Attia Qammar</a>, <a href="/search/?searchtype=author&query=Shi%2C+F">Feifei Shi</a>, <a href="/search/?searchtype=author&query=Ning%2C+H">Huansheng Ning</a>, <a href="/search/?searchtype=author&query=Dhelim%2C+S">Sahraoui Dhelim</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="2308.07193v1-abstract-short" style="display: inline;"> Wearable devices like smart glasses have gained popularity across various applications. However, their limited computational capabilities pose challenges for tasks that require extensive processing, such as image and video processing, leading to drained device batteries. To address this, offloading such tasks to nearby powerful remote devices, such as mobile devices or remote servers, has emerged… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.07193v1-abstract-full').style.display = 'inline'; document.getElementById('2308.07193v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2308.07193v1-abstract-full" style="display: none;"> Wearable devices like smart glasses have gained popularity across various applications. However, their limited computational capabilities pose challenges for tasks that require extensive processing, such as image and video processing, leading to drained device batteries. To address this, offloading such tasks to nearby powerful remote devices, such as mobile devices or remote servers, has emerged as a promising solution. This paper focuses on analyzing task-offloading scenarios for a healthcare monitoring application performed on smart wearable glasses, aiming to identify the optimal conditions for offloading. The study evaluates performance metrics including task completion time, computing capabilities, and energy consumption under realistic conditions. A specific use case is explored within an indoor area like an airport, where security agents wearing smart glasses to detect elevated body temperature in individuals, potentially indicating COVID-19. The findings highlight the potential benefits of task offloading for wearable devices in healthcare settings, demonstrating its practicality and relevance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.07193v1-abstract-full').style.display = 'none'; document.getElementById('2308.07193v1-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 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2308.04442">arXiv:2308.04442</a> <span> [<a href="https://arxiv.org/pdf/2308.04442">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</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"> Blockchain-based Optimized Client Selection and Privacy Preserved Framework for Federated Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Qammar%2C+A">Attia Qammar</a>, <a href="/search/?searchtype=author&query=Naouri%2C+A">Abdenacer Naouri</a>, <a href="/search/?searchtype=author&query=Ding%2C+J">Jianguo Ding</a>, <a href="/search/?searchtype=author&query=Ning%2C+H">Huansheng Ning</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="2308.04442v1-abstract-short" style="display: inline;"> Federated learning is a distributed mechanism that trained large-scale neural network models with the participation of multiple clients and data remains on their devices, only sharing the local model updates. With this feature, federated learning is considered a secure solution for data privacy issues. However, the typical FL structure relies on the client-server, which leads to the single-point-o… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.04442v1-abstract-full').style.display = 'inline'; document.getElementById('2308.04442v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2308.04442v1-abstract-full" style="display: none;"> Federated learning is a distributed mechanism that trained large-scale neural network models with the participation of multiple clients and data remains on their devices, only sharing the local model updates. With this feature, federated learning is considered a secure solution for data privacy issues. However, the typical FL structure relies on the client-server, which leads to the single-point-of-failure (SPoF) attack, and the random selection of clients for model training compromised the model accuracy. Furthermore, adversaries try for inference attacks i.e., attack on privacy leads to gradient leakage attacks. We proposed the blockchain-based optimized client selection and privacy-preserved framework in this context. We designed the three kinds of smart contracts such as 1) registration of clients 2) forward bidding to select optimized clients for FL model training 3) payment settlement and reward smart contracts. Moreover, fully homomorphic encryption with Cheon, Kim, Kim, and Song (CKKS) method is implemented before transmitting the local model updates to the server. Finally, we evaluated our proposed method on the benchmark dataset and compared it with state-of-the-art studies. Consequently, we achieved a higher accuracy rate and privacy-preserved FL framework with decentralized nature. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.04442v1-abstract-full').style.display = 'none'; document.getElementById('2308.04442v1-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 July, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.09255">arXiv:2306.09255</a> <span> [<a href="https://arxiv.org/pdf/2306.09255">pdf</a>, <a href="https://arxiv.org/format/2306.09255">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</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="Computers and Society">cs.CY</span> </div> </div> <p class="title is-5 mathjax"> Chatbots to ChatGPT in a Cybersecurity Space: Evolution, Vulnerabilities, Attacks, Challenges, and Future Recommendations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Qammar%2C+A">Attia Qammar</a>, <a href="/search/?searchtype=author&query=Wang%2C+H">Hongmei Wang</a>, <a href="/search/?searchtype=author&query=Ding%2C+J">Jianguo Ding</a>, <a href="/search/?searchtype=author&query=Naouri%2C+A">Abdenacer Naouri</a>, <a href="/search/?searchtype=author&query=Daneshmand%2C+M">Mahmoud Daneshmand</a>, <a href="/search/?searchtype=author&query=Ning%2C+H">Huansheng Ning</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="2306.09255v1-abstract-short" style="display: inline;"> Chatbots shifted from rule-based to artificial intelligence techniques and gained traction in medicine, shopping, customer services, food delivery, education, and research. OpenAI developed ChatGPT blizzard on the Internet as it crossed one million users within five days of its launch. However, with the enhanced popularity, chatbots experienced cybersecurity threats and vulnerabilities. This paper… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.09255v1-abstract-full').style.display = 'inline'; document.getElementById('2306.09255v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.09255v1-abstract-full" style="display: none;"> Chatbots shifted from rule-based to artificial intelligence techniques and gained traction in medicine, shopping, customer services, food delivery, education, and research. OpenAI developed ChatGPT blizzard on the Internet as it crossed one million users within five days of its launch. However, with the enhanced popularity, chatbots experienced cybersecurity threats and vulnerabilities. This paper discussed the relevant literature, reports, and explanatory incident attacks generated against chatbots. Our initial point is to explore the timeline of chatbots from ELIZA (an early natural language processing computer program) to GPT-4 and provide the working mechanism of ChatGPT. Subsequently, we explored the cybersecurity attacks and vulnerabilities in chatbots. Besides, we investigated the ChatGPT, specifically in the context of creating the malware code, phishing emails, undetectable zero-day attacks, and generation of macros and LOLBINs. Furthermore, the history of cyberattacks and vulnerabilities exploited by cybercriminals are discussed, particularly considering the risk and vulnerabilities in ChatGPT. Addressing these threats and vulnerabilities requires specific strategies and measures to reduce the harmful consequences. Therefore, the future directions to address the challenges were presented. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.09255v1-abstract-full').style.display = 'none'; document.getElementById('2306.09255v1-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 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.01988">arXiv:2306.01988</a> <span> [<a href="https://arxiv.org/pdf/2306.01988">pdf</a>, <a href="https://arxiv.org/format/2306.01988">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"> Lightweight Structure-aware Transformer Network for VHR Remote Sensing Image Change Detection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Lei%2C+T">Tao Lei</a>, <a href="/search/?searchtype=author&query=Xu%2C+Y">Yetong Xu</a>, <a href="/search/?searchtype=author&query=Ning%2C+H">Hailong Ning</a>, <a href="/search/?searchtype=author&query=Lv%2C+Z">Zhiyong Lv</a>, <a href="/search/?searchtype=author&query=Min%2C+C">Chongdan Min</a>, <a href="/search/?searchtype=author&query=Jin%2C+Y">Yaochu Jin</a>, <a href="/search/?searchtype=author&query=Nandi%2C+A+K">Asoke K. Nandi</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="2306.01988v1-abstract-short" style="display: inline;"> Popular Transformer networks have been successfully applied to remote sensing (RS) image change detection (CD) identifications and achieve better results than most convolutional neural networks (CNNs), but they still suffer from two main problems. First, the computational complexity of the Transformer grows quadratically with the increase of image spatial resolution, which is unfavorable to very h… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.01988v1-abstract-full').style.display = 'inline'; document.getElementById('2306.01988v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.01988v1-abstract-full" style="display: none;"> Popular Transformer networks have been successfully applied to remote sensing (RS) image change detection (CD) identifications and achieve better results than most convolutional neural networks (CNNs), but they still suffer from two main problems. First, the computational complexity of the Transformer grows quadratically with the increase of image spatial resolution, which is unfavorable to very high-resolution (VHR) RS images. Second, these popular Transformer networks tend to ignore the importance of fine-grained features, which results in poor edge integrity and internal tightness for largely changed objects and leads to the loss of small changed objects. To address the above issues, this Letter proposes a Lightweight Structure-aware Transformer (LSAT) network for RS image CD. The proposed LSAT has two advantages. First, a Cross-dimension Interactive Self-attention (CISA) module with linear complexity is designed to replace the vanilla self-attention in visual Transformer, which effectively reduces the computational complexity while improving the feature representation ability of the proposed LSAT. Second, a Structure-aware Enhancement Module (SAEM) is designed to enhance difference features and edge detail information, which can achieve double enhancement by difference refinement and detail aggregation so as to obtain fine-grained features of bi-temporal RS images. Experimental results show that the proposed LSAT achieves significant improvement in detection accuracy and offers a better tradeoff between accuracy and computational costs than most state-of-the-art CD methods for VHR RS images. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.01988v1-abstract-full').style.display = 'none'; document.getElementById('2306.01988v1-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 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.01163">arXiv:2306.01163</a> <span> [<a href="https://arxiv.org/pdf/2306.01163">pdf</a>, <a href="https://arxiv.org/format/2306.01163">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> </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/TCSS.2024.3360518">10.1109/TCSS.2024.3360518 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> A Multi-Modal Latent-Features based Service Recommendation System for the Social Internet of Things </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Khelloufi%2C+A">Amar Khelloufi</a>, <a href="/search/?searchtype=author&query=Ning%2C+H">Huansheng Ning</a>, <a href="/search/?searchtype=author&query=Naouri%2C+A">Abdenacer Naouri</a>, <a href="/search/?searchtype=author&query=Sada%2C+A+B">Abdelkarim Ben Sada</a>, <a href="/search/?searchtype=author&query=Qammar%2C+A">Attia Qammar</a>, <a href="/search/?searchtype=author&query=Khalil%2C+A">Abdelkader Khalil</a>, <a href="/search/?searchtype=author&query=Dhelim%2C+S">Sahraoui Dhelim</a>, <a href="/search/?searchtype=author&query=Mao%2C+L">Lingfeng Mao</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="2306.01163v2-abstract-short" style="display: inline;"> The Social Internet of Things (SIoT), is revolutionizing how we interact with our everyday lives. By adding the social dimension to connecting devices, the SIoT has the potential to drastically change the way we interact with smart devices. This connected infrastructure allows for unprecedented levels of convenience, automation, and access to information, allowing us to do more with less effort. H… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.01163v2-abstract-full').style.display = 'inline'; document.getElementById('2306.01163v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.01163v2-abstract-full" style="display: none;"> The Social Internet of Things (SIoT), is revolutionizing how we interact with our everyday lives. By adding the social dimension to connecting devices, the SIoT has the potential to drastically change the way we interact with smart devices. This connected infrastructure allows for unprecedented levels of convenience, automation, and access to information, allowing us to do more with less effort. However, this revolutionary new technology also brings an eager need for service recommendation systems. As the SIoT grows in scope and complexity, it becomes increasingly important for businesses and individuals, and SIoT objects alike to have reliable sources for products, services, and information that are tailored to their specific needs. Few works have been proposed to provide service recommendations for SIoT environments. However, these efforts have been confined to only focusing on modeling user-item interactions using contextual information, devices' SIoT relationships, and correlation social groups but these schemes do not account for latent semantic item-item structures underlying the sparse multi-modal contents in SIoT environment. In this paper, we propose a latent-based SIoT recommendation system that learns item-item structures and aggregates multiple modalities to obtain latent item graphs which are then used in graph convolutions to inject high-order affinities into item representations. Experiments showed that the proposed recommendation system outperformed state-of-the-art SIoT recommendation methods and validated its efficacy at mining latent relationships from multi-modal features. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.01163v2-abstract-full').style.display = 'none'; document.getElementById('2306.01163v2-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 1 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">Published in IEEE Transactions on Computational Social Systems</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.06453">arXiv:2305.06453</a> <span> [<a href="https://arxiv.org/pdf/2305.06453">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> </div> </div> <p class="title is-5 mathjax"> Autonomous GIS: the next-generation AI-powered GIS </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+Z">Zhenlong Li</a>, <a href="/search/?searchtype=author&query=Ning%2C+H">Huan Ning</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="2305.06453v4-abstract-short" style="display: inline;"> Large Language Models (LLMs), such as ChatGPT, demonstrate a strong understanding of human natural language and have been explored and applied in various fields, including reasoning, creative writing, code generation, translation, and information retrieval. By adopting LLM as the reasoning core, we introduce Autonomous GIS as an AI-powered geographic information system (GIS) that leverages the LLM… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.06453v4-abstract-full').style.display = 'inline'; document.getElementById('2305.06453v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.06453v4-abstract-full" style="display: none;"> Large Language Models (LLMs), such as ChatGPT, demonstrate a strong understanding of human natural language and have been explored and applied in various fields, including reasoning, creative writing, code generation, translation, and information retrieval. By adopting LLM as the reasoning core, we introduce Autonomous GIS as an AI-powered geographic information system (GIS) that leverages the LLM's general abilities in natural language understanding, reasoning, and coding for addressing spatial problems with automatic spatial data collection, analysis, and visualization. We envision that autonomous GIS will need to achieve five autonomous goals: self-generating, self-organizing, self-verifying, self-executing, and self-growing. We developed a prototype system called LLM-Geo using the GPT-4 API in a Python environment, demonstrating what an autonomous GIS looks like and how it delivers expected results without human intervention using three case studies. For all case studies, LLM-Geo was able to return accurate results, including aggregated numbers, graphs, and maps, significantly reducing manual operation time. Although still in its infancy and lacking several important modules such as logging and code testing, LLM-Geo demonstrates a potential path toward the next-generation AI-powered GIS. We advocate for the GIScience community to dedicate more effort to the research and development of autonomous GIS, making spatial analysis easier, faster, and more accessible to a broader audience. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.06453v4-abstract-full').style.display = 'none'; document.getElementById('2305.06453v4-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 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.00894">arXiv:2305.00894</a> <span> [<a href="https://arxiv.org/pdf/2305.00894">pdf</a>, <a href="https://arxiv.org/format/2305.00894">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Nuclear Experiment">nucl-ex</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</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/10.1088/1674-1137/ad597b">10.1088/1674-1137/ad597b <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Searching for $^{76}$Ge neutrinoless double beta decay with the CDEX-1B experiment </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zhang%2C+B+T">B. T. Zhang</a>, <a href="/search/?searchtype=author&query=Wang%2C+J+Z">J. Z. Wang</a>, <a href="/search/?searchtype=author&query=Yang%2C+L+T">L. T. Yang</a>, <a href="/search/?searchtype=author&query=Yue%2C+Q">Q. Yue</a>, <a href="/search/?searchtype=author&query=Kang%2C+K+J">K. J. Kang</a>, <a href="/search/?searchtype=author&query=Li%2C+Y+J">Y. J. Li</a>, <a href="/search/?searchtype=author&query=An%2C+H+P">H. P. An</a>, <a href="/search/?searchtype=author&query=C.%2C+G">Greeshma C.</a>, <a href="/search/?searchtype=author&query=Chang%2C+J+P">J. P. Chang</a>, <a href="/search/?searchtype=author&query=Chen%2C+Y+H">Y. H. Chen</a>, <a href="/search/?searchtype=author&query=Cheng%2C+J+P">J. P. Cheng</a>, <a href="/search/?searchtype=author&query=Dai%2C+W+H">W. H. Dai</a>, <a href="/search/?searchtype=author&query=Deng%2C+Z">Z. Deng</a>, <a href="/search/?searchtype=author&query=Fang%2C+C+H">C. H. Fang</a>, <a href="/search/?searchtype=author&query=Geng%2C+X+P">X. P. Geng</a>, <a href="/search/?searchtype=author&query=Gong%2C+H">H. Gong</a>, <a href="/search/?searchtype=author&query=Guo%2C+Q+J">Q. J. Guo</a>, <a href="/search/?searchtype=author&query=Guo%2C+X+Y">X. Y. Guo</a>, <a href="/search/?searchtype=author&query=He%2C+L">L. He</a>, <a href="/search/?searchtype=author&query=He%2C+S+M">S. M. He</a>, <a href="/search/?searchtype=author&query=Hu%2C+J+W">J. W. Hu</a>, <a href="/search/?searchtype=author&query=Huang%2C+H+X">H. X. Huang</a>, <a href="/search/?searchtype=author&query=Huang%2C+T+C">T. C. Huang</a>, <a href="/search/?searchtype=author&query=Jia%2C+H+T">H. T. Jia</a>, <a href="/search/?searchtype=author&query=Jiang%2C+X">X. Jiang</a> , et al. (60 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="2305.00894v3-abstract-short" style="display: inline;"> We operated a p-type point contact high purity germanium (PPCGe) detector (CDEX-1B, 1.008 kg) in the China Jinping Underground Laboratory (CJPL) for 500.3 days to search for neutrinoless double beta ($0谓尾尾$) decay of $^{76}$Ge. A total of 504.3 kg$\cdot$day effective exposure data was accumulated. The anti-coincidence and the multi/single-site event (MSE/SSE) discrimination methods were used to su… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.00894v3-abstract-full').style.display = 'inline'; document.getElementById('2305.00894v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.00894v3-abstract-full" style="display: none;"> We operated a p-type point contact high purity germanium (PPCGe) detector (CDEX-1B, 1.008 kg) in the China Jinping Underground Laboratory (CJPL) for 500.3 days to search for neutrinoless double beta ($0谓尾尾$) decay of $^{76}$Ge. A total of 504.3 kg$\cdot$day effective exposure data was accumulated. The anti-coincidence and the multi/single-site event (MSE/SSE) discrimination methods were used to suppress the background in the energy region of interest (ROI, 1989$-$2089 keV for this work) with a factor of 23. A background level of 0.33 counts/(keV$\cdot$kg$\cdot$yr) was realized. The lower limit on the half life of $^{76}$Ge $0谓尾尾$ decay was constrained as $T_{1/2}^{0谓}\ > \ {1.0}\times 10^{23}\ \rm yr\ (90\% \ C.L.)$, corresponding to the upper limits on the effective Majorana neutrino mass: $\langle m_{尾尾}\rangle < $3.2$-$7.5$\ \mathrm{eV}$. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.00894v3-abstract-full').style.display = 'none'; document.getElementById('2305.00894v3-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 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 1 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">11 pages, 12 figures, 2 tables. Version updated to match CPC version</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Chin. Phys. C 48, 101001 (2024) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2304.05571">arXiv:2304.05571</a> <span> [<a href="https://arxiv.org/pdf/2304.05571">pdf</a>, <a href="https://arxiv.org/format/2304.05571">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"> SGL: Structure Guidance Learning for Camera Localization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zhang%2C+X">Xudong Zhang</a>, <a href="/search/?searchtype=author&query=Gao%2C+S">Shuang Gao</a>, <a href="/search/?searchtype=author&query=Nan%2C+X">Xiaohu Nan</a>, <a href="/search/?searchtype=author&query=Ning%2C+H">Haikuan Ning</a>, <a href="/search/?searchtype=author&query=Yang%2C+Y">Yuchen Yang</a>, <a href="/search/?searchtype=author&query=Ping%2C+Y">Yishan Ping</a>, <a href="/search/?searchtype=author&query=Wan%2C+J">Jixiang Wan</a>, <a href="/search/?searchtype=author&query=Dong%2C+S">Shuzhou Dong</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Jijunnan Li</a>, <a href="/search/?searchtype=author&query=Guo%2C+Y">Yandong Guo</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="2304.05571v1-abstract-short" style="display: inline;"> Camera localization is a classical computer vision task that serves various Artificial Intelligence and Robotics applications. With the rapid developments of Deep Neural Networks (DNNs), end-to-end visual localization methods are prosperous in recent years. In this work, we focus on the scene coordinate prediction ones and propose a network architecture named as Structure Guidance Learning (SGL) w… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.05571v1-abstract-full').style.display = 'inline'; document.getElementById('2304.05571v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2304.05571v1-abstract-full" style="display: none;"> Camera localization is a classical computer vision task that serves various Artificial Intelligence and Robotics applications. With the rapid developments of Deep Neural Networks (DNNs), end-to-end visual localization methods are prosperous in recent years. In this work, we focus on the scene coordinate prediction ones and propose a network architecture named as Structure Guidance Learning (SGL) which utilizes the receptive branch and the structure branch to extract both high-level and low-level features to estimate the 3D coordinates. We design a confidence strategy to refine and filter the predicted 3D observations, which enables us to estimate the camera poses by employing the Perspective-n-Point (PnP) with RANSAC. In the training part, we design the Bundle Adjustment trainer to help the network fit the scenes better. Comparisons with some state-of-the-art (SOTA) methods and sufficient ablation experiments confirm the validity of our proposed architecture. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.05571v1-abstract-full').style.display = 'none'; document.getElementById('2304.05571v1-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 April, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2303.11100">arXiv:2303.11100</a> <span> [<a href="https://arxiv.org/pdf/2303.11100">pdf</a>, <a href="https://arxiv.org/format/2303.11100">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"> A Multi-Task Deep Learning Approach for Sensor-based Human Activity Recognition and Segmentation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Duan%2C+F">Furong Duan</a>, <a href="/search/?searchtype=author&query=Zhu%2C+T">Tao Zhu</a>, <a href="/search/?searchtype=author&query=Wang%2C+J">Jinqiang Wang</a>, <a href="/search/?searchtype=author&query=Chen%2C+L">Liming Chen</a>, <a href="/search/?searchtype=author&query=Ning%2C+H">Huansheng Ning</a>, <a href="/search/?searchtype=author&query=Wan%2C+Y">Yaping Wan</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="2303.11100v1-abstract-short" style="display: inline;"> Sensor-based human activity segmentation and recognition are two important and challenging problems in many real-world applications and they have drawn increasing attention from the deep learning community in recent years. Most of the existing deep learning works were designed based on pre-segmented sensor streams and they have treated activity segmentation and recognition as two separate tasks. I… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.11100v1-abstract-full').style.display = 'inline'; document.getElementById('2303.11100v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2303.11100v1-abstract-full" style="display: none;"> Sensor-based human activity segmentation and recognition are two important and challenging problems in many real-world applications and they have drawn increasing attention from the deep learning community in recent years. Most of the existing deep learning works were designed based on pre-segmented sensor streams and they have treated activity segmentation and recognition as two separate tasks. In practice, performing data stream segmentation is very challenging. We believe that both activity segmentation and recognition may convey unique information which can complement each other to improve the performance of the two tasks. In this paper, we firstly proposes a new multitask deep neural network to solve the two tasks simultaneously. The proposed neural network adopts selective convolution and features multiscale windows to segment activities of long or short time durations. First, multiple windows of different scales are generated to center on each unit of the feature sequence. Then, the model is trained to predict, for each window, the activity class and the offset to the true activity boundaries. Finally, overlapping windows are filtered out by non-maximum suppression, and adjacent windows of the same activity are concatenated to complete the segmentation task. Extensive experiments were conducted on eight popular benchmarking datasets, and the results show that our proposed method outperforms the state-of-the-art methods both for activity recognition and segmentation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.11100v1-abstract-full').style.display = 'none'; document.getElementById('2303.11100v1-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 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 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">14 pages, 14 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/2302.05948">arXiv:2302.05948</a> <span> [<a href="https://arxiv.org/pdf/2302.05948">pdf</a>, <a href="https://arxiv.org/format/2302.05948">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> <span class="tag is-small is-grey 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/10.1007/s10586-024-04409-3">10.1007/s10586-024-04409-3 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Efficient Fog Node Placement using Nature-Inspired Metaheuristic for IoT Applications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Naouri%2C+A">Abdenacer Naouri</a>, <a href="/search/?searchtype=author&query=Nouri%2C+N+A">Nabil Abdelkader Nouri</a>, <a href="/search/?searchtype=author&query=Dhelim%2C+S">Sahraoui Dhelim</a>, <a href="/search/?searchtype=author&query=Khelloufi%2C+A">Amar Khelloufi</a>, <a href="/search/?searchtype=author&query=Sada%2C+A+B">Abdelkarim Ben Sada</a>, <a href="/search/?searchtype=author&query=Ning%2C+H">Huansheng Ning</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="2302.05948v1-abstract-short" style="display: inline;"> Managing the explosion of data from the edge to the cloud requires intelligent supervision such as fog node deployments, which is an essential task to assess network operability. To ensure network operability, the deployment process must be carried out effectively in terms of two main factors: connectivity and coverage. The network connectivity is based on fog node deployment which determines the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.05948v1-abstract-full').style.display = 'inline'; document.getElementById('2302.05948v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2302.05948v1-abstract-full" style="display: none;"> Managing the explosion of data from the edge to the cloud requires intelligent supervision such as fog node deployments, which is an essential task to assess network operability. To ensure network operability, the deployment process must be carried out effectively in terms of two main factors: connectivity and coverage. The network connectivity is based on fog node deployment which determines the physical topology of the network while the coverage determines the network accessibility. Both have a significant impact on network performance and guarantee the network QoS. Determining an optimum fog node deployment method that minimizes cost, reduces computation and communication overhead, and provides a high degree of network connection coverage is extremely hard. Therefore, maximizing coverage as well as preserving network connectivity is a non-trivial problem. In this paper, we proposed a fog deployment algorithm that can effectively connect the fog nodes and cover all edge devices. Firstly, we formulate fog deployment as an instance of multi-objective optimization problems with a large search space. Then, we leverage Marine Predator Algorithm (MPA) to tackle the deployment problem and prove that MPA is well-suited for fog node deployment due to its rapid convergence and low computational complexity compared to other population-based algorithms. Finally, we evaluate the proposed algorithm on a different benchmark of generated instances with various fog scenario configurations. The experimental results demonstrate that our proposed algorithm is capable of providing very promising results when compared to state-of-the-art methods for determining an optimal deployment of fog nodes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.05948v1-abstract-full').style.display = 'none'; document.getElementById('2302.05948v1-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 February, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 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">Cluster Comput (2024)</span> </p> </li> </ol> <nav class="pagination is-small is-centered breathe-horizontal" role="navigation" aria-label="pagination"> <a href="" 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