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is-link">Go</button> </div> </div> </form> </div> </div> <nav class="pagination is-small is-centered breathe-horizontal" role="navigation" aria-label="pagination"> <a href="" class="pagination-previous is-invisible">Previous </a> <a href="/search/?searchtype=author&query=Ghosh%2C+M&start=50" class="pagination-next" >Next </a> <ul class="pagination-list"> <li> <a href="/search/?searchtype=author&query=Ghosh%2C+M&start=0" class="pagination-link is-current" aria-label="Goto page 1">1 </a> </li> <li> <a href="/search/?searchtype=author&query=Ghosh%2C+M&start=50" class="pagination-link " aria-label="Page 2" aria-current="page">2 </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/2502.07500">arXiv:2502.07500</a> <span> [<a href="https://arxiv.org/pdf/2502.07500">pdf</a>, <a href="https://arxiv.org/format/2502.07500">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"> Unified Graph Networks (UGN): A Deep Neural Framework for Solving Graph Problems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Dawn%2C+R">Rudrajit Dawn</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Madhusudan Ghosh</a>, <a href="/search/cs?searchtype=author&query=Basuchowdhuri%2C+P">Partha Basuchowdhuri</a>, <a href="/search/cs?searchtype=author&query=Naskar%2C+S+K">Sudip Kumar Naskar</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="2502.07500v1-abstract-short" style="display: inline;"> Deep neural networks have enabled researchers to create powerful generalized frameworks, such as transformers, that can be used to solve well-studied problems in various application domains, such as text and image. However, such generalized frameworks are not available for solving graph problems. Graph structures are ubiquitous in many applications around us and many graph problems have been widel… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.07500v1-abstract-full').style.display = 'inline'; document.getElementById('2502.07500v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.07500v1-abstract-full" style="display: none;"> Deep neural networks have enabled researchers to create powerful generalized frameworks, such as transformers, that can be used to solve well-studied problems in various application domains, such as text and image. However, such generalized frameworks are not available for solving graph problems. Graph structures are ubiquitous in many applications around us and many graph problems have been widely studied over years. In recent times, there has been a surge in deep neural network based approaches to solve graph problems, with growing availability of graph structured datasets across diverse domains. Nevertheless, existing methods are mostly tailored to solve a specific task and lack the capability to create a generalized model leading to solutions for different downstream tasks. In this work, we propose a novel, resource-efficient framework named \emph{U}nified \emph{G}raph \emph{N}etwork (UGN) by leveraging the feature extraction capability of graph convolutional neural networks (GCN) and 2-dimensional convolutional neural networks (Conv2D). UGN unifies various graph learning tasks, such as link prediction, node classification, community detection, graph-to-graph translation, knowledge graph completion, and more, within a cohesive framework, while exercising minimal task-specific extensions (e.g., formation of supernodes for coarsening massive networks to increase scalability, use of \textit{mean target connectivity matrix} (MTCM) representation for achieving scalability in graph translation task, etc.) to enhance the generalization capability of graph learning and analysis. We test the novel UGN framework for six uncorrelated graph problems, using twelve different datasets. Experimental results show that UGN outperforms the state-of-the-art baselines by a significant margin on ten datasets, while producing comparable results on the remaining dataset. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.07500v1-abstract-full').style.display = 'none'; document.getElementById('2502.07500v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.05826">arXiv:2501.05826</a> <span> [<a href="https://arxiv.org/pdf/2501.05826">pdf</a>, <a href="https://arxiv.org/format/2501.05826">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="Artificial Intelligence">cs.AI</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"> AI-Driven Diabetic Retinopathy Screening: Multicentric Validation of AIDRSS in India </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Dey%2C+A+K">Amit Kr Dey</a>, <a href="/search/cs?searchtype=author&query=Walia%2C+P">Pradeep Walia</a>, <a href="/search/cs?searchtype=author&query=Somvanshi%2C+G">Girish Somvanshi</a>, <a href="/search/cs?searchtype=author&query=Ali%2C+A">Abrar Ali</a>, <a href="/search/cs?searchtype=author&query=Das%2C+S">Sagarnil Das</a>, <a href="/search/cs?searchtype=author&query=Paul%2C+P">Pallabi Paul</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Minakhi Ghosh</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="2501.05826v2-abstract-short" style="display: inline;"> Purpose: Diabetic retinopathy (DR) is a major cause of vision loss, particularly in India, where access to retina specialists is limited in rural areas. This study aims to evaluate the Artificial Intelligence-based Diabetic Retinopathy Screening System (AIDRSS) for DR detection and prevalence assessment, addressing the growing need for scalable, automated screening solutions in resource-limited se… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.05826v2-abstract-full').style.display = 'inline'; document.getElementById('2501.05826v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.05826v2-abstract-full" style="display: none;"> Purpose: Diabetic retinopathy (DR) is a major cause of vision loss, particularly in India, where access to retina specialists is limited in rural areas. This study aims to evaluate the Artificial Intelligence-based Diabetic Retinopathy Screening System (AIDRSS) for DR detection and prevalence assessment, addressing the growing need for scalable, automated screening solutions in resource-limited settings. Approach: A multicentric, cross-sectional study was conducted in Kolkata, India, involving 5,029 participants and 10,058 macula-centric retinal fundus images. The AIDRSS employed a deep learning algorithm with 50 million trainable parameters, integrated with Contrast Limited Adaptive Histogram Equalization (CLAHE) preprocessing for enhanced image quality. DR was graded using the International Clinical Diabetic Retinopathy (ICDR) Scale, categorizing disease into five stages (DR0 to DR4). Statistical metrics including sensitivity, specificity, and prevalence rates were evaluated against expert retina specialist assessments. Results: The prevalence of DR in the general population was 13.7%, rising to 38.2% among individuals with elevated random blood glucose levels. The AIDRSS achieved an overall sensitivity of 92%, specificity of 88%, and 100% sensitivity for detecting referable DR (DR3 and DR4). These results demonstrate the system's robust performance in accurately identifying and grading DR in a diverse population. Conclusions: AIDRSS provides a reliable, scalable solution for early DR detection in resource-constrained environments. Its integration of advanced AI techniques ensures high diagnostic accuracy, with potential to significantly reduce the burden of diabetes-related vision loss in underserved regions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.05826v2-abstract-full').style.display = 'none'; document.getElementById('2501.05826v2-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 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </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">22 pages, 5 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.14718">arXiv:2412.14718</a> <span> [<a href="https://arxiv.org/pdf/2412.14718">pdf</a>, <a href="https://arxiv.org/format/2412.14718">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="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> </div> <p class="title is-5 mathjax"> A Comprehensive Forecasting Framework based on Multi-Stage Hierarchical Forecasting Reconciliation and Adjustment </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Yang%2C+Z">Zhengchao Yang</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Mithun Ghosh</a>, <a href="/search/cs?searchtype=author&query=Saha%2C+A">Anish Saha</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+D">Dong Xu</a>, <a href="/search/cs?searchtype=author&query=Shmakov%2C+K">Konstantin Shmakov</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+K">Kuang-chih Lee</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2412.14718v1-abstract-short" style="display: inline;"> Ads demand forecasting for Walmart's ad products plays a critical role in enabling effective resource planning, allocation, and management of ads performance. In this paper, we introduce a comprehensive demand forecasting system that tackles hierarchical time series forecasting in business settings. Though traditional hierarchical reconciliation methods ensure forecasting coherence, they often tra… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.14718v1-abstract-full').style.display = 'inline'; document.getElementById('2412.14718v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.14718v1-abstract-full" style="display: none;"> Ads demand forecasting for Walmart's ad products plays a critical role in enabling effective resource planning, allocation, and management of ads performance. In this paper, we introduce a comprehensive demand forecasting system that tackles hierarchical time series forecasting in business settings. Though traditional hierarchical reconciliation methods ensure forecasting coherence, they often trade off accuracy for coherence especially at lower levels and fail to capture the seasonality unique to each time-series in the hierarchy. Thus, we propose a novel framework "Multi-Stage Hierarchical Forecasting Reconciliation and Adjustment (Multi-Stage HiFoReAd)" to address the challenges of preserving seasonality, ensuring coherence, and improving accuracy. Our system first utilizes diverse models, ensembled through Bayesian Optimization (BO), achieving base forecasts. The generated base forecasts are then passed into the Multi-Stage HiFoReAd framework. The initial stage refines the hierarchy using Top-Down forecasts and "harmonic alignment." The second stage aligns the higher levels' forecasts using MinTrace algorithm, following which the last two levels undergo "harmonic alignment" and "stratified scaling", to eventually achieve accurate and coherent forecasts across the whole hierarchy. Our experiments on Walmart's internal Ads-demand dataset and 3 other public datasets, each with 4 hierarchical levels, demonstrate that the average Absolute Percentage Error from the cross-validation sets improve from 3% to 40% across levels against BO-ensemble of models (LGBM, MSTL+ETS, Prophet) as well as from 1.2% to 92.9% against State-Of-The-Art models. In addition, the forecasts at all hierarchical levels are proved to be coherent. The proposed framework has been deployed and leveraged by Walmart's ads, sales and operations teams to track future demands, make informed decisions and plan resources. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.14718v1-abstract-full').style.display = 'none'; document.getElementById('2412.14718v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 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">Published in 2024 IEEE International Conference on Big Data (BigData)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.15801">arXiv:2411.15801</a> <span> [<a href="https://arxiv.org/pdf/2411.15801">pdf</a>, <a href="https://arxiv.org/format/2411.15801">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Multimedia">cs.MM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> A review on Machine Learning based User-Centric Multimedia Streaming Techniques </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Monalisa Ghosh</a>, <a href="/search/cs?searchtype=author&query=Singhal%2C+C">Chetna Singhal</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="2411.15801v1-abstract-short" style="display: inline;"> The multimedia content and streaming are a major means of information exchange in the modern era and there is an increasing demand for such services. This coupled with the advancement of future wireless networks B5G/6G and the proliferation of intelligent handheld mobile devices, has facilitated the availability of multimedia content to heterogeneous mobile users. Apart from the conventional video… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.15801v1-abstract-full').style.display = 'inline'; document.getElementById('2411.15801v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.15801v1-abstract-full" style="display: none;"> The multimedia content and streaming are a major means of information exchange in the modern era and there is an increasing demand for such services. This coupled with the advancement of future wireless networks B5G/6G and the proliferation of intelligent handheld mobile devices, has facilitated the availability of multimedia content to heterogeneous mobile users. Apart from the conventional video, the 360$^o$ videos have gained popularity with the emerging virtual reality applications. All formats of videos (conventional and 360$^o$) undergo processing, compression, and transmission across dynamic wireless channels with restricted bandwidth to facilitate the streaming services. This causes video impairments, leading to quality degradation and poses challenges in delivering good Quality-of-Experience (QoE) to the viewers. The QoE is a prominent subjective quality measure to assess multimedia services. This requires end-to-end QoE evaluation. Efficient multimedia streaming techniques can improve the service quality while dealing with dynamic network and end-user challenges. A paradigm shift in user-centric multimedia services is envisioned with a focus on Machine Learning (ML) based QoE modeling and streaming strategies. This survey paper presents a comprehensive overview of the overall and continuous, time varying QoE modeling for the purpose of QoE management in multimedia services. It also examines the recent research on intelligent and adaptive multimedia streaming strategies, with a special emphasis on ML based techniques for video (conventional and 360$^o$) streaming. This paper discusses the overall and continuous QoE modeling to optimize the end-user viewing experience, efficient video streaming with a focus on user-centric strategies, associated datasets for modeling and streaming, along with existing shortcoming and open challenges. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.15801v1-abstract-full').style.display = 'none'; document.getElementById('2411.15801v1-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 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 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">Computer Communications</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.13159">arXiv:2410.13159</a> <span> [<a href="https://arxiv.org/pdf/2410.13159">pdf</a>, <a href="https://arxiv.org/format/2410.13159">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="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Data Driven Environmental Awareness Using Wireless Signals </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Nasiri%2C+H">Hossein Nasiri</a>, <a href="/search/cs?searchtype=author&query=Dogan-Tusha%2C+S">Seda Dogan-Tusha</a>, <a href="/search/cs?searchtype=author&query=Rochman%2C+M+I">Muhammad Iqbal Rochman</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Monisha Ghosh</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="2410.13159v2-abstract-short" style="display: inline;"> Robust classification of the operational environment of wireless devices is becoming increasingly important for wireless network optimization, particularly in a shared spectrum environment. Distinguishing between indoor and outdoor devices can enhance reliability and improve coexistence with existing, outdoor, incumbents. For instance, the unlicensed but shared 6 GHz band (5.925 - 7.125 GHz) enabl… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.13159v2-abstract-full').style.display = 'inline'; document.getElementById('2410.13159v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.13159v2-abstract-full" style="display: none;"> Robust classification of the operational environment of wireless devices is becoming increasingly important for wireless network optimization, particularly in a shared spectrum environment. Distinguishing between indoor and outdoor devices can enhance reliability and improve coexistence with existing, outdoor, incumbents. For instance, the unlicensed but shared 6 GHz band (5.925 - 7.125 GHz) enables sharing by imposing lower transmit power for indoor unlicensed devices and a spectrum coordination requirement for outdoor devices. Further, indoor devices are prohibited from using battery power, external antennas, and weatherization to prevent outdoor operations. As these rules may be circumvented, we propose a robust indoor/outdoor classification method by leveraging the fact that the radio-frequency environment faced by a device are quite different indoors and outdoors. We first collect signal strength data from all cellular and Wi-Fi bands that can be received by a smartphone in various environments (indoor interior, indoor near windows, and outdoors), along with GPS accuracy, and then evaluate three machine learning (ML) methods: deep neural network (DNN), decision tree, and random forest to perform classification into these three categories. Our results indicate that the DNN model performs the best, particularly in minimizing the most important classification error, that of classifying outdoor devices as indoor interior devices. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.13159v2-abstract-full').style.display = 'none'; document.getElementById('2410.13159v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 16 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/2409.09704">arXiv:2409.09704</a> <span> [<a href="https://arxiv.org/pdf/2409.09704">pdf</a>, <a href="https://arxiv.org/format/2409.09704">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="Information Retrieval">cs.IR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> <div 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/10.1016/j.ymeth.2024.04.005">10.1016/j.ymeth.2024.04.005 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> AlpaPICO: Extraction of PICO Frames from Clinical Trial Documents Using LLMs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Madhusudan Ghosh</a>, <a href="/search/cs?searchtype=author&query=Mukherjee%2C+S">Shrimon Mukherjee</a>, <a href="/search/cs?searchtype=author&query=Ganguly%2C+A">Asmit Ganguly</a>, <a href="/search/cs?searchtype=author&query=Basuchowdhuri%2C+P">Partha Basuchowdhuri</a>, <a href="/search/cs?searchtype=author&query=Naskar%2C+S+K">Sudip Kumar Naskar</a>, <a href="/search/cs?searchtype=author&query=Ganguly%2C+D">Debasis Ganguly</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="2409.09704v1-abstract-short" style="display: inline;"> In recent years, there has been a surge in the publication of clinical trial reports, making it challenging to conduct systematic reviews. Automatically extracting Population, Intervention, Comparator, and Outcome (PICO) from clinical trial studies can alleviate the traditionally time-consuming process of manually scrutinizing systematic reviews. Existing approaches of PICO frame extraction involv… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.09704v1-abstract-full').style.display = 'inline'; document.getElementById('2409.09704v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.09704v1-abstract-full" style="display: none;"> In recent years, there has been a surge in the publication of clinical trial reports, making it challenging to conduct systematic reviews. Automatically extracting Population, Intervention, Comparator, and Outcome (PICO) from clinical trial studies can alleviate the traditionally time-consuming process of manually scrutinizing systematic reviews. Existing approaches of PICO frame extraction involves supervised approach that relies on the existence of manually annotated data points in the form of BIO label tagging. Recent approaches, such as In-Context Learning (ICL), which has been shown to be effective for a number of downstream NLP tasks, require the use of labeled examples. In this work, we adopt ICL strategy by employing the pretrained knowledge of Large Language Models (LLMs), gathered during the pretraining phase of an LLM, to automatically extract the PICO-related terminologies from clinical trial documents in unsupervised set up to bypass the availability of large number of annotated data instances. Additionally, to showcase the highest effectiveness of LLM in oracle scenario where large number of annotated samples are available, we adopt the instruction tuning strategy by employing Low Rank Adaptation (LORA) to conduct the training of gigantic model in low resource environment for the PICO frame extraction task. Our empirical results show that our proposed ICL-based framework produces comparable results on all the version of EBM-NLP datasets and the proposed instruction tuned version of our framework produces state-of-the-art results on all the different EBM-NLP datasets. Our project is available at \url{https://github.com/shrimonmuke0202/AlpaPICO.git}. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.09704v1-abstract-full').style.display = 'none'; document.getElementById('2409.09704v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 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">Accepted at Methods</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.04737">arXiv:2409.04737</a> <span> [<a href="https://arxiv.org/pdf/2409.04737">pdf</a>, <a href="https://arxiv.org/format/2409.04737">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="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> CrysAtom: Distributed Representation of Atoms for Crystal Property Prediction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Mukherjee%2C+S">Shrimon Mukherjee</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Madhusudan Ghosh</a>, <a href="/search/cs?searchtype=author&query=Basuchowdhuri%2C+P">Partha Basuchowdhuri</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="2409.04737v1-abstract-short" style="display: inline;"> Application of artificial intelligence (AI) has been ubiquitous in the growth of research in the areas of basic sciences. Frequent use of machine learning (ML) and deep learning (DL) based methodologies by researchers has resulted in significant advancements in the last decade. These techniques led to notable performance enhancements in different tasks such as protein structure prediction, drug-ta… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.04737v1-abstract-full').style.display = 'inline'; document.getElementById('2409.04737v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.04737v1-abstract-full" style="display: none;"> Application of artificial intelligence (AI) has been ubiquitous in the growth of research in the areas of basic sciences. Frequent use of machine learning (ML) and deep learning (DL) based methodologies by researchers has resulted in significant advancements in the last decade. These techniques led to notable performance enhancements in different tasks such as protein structure prediction, drug-target binding affinity prediction, and molecular property prediction. In material science literature, it is well-known that crystalline materials exhibit topological structures. Such topological structures may be represented as graphs and utilization of graph neural network (GNN) based approaches could help encoding them into an augmented representation space. Primarily, such frameworks adopt supervised learning techniques targeted towards downstream property prediction tasks on the basis of electronic properties (formation energy, bandgap, total energy, etc.) and crystalline structures. Generally, such type of frameworks rely highly on the handcrafted atom feature representations along with the structural representations. In this paper, we propose an unsupervised framework namely, CrysAtom, using untagged crystal data to generate dense vector representation of atoms, which can be utilized in existing GNN-based property predictor models to accurately predict important properties of crystals. Empirical results show that our dense representation embeds chemical properties of atoms and enhance the performance of the baseline property predictor models significantly. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.04737v1-abstract-full').style.display = 'none'; document.getElementById('2409.04737v1-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 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/2405.04373">arXiv:2405.04373</a> <span> [<a href="https://arxiv.org/pdf/2405.04373">pdf</a>, <a href="https://arxiv.org/format/2405.04373">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> </div> </div> <p class="title is-5 mathjax"> Leveraging LSTM and GAN for Modern Malware Detection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Gupta%2C+I">Ishita Gupta</a>, <a href="/search/cs?searchtype=author&query=Kumari%2C+S">Sneha Kumari</a>, <a href="/search/cs?searchtype=author&query=Jha%2C+P">Priya Jha</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Mohona Ghosh</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="2405.04373v1-abstract-short" style="display: inline;"> The malware booming is a cyberspace equal to the effect of climate change to ecosystems in terms of danger. In the case of significant investments in cybersecurity technologies and staff training, the global community has become locked up in the eternal war with cyber security threats. The multi-form and changing faces of malware are continuously pushing the boundaries of the cybersecurity practit… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.04373v1-abstract-full').style.display = 'inline'; document.getElementById('2405.04373v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.04373v1-abstract-full" style="display: none;"> The malware booming is a cyberspace equal to the effect of climate change to ecosystems in terms of danger. In the case of significant investments in cybersecurity technologies and staff training, the global community has become locked up in the eternal war with cyber security threats. The multi-form and changing faces of malware are continuously pushing the boundaries of the cybersecurity practitioners employ various approaches like detection and mitigate in coping with this issue. Some old mannerisms like signature-based detection and behavioral analysis are slow to adapt to the speedy evolution of malware types. Consequently, this paper proposes the utilization of the Deep Learning Model, LSTM networks, and GANs to amplify malware detection accuracy and speed. A fast-growing, state-of-the-art technology that leverages raw bytestream-based data and deep learning architectures, the AI technology provides better accuracy and performance than the traditional methods. Integration of LSTM and GAN model is the technique that is used for the synthetic generation of data, leading to the expansion of the training datasets, and as a result, the detection accuracy is improved. The paper uses the VirusShare dataset which has more than one million unique samples of the malware as the training and evaluation set for the presented models. Through thorough data preparation including tokenization, augmentation, as well as model training, the LSTM and GAN models convey the better performance in the tasks compared to straight classifiers. The research outcomes come out with 98% accuracy that shows the efficiency of deep learning plays a decisive role in proactive cybersecurity defense. Aside from that, the paper studies the output of ensemble learning and model fusion methods as a way to reduce biases and lift model complexity. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.04373v1-abstract-full').style.display = 'none'; document.getElementById('2405.04373v1-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 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">11 pages</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> Paper ID: IST-BDE-MNNR-170524-5719 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.19411">arXiv:2402.19411</a> <span> [<a href="https://arxiv.org/pdf/2402.19411">pdf</a>, <a href="https://arxiv.org/ps/2402.19411">ps</a>, <a href="https://arxiv.org/format/2402.19411">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="Computation and Language">cs.CL</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"> PaECTER: Patent-level Representation Learning using Citation-informed Transformers </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Mainak Ghosh</a>, <a href="/search/cs?searchtype=author&query=Erhardt%2C+S">Sebastian Erhardt</a>, <a href="/search/cs?searchtype=author&query=Rose%2C+M+E">Michael E. Rose</a>, <a href="/search/cs?searchtype=author&query=Buunk%2C+E">Erik Buunk</a>, <a href="/search/cs?searchtype=author&query=Harhoff%2C+D">Dietmar Harhoff</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="2402.19411v1-abstract-short" style="display: inline;"> PaECTER is a publicly available, open-source document-level encoder specific for patents. We fine-tune BERT for Patents with examiner-added citation information to generate numerical representations for patent documents. PaECTER performs better in similarity tasks than current state-of-the-art models used in the patent domain. More specifically, our model outperforms the next-best patent specific… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.19411v1-abstract-full').style.display = 'inline'; document.getElementById('2402.19411v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.19411v1-abstract-full" style="display: none;"> PaECTER is a publicly available, open-source document-level encoder specific for patents. We fine-tune BERT for Patents with examiner-added citation information to generate numerical representations for patent documents. PaECTER performs better in similarity tasks than current state-of-the-art models used in the patent domain. More specifically, our model outperforms the next-best patent specific pre-trained language model (BERT for Patents) on our patent citation prediction test dataset on two different rank evaluation metrics. PaECTER predicts at least one most similar patent at a rank of 1.32 on average when compared against 25 irrelevant patents. Numerical representations generated by PaECTER from patent text can be used for downstream tasks such as classification, tracing knowledge flows, or semantic similarity search. Semantic similarity search is especially relevant in the context of prior art search for both inventors and patent examiners. PaECTER is available on Hugging Face. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.19411v1-abstract-full').style.display = 'none'; document.getElementById('2402.19411v1-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 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">7 pages, 3 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.05226">arXiv:2402.05226</a> <span> [<a href="https://arxiv.org/pdf/2402.05226">pdf</a>, <a href="https://arxiv.org/ps/2402.05226">ps</a>, <a href="https://arxiv.org/format/2402.05226">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> </div> </div> <p class="title is-5 mathjax"> A Comprehensive Analysis of Secondary Coexistence in a Real-World CBRS Deployment </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Tusha%2C+A">Armed Tusha</a>, <a href="/search/cs?searchtype=author&query=Dogan-Tusha%2C+S">Seda Dogan-Tusha</a>, <a href="/search/cs?searchtype=author&query=Nasiri%2C+H">Hossein Nasiri</a>, <a href="/search/cs?searchtype=author&query=Rochman%2C+M+I">Muhammad I. Rochman</a>, <a href="/search/cs?searchtype=author&query=McGuire%2C+P">Patrick McGuire</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Monisha Ghosh</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="2402.05226v2-abstract-short" style="display: inline;"> The Federal Communications Commission (FCC) in the U.S. has made the Citizens Broadband Radio Service (CBRS) band (3.55 - 3.7 GHz) available for commercial wireless usage under a shared approach using a three-tier hierarchical architecture, where the federal incumbent is the highest priority Tier 1 user, Priority Access License (PAL) holders, who have paid for licenses, are Tier 2 users and Tier 3… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.05226v2-abstract-full').style.display = 'inline'; document.getElementById('2402.05226v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.05226v2-abstract-full" style="display: none;"> The Federal Communications Commission (FCC) in the U.S. has made the Citizens Broadband Radio Service (CBRS) band (3.55 - 3.7 GHz) available for commercial wireless usage under a shared approach using a three-tier hierarchical architecture, where the federal incumbent is the highest priority Tier 1 user, Priority Access License (PAL) holders, who have paid for licenses, are Tier 2 users and Tier 3 users operate under General Authorized Access (GAA), without license fees or protection from higher priority users. The Spectrum Access System (SAS) ensures that higher priority users are protected from interference from lower priority users. However, the lowest priority GAA users are not given any protection from each other by the SAS and are expected to not cause any harmful interference to Tier 1 and Tier 2 users. As the deployments of GAA devices grow, the potential for secondary interference between GAA users increases, especially since the SAS architecture does not allow dynamic channel switching when faced with interference. In this paper, we present a first-of-its-kind extensive measurement campaign of a commercial CBRS network deployed in the city of South Bend, IN, that quantifies both co-channel interference (CCI) and adjacent channel interference (ACI) caused by competing GAA devices and C-band 5G, respectively. We (i) identify a particular CCI scenario and improve performance by changing the frequency allocation based on our study of other allocations in the vicinity and (ii) quantify ACI from 5G in C-band (3.7 GHz) on CBRS throughput. We conclude that (i) CCI and ACI for GAA users is not handled well by the SAS, (ii) proper frequency allocation for GAA requires additional analysis of interference from other GAA users followed by dynamical channel selection, and (iii) utilization of immediate adjacent channels by high power 5G deployments limits the performance of CBRS. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.05226v2-abstract-full').style.display = 'none'; document.getElementById('2402.05226v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 7 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.00957">arXiv:2312.00957</a> <span> [<a href="https://arxiv.org/pdf/2312.00957">pdf</a>, <a href="https://arxiv.org/format/2312.00957">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> </div> </div> <p class="title is-5 mathjax"> A Comprehensive Real-World Evaluation of 5G Improvements over 4G in Low- and Mid-Bands </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Rochman%2C+M+I">Muhammad Iqbal Rochman</a>, <a href="/search/cs?searchtype=author&query=Ye%2C+W">Wei Ye</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+Z">Zhi-Li Zhang</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Monisha Ghosh</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="2312.00957v2-abstract-short" style="display: inline;"> As discussions around 6G begin, it is important to carefully quantify the spectral efficiency gains actually realized by deployed 5G networks as compared to 4G through various enhancements such as higher modulation, beamforming, and MIMO. This will inform the design of future cellular systems, especially in the mid-bands, which provide a good balance between bandwidth and propagation. Similar to 4… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.00957v2-abstract-full').style.display = 'inline'; document.getElementById('2312.00957v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.00957v2-abstract-full" style="display: none;"> As discussions around 6G begin, it is important to carefully quantify the spectral efficiency gains actually realized by deployed 5G networks as compared to 4G through various enhancements such as higher modulation, beamforming, and MIMO. This will inform the design of future cellular systems, especially in the mid-bands, which provide a good balance between bandwidth and propagation. Similar to 4G, 5G also utilizes low-band (<1 GHz) and mid-band spectrum (1 to 6 GHz), and hence comparing the performance of 4G and 5G in these bands will provide insights into how further improvements can be attained. In this work, we address a crucial question: is the performance boost in 5G compared to 4G primarily a result of increased bandwidth, or do the other enhancements play significant roles, and if so, under what circumstances? Hence, we conduct city-wide measurements of 4G and 5G cellular networks deployed in low- and mid-bands in Chicago and Minneapolis, and carefully quantify the contributions of different aspects of 5G advancements to its improved throughput performance. Our analyses show that (i) compared to 4G, the throughput improvement in 5G today is mainly influenced by the wider channel bandwidth, both from single channels and channel aggregation, (ii) in addition to wider channels, improved 5G throughput requires better signal conditions, which can be delivered by denser deployment and/or use of beamforming in mid-bands, (iii) the channel rank in real-world environments rarely supports the full 4 layers of 4x4 MIMO and (iv) advanced features such as MU-MIMO and higher order modulation such as 1024-QAM have yet to be widely deployed. These observations and conclusions lead one to consider designing the next generation of cellular systems to have wider channels, perhaps with improved channel aggregation, dense deployment with more beams. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.00957v2-abstract-full').style.display = 'none'; document.getElementById('2312.00957v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 1 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/2311.10316">arXiv:2311.10316</a> <span> [<a href="https://arxiv.org/pdf/2311.10316">pdf</a>, <a href="https://arxiv.org/format/2311.10316">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"> Graph Sparsifications using Neural Network Assisted Monte Carlo Tree Search </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chiu%2C+A">Alvin Chiu</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Mithun Ghosh</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+R">Reyan Ahmed</a>, <a href="/search/cs?searchtype=author&query=Jun%2C+K">Kwang-Sung Jun</a>, <a href="/search/cs?searchtype=author&query=Kobourov%2C+S">Stephen Kobourov</a>, <a href="/search/cs?searchtype=author&query=Goodrich%2C+M+T">Michael T. Goodrich</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2311.10316v1-abstract-short" style="display: inline;"> Graph neural networks have been successful for machine learning, as well as for combinatorial and graph problems such as the Subgraph Isomorphism Problem and the Traveling Salesman Problem. We describe an approach for computing graph sparsifiers by combining a graph neural network and Monte Carlo Tree Search. We first train a graph neural network that takes as input a partial solution and proposes… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.10316v1-abstract-full').style.display = 'inline'; document.getElementById('2311.10316v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.10316v1-abstract-full" style="display: none;"> Graph neural networks have been successful for machine learning, as well as for combinatorial and graph problems such as the Subgraph Isomorphism Problem and the Traveling Salesman Problem. We describe an approach for computing graph sparsifiers by combining a graph neural network and Monte Carlo Tree Search. We first train a graph neural network that takes as input a partial solution and proposes a new node to be added as output. This neural network is then used in a Monte Carlo search to compute a sparsifier. The proposed method consistently outperforms several standard approximation algorithms on different types of graphs and often finds the optimal solution. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.10316v1-abstract-full').style.display = 'none'; document.getElementById('2311.10316v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">arXiv admin note: substantial text overlap with arXiv:2305.00535</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.03401">arXiv:2311.03401</a> <span> [<a href="https://arxiv.org/pdf/2311.03401">pdf</a>, <a href="https://arxiv.org/format/2311.03401">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="Digital Libraries">cs.DL</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"> Enhancing AI Research Paper Analysis: Methodology Component Extraction using Factored Transformer-based Sequence Modeling Approach </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Madhusudan Ghosh</a>, <a href="/search/cs?searchtype=author&query=Ganguly%2C+D">Debasis Ganguly</a>, <a href="/search/cs?searchtype=author&query=Basuchowdhuri%2C+P">Partha Basuchowdhuri</a>, <a href="/search/cs?searchtype=author&query=Naskar%2C+S+K">Sudip Kumar Naskar</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2311.03401v1-abstract-short" style="display: inline;"> Research in scientific disciplines evolves, often rapidly, over time with the emergence of novel methodologies and their associated terminologies. While methodologies themselves being conceptual in nature and rather difficult to automatically extract and characterise, in this paper, we seek to develop supervised models for automatic extraction of the names of the various constituents of a methodol… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.03401v1-abstract-full').style.display = 'inline'; document.getElementById('2311.03401v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.03401v1-abstract-full" style="display: none;"> Research in scientific disciplines evolves, often rapidly, over time with the emergence of novel methodologies and their associated terminologies. While methodologies themselves being conceptual in nature and rather difficult to automatically extract and characterise, in this paper, we seek to develop supervised models for automatic extraction of the names of the various constituents of a methodology, e.g., `R-CNN', `ELMo' etc. The main research challenge for this task is effectively modeling the contexts around these methodology component names in a few-shot or even a zero-shot setting. The main contributions of this paper towards effectively identifying new evolving scientific methodology names are as follows: i) we propose a factored approach to sequence modeling, which leverages a broad-level category information of methodology domains, e.g., `NLP', `RL' etc.; ii) to demonstrate the feasibility of our proposed approach of identifying methodology component names under a practical setting of fast evolving AI literature, we conduct experiments following a simulated chronological setup (newer methodologies not seen during the training process); iii) our experiments demonstrate that the factored approach outperforms state-of-the-art baselines by margins of up to 9.257\% for the methodology extraction task with the few-shot setup. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.03401v1-abstract-full').style.display = 'none'; document.getElementById('2311.03401v1-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 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.03038">arXiv:2309.03038</a> <span> [<a href="https://arxiv.org/pdf/2309.03038">pdf</a>, <a href="https://arxiv.org/format/2309.03038">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="Signal Processing">eess.SP</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/OJCOMS.2024.3373368">10.1109/OJCOMS.2024.3373368 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Cellular Wireless Networks in the Upper Mid-Band </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kang%2C+S">Seongjoon Kang</a>, <a href="/search/cs?searchtype=author&query=Mezzavilla%2C+M">Marco Mezzavilla</a>, <a href="/search/cs?searchtype=author&query=Rangan%2C+S">Sundeep Rangan</a>, <a href="/search/cs?searchtype=author&query=Madanayake%2C+A">Arjuna Madanayake</a>, <a href="/search/cs?searchtype=author&query=Venkatakrishnan%2C+S+B">Satheesh Bojja Venkatakrishnan</a>, <a href="/search/cs?searchtype=author&query=Hellbourg%2C+G">Gregory Hellbourg</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Monisha Ghosh</a>, <a href="/search/cs?searchtype=author&query=Rahmani%2C+H">Hamed Rahmani</a>, <a href="/search/cs?searchtype=author&query=Dhananjay%2C+A">Aditya Dhananjay</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="2309.03038v6-abstract-short" style="display: inline;"> The upper mid-band - roughly from 7 to 24 GHz - has attracted considerable recent interest for new cellular services. This frequency range has vastly more spectrum than the highly congested bands below 7 GHz while offering more favorable propagation and coverage than the millimeter wave (mmWave) frequencies. The upper mid-band can thus provide a powerful and complementary frequency range to balanc… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.03038v6-abstract-full').style.display = 'inline'; document.getElementById('2309.03038v6-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.03038v6-abstract-full" style="display: none;"> The upper mid-band - roughly from 7 to 24 GHz - has attracted considerable recent interest for new cellular services. This frequency range has vastly more spectrum than the highly congested bands below 7 GHz while offering more favorable propagation and coverage than the millimeter wave (mmWave) frequencies. The upper mid-band can thus provide a powerful and complementary frequency range to balance coverage and capacity. Realizing the full potential of these bands, however, will require fundamental changes to the design of cellular systems. Most importantly, spectrum will likely need to be shared with incumbents including communication satellites, military RADAR, and radio astronomy. Also, the upper mid-band is simply a vast frequency range. Due to this wide bandwidth, combined with the directional nature of transmission and intermittent occupancy of incumbents, cellular systems will need to be agile to sense and intelligently use large spatial and frequency degrees of freedom. This paper attempts to provide an initial assessment of the feasibility and potential gains of wideband cellular systems operating in the upper mid-band. The study includes: (1) a system study to assess potential gains of multi-band systems in a representative dense urban environment and illustrate the value of wide band system with dynamic frequency selectivity; (2) an evaluation of potential cross interference between satellites and terrestrial cellular services and interference nulling to reduce that interference; and (3) design and evaluation of a compact multi-band antenna array structure. Leveraging these preliminary results, we identify potential future research directions to realize next-generation systems in these frequencies. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.03038v6-abstract-full').style.display = 'none'; document.getElementById('2309.03038v6-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 6 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">18 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2307.00235">arXiv:2307.00235</a> <span> [<a href="https://arxiv.org/pdf/2307.00235">pdf</a>, <a href="https://arxiv.org/format/2307.00235">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> </div> </div> <p class="title is-5 mathjax"> Evaluating The Interference Potential in 6 GHz: An Extensive Measurement Campaign of A Dense Indoor Wi-Fi 6E Network </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Dogan-Tusha%2C+S">Seda Dogan-Tusha</a>, <a href="/search/cs?searchtype=author&query=Rochman%2C+M+I">Muhammad Iqbal Rochman</a>, <a href="/search/cs?searchtype=author&query=Tusha%2C+A">Armed Tusha</a>, <a href="/search/cs?searchtype=author&query=Nasiri%2C+H">Hossein Nasiri</a>, <a href="/search/cs?searchtype=author&query=Helzerman%2C+J">James Helzerman</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Monisha Ghosh</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2307.00235v2-abstract-short" style="display: inline;"> The Federal Communications Commission (FCC) has allocated the 6 GHz band (5.925 - 7.125 GHz) for unlicensed, shared use in the US. Incumbents in the band are protected via Low Power Indoor (LPI) rules that do not require the use of an Automatic Frequency Control (AFC) mechanism and Standard Power (SP) rules which do. As the deployment of Wi-Fi 6E APs implementing LPI rules have been increasing, th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.00235v2-abstract-full').style.display = 'inline'; document.getElementById('2307.00235v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2307.00235v2-abstract-full" style="display: none;"> The Federal Communications Commission (FCC) has allocated the 6 GHz band (5.925 - 7.125 GHz) for unlicensed, shared use in the US. Incumbents in the band are protected via Low Power Indoor (LPI) rules that do not require the use of an Automatic Frequency Control (AFC) mechanism and Standard Power (SP) rules which do. As the deployment of Wi-Fi 6E APs implementing LPI rules have been increasing, there is limited research examining the real-world interference potential of dense LPI deployments to fixed links, which remains a concern for incumbents. We have conducted a first-of-its-kind extensive measurement campaign of a dense indoor Wi-Fi 6E network at the University of Michigan, which includes walking, driving, and drone measurements to assess outdoor beacon Received Signal Strength Indicator (RSSI), building entry loss (BEL), channel utilization, and appropriate enabling signal level for a proposed client-to-client (C2C) mode in 6 GHz. Our detailed measurements under various conditions show median outdoor RSSI between -75 dBm and -85 dBm, BEL between 12 dB and 16 dB through double-pane low-emission windows, and only 5% of indoor Basic Service Set Identifiers (BSSIDs) observed outdoors. Our overall conclusion is that the probability of interference to incumbent fixed links is low, but more research is required to determine the appropriate signal level for the C2C enabling signal. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.00235v2-abstract-full').style.display = 'none'; document.getElementById('2307.00235v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 1 July, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.01515">arXiv:2305.01515</a> <span> [<a href="https://arxiv.org/pdf/2305.01515">pdf</a>, <a href="https://arxiv.org/format/2305.01515">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Performance">cs.PF</span> </div> </div> <p class="title is-5 mathjax"> MTrainS: Improving DLRM training efficiency using heterogeneous memories </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kassa%2C+H+T">Hiwot Tadese Kassa</a>, <a href="/search/cs?searchtype=author&query=Johnson%2C+P">Paul Johnson</a>, <a href="/search/cs?searchtype=author&query=Akers%2C+J">Jason Akers</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Mrinmoy Ghosh</a>, <a href="/search/cs?searchtype=author&query=Tulloch%2C+A">Andrew Tulloch</a>, <a href="/search/cs?searchtype=author&query=Mudigere%2C+D">Dheevatsa Mudigere</a>, <a href="/search/cs?searchtype=author&query=Park%2C+J">Jongsoo Park</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+X">Xing Liu</a>, <a href="/search/cs?searchtype=author&query=Dreslinski%2C+R">Ronald Dreslinski</a>, <a href="/search/cs?searchtype=author&query=Ardestani%2C+E+K">Ehsan K. Ardestani</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="2305.01515v1-abstract-short" style="display: inline;"> Recommendation models are very large, requiring terabytes (TB) of memory during training. In pursuit of better quality, the model size and complexity grow over time, which requires additional training data to avoid overfitting. This model growth demands a large number of resources in data centers. Hence, training efficiency is becoming considerably more important to keep the data center power dema… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.01515v1-abstract-full').style.display = 'inline'; document.getElementById('2305.01515v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.01515v1-abstract-full" style="display: none;"> Recommendation models are very large, requiring terabytes (TB) of memory during training. In pursuit of better quality, the model size and complexity grow over time, which requires additional training data to avoid overfitting. This model growth demands a large number of resources in data centers. Hence, training efficiency is becoming considerably more important to keep the data center power demand manageable. In Deep Learning Recommendation Models (DLRM), sparse features capturing categorical inputs through embedding tables are the major contributors to model size and require high memory bandwidth. In this paper, we study the bandwidth requirement and locality of embedding tables in real-world deployed models. We observe that the bandwidth requirement is not uniform across different tables and that embedding tables show high temporal locality. We then design MTrainS, which leverages heterogeneous memory, including byte and block addressable Storage Class Memory for DLRM hierarchically. MTrainS allows for higher memory capacity per node and increases training efficiency by lowering the need to scale out to multiple hosts in memory capacity bound use cases. By optimizing the platform memory hierarchy, we reduce the number of nodes for training by 4-8X, saving power and cost of training while meeting our target training performance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.01515v1-abstract-full').style.display = 'none'; document.getElementById('2305.01515v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 April, 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/2305.00535">arXiv:2305.00535</a> <span> [<a href="https://arxiv.org/pdf/2305.00535">pdf</a>, <a href="https://arxiv.org/format/2305.00535">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="Data Structures and Algorithms">cs.DS</span> </div> </div> <p class="title is-5 mathjax"> Nearly Optimal Steiner Trees using Graph Neural Network Assisted Monte Carlo Tree Search </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ahmed%2C+R">Reyan Ahmed</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Mithun Ghosh</a>, <a href="/search/cs?searchtype=author&query=Jun%2C+K">Kwang-Sung Jun</a>, <a href="/search/cs?searchtype=author&query=Kobourov%2C+S">Stephen Kobourov</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="2305.00535v1-abstract-short" style="display: inline;"> Graph neural networks are useful for learning problems, as well as for combinatorial and graph problems such as the Subgraph Isomorphism Problem and the Traveling Salesman Problem. We describe an approach for computing Steiner Trees by combining a graph neural network and Monte Carlo Tree Search. We first train a graph neural network that takes as input a partial solution and proposes a new node t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.00535v1-abstract-full').style.display = 'inline'; document.getElementById('2305.00535v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.00535v1-abstract-full" style="display: none;"> Graph neural networks are useful for learning problems, as well as for combinatorial and graph problems such as the Subgraph Isomorphism Problem and the Traveling Salesman Problem. We describe an approach for computing Steiner Trees by combining a graph neural network and Monte Carlo Tree Search. We first train a graph neural network that takes as input a partial solution and proposes a new node to be added as output. This neural network is then used in a Monte Carlo search to compute a Steiner tree. The proposed method consistently outperforms the standard 2-approximation algorithm on many different types of graphs and often finds the optimal solution. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.00535v1-abstract-full').style.display = 'none'; document.getElementById('2305.00535v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 April, 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/2304.07690">arXiv:2304.07690</a> <span> [<a href="https://arxiv.org/pdf/2304.07690">pdf</a>, <a href="https://arxiv.org/format/2304.07690">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> </div> </div> <p class="title is-5 mathjax"> A Measurement Study of the Impact of Adjacent Channel Interference between C-band and CBRS </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Rochman%2C+M+I">Muhammad Iqbal Rochman</a>, <a href="/search/cs?searchtype=author&query=Sathya%2C+V">Vanlin Sathya</a>, <a href="/search/cs?searchtype=author&query=Payne%2C+B">Bill Payne</a>, <a href="/search/cs?searchtype=author&query=Yavuz%2C+M">Mehmet Yavuz</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Monisha Ghosh</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="2304.07690v2-abstract-short" style="display: inline;"> The 3.7 - 3.98 GHz frequency band (also known as C-band) was recently allocated in the US for the deployment of 5G cellular services. Prior to this, the lower adjacent band, 3.55 - 3.7 GHz, had been allocated to Citizens Broadband Radio Service (CBRS), where the entire 150 MHz can be used for free by Tier 3 General Authorized Access (GAA) users, but access to the spectrum needs to be authorized by… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.07690v2-abstract-full').style.display = 'inline'; document.getElementById('2304.07690v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2304.07690v2-abstract-full" style="display: none;"> The 3.7 - 3.98 GHz frequency band (also known as C-band) was recently allocated in the US for the deployment of 5G cellular services. Prior to this, the lower adjacent band, 3.55 - 3.7 GHz, had been allocated to Citizens Broadband Radio Service (CBRS), where the entire 150 MHz can be used for free by Tier 3 General Authorized Access (GAA) users, but access to the spectrum needs to be authorized by the Spectrum Access System (SAS). GAA users are allowed on a channel only when there are no Tier 1 Incumbents (Navy radars) or Tier 2 Priority Access License (PAL) users in the area. However, since there are no guard bands between GAA and C-band, and both systems employ Time Division Duplexing (TDD) where the uplink/downlink configurations are not synchronized, adjacent channel interference can potentially reduce the performance of both systems. In this paper, we quantify the effect of this mutual interference by performing experiments with a real-world deployment. We observe significant downlink throughput reductions on both systems when two devices are in close proximity to each other, and one is transmitting uplink while the other is transmitting downlink: 60% for 4G CBRS and 43% for 5G C-band. We believe that this is the first paper to demonstrate this in a real deployment. This throughput degradation was reduced when the CBSD changed its channel and operated 20 MHz away from C-band, essentially creating a guard band between the channels. We also demonstrate the improvement in latency under adjacent channel interference by implementing MicroSlicing at the CBSD. Our results indicate that addressing adjacent channel interference due to the lack of guard bands and TDD configuration mismatch is crucial to improving the performance of both CBRS and C-band systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.07690v2-abstract-full').style.display = 'none'; document.getElementById('2304.07690v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 April, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 16 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/2302.10257">arXiv:2302.10257</a> <span> [<a href="https://arxiv.org/pdf/2302.10257">pdf</a>, <a href="https://arxiv.org/format/2302.10257">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Secrecy Outage Analysis of Energy Harvesting Relay-based Mixed UOWC-RF Network with Multiple Eavesdroppers </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ghosh%2C+M+K">Moloy Kumar Ghosh</a>, <a href="/search/cs?searchtype=author&query=Kundu%2C+M+K">Milton Kumar Kundu</a>, <a href="/search/cs?searchtype=author&query=Ibrahim%2C+M">Md Ibrahim</a>, <a href="/search/cs?searchtype=author&query=Badrudduza%2C+A+S+M">A. S. M. Badrudduza</a>, <a href="/search/cs?searchtype=author&query=Anower%2C+M+S">Md. Shamim Anower</a>, <a href="/search/cs?searchtype=author&query=Ansari%2C+I+S">Imran Shafique Ansari</a>, <a href="/search/cs?searchtype=author&query=Shaikhi%2C+A+A">Ali A. Shaikhi</a>, <a href="/search/cs?searchtype=author&query=Mohandes%2C+M+A">Mohammed A. Mohandes</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="2302.10257v1-abstract-short" style="display: inline;"> This work deals with the physical layer security performance of a dual-hop underwater optical communication (UOWC)-radio frequency (RF) network under the intruding attempts of multiple eavesdroppers via RF links. The intermediate decode and forward relay node between the underwater source and the destination transforms the optical signal into electrical form and re-transmits it to the destination… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.10257v1-abstract-full').style.display = 'inline'; document.getElementById('2302.10257v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2302.10257v1-abstract-full" style="display: none;"> This work deals with the physical layer security performance of a dual-hop underwater optical communication (UOWC)-radio frequency (RF) network under the intruding attempts of multiple eavesdroppers via RF links. The intermediate decode and forward relay node between the underwater source and the destination transforms the optical signal into electrical form and re-transmits it to the destination node with the help of harvested energy by the relay from an integrated power beacon within the system. The source-to-relay link (UOWC) follows a mixture exponential generalized Gamma turbulence with pointing error impairments whereas all the remaining links (RF) undergo $魏-渭$ shadowed fading. With regards to the types of intruders, herein two scenarios are considered, i.e., colluding (\textit{Scenario-I}) and non-colluding (\textit{Scenario-II}) eavesdroppers and the analytical expressions of secure outage probability, probability of strictly positive secrecy capacity, and effective secrecy throughput are derived in closed form for each scenario. Furthermore, the impacts of UOWC and RF channel parameters as well as detection techniques on secrecy capacity are demonstrated, and following this a comparison between the two considered scenarios is demonstrated that reveals the collusion between the eavesdroppers imposes the most harmful threat on secrecy throughput but a better secrecy level can be attained adopting diversity at the destination and power beacon nodes along with heterodyne detection rather than intensity modulation and direct detection technique. Finally, all the derived expressions are corroborated via Monte Carlo simulations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.10257v1-abstract-full').style.display = 'none'; document.getElementById('2302.10257v1-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 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">No</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2301.00200">arXiv:2301.00200</a> <span> [<a href="https://arxiv.org/pdf/2301.00200">pdf</a>, <a href="https://arxiv.org/format/2301.00200">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Logic Mill -- A Knowledge Navigation System </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Erhardt%2C+S">Sebastian Erhardt</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Mainak Ghosh</a>, <a href="/search/cs?searchtype=author&query=Buunk%2C+E">Erik Buunk</a>, <a href="/search/cs?searchtype=author&query=Rose%2C+M+E">Michael E. Rose</a>, <a href="/search/cs?searchtype=author&query=Harhoff%2C+D">Dietmar Harhoff</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="2301.00200v2-abstract-short" style="display: inline;"> Logic Mill is a scalable and openly accessible software system that identifies semantically similar documents within either one domain-specific corpus or multi-domain corpora. It uses advanced Natural Language Processing (NLP) techniques to generate numerical representations of documents. Currently it leverages a large pre-trained language model to generate these document representations. The syst… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.00200v2-abstract-full').style.display = 'inline'; document.getElementById('2301.00200v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2301.00200v2-abstract-full" style="display: none;"> Logic Mill is a scalable and openly accessible software system that identifies semantically similar documents within either one domain-specific corpus or multi-domain corpora. It uses advanced Natural Language Processing (NLP) techniques to generate numerical representations of documents. Currently it leverages a large pre-trained language model to generate these document representations. The system focuses on scientific publications and patent documents and contains more than 200 million documents. It is easily accessible via a simple Application Programming Interface (API) or via a web interface. Moreover, it is continuously being updated and can be extended to text corpora from other domains. We see this system as a general-purpose tool for future research applications in the social sciences and other domains. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.00200v2-abstract-full').style.display = 'none'; document.getElementById('2301.00200v2-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 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 31 December, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 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">10 pages, 3 figures, 1 table</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Proceedings of the 5th Workshop on Patent Text Mining and Semantic Technologies (PatentSemTech 2024), Washington D.C., USA, July 28th, 2024 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2207.04199">arXiv:2207.04199</a> <span> [<a href="https://arxiv.org/pdf/2207.04199">pdf</a>, <a href="https://arxiv.org/format/2207.04199">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Databases">cs.DB</span> </div> <div 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/10.1007/978-3-031-15116-3_1">10.1007/978-3-031-15116-3_1 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Serving Hybrid-Cloud SQL Interactive Queries at Twitter </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Tang%2C+C">Chunxu Tang</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+B">Beinan Wang</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+H">Huijun Wu</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+Z">Zhenzhao Wang</a>, <a href="/search/cs?searchtype=author&query=Li%2C+Y">Yao Li</a>, <a href="/search/cs?searchtype=author&query=Channapattan%2C+V">Vrushali Channapattan</a>, <a href="/search/cs?searchtype=author&query=Luo%2C+Z">Zhenxiao Luo</a>, <a href="/search/cs?searchtype=author&query=Kabra%2C+R">Ruchin Kabra</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Mainak Ghosh</a>, <a href="/search/cs?searchtype=author&query=Navadiya%2C+N+K">Nikhil Kantibhai Navadiya</a>, <a href="/search/cs?searchtype=author&query=Mishra%2C+P">Prachi Mishra</a>, <a href="/search/cs?searchtype=author&query=Mukhedkar%2C+P">Prateek Mukhedkar</a>, <a href="/search/cs?searchtype=author&query=Lu%2C+A">Anneliese Lu</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="2207.04199v1-abstract-short" style="display: inline;"> The demand for data analytics has been consistently increasing in the past years at Twitter. In order to fulfill the requirements and provide a highly scalable and available query experience, a large-scale in-house SQL system is heavily relied on. Recently, we evolved the SQL system into a hybrid-cloud SQL federation system, compliant with Twitter's Partly Cloudy strategy. The hybrid-cloud SQL fed… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.04199v1-abstract-full').style.display = 'inline'; document.getElementById('2207.04199v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2207.04199v1-abstract-full" style="display: none;"> The demand for data analytics has been consistently increasing in the past years at Twitter. In order to fulfill the requirements and provide a highly scalable and available query experience, a large-scale in-house SQL system is heavily relied on. Recently, we evolved the SQL system into a hybrid-cloud SQL federation system, compliant with Twitter's Partly Cloudy strategy. The hybrid-cloud SQL federation system is capable of processing queries across Twitter's data centers and the public cloud, interacting with around 10PB of data per day. In this paper, the design of the hybrid-cloud SQL federation system is presented, which consists of query, cluster, and storage federations. We identify challenges in a modern SQL system and demonstrate how our system addresses them with some important design decisions. We also conduct qualitative examinations and summarize instructive lessons learned from the development and operation of such a SQL system. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.04199v1-abstract-full').style.display = 'none'; document.getElementById('2207.04199v1-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> 9 July, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2022. </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">Submitted to ECSA 2021 post-proceedings</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2204.11338">arXiv:2204.11338</a> <span> [<a href="https://arxiv.org/pdf/2204.11338">pdf</a>, <a href="https://arxiv.org/format/2204.11338">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Databases">cs.DB</span> <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"> Taming Hybrid-Cloud Fast and Scalable Graph Analytics at Twitter </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Tang%2C+C">Chunxu Tang</a>, <a href="/search/cs?searchtype=author&query=Li%2C+Y">Yao Li</a>, <a href="/search/cs?searchtype=author&query=Luo%2C+Z">Zhenxiao Luo</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Mainak Ghosh</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+H">Huijun Wu</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+L">Lu Zhang</a>, <a href="/search/cs?searchtype=author&query=Lu%2C+A">Anneliese Lu</a>, <a href="/search/cs?searchtype=author&query=Kabra%2C+R">Ruchin Kabra</a>, <a href="/search/cs?searchtype=author&query=Navadiya%2C+N+K">Nikhil Kantibhai Navadiya</a>, <a href="/search/cs?searchtype=author&query=Mishra%2C+P">Prachi Mishra</a>, <a href="/search/cs?searchtype=author&query=Mukhedkar%2C+P">Prateek Mukhedkar</a>, <a href="/search/cs?searchtype=author&query=Channapattan%2C+V">Vrushali Channapattan</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="2204.11338v2-abstract-short" style="display: inline;"> We have witnessed a boosted demand for graph analytics at Twitter in recent years, and graph analytics has become one of the key parts of Twitter's large-scale data analytics and machine learning for driving engagement, serving the most relevant content, and promoting healthier conversations. However, infrastructure for graph analytics has historically not been an area of investment at Twitter, re… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.11338v2-abstract-full').style.display = 'inline'; document.getElementById('2204.11338v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2204.11338v2-abstract-full" style="display: none;"> We have witnessed a boosted demand for graph analytics at Twitter in recent years, and graph analytics has become one of the key parts of Twitter's large-scale data analytics and machine learning for driving engagement, serving the most relevant content, and promoting healthier conversations. However, infrastructure for graph analytics has historically not been an area of investment at Twitter, resulting in a long timeline and huge engineering effort for each project to deal with graphs at the Twitter scale. How do we build a unified graph analytics user experience to fulfill modern data analytics on various graph scales spanning from thousands to hundreds of billions of vertices and edges? To bring fast and scalable graph analytics capability into production, we investigate the challenges we are facing in large-scale graph analytics at Twitter and propose a unified graph analytics platform for efficient, scalable, and reliable graph analytics across on-premises and cloud, to fulfill the requirements of diverse graph use cases and challenging scales. We also conduct quantitative benchmarking on Twitter's production-level graph use cases between popular graph analytics frameworks to certify our solution. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.11338v2-abstract-full').style.display = 'none'; document.getElementById('2204.11338v2-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 August, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 24 April, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">6 pages, 7 figures, accepted at IEEE GLOBECOM 2022</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2204.05529">arXiv:2204.05529</a> <span> [<a href="https://arxiv.org/pdf/2204.05529">pdf</a>, <a href="https://arxiv.org/format/2204.05529">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Databases">cs.DB</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</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/IC2E52221.2021.00030">10.1109/IC2E52221.2021.00030 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Forecasting SQL Query Cost at Twitter </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Tang%2C+C">Chunxu Tang</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+B">Beinan Wang</a>, <a href="/search/cs?searchtype=author&query=Luo%2C+Z">Zhenxiao Luo</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+H">Huijun Wu</a>, <a href="/search/cs?searchtype=author&query=Dasan%2C+S">Shajan Dasan</a>, <a href="/search/cs?searchtype=author&query=Fu%2C+M">Maosong Fu</a>, <a href="/search/cs?searchtype=author&query=Li%2C+Y">Yao Li</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Mainak Ghosh</a>, <a href="/search/cs?searchtype=author&query=Kabra%2C+R">Ruchin Kabra</a>, <a href="/search/cs?searchtype=author&query=Navadiya%2C+N+K">Nikhil Kantibhai Navadiya</a>, <a href="/search/cs?searchtype=author&query=Cheng%2C+D">Da Cheng</a>, <a href="/search/cs?searchtype=author&query=Dai%2C+F">Fred Dai</a>, <a href="/search/cs?searchtype=author&query=Channapattan%2C+V">Vrushali Channapattan</a>, <a href="/search/cs?searchtype=author&query=Mishra%2C+P">Prachi Mishra</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="2204.05529v1-abstract-short" style="display: inline;"> With the advent of the Big Data era, it is usually computationally expensive to calculate the resource usages of a SQL query with traditional DBMS approaches. Can we estimate the cost of each query more efficiently without any computation in a SQL engine kernel? Can machine learning techniques help to estimate SQL query resource utilization? The answers are yes. We propose a SQL query cost predict… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.05529v1-abstract-full').style.display = 'inline'; document.getElementById('2204.05529v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2204.05529v1-abstract-full" style="display: none;"> With the advent of the Big Data era, it is usually computationally expensive to calculate the resource usages of a SQL query with traditional DBMS approaches. Can we estimate the cost of each query more efficiently without any computation in a SQL engine kernel? Can machine learning techniques help to estimate SQL query resource utilization? The answers are yes. We propose a SQL query cost predictor service, which employs machine learning techniques to train models from historical query request logs and rapidly forecasts the CPU and memory resource usages of online queries without any computation in a SQL engine. At Twitter, infrastructure engineers are maintaining a large-scale SQL federation system across on-premises and cloud data centers for serving ad-hoc queries. The proposed service can help to improve query scheduling by relieving the issue of imbalanced online analytical processing (OLAP) workloads in the SQL engine clusters. It can also assist in enabling preemptive scaling. Additionally, the proposed approach uses plain SQL statements for the model training and online prediction, indicating it is both hardware and software-agnostic. The method can be generalized to broader SQL systems and heterogeneous environments. The models can achieve 97.9\% accuracy for CPU usage prediction and 97\% accuracy for memory usage prediction. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.05529v1-abstract-full').style.display = 'none'; document.getElementById('2204.05529v1-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 April, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2022. </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">2021 IEEE International Conference on Cloud Engineering (IC2E). IEEE, 2021</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2202.05422">arXiv:2202.05422</a> <span> [<a href="https://arxiv.org/pdf/2202.05422">pdf</a>, <a href="https://arxiv.org/ps/2202.05422">ps</a>, <a href="https://arxiv.org/format/2202.05422">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">stat.ML</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"> Posterior Consistency for Bayesian Relevance Vector Machines </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Fang%2C+X">Xiao Fang</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Malay Ghosh</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="2202.05422v1-abstract-short" style="display: inline;"> Statistical modeling and inference problems with sample sizes substantially smaller than the number of available covariates are challenging. Chakraborty et al. (2012) did a full hierarchical Bayesian analysis of nonlinear regression in such situations using relevance vector machines based on reproducing kernel Hilbert space (RKHS). But they did not provide any theoretical properties associated wit… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.05422v1-abstract-full').style.display = 'inline'; document.getElementById('2202.05422v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2202.05422v1-abstract-full" style="display: none;"> Statistical modeling and inference problems with sample sizes substantially smaller than the number of available covariates are challenging. Chakraborty et al. (2012) did a full hierarchical Bayesian analysis of nonlinear regression in such situations using relevance vector machines based on reproducing kernel Hilbert space (RKHS). But they did not provide any theoretical properties associated with their procedure. The present paper revisits their problem, introduces a new class of global-local priors different from theirs, and provides results on posterior consistency as well as posterior contraction rates <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.05422v1-abstract-full').style.display = 'none'; document.getElementById('2202.05422v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 February, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2202.04830">arXiv:2202.04830</a> <span> [<a href="https://arxiv.org/pdf/2202.04830">pdf</a>, <a href="https://arxiv.org/format/2202.04830">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> </div> </div> <p class="title is-5 mathjax"> Impact of Device Thermal Performance on 5G mmWave Communication Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Rochman%2C+M+I">Muhammad Iqbal Rochman</a>, <a href="/search/cs?searchtype=author&query=Fernandez%2C+D">Damian Fernandez</a>, <a href="/search/cs?searchtype=author&query=Nunez%2C+N">Norlen Nunez</a>, <a href="/search/cs?searchtype=author&query=Sathya%2C+V">Vanlin Sathya</a>, <a href="/search/cs?searchtype=author&query=Ibrahim%2C+A+S">Ahmed S. Ibrahim</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Monisha Ghosh</a>, <a href="/search/cs?searchtype=author&query=Payne%2C+W">William Payne</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="2202.04830v3-abstract-short" style="display: inline;"> 5G millimeter wave (mmWave) cellular networks have been reported to deliver 1-2 Gbps downlink throughput, via speed-tests. However, these speed-tests capture only a few seconds of throughput and are not representative of sustained throughput over several minutes. We report the first measurements of sustained throughput in three cities, Miami, Chicago, and San Francisco, where we observe throughput… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.04830v3-abstract-full').style.display = 'inline'; document.getElementById('2202.04830v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2202.04830v3-abstract-full" style="display: none;"> 5G millimeter wave (mmWave) cellular networks have been reported to deliver 1-2 Gbps downlink throughput, via speed-tests. However, these speed-tests capture only a few seconds of throughput and are not representative of sustained throughput over several minutes. We report the first measurements of sustained throughput in three cities, Miami, Chicago, and San Francisco, where we observe throughput throttling due to rising skin temperature of the phone when it is connected to a deployed 5G mmWave base-station (BS). Radio Resource Control (RRC) messaging between the phone and the BS indicates the reduction in the number of aggregated mmWave channels from 4 to 1 followed by a switch to 4G. We corroborate these measurements with infra-red images as the phone heats up. Thus, mmWave throughput will be limited not by network characteristics but by device thermal management. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.04830v3-abstract-full').style.display = 'none'; document.getElementById('2202.04830v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 June, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 February, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2201.06872">arXiv:2201.06872</a> <span> [<a href="https://arxiv.org/pdf/2201.06872">pdf</a>, <a href="https://arxiv.org/format/2201.06872">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="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> Deep Graph Convolutional Network and LSTM based approach for predicting drug-target binding affinity </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Mukherjee%2C+S">Shrimon Mukherjee</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Madhusudan Ghosh</a>, <a href="/search/cs?searchtype=author&query=Basuchowdhuri%2C+P">Partha Basuchowdhuri</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="2201.06872v1-abstract-short" style="display: inline;"> Development of new drugs is an expensive and time-consuming process. Due to the world-wide SARS-CoV-2 outbreak, it is essential that new drugs for SARS-CoV-2 are developed as soon as possible. Drug repurposing techniques can reduce the time span needed to develop new drugs by probing the list of existing FDA-approved drugs and their properties to reuse them for combating the new disease. We propos… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.06872v1-abstract-full').style.display = 'inline'; document.getElementById('2201.06872v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2201.06872v1-abstract-full" style="display: none;"> Development of new drugs is an expensive and time-consuming process. Due to the world-wide SARS-CoV-2 outbreak, it is essential that new drugs for SARS-CoV-2 are developed as soon as possible. Drug repurposing techniques can reduce the time span needed to develop new drugs by probing the list of existing FDA-approved drugs and their properties to reuse them for combating the new disease. We propose a novel architecture DeepGLSTM, which is a Graph Convolutional network and LSTM based method that predicts binding affinity values between the FDA-approved drugs and the viral proteins of SARS-CoV-2. Our proposed model has been trained on Davis, KIBA (Kinase Inhibitor Bioactivity), DTC (Drug Target Commons), Metz, ToxCast and STITCH datasets. We use our novel architecture to predict a Combined Score (calculated using Davis and KIBA score) of 2,304 FDA-approved drugs against 5 viral proteins. On the basis of the Combined Score, we prepare a list of the top-18 drugs with the highest binding affinity for 5 viral proteins present in SARS-CoV-2. Subsequently, this list may be used for the creation of new useful drugs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.06872v1-abstract-full').style.display = 'none'; document.getElementById('2201.06872v1-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 January, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted for publication in the proceedings of SIAM Data Mining conference 2022 (SDM'22)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2110.15426">arXiv:2110.15426</a> <span> [<a href="https://arxiv.org/pdf/2110.15426">pdf</a>, <a href="https://arxiv.org/format/2110.15426">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="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> RadBERT-CL: Factually-Aware Contrastive Learning For Radiology Report Classification </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jaiswal%2C+A">Ajay Jaiswal</a>, <a href="/search/cs?searchtype=author&query=Tang%2C+L">Liyan Tang</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Meheli Ghosh</a>, <a href="/search/cs?searchtype=author&query=Rousseau%2C+J">Justin Rousseau</a>, <a href="/search/cs?searchtype=author&query=Peng%2C+Y">Yifan Peng</a>, <a href="/search/cs?searchtype=author&query=Ding%2C+Y">Ying Ding</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="2110.15426v2-abstract-short" style="display: inline;"> Radiology reports are unstructured and contain the imaging findings and corresponding diagnoses transcribed by radiologists which include clinical facts and negated and/or uncertain statements. Extracting pathologic findings and diagnoses from radiology reports is important for quality control, population health, and monitoring of disease progress. Existing works, primarily rely either on rule-bas… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.15426v2-abstract-full').style.display = 'inline'; document.getElementById('2110.15426v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2110.15426v2-abstract-full" style="display: none;"> Radiology reports are unstructured and contain the imaging findings and corresponding diagnoses transcribed by radiologists which include clinical facts and negated and/or uncertain statements. Extracting pathologic findings and diagnoses from radiology reports is important for quality control, population health, and monitoring of disease progress. Existing works, primarily rely either on rule-based systems or transformer-based pre-trained model fine-tuning, but could not take the factual and uncertain information into consideration, and therefore generate false-positive outputs. In this work, we introduce three sedulous augmentation techniques which retain factual and critical information while generating augmentations for contrastive learning. We introduce RadBERT-CL, which fuses these information into BlueBert via a self-supervised contrastive loss. Our experiments on MIMIC-CXR show superior performance of RadBERT-CL on fine-tuning for multi-class, multi-label report classification. We illustrate that when few labeled data are available, RadBERT-CL outperforms conventional SOTA transformers (BERT/BlueBert) by significantly larger margins (6-11%). We also show that the representations learned by RadBERT-CL can capture critical medical information in the latent space. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.15426v2-abstract-full').style.display = 'none'; document.getElementById('2110.15426v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 November, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 28 October, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2110.10575">arXiv:2110.10575</a> <span> [<a href="https://arxiv.org/pdf/2110.10575">pdf</a>, <a href="https://arxiv.org/format/2110.10575">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> SocialVisTUM: An Interactive Visualization Toolkit for Correlated Neural Topic Models on Social Media Opinion Mining </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Hagerer%2C+G+J">Gerhard Johann Hagerer</a>, <a href="/search/cs?searchtype=author&query=Kirchhoff%2C+M">Martin Kirchhoff</a>, <a href="/search/cs?searchtype=author&query=Danner%2C+H">Hannah Danner</a>, <a href="/search/cs?searchtype=author&query=Pesch%2C+R">Robert Pesch</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Mainak Ghosh</a>, <a href="/search/cs?searchtype=author&query=Roy%2C+A">Archishman Roy</a>, <a href="/search/cs?searchtype=author&query=Zhao%2C+J">Jiaxi Zhao</a>, <a href="/search/cs?searchtype=author&query=Groh%2C+G">Georg Groh</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="2110.10575v2-abstract-short" style="display: inline;"> Recent research in opinion mining proposed word embedding-based topic modeling methods that provide superior coherence compared to traditional topic modeling. In this paper, we demonstrate how these methods can be used to display correlated topic models on social media texts using SocialVisTUM, our proposed interactive visualization toolkit. It displays a graph with topics as nodes and their corre… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.10575v2-abstract-full').style.display = 'inline'; document.getElementById('2110.10575v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2110.10575v2-abstract-full" style="display: none;"> Recent research in opinion mining proposed word embedding-based topic modeling methods that provide superior coherence compared to traditional topic modeling. In this paper, we demonstrate how these methods can be used to display correlated topic models on social media texts using SocialVisTUM, our proposed interactive visualization toolkit. It displays a graph with topics as nodes and their correlations as edges. Further details are displayed interactively to support the exploration of large text collections, e.g., representative words and sentences of topics, topic and sentiment distributions, hierarchical topic clustering, and customizable, predefined topic labels. The toolkit optimizes automatically on custom data for optimal coherence. We show a working instance of the toolkit on data crawled from English social media discussions about organic food consumption. The visualization confirms findings of a qualitative consumer research study. SocialVisTUM and its training procedures are accessible online. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.10575v2-abstract-full').style.display = 'none'; document.getElementById('2110.10575v2-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">v1</span> submitted 20 October, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2021. </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">Demo paper accepted for publication on RANLP 2021; 8 pages, 5 figures, 1 table</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> RANLP-2021 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2110.05321">arXiv:2110.05321</a> <span> [<a href="https://arxiv.org/pdf/2110.05321">pdf</a>, <a href="https://arxiv.org/format/2110.05321">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="Emerging Technologies">cs.ET</span> </div> </div> <p class="title is-5 mathjax"> Quantum solutions to possible challenges of Blockchain technology </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Dey%2C+N">Nivedita Dey</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Mrityunjay Ghosh</a>, <a href="/search/cs?searchtype=author&query=Chakrabarti%2C+A">Amlan Chakrabarti</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="2110.05321v1-abstract-short" style="display: inline;"> Technological advancements of Blockchain and other Distributed Ledger Techniques (DLTs) promise to provide significant advantages to applications seeking transparency, redundancy, and accountability. Actual adoption of these emerging technologies requires incorporating cost-effective, fast, QoS-enabled, secure, and scalable design. With the recent advent of quantum computing, the security of curre… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.05321v1-abstract-full').style.display = 'inline'; document.getElementById('2110.05321v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2110.05321v1-abstract-full" style="display: none;"> Technological advancements of Blockchain and other Distributed Ledger Techniques (DLTs) promise to provide significant advantages to applications seeking transparency, redundancy, and accountability. Actual adoption of these emerging technologies requires incorporating cost-effective, fast, QoS-enabled, secure, and scalable design. With the recent advent of quantum computing, the security of current blockchain cryptosystems can be compromised to a greater extent. Quantum algorithms like Shor's large integer factorization algorithm and Grover's unstructured database search algorithm can provide exponential and quadratic speedup, respectively, in contrast to their classical counterpart. This can put threats on both public-key cryptosystems and hash functions, which necessarily demands to migrate from classical cryptography to quantum-secure cryptography. Moreover, the computational latency of blockchain platforms causes slow transaction speed, so quantum computing principles might provide significant speedup and scalability in transaction processing and accelerating the mining process. For such purpose, this article first studies current and future classical state-of-the-art blockchain scalability and security primitives. The relevant quantum-safe blockchain cryptosystem initiatives which have been taken by Bitcoin, Ethereum, Corda, etc. are stated and compared with respect to key sizes, hash length, execution time, computational overhead, and energy efficiency. Post Quantum Cryptographic algorithms like Code-based, Lattice-based, Multivariate-based, and other schemes are not well suited for classical blockchain technology due to several disadvantages in practical implementation. Decryption latency, massive consumption of computational resources, and increased key size are few challenges that can hinder blockchain performance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.05321v1-abstract-full').style.display = 'none'; document.getElementById('2110.05321v1-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 October, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2108.08368">arXiv:2108.08368</a> <span> [<a href="https://arxiv.org/pdf/2108.08368">pdf</a>, <a href="https://arxiv.org/format/2108.08368">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"> Computing Steiner Trees using Graph Neural Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ahmed%2C+R">Reyan Ahmed</a>, <a href="/search/cs?searchtype=author&query=Turja%2C+M+A">Md Asadullah Turja</a>, <a href="/search/cs?searchtype=author&query=Sahneh%2C+F+D">Faryad Darabi Sahneh</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Mithun Ghosh</a>, <a href="/search/cs?searchtype=author&query=Hamm%2C+K">Keaton Hamm</a>, <a href="/search/cs?searchtype=author&query=Kobourov%2C+S">Stephen Kobourov</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="2108.08368v1-abstract-short" style="display: inline;"> Graph neural networks have been successful in many learning problems and real-world applications. A recent line of research explores the power of graph neural networks to solve combinatorial and graph algorithmic problems such as subgraph isomorphism, detecting cliques, and the traveling salesman problem. However, many NP-complete problems are as of yet unexplored using this method. In this paper,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.08368v1-abstract-full').style.display = 'inline'; document.getElementById('2108.08368v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2108.08368v1-abstract-full" style="display: none;"> Graph neural networks have been successful in many learning problems and real-world applications. A recent line of research explores the power of graph neural networks to solve combinatorial and graph algorithmic problems such as subgraph isomorphism, detecting cliques, and the traveling salesman problem. However, many NP-complete problems are as of yet unexplored using this method. In this paper, we tackle the Steiner Tree Problem. We employ four learning frameworks to compute low cost Steiner trees: feed-forward neural networks, graph neural networks, graph convolutional networks, and a graph attention model. We use these frameworks in two fundamentally different ways: 1) to train the models to learn the actual Steiner tree nodes, 2) to train the model to learn good Steiner point candidates to be connected to the constructed tree using a shortest path in a greedy fashion. We illustrate the robustness of our heuristics on several random graph generation models as well as the SteinLib data library. Our finding suggests that the out-of-the-box application of GNN methods does worse than the classic 2-approximation method. However, when combined with a greedy shortest path construction, it even does slightly better than the 2-approximation algorithm. This result sheds light on the fundamental capabilities and limitations of graph learning techniques on classical NP-complete problems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.08368v1-abstract-full').style.display = 'none'; document.getElementById('2108.08368v1-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 August, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2108.00453">arXiv:2108.00453</a> <span> [<a href="https://arxiv.org/pdf/2108.00453">pdf</a>, <a href="https://arxiv.org/ps/2108.00453">ps</a>, <a href="https://arxiv.org/format/2108.00453">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> </div> </div> <p class="title is-5 mathjax"> A Comparison Study of Cellular Deployments in Chicago and Miami Using Apps on Smartphones </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Rochman%2C+M+I">Muhammad Iqbal Rochman</a>, <a href="/search/cs?searchtype=author&query=Sathya%2C+V">Vanlin Sathya</a>, <a href="/search/cs?searchtype=author&query=Nunez%2C+N">Norlen Nunez</a>, <a href="/search/cs?searchtype=author&query=Fernandez%2C+D">Damian Fernandez</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Monisha Ghosh</a>, <a href="/search/cs?searchtype=author&query=Ibrahim%2C+A+S">Ahmed S. Ibrahim</a>, <a href="/search/cs?searchtype=author&query=Payne%2C+W">William Payne</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="2108.00453v3-abstract-short" style="display: inline;"> Cellular operators have begun deploying 5G New Radio (NR) in all available bands: low (< 1 GHz), mid (1 - 6 GHz), and high (> 24 GHz) to exploit the different capabilities of each. At the same time, traditional 4G Long Term Evolution (LTE) deployments are being enhanced with the addition of bands in the unlicensed 5 GHz (using License Assisted Access, or LAA) and the 3.5 GHz Citizens Broadband Rad… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.00453v3-abstract-full').style.display = 'inline'; document.getElementById('2108.00453v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2108.00453v3-abstract-full" style="display: none;"> Cellular operators have begun deploying 5G New Radio (NR) in all available bands: low (< 1 GHz), mid (1 - 6 GHz), and high (> 24 GHz) to exploit the different capabilities of each. At the same time, traditional 4G Long Term Evolution (LTE) deployments are being enhanced with the addition of bands in the unlicensed 5 GHz (using License Assisted Access, or LAA) and the 3.5 GHz Citizens Broadband Radio Service (CBRS) resulting in throughput performance comparable to 5G in mid-band. We present a detailed study comparing 4G and 5G deployments, in all bands in Chicago, and focused mmWave measurements and analysis in Miami. Our methodology, based on commercial and custom apps, is scalable for crowdsourcing measurements on a large scale and provides detailed data (throughput, latency, signal strength, etc.) on actual deployments. Our main conclusions based on the measurements are (i) optimized 4G networks in mid-band are comparable in both throughput and latency to current deployments of 5G (both standalone (SA) and non-standalone (NSA)) and (ii) mmWave 5G, even in NSA mode, can deliver multi-Gbps throughput reliably if the installation is dense enough, but performance is still brittle due to the propagation limitations imposed by distance and body loss. Thus, while 5G demonstrates significant early promise, further work needs to be done to ensure that the stated goals of 5G are met. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.00453v3-abstract-full').style.display = 'none'; document.getElementById('2108.00453v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 August, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 1 August, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted in ACM WiNTECH 2021</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2103.15591">arXiv:2103.15591</a> <span> [<a href="https://arxiv.org/pdf/2103.15591">pdf</a>, <a href="https://arxiv.org/ps/2103.15591">ps</a>, <a href="https://arxiv.org/format/2103.15591">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> </div> </div> <p class="title is-5 mathjax"> Hidden-nodes in coexisting LAA & Wi-Fi: a measurement study of real deployments </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sathya%2C+V">Vanlin Sathya</a>, <a href="/search/cs?searchtype=author&query=Rochman%2C+M+I">Muhammad Iqbal Rochman</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Monisha Ghosh</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="2103.15591v2-abstract-short" style="display: inline;"> LTE-Licensed Assisted Access (LAA) networks are beginning to be deployed widely in major metropolitan areas in the US in the unlicensed 5 GHz bands, which have existing dense deployments of Wi-Fi. This provides a real-world opportunity to study the problems due to hidden-node scenarios between LAA and Wi-Fi. The hidden node problem has been well studied in the context of overlapping Wi-Fi APs. How… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2103.15591v2-abstract-full').style.display = 'inline'; document.getElementById('2103.15591v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2103.15591v2-abstract-full" style="display: none;"> LTE-Licensed Assisted Access (LAA) networks are beginning to be deployed widely in major metropolitan areas in the US in the unlicensed 5 GHz bands, which have existing dense deployments of Wi-Fi. This provides a real-world opportunity to study the problems due to hidden-node scenarios between LAA and Wi-Fi. The hidden node problem has been well studied in the context of overlapping Wi-Fi APs. However, when Wi-Fi coexists with LAA, the hidden node problem is exacerbated since LAA cannot use the well-known Request-to-Send (RTS)/Clear to-Send (CTS) mechanism to resolve contentions, resulting in throughput degradation for Wi-Fi. In this paper, we describe detailed measurements and conclusions from experiments on the campus of the University of Chicago which presents a perfect hidden node scenario where Wi-Fi access points (APs) controlled by us and an LAA base-station (BS) deployed by AT&T are hidden from each other, but the clients are not. We performed careful experiments in three different regions of the coexistence area: (i) clients midway between LAA & Wi-Fi; (ii) clients close to the Wi-Fi AP; and (iii) clients close to the LAA BS. Our results show that in a situation where LAA uses an aggregate of three unlicensed channels (60 MHz bandwidth) which overlap with an 80 MHz Wi-Fi transmission, the Wi-Fi throughput at client devices suffers considerably. Overall, Wi-Fi performance is impacted by the hidden node problem more severely than LAA. In the best outdoor conditions, the throughput of LAA and Wi-Fi is reduced by 35% and 97% respectively when coexisting with each other as compared when the other system is not present. Furthermore, we conclude that when both LAA and Wi-Fi use multiple 20 MHz channels and there are multiple Wi-Fi APs coexisting with LAA on the same set of channels, the choice of Wi-Fi primary channels can have a significant impact on LAA throughput. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2103.15591v2-abstract-full').style.display = 'none'; document.getElementById('2103.15591v2-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> 31 March, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 29 March, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2021. </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">IEEE ICC 2021 Workshop on Spectrum Sharing Technology for Next-Generation Communications</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2102.05439">arXiv:2102.05439</a> <span>  </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="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Student sentiment Analysis Using Classification With Feature Extraction Techniques </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Tamrakar%2C+L">Latika Tamrakar</a>, <a href="/search/cs?searchtype=author&query=Shrivastava%2C+D+P">Dr. Padmavati Shrivastava</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S+M">S. M. Ghosh</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="2102.05439v2-abstract-short" style="display: inline;"> Technical growths have empowered, numerous revolutions in the educational system by acquainting with technology into the classroom and by elevating the learning experience. Nowadays Web-based learning is getting much popularity. This paper describes the web-based learning and their effectiveness towards students. One of the prime factors in education or learning system is feedback; it is beneficia… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2102.05439v2-abstract-full').style.display = 'inline'; document.getElementById('2102.05439v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2102.05439v2-abstract-full" style="display: none;"> Technical growths have empowered, numerous revolutions in the educational system by acquainting with technology into the classroom and by elevating the learning experience. Nowadays Web-based learning is getting much popularity. This paper describes the web-based learning and their effectiveness towards students. One of the prime factors in education or learning system is feedback; it is beneficial to learning if it must be used effectively. In this paper, we worked on how machine learning techniques like Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT) can be applied over Web-based learning, emphasis given on sentiment present in the feedback students. We also work on two types of Feature Extraction Technique (FETs) namely Count Vector (CVr) or Bag of Words) (BoW) and Term Frequency and Inverse Document Frequency (TF-IDF) Vector. In the research study, it is our goal for our proposed LR, SVM, NB, and DT models to classify the presence of Student Feedback Dataset (SFB) with improved accuracy with cleaned dataset and feature extraction techniques. The SFB is one of the significant concerns among the student sentimental analysis. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2102.05439v2-abstract-full').style.display = 'none'; document.getElementById('2102.05439v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 March, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 1 February, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2021. </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">need to rework in this paper</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2010.15012">arXiv:2010.15012</a> <span> [<a href="https://arxiv.org/pdf/2010.15012">pdf</a>, <a href="https://arxiv.org/ps/2010.15012">ps</a>, <a href="https://arxiv.org/format/2010.15012">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="Performance">cs.PF</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"> Measurement-based coexistence studies of LAA & Wi-Fi deployments in Chicago </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sathya%2C+V">Vanlin Sathya</a>, <a href="/search/cs?searchtype=author&query=Rochman%2C+M+I">Muhammad Iqbal Rochman</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Monisha Ghosh</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="2010.15012v1-abstract-short" style="display: inline;"> LTE-Licensed Assisted Access (LAA) networks are beginning to be deployed widely in major metropolitan areas in the US in the unlicensed 5 GHz bands, which have existing dense deployments of Wi-Fi as well. Various aspects of the coexistence scenarios such deployments give rise to have been considered ina vast body of academic and industry research. However, there is very little data and research on… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.15012v1-abstract-full').style.display = 'inline'; document.getElementById('2010.15012v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2010.15012v1-abstract-full" style="display: none;"> LTE-Licensed Assisted Access (LAA) networks are beginning to be deployed widely in major metropolitan areas in the US in the unlicensed 5 GHz bands, which have existing dense deployments of Wi-Fi as well. Various aspects of the coexistence scenarios such deployments give rise to have been considered ina vast body of academic and industry research. However, there is very little data and research on how these coexisting networks will behave in practice. The question of fair coexistence between Wi-Fi and LAA has moved from a theoretical question to reality. The recent roll-out of LAA deployments provides an opportunity to collect data on the operation of these networks as well as studying coexistence issues on the ground. In this paper we describe the first results of a measurement campaign conducted over many months, using custom apps as well as off-the-shelf tools, in several areas of Chicago where the major carriers have been expanding LAA deployments. The measurements reveal that coexistence between LAA and Wi-Fi in dense, urban environments where both systems aggregate multiple channels, continues to be a challenging problem that requires further research. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.15012v1-abstract-full').style.display = 'none'; document.getElementById('2010.15012v1-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 October, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2020. </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">IEEE Wireless Communication Magazine, October 2020</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2010.08053">arXiv:2010.08053</a> <span> [<a href="https://arxiv.org/pdf/2010.08053">pdf</a>, <a href="https://arxiv.org/format/2010.08053">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Emerging Technologies">cs.ET</span> </div> </div> <p class="title is-5 mathjax"> QDLC -- The Quantum Development Life Cycle </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Dey%2C+N">Nivedita Dey</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Mrityunjay Ghosh</a>, <a href="/search/cs?searchtype=author&query=kundu%2C+S+S">Subhra Samir kundu</a>, <a href="/search/cs?searchtype=author&query=Chakrabarti%2C+A">Amlan Chakrabarti</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="2010.08053v1-abstract-short" style="display: inline;"> The magnificence grandeur of quantum computing lies in the inherent nature of quantum particles to exhibit true parallelism, which can be realized by indubitably fascinating theories of quantum physics. The possibilities opened by quantum computation (QC) is no where analogous to any classical simulation as quantum computers can efficiently simulate the complex dynamics of strongly correlated inte… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.08053v1-abstract-full').style.display = 'inline'; document.getElementById('2010.08053v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2010.08053v1-abstract-full" style="display: none;"> The magnificence grandeur of quantum computing lies in the inherent nature of quantum particles to exhibit true parallelism, which can be realized by indubitably fascinating theories of quantum physics. The possibilities opened by quantum computation (QC) is no where analogous to any classical simulation as quantum computers can efficiently simulate the complex dynamics of strongly correlated inter-facial systems. But, unfolding mysteries and leading to revolutionary breakthroughs in quantum computing are often challenged by lack of research and development potential in developing qubits with longer coherence interval, scaling qubit count, incorporating quantum error correction to name a few. Putting the first footstep into explorative quantum research by researchers and developers is also inherently ambiguous - due to lack of definitive steps in building up a quantum enabled customized computing stack. Difference in behavioral pattern of underlying system, early-stage noisy device, implementation barriers and performance metric cause hindrance in full adoption of existing classical SDLC suites for quantum product development. This in turn, necessitates to devise systematic and cost-effective techniques to quantum software development through a Quantum Development Life Cycle (QDLC) model, specifying the distinguished features and functionalities of quantum feasibility study, quantum requirement specification, quantum system design, quantum software coding and implementation, quantum testing and quantum software quality management. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.08053v1-abstract-full').style.display = 'none'; document.getElementById('2010.08053v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 October, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">18 pages, 4 tables, 6 diagrams</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2010.07413">arXiv:2010.07413</a> <span> [<a href="https://arxiv.org/pdf/2010.07413">pdf</a>, <a href="https://arxiv.org/format/2010.07413">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Emerging Technologies">cs.ET</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/10.1049/qtc2.12023">10.1049/qtc2.12023 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> A Novel Quantum Algorithm for Ant Colony Optimization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Mrityunjay Ghosh</a>, <a href="/search/cs?searchtype=author&query=Dey%2C+N">Nivedita Dey</a>, <a href="/search/cs?searchtype=author&query=Mitra%2C+D">Debdeep Mitra</a>, <a href="/search/cs?searchtype=author&query=Chakrabarti%2C+A">Amlan Chakrabarti</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="2010.07413v2-abstract-short" style="display: inline;"> Ant colony optimization (ACO) is a commonly used meta-heuristic to solve complex combinatorial optimization problems like traveling salesman problem (TSP), vehicle routing problem (VRP), etc. However, classical ACO algorithms provide better optimal solutions but do not reduce computation time overhead to a significant extent. Algorithmic speed-up can be achieved by using parallelism offered by qua… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.07413v2-abstract-full').style.display = 'inline'; document.getElementById('2010.07413v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2010.07413v2-abstract-full" style="display: none;"> Ant colony optimization (ACO) is a commonly used meta-heuristic to solve complex combinatorial optimization problems like traveling salesman problem (TSP), vehicle routing problem (VRP), etc. However, classical ACO algorithms provide better optimal solutions but do not reduce computation time overhead to a significant extent. Algorithmic speed-up can be achieved by using parallelism offered by quantum computing. Existing quantum algorithms to solve ACO are either quantum-inspired classical algorithms or hybrid quantum-classical algorithms. Since all these algorithms need the intervention of classical computing, leveraging the true potential of quantum computing on real quantum hardware remains a challenge. This paper's main contribution is to propose a fully quantum algorithm to solve ACO, enhancing the quantum information processing toolbox in the fault-tolerant quantum computing (FTQC) era. We have Solved the Single Source Single Destination (SSSD) shortest-path problem using our proposed adaptive quantum circuit for representing dynamic pheromone updating strategy in real IBMQ devices. Our quantum ACO technique can be further used as a quantum ORACLE to solve complex optimization problems in a fully quantum setup with significant speed up upon the availability of more qubits. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.07413v2-abstract-full').style.display = 'none'; document.getElementById('2010.07413v2-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 September, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 14 October, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">13 pages, 13 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> IET Quantum Communication 2021 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2009.01874">arXiv:2009.01874</a> <span> [<a href="https://arxiv.org/pdf/2009.01874">pdf</a>, <a href="https://arxiv.org/format/2009.01874">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Complexity">cs.CC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Combinatorics">math.CO</span> </div> </div> <p class="title is-5 mathjax"> Sum-of-Squares Lower Bounds for Sherrington-Kirkpatrick via Planted Affine Planes </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Mrinalkanti Ghosh</a>, <a href="/search/cs?searchtype=author&query=Jeronimo%2C+F+G">Fernando Granha Jeronimo</a>, <a href="/search/cs?searchtype=author&query=Jones%2C+C">Chris Jones</a>, <a href="/search/cs?searchtype=author&query=Potechin%2C+A">Aaron Potechin</a>, <a href="/search/cs?searchtype=author&query=Rajendran%2C+G">Goutham Rajendran</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="2009.01874v1-abstract-short" style="display: inline;"> The Sum-of-Squares (SoS) hierarchy is a semi-definite programming meta-algorithm that captures state-of-the-art polynomial time guarantees for many optimization problems such as Max-$k$-CSPs and Tensor PCA. On the flip side, a SoS lower bound provides evidence of hardness, which is particularly relevant to average-case problems for which NP-hardness may not be available. In this paper, we consid… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2009.01874v1-abstract-full').style.display = 'inline'; document.getElementById('2009.01874v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2009.01874v1-abstract-full" style="display: none;"> The Sum-of-Squares (SoS) hierarchy is a semi-definite programming meta-algorithm that captures state-of-the-art polynomial time guarantees for many optimization problems such as Max-$k$-CSPs and Tensor PCA. On the flip side, a SoS lower bound provides evidence of hardness, which is particularly relevant to average-case problems for which NP-hardness may not be available. In this paper, we consider the following average case problem, which we call the \emph{Planted Affine Planes} (PAP) problem: Given $m$ random vectors $d_1,\ldots,d_m$ in $\mathbb{R}^n$, can we prove that there is no vector $v \in \mathbb{R}^n$ such that for all $u \in [m]$, $\langle v, d_u\rangle^2 = 1$? In other words, can we prove that $m$ random vectors are not all contained in two parallel hyperplanes at equal distance from the origin? We prove that for $m \leq n^{3/2-蔚}$, with high probability, degree-$n^{惟(蔚)}$ SoS fails to refute the existence of such a vector $v$. When the vectors $d_1,\ldots,d_m$ are chosen from the multivariate normal distribution, the PAP problem is equivalent to the problem of proving that a random $n$-dimensional subspace of $\mathbb{R}^m$ does not contain a boolean vector. As shown by Mohanty--Raghavendra--Xu [STOC 2020], a lower bound for this problem implies a lower bound for the problem of certifying energy upper bounds on the Sherrington-Kirkpatrick Hamiltonian, and so our lower bound implies a degree-$n^{惟(蔚)}$ SoS lower bound for the certification version of the Sherrington-Kirkpatrick problem. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2009.01874v1-abstract-full').style.display = 'none'; document.getElementById('2009.01874v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 September, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2020. </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">68 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2007.06804">arXiv:2007.06804</a> <span> [<a href="https://arxiv.org/pdf/2007.06804">pdf</a>, <a href="https://arxiv.org/format/2007.06804">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Emerging Technologies">cs.ET</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantum Physics">quant-ph</span> </div> </div> <p class="title is-5 mathjax"> 2D Qubit Placement of Quantum Circuits using LONGPATH </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Mrityunjay Ghosh</a>, <a href="/search/cs?searchtype=author&query=Dey%2C+N">Nivedita Dey</a>, <a href="/search/cs?searchtype=author&query=Mitra%2C+D">Debdeep Mitra</a>, <a href="/search/cs?searchtype=author&query=Chakrabarti%2C+A">Amlan Chakrabarti</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="2007.06804v1-abstract-short" style="display: inline;"> In order to achieve speedup over conventional classical computing for finding solution of computationally hard problems, quantum computing was introduced. Quantum algorithms can be simulated in a pseudo quantum environment, but implementation involves realization of quantum circuits through physical synthesis of quantum gates. This requires decomposition of complex quantum gates into a cascade of… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.06804v1-abstract-full').style.display = 'inline'; document.getElementById('2007.06804v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2007.06804v1-abstract-full" style="display: none;"> In order to achieve speedup over conventional classical computing for finding solution of computationally hard problems, quantum computing was introduced. Quantum algorithms can be simulated in a pseudo quantum environment, but implementation involves realization of quantum circuits through physical synthesis of quantum gates. This requires decomposition of complex quantum gates into a cascade of simple one qubit and two qubit gates. The methodological framework for physical synthesis imposes a constraint regarding placement of operands (qubits) and operators. If physical qubits can be placed on a grid, where each node of the grid represents a qubit then quantum gates can only be operated on adjacent qubits, otherwise SWAP gates must be inserted to convert non-Linear Nearest Neighbor architecture to Linear Nearest Neighbor architecture. Insertion of SWAP gates should be made optimal to reduce cumulative cost of physical implementation. A schedule layout generation is required for placement and routing apriori to actual implementation. In this paper, two algorithms are proposed to optimize the number of SWAP gates in any arbitrary quantum circuit. The first algorithm is intended to start with generation of an interaction graph followed by finding the longest path starting from the node with maximum degree. The second algorithm optimizes the number of SWAP gates between any pair of non-neighbouring qubits. Our proposed approach has a significant reduction in number of SWAP gates in 1D and 2D NTC architecture. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.06804v1-abstract-full').style.display = 'none'; document.getElementById('2007.06804v1-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 July, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2020. </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">Advanced Computing and Systems for Security, SpringerLink, Volume 10</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2005.04599">arXiv:2005.04599</a> <span> [<a href="https://arxiv.org/pdf/2005.04599">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neural and Evolutionary Computing">cs.NE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</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/10.1016/j.engappai.2020.103847">10.1016/j.engappai.2020.103847 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Fuzzy Mutation Embedded Hybrids of Gravitational Search and Particle Swarm Optimization Methods for Engineering Design Problems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kar%2C+D">Devroop Kar</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Manosij Ghosh</a>, <a href="/search/cs?searchtype=author&query=Guha%2C+R">Ritam Guha</a>, <a href="/search/cs?searchtype=author&query=Sarkar%2C+R">Ram Sarkar</a>, <a href="/search/cs?searchtype=author&query=Garc%C3%ADa-Hern%C3%A1ndez%2C+L">Laura Garc铆a-Hern谩ndez</a>, <a href="/search/cs?searchtype=author&query=Abraham%2C+A">Ajith Abraham</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="2005.04599v1-abstract-short" style="display: inline;"> Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO) are nature-inspired, swarm-based optimization algorithms respectively. Though they have been widely used for single-objective optimization since their inception, they suffer from premature convergence. Even though the hybrids of GSA and PSO perform much better, the problem remains. Hence, to solve this issue we have propose… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.04599v1-abstract-full').style.display = 'inline'; document.getElementById('2005.04599v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2005.04599v1-abstract-full" style="display: none;"> Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO) are nature-inspired, swarm-based optimization algorithms respectively. Though they have been widely used for single-objective optimization since their inception, they suffer from premature convergence. Even though the hybrids of GSA and PSO perform much better, the problem remains. Hence, to solve this issue we have proposed a fuzzy mutation model for two hybrid versions of PSO and GSA - Gravitational Particle Swarm (GPS) and PSOGSA. The developed algorithms are called Mutation based GPS (MGPS) and Mutation based PSOGSA (MPSOGSA). The mutation operator is based on a fuzzy model where the probability of mutation has been calculated based on the closeness of particle to population centroid and improvement in the particle value. We have evaluated these two new algorithms on 23 benchmark functions of three categories (unimodal, multi-modal and multi-modal with fixed dimension). The experimental outcome shows that our proposed model outperforms their corresponding ancestors, MGPS outperforms GPS 13 out of 23 times (56.52%) and MPSOGSA outperforms PSOGSA 17 times out of 23 (73.91 %). We have also compared our results against those of recent optimization algorithms such as Sine Cosine Algorithm (SCA), Opposition-Based SCA, and Volleyball Premier League Algorithm (VPL). In addition, we have applied our proposed algorithms on some classic engineering design problems and the outcomes are satisfactory. The related codes of the proposed algorithms can be found in this link: Fuzzy-Mutation-Embedded-Hybrids-of-GSA-and-PSO. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.04599v1-abstract-full').style.display = 'none'; document.getElementById('2005.04599v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 May, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">33 pages, 18 figures, submitted to Engineering Applications of Artificial Intelligence, Elsevier</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2005.04596">arXiv:2005.04596</a> <span> [<a href="https://arxiv.org/pdf/2005.04596">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neural and Evolutionary Computing">cs.NE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="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"> A Hybrid Swarm and Gravitation based feature selection algorithm for Handwritten Indic Script Classification problem </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Guha%2C+R">Ritam Guha</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Manosij Ghosh</a>, <a href="/search/cs?searchtype=author&query=Singh%2C+P+K">Pawan Kumar Singh</a>, <a href="/search/cs?searchtype=author&query=Sarkar%2C+R">Ram Sarkar</a>, <a href="/search/cs?searchtype=author&query=Nasipuri%2C+M">Mita Nasipuri</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="2005.04596v1-abstract-short" style="display: inline;"> In any multi-script environment, handwritten script classification is of paramount importance before the document images are fed to their respective Optical Character Recognition (OCR) engines. Over the years, this complex pattern classification problem has been solved by researchers proposing various feature vectors mostly having large dimension, thereby increasing the computation complexity of t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.04596v1-abstract-full').style.display = 'inline'; document.getElementById('2005.04596v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2005.04596v1-abstract-full" style="display: none;"> In any multi-script environment, handwritten script classification is of paramount importance before the document images are fed to their respective Optical Character Recognition (OCR) engines. Over the years, this complex pattern classification problem has been solved by researchers proposing various feature vectors mostly having large dimension, thereby increasing the computation complexity of the whole classification model. Feature Selection (FS) can serve as an intermediate step to reduce the size of the feature vectors by restricting them only to the essential and relevant features. In our paper, we have addressed this issue by introducing a new FS algorithm, called Hybrid Swarm and Gravitation based FS (HSGFS). This algorithm is made to run on 3 feature vectors introduced in the literature recently - Distance-Hough Transform (DHT), Histogram of Oriented Gradients (HOG) and Modified log-Gabor (MLG) filter Transform. Three state-of-the-art classifiers namely, Multi-Layer Perceptron (MLP), K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) are used for the handwritten script classification. Handwritten datasets, prepared at block, text-line and word level, consisting of officially recognized 12 Indic scripts are used for the evaluation of our method. An average improvement in the range of 2-5 % is achieved in the classification accuracies by utilizing only about 75-80 % of the original feature vectors on all three datasets. The proposed methodology also shows better performance when compared to some popularly used FS models. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.04596v1-abstract-full').style.display = 'none'; document.getElementById('2005.04596v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 May, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2020. </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">37 pages, 22 figures, submitted to Multimedia Tools and Applications, Springer</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2005.04593">arXiv:2005.04593</a> <span> [<a href="https://arxiv.org/pdf/2005.04593">pdf</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 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/s00500-020-05183-1">10.1007/s00500-020-05183-1 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Embedded Chaotic Whale Survival Algorithm for Filter-Wrapper Feature Selection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Guha%2C+R">Ritam Guha</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Manosij Ghosh</a>, <a href="/search/cs?searchtype=author&query=Mutsuddi%2C+S">Shyok Mutsuddi</a>, <a href="/search/cs?searchtype=author&query=Sarkar%2C+R">Ram Sarkar</a>, <a href="/search/cs?searchtype=author&query=Mirjalili%2C+S">Seyedali Mirjalili</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="2005.04593v1-abstract-short" style="display: inline;"> Classification accuracy provided by a machine learning model depends a lot on the feature set used in the learning process. Feature Selection (FS) is an important and challenging pre-processing technique which helps to identify only the relevant features from a dataset thereby reducing the feature dimension as well as improving the classification accuracy at the same time. The binary version of Wh… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.04593v1-abstract-full').style.display = 'inline'; document.getElementById('2005.04593v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2005.04593v1-abstract-full" style="display: none;"> Classification accuracy provided by a machine learning model depends a lot on the feature set used in the learning process. Feature Selection (FS) is an important and challenging pre-processing technique which helps to identify only the relevant features from a dataset thereby reducing the feature dimension as well as improving the classification accuracy at the same time. The binary version of Whale Optimization Algorithm (WOA) is a popular FS technique which is inspired from the foraging behavior of humpback whales. In this paper, an embedded version of WOA called Embedded Chaotic Whale Survival Algorithm (ECWSA) has been proposed which uses its wrapper process to achieve high classification accuracy and a filter approach to further refine the selected subset with low computation cost. Chaos has been introduced in the ECWSA to guide selection of the type of movement followed by the whales while searching for prey. A fitness-dependent death mechanism has also been introduced in the system of whales which is inspired from the real-life scenario in which whales die if they are unable to catch their prey. The proposed method has been evaluated on 18 well-known UCI datasets and compared with its predecessors as well as some other popular FS methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.04593v1-abstract-full').style.display = 'none'; document.getElementById('2005.04593v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 May, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2020. </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">28 pages, 6 figures, submitted a minor revision to Soft Computing, Springer</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2003.13652">arXiv:2003.13652</a> <span> [<a href="https://arxiv.org/pdf/2003.13652">pdf</a>, <a href="https://arxiv.org/format/2003.13652">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="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"> Machine Learning enabled Spectrum Sharing in Dense LTE-U/Wi-Fi Coexistence Scenarios </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Dziedzic%2C+A">Adam Dziedzic</a>, <a href="/search/cs?searchtype=author&query=Sathya%2C+V">Vanlin Sathya</a>, <a href="/search/cs?searchtype=author&query=Rochman%2C+M+I">Muhammad Iqbal Rochman</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Monisha Ghosh</a>, <a href="/search/cs?searchtype=author&query=Krishnan%2C+S">Sanjay Krishnan</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="2003.13652v1-abstract-short" style="display: inline;"> The application of Machine Learning (ML) techniques to complex engineering problems has proved to be an attractive and efficient solution. ML has been successfully applied to several practical tasks like image recognition, automating industrial operations, etc. The promise of ML techniques in solving non-linear problems influenced this work which aims to apply known ML techniques and develop new o… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2003.13652v1-abstract-full').style.display = 'inline'; document.getElementById('2003.13652v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2003.13652v1-abstract-full" style="display: none;"> The application of Machine Learning (ML) techniques to complex engineering problems has proved to be an attractive and efficient solution. ML has been successfully applied to several practical tasks like image recognition, automating industrial operations, etc. The promise of ML techniques in solving non-linear problems influenced this work which aims to apply known ML techniques and develop new ones for wireless spectrum sharing between Wi-Fi and LTE in the unlicensed spectrum. In this work, we focus on the LTE-Unlicensed (LTE-U) specification developed by the LTE-U Forum, which uses the duty-cycle approach for fair coexistence. The specification suggests reducing the duty cycle at the LTE-U base-station (BS) when the number of co-channel Wi-Fi basic service sets (BSSs) increases from one to two or more. However, without decoding the Wi-Fi packets, detecting the number of Wi-Fi BSSs operating on the channel in real-time is a challenging problem. In this work, we demonstrate a novel ML-based approach which solves this problem by using energy values observed during the LTE-U OFF duration. It is relatively straightforward to observe only the energy values during the LTE-U BS OFF time compared to decoding the entire Wi-Fi packet, which would require a full Wi-Fi receiver at the LTE-U base-station. We implement and validate the proposed ML-based approach by real-time experiments and demonstrate that there exist distinct patterns between the energy distributions between one and many Wi-Fi AP transmissions. The proposed ML-based approach results in a higher accuracy (close to 99\% in all cases) as compared to the existing auto-correlation (AC) and energy detection (ED) approaches. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2003.13652v1-abstract-full').style.display = 'none'; document.getElementById('2003.13652v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 March, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted at IEEE Open Journal of Vehicular Technology. arXiv admin note: substantial text overlap with arXiv:1911.09292</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1911.09292">arXiv:1911.09292</a> <span> [<a href="https://arxiv.org/pdf/1911.09292">pdf</a>, <a href="https://arxiv.org/format/1911.09292">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="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Machine Learning based detection of multiple Wi-Fi BSSs for LTE-U CSAT </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sathya%2C+V">Vanlin Sathya</a>, <a href="/search/cs?searchtype=author&query=Dziedzic%2C+A">Adam Dziedzic</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Monisha Ghosh</a>, <a href="/search/cs?searchtype=author&query=Krishnan%2C+S">Sanjay Krishnan</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="1911.09292v1-abstract-short" style="display: inline;"> According to the LTE-U Forum specification, a LTE-U base-station (BS) reduces its duty cycle from 50% to 33% when it senses an increase in the number of co-channel Wi-Fi basic service sets (BSSs) from one to two. The detection of the number of Wi-Fi BSSs that are operating on the channel in real-time, without decoding the Wi-Fi packets, still remains a challenge. In this paper, we present a novel… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.09292v1-abstract-full').style.display = 'inline'; document.getElementById('1911.09292v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1911.09292v1-abstract-full" style="display: none;"> According to the LTE-U Forum specification, a LTE-U base-station (BS) reduces its duty cycle from 50% to 33% when it senses an increase in the number of co-channel Wi-Fi basic service sets (BSSs) from one to two. The detection of the number of Wi-Fi BSSs that are operating on the channel in real-time, without decoding the Wi-Fi packets, still remains a challenge. In this paper, we present a novel machine learning (ML) approach that solves the problem by using energy values observed during LTE-U OFF duration. Observing the energy values (at LTE-U BS OFF time) is a much simpler operation than decoding the entire Wi-Fi packets. In this work, we implement and validate the proposed ML based approach in real-time experiments, and demonstrate that there are two distinct patterns between one and two Wi-Fi APs. This approach delivers an accuracy close to 100% compared to auto-correlation (AC) and energy detection (ED) approaches. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.09292v1-abstract-full').style.display = 'none'; document.getElementById('1911.09292v1-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, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2019. </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 at International Conference on Computing, Networking and Communications (ICNC 2020)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1903.03512">arXiv:1903.03512</a> <span> [<a href="https://arxiv.org/pdf/1903.03512">pdf</a>, <a href="https://arxiv.org/format/1903.03512">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> </div> </div> <p class="title is-5 mathjax"> AgentBuddy: A Contextual Bandit based Decision Support System for Customer Support Agents </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ganu%2C+H">Hrishikesh Ganu</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Mithun Ghosh</a>, <a href="/search/cs?searchtype=author&query=Roshan%2C+S">Shashi Roshan</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="1903.03512v1-abstract-short" style="display: inline;"> In this short paper, we present early insights from a Decision Support System for Customer Support Agents (CSAs) serving customers of a leading accounting software. The system is under development and is designed to provide suggestions to CSAs to make them more productive. A unique aspect of the solution is the use of bandit algorithms to create a tractable human-in-the-loop system that can learn… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1903.03512v1-abstract-full').style.display = 'inline'; document.getElementById('1903.03512v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1903.03512v1-abstract-full" style="display: none;"> In this short paper, we present early insights from a Decision Support System for Customer Support Agents (CSAs) serving customers of a leading accounting software. The system is under development and is designed to provide suggestions to CSAs to make them more productive. A unique aspect of the solution is the use of bandit algorithms to create a tractable human-in-the-loop system that can learn from CSAs in an online fashion. In addition to discussing the ML aspects, we also bring out important insights we gleaned from early feedback from CSAs. These insights motivate our future work and also might be of wider interest to ML practitioners. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1903.03512v1-abstract-full').style.display = 'none'; document.getElementById('1903.03512v1-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, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2019. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1809.05928">arXiv:1809.05928</a> <span> [<a href="https://arxiv.org/pdf/1809.05928">pdf</a>, <a href="https://arxiv.org/format/1809.05928">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Performance">cs.PF</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/TBDATA.2018.2871114">10.1109/TBDATA.2018.2871114 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> I/O Workload Management for All-Flash Datacenter Storage Systems Based on Total Cost of Ownership </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Yang%2C+Z">Zhengyu Yang</a>, <a href="/search/cs?searchtype=author&query=Awasthi%2C+M">Manu Awasthi</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Mrinmoy Ghosh</a>, <a href="/search/cs?searchtype=author&query=Bhimani%2C+J">Janki Bhimani</a>, <a href="/search/cs?searchtype=author&query=Mi%2C+N">Ningfang Mi</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="1809.05928v3-abstract-short" style="display: inline;"> Recently, the capital expenditure of flash-based Solid State Driver (SSDs) keeps declining and the storage capacity of SSDs keeps increasing. As a result, all-flash storage systems have started to become more economically viable for large shared storage installations in datacenters, where metrics like Total Cost of Ownership (TCO) are of paramount importance. On the other hand, flash devices suffe… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1809.05928v3-abstract-full').style.display = 'inline'; document.getElementById('1809.05928v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1809.05928v3-abstract-full" style="display: none;"> Recently, the capital expenditure of flash-based Solid State Driver (SSDs) keeps declining and the storage capacity of SSDs keeps increasing. As a result, all-flash storage systems have started to become more economically viable for large shared storage installations in datacenters, where metrics like Total Cost of Ownership (TCO) are of paramount importance. On the other hand, flash devices suffer from write amplification, which, if unaccounted, can substantially increase the TCO of a storage system. In this paper, we first develop a TCO model for datacenter all-flash storage systems, and then plug a Write Amplification model (WAF) of NVMe SSDs we build based on empirical data into this TCO model. Our new WAF model accounts for workload characteristics like write rate and percentage of sequential writes. Furthermore, using both the TCO and WAF models as the optimization criterion, we design new flash resource management schemes (MINTCO) to guide datacenter managers to make workload allocation decisions under the consideration of TCO for SSDs. Based on that, we also develop MINTCO-RAID to support RAID SSDs and MINTCO-OFFLINE to optimize the offline workload-disk deployment problem during the initialization phase. Experimental results show that MINTCO can reduce the TCO and keep relatively high throughput and space utilization of the entire datacenter storage resources. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1809.05928v3-abstract-full').style.display = 'none'; document.getElementById('1809.05928v3-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 July, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 16 September, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2018. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1808.07097">arXiv:1808.07097</a> <span> [<a href="https://arxiv.org/pdf/1808.07097">pdf</a>, <a href="https://arxiv.org/format/1808.07097">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> <div 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/JSTSP.2019.2905226">10.1109/JSTSP.2019.2905226 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Angle Feedback for NOMA Transmission in mmWave Drone Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Rupasinghe%2C+N">Nadisanka Rupasinghe</a>, <a href="/search/cs?searchtype=author&query=Yapici%2C+Y">Yavuz Yapici</a>, <a href="/search/cs?searchtype=author&query=Guvenc%2C+I">Ismail Guvenc</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Monisha Ghosh</a>, <a href="/search/cs?searchtype=author&query=Kakishima%2C+Y">Yuichi Kakishima</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="1808.07097v2-abstract-short" style="display: inline;"> In this paper, we consider an unmanned aerial vehicle (UAV) based wireless network using non-orthogonal multiple access (NOMA) transmission in millimeter-wave frequencies to deliver broadband data in a spectrally efficient fashion at hotspot scenarios. The necessity for the NOMA transmitter to gather information on user channel quality becomes a major drawback in practical deployments. We therefor… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1808.07097v2-abstract-full').style.display = 'inline'; document.getElementById('1808.07097v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1808.07097v2-abstract-full" style="display: none;"> In this paper, we consider an unmanned aerial vehicle (UAV) based wireless network using non-orthogonal multiple access (NOMA) transmission in millimeter-wave frequencies to deliver broadband data in a spectrally efficient fashion at hotspot scenarios. The necessity for the NOMA transmitter to gather information on user channel quality becomes a major drawback in practical deployments. We therefore consider various limited feedback schemes for NOMA transmission, to relieve the complexity of tracking and feeding back the full channel state information (CSI) of the users. In particular, through beamforming we allow NOMA to exploit the space domain, and hence the user angle emerges as a promising (yet novel) limited feedback scheme. We show that as the user region for NOMA transmission gets wider, the users become more distinctive at the transmitter side with respect to their angles, making user angle feedback a better alternative than distance feedback in such scenarios. We rigorously derive and analyze the outage sum rate performance for NOMA transmission considering various user ordering strategies involving full CSI, angle, and distance feedback schemes. Our analytical results for NOMA outage sum rates using those feedback schemes match closely with simulations, and provide useful insights on properly choosing a limited feedback scheme for different deployment geometries and operating configurations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1808.07097v2-abstract-full').style.display = 'none'; document.getElementById('1808.07097v2-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 February, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 21 August, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2018. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1807.07230">arXiv:1807.07230</a> <span> [<a href="https://arxiv.org/pdf/1807.07230">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> UAV-Based in-band Integrated Access and Backhaul for 5G Communications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Fouda%2C+A">Abdurrahman Fouda</a>, <a href="/search/cs?searchtype=author&query=Ibrahim%2C+A+S">Ahmed S. Ibrahim</a>, <a href="/search/cs?searchtype=author&query=Guvenc%2C+I">Ismail Guvenc</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Monisha Ghosh</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="1807.07230v1-abstract-short" style="display: inline;"> We introduce the concept of using unmanned aerial vehicles (UAVs) as drone base stations for in-band Integrated Access and Backhaul (IB-IAB) scenarios for 5G networks. We first present a system model for forward link transmissions in an IB-IAB multi-tier drone cellular network. We then investigate the key challenges of this scenario and propose a framework that utilizes the flying capabilities of… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1807.07230v1-abstract-full').style.display = 'inline'; document.getElementById('1807.07230v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1807.07230v1-abstract-full" style="display: none;"> We introduce the concept of using unmanned aerial vehicles (UAVs) as drone base stations for in-band Integrated Access and Backhaul (IB-IAB) scenarios for 5G networks. We first present a system model for forward link transmissions in an IB-IAB multi-tier drone cellular network. We then investigate the key challenges of this scenario and propose a framework that utilizes the flying capabilities of the UAVs as the main degree of freedom to find the optimal precoder design for the backhaul links, user-base station association, UAV 3D hovering locations, and power allocations. We discuss how the proposed algorithm can be utilized to optimize the network performance in both large and small scales. Finally, we use an exhaustive search-based solution to demonstrate the performance gains that can be achieved from the presented algorithm in terms of the received signal to interference plus noise ratio (SINR) and overall network sum-rate. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1807.07230v1-abstract-full').style.display = 'none'; document.getElementById('1807.07230v1-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 July, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2018. </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">To be presented in the proceedings of VTC-Fall'18, Chicago, IL</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1804.03644">arXiv:1804.03644</a> <span> [<a href="https://arxiv.org/pdf/1804.03644">pdf</a>, <a href="https://arxiv.org/format/1804.03644">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Data Structures and Algorithms">cs.DS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Functional Analysis">math.FA</span> </div> </div> <p class="title is-5 mathjax"> Approximating Operator Norms via Generalized Krivine Rounding </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bhattiprolu%2C+V">Vijay Bhattiprolu</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Mrinalkanti Ghosh</a>, <a href="/search/cs?searchtype=author&query=Guruswami%2C+V">Venkatesan Guruswami</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+E">Euiwoong Lee</a>, <a href="/search/cs?searchtype=author&query=Tulsiani%2C+M">Madhur Tulsiani</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="1804.03644v2-abstract-short" style="display: inline;"> We consider the $(\ell_p,\ell_r)$-Grothendieck problem, which seeks to maximize the bilinear form $y^T A x$ for an input matrix $A$ over vectors $x,y$ with $\|x\|_p=\|y\|_r=1$. The problem is equivalent to computing the $p \to r^*$ operator norm of $A$. The case $p=r=\infty$ corresponds to the classical Grothendieck problem. Our main result is an algorithm for arbitrary $p,r \ge 2$ with approximat… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1804.03644v2-abstract-full').style.display = 'inline'; document.getElementById('1804.03644v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1804.03644v2-abstract-full" style="display: none;"> We consider the $(\ell_p,\ell_r)$-Grothendieck problem, which seeks to maximize the bilinear form $y^T A x$ for an input matrix $A$ over vectors $x,y$ with $\|x\|_p=\|y\|_r=1$. The problem is equivalent to computing the $p \to r^*$ operator norm of $A$. The case $p=r=\infty$ corresponds to the classical Grothendieck problem. Our main result is an algorithm for arbitrary $p,r \ge 2$ with approximation ratio $(1+蔚_0)/(\sinh^{-1}(1)\cdot 纬_{p^*} \,纬_{r^*})$ for some fixed $蔚_0 \le 0.00863$. Comparing this with Krivine's approximation ratio of $(蟺/2)/\sinh^{-1}(1)$ for the original Grothendieck problem, our guarantee is off from the best known hardness factor of $(纬_{p^*} 纬_{r^*})^{-1}$ for the problem by a factor similar to Krivine's defect. Our approximation follows by bounding the value of the natural vector relaxation for the problem which is convex when $p,r \ge 2$. We give a generalization of random hyperplane rounding and relate the performance of this rounding to certain hypergeometric functions, which prescribe necessary transformations to the vector solution before the rounding is applied. Unlike Krivine's Rounding where the relevant hypergeometric function was $\arcsin$, we have to study a family of hypergeometric functions. The bulk of our technical work then involves methods from complex analysis to gain detailed information about the Taylor series coefficients of the inverses of these hypergeometric functions, which then dictate our approximation factor. Our result also implies improved bounds for "factorization through $\ell_{2}^{\,n}$" of operators from $\ell_{p}^{\,n}$ to $\ell_{q}^{\,m}$ (when $p\geq 2 \geq q$)--- such bounds are of significant interest in functional analysis and our work provides modest supplementary evidence for an intriguing parallel between factorizability, and constant-factor approximability. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1804.03644v2-abstract-full').style.display = 'none'; document.getElementById('1804.03644v2-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 November, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 April, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2018. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1804.02994">arXiv:1804.02994</a> <span> [<a href="https://arxiv.org/pdf/1804.02994">pdf</a>, <a href="https://arxiv.org/format/1804.02994">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> </div> </div> <p class="title is-5 mathjax"> Analysis of CSAT performance in Wi-Fi and LTE-U Coexistence </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sathya%2C+V">Vanlin Sathya</a>, <a href="/search/cs?searchtype=author&query=Mehrnoush%2C+M">Morteza Mehrnoush</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Monisha Ghosh</a>, <a href="/search/cs?searchtype=author&query=Roy%2C+S">Sumit Roy</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="1804.02994v1-abstract-short" style="display: inline;"> In this paper, we study energy-based Carrier Sense Adaptive Transmission (CSAT) for use with LTE-U and investigate the performance in Wi-Fi/LTE-U coexistence using theoretical analysis and experimental verification using NI USRPs. According to the LTE-U forum specification, if an LTE-U base station (BS) finds a vacant channel, it can transmit for up to 20 ms and turn OFF its transmission for only… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1804.02994v1-abstract-full').style.display = 'inline'; document.getElementById('1804.02994v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1804.02994v1-abstract-full" style="display: none;"> In this paper, we study energy-based Carrier Sense Adaptive Transmission (CSAT) for use with LTE-U and investigate the performance in Wi-Fi/LTE-U coexistence using theoretical analysis and experimental verification using NI USRPs. According to the LTE-U forum specification, if an LTE-U base station (BS) finds a vacant channel, it can transmit for up to 20 ms and turn OFF its transmission for only 1 ms, resulting in a maximum duty cycle of 95%. In a dense deployment of LTE-U and Wi-Fi, it is very likely that a Wi-Fi access point (AP) will wish to use the same channel. It will start transmission by trying to transmit association packets (using carrier sense multiple access with collision avoidance (CSMA/CA)) through the 1 ms LTE-U OFF duration. Since this duration is very small, it leads to increased association packet drops and thus delays the Wi-Fi association process. Once LTE-U, using CSAT, detects Wi-Fi, it should scale back the duty cycle to 50%. We demonstrate in this paper, using an experimental platform as well as theoretical analysis, that if LTE-U is using a 95% duty cycle, energy based CSAT will take a much longer time to scale back the duty cycle due to the beacon drops and delays in the reception. Hence, in order to maintain association fairness with Wi-Fi, we propose that a LTE-U BS should not transmit at maximum duty cycles (95%), even if the channel is sensed to be vacant. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1804.02994v1-abstract-full').style.display = 'none'; document.getElementById('1804.02994v1-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 April, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2018. </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">IEEE ICC 2018 Workshop on 5G Ultra Dense Network (5G-UDN)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1803.02444">arXiv:1803.02444</a> <span> [<a href="https://arxiv.org/pdf/1803.02444">pdf</a>, <a href="https://arxiv.org/format/1803.02444">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> </div> </div> <p class="title is-5 mathjax"> Analytical Modeling of Wi-Fi and LTE-LAA Coexistence: Throughput and Impact of Energy Detection Threshold </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Mehrnoush%2C+M">Morteza Mehrnoush</a>, <a href="/search/cs?searchtype=author&query=Sathya%2C+V">Vanlin Sathya</a>, <a href="/search/cs?searchtype=author&query=Roy%2C+S">Sumit Roy</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+M">Monisha Ghosh</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="1803.02444v1-abstract-short" style="display: inline;"> With both small-cell LTE and Wi-Fi networks available as alternatives for deployment in unlicensed bands (notably 5 GHz), the investigation into their coexistence is a topic of active interest, primarily driven by industry groups. 3GPP has recently standardized LTE Licensed Assisted Access (LTE-LAA) that seeks to make LTE more co-existence friendly with Wi-Fi by incorporating similar sensing and b… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1803.02444v1-abstract-full').style.display = 'inline'; document.getElementById('1803.02444v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1803.02444v1-abstract-full" style="display: none;"> With both small-cell LTE and Wi-Fi networks available as alternatives for deployment in unlicensed bands (notably 5 GHz), the investigation into their coexistence is a topic of active interest, primarily driven by industry groups. 3GPP has recently standardized LTE Licensed Assisted Access (LTE-LAA) that seeks to make LTE more co-existence friendly with Wi-Fi by incorporating similar sensing and back-off features. Nonetheless, the results presented by industry groups offer little consensus on important issues like respective network parameter settings that promote "fair access" as required by 3GPP. Answers to such key system deployment aspects, in turn, require credible analytical models, on which there has been little progress to date. Accordingly, in one of the first work of its kind, we develop a new framework for estimating the throughput of Wi-Fi and LTE-LAA in coexistence scenarios via suitable modifications to the celebrated Bianchi \cite{Bianchi} model. The impact of various network parameters such as energy detection (ED) threshold on Wi-Fi and LTE-LAA coexistence is explored as a byproduct and corroborated via a National Instrument (NI) experimental testbed that validates the results for LTE-LAA access priority class 1 and 3. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1803.02444v1-abstract-full').style.display = 'none'; document.getElementById('1803.02444v1-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 March, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2018. </p> </li> </ol> <nav class="pagination is-small is-centered breathe-horizontal" role="navigation" aria-label="pagination"> <a href="" class="pagination-previous is-invisible">Previous </a> <a href="/search/?searchtype=author&query=Ghosh%2C+M&start=50" 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