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tooltip is-tooltip-top" data-tooltip="Emerging Technologies">cs.ET</span> </div> </div> <p class="title is-5 mathjax"> Energy-Efficient Cryogenic Neuromorphic Network with Superconducting Memristor </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Islam%2C+M+M">Md Mazharul Islam</a>, <a href="/search/cs?searchtype=author&query=Steed%2C+J">Julia Steed</a>, <a href="/search/cs?searchtype=author&query=Patel%2C+K">Karan Patel</a>, <a href="/search/cs?searchtype=author&query=Schuman%2C+C">Catherine Schuman</a>, <a href="/search/cs?searchtype=author&query=Aziz%2C+A">Ahmedullah Aziz</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="2501.07683v1-abstract-short" style="display: inline;"> Cryogenic neuromorphic systems, inspired by the brains unparalleled efficiency, present a promising paradigm for next generation computing architectures.This work introduces a fully integrated neuromorphic framework that combines superconducting memristor(SM) based spiking neurons and synapse topologies to achieve a low power neuromorphic network with non volatile synaptic strength.This neurosynap… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.07683v1-abstract-full').style.display = 'inline'; document.getElementById('2501.07683v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.07683v1-abstract-full" style="display: none;"> Cryogenic neuromorphic systems, inspired by the brains unparalleled efficiency, present a promising paradigm for next generation computing architectures.This work introduces a fully integrated neuromorphic framework that combines superconducting memristor(SM) based spiking neurons and synapse topologies to achieve a low power neuromorphic network with non volatile synaptic strength.This neurosynaptic framework is validated by implementing the cart pole control task, a dynamic decision making problem requiring real time computation.Through detailed simulations, we demonstrate the network's ability to execute this task with an average fitness of 5965 timesteps across 1000 randomized test episodes, with 40 percent achieving the target fitness of 15,000 timesteps (0.02s per timestep).The system achieves 23 distinct spiking rates across neurons, ensuring efficient information encoding.Our findings establish the potential of SM based cryogenic neuromorphic systems to address the energy and scalability limitations of traditional computing, paving the way for biologically inspired, ultra low power computational frameworks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.07683v1-abstract-full').style.display = 'none'; document.getElementById('2501.07683v1-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">originally announced</span> January 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.16925">arXiv:2412.16925</a> <span> [<a href="https://arxiv.org/pdf/2412.16925">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Quantifying Public Response to COVID-19 Events: Introducing the Community Sentiment and Engagement Index </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Thakur%2C+N">Nirmalya Thakur</a>, <a href="/search/cs?searchtype=author&query=Patel%2C+K+A">Kesha A. Patel</a>, <a href="/search/cs?searchtype=author&query=Poon%2C+A">Audrey Poon</a>, <a href="/search/cs?searchtype=author&query=Cui%2C+S">Shuqi Cui</a>, <a href="/search/cs?searchtype=author&query=Azizi%2C+N">Nazif Azizi</a>, <a href="/search/cs?searchtype=author&query=Shah%2C+R">Rishika Shah</a>, <a href="/search/cs?searchtype=author&query=Shah%2C+R">Riyan Shah</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="2412.16925v1-abstract-short" style="display: inline;"> This study introduces the Community Sentiment and Engagement Index (CSEI), developed to capture nuanced public sentiment and engagement variations on social media, particularly in response to major events related to COVID-19. Constructed with diverse sentiment indicators, CSEI integrates features like engagement, daily post count, compound sentiment, fine-grain sentiments (fear, surprise, joy, sad… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.16925v1-abstract-full').style.display = 'inline'; document.getElementById('2412.16925v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.16925v1-abstract-full" style="display: none;"> This study introduces the Community Sentiment and Engagement Index (CSEI), developed to capture nuanced public sentiment and engagement variations on social media, particularly in response to major events related to COVID-19. Constructed with diverse sentiment indicators, CSEI integrates features like engagement, daily post count, compound sentiment, fine-grain sentiments (fear, surprise, joy, sadness, anger, disgust, and neutral), readability, offensiveness, and domain diversity. Each component is systematically weighted through a multi-step Principal Component Analysis (PCA)-based framework, prioritizing features according to their variance contributions across temporal sentiment shifts. This approach dynamically adjusts component importance, enabling CSEI to precisely capture high-sensitivity shifts in public sentiment. The development of CSEI showed statistically significant correlations with its constituent features, underscoring internal consistency and sensitivity to specific sentiment dimensions. CSEI's responsiveness was validated using a dataset of 4,510,178 Reddit posts about COVID-19. The analysis focused on 15 major events, including the WHO's declaration of COVID-19 as a pandemic, the first reported cases of COVID-19 across different countries, national lockdowns, vaccine developments, and crucial public health measures. Cumulative changes in CSEI revealed prominent peaks and valleys aligned with these events, indicating significant patterns in public sentiment across different phases of the pandemic. Pearson correlation analysis further confirmed a statistically significant relationship between CSEI daily fluctuations and these events (p = 0.0428), highlighting the capacity of CSEI to infer and interpret shifts in public sentiment and engagement in response to major events related to COVID-19. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.16925v1-abstract-full').style.display = 'none'; document.getElementById('2412.16925v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 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">ACM Class:</span> I.2.7; I.2.8; I.5.4; K.4.2; H.2.8; I.2.6 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.08984">arXiv:2412.08984</a> <span> [<a href="https://arxiv.org/pdf/2412.08984">pdf</a>, <a href="https://arxiv.org/format/2412.08984">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</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"> Predicting Emergency Department Visits for Patients with Type II Diabetes </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Alizadeh%2C+J+M">Javad M Alizadeh</a>, <a href="/search/cs?searchtype=author&query=Patel%2C+J+S">Jay S Patel</a>, <a href="/search/cs?searchtype=author&query=Tajeu%2C+G">Gabriel Tajeu</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+Y">Yuzhou Chen</a>, <a href="/search/cs?searchtype=author&query=Hollin%2C+I+L">Ilene L Hollin</a>, <a href="/search/cs?searchtype=author&query=Patel%2C+M+K">Mukesh K Patel</a>, <a href="/search/cs?searchtype=author&query=Fei%2C+J">Junchao Fei</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+H">Huanmei Wu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2412.08984v1-abstract-short" style="display: inline;"> Over 30 million Americans are affected by Type II diabetes (T2D), a treatable condition with significant health risks. This study aims to develop and validate predictive models using machine learning (ML) techniques to estimate emergency department (ED) visits among patients with T2D. Data for these patients was obtained from the HealthShare Exchange (HSX), focusing on demographic details, diagnos… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.08984v1-abstract-full').style.display = 'inline'; document.getElementById('2412.08984v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.08984v1-abstract-full" style="display: none;"> Over 30 million Americans are affected by Type II diabetes (T2D), a treatable condition with significant health risks. This study aims to develop and validate predictive models using machine learning (ML) techniques to estimate emergency department (ED) visits among patients with T2D. Data for these patients was obtained from the HealthShare Exchange (HSX), focusing on demographic details, diagnoses, and vital signs. Our sample contained 34,151 patients diagnosed with T2D which resulted in 703,065 visits overall between 2017 and 2021. A workflow integrated EMR data with SDoH for ML predictions. A total of 87 out of 2,555 features were selected for model construction. Various machine learning algorithms, including CatBoost, Ensemble Learning, K-nearest Neighbors (KNN), Support Vector Classification (SVC), Random Forest, and Extreme Gradient Boosting (XGBoost), were employed with tenfold cross-validation to predict whether a patient is at risk of an ED visit. The ROC curves for Random Forest, XGBoost, Ensemble Learning, CatBoost, KNN, and SVC, were 0.82, 0.82, 0.82, 0.81, 0.72, 0.68, respectively. Ensemble Learning and Random Forest models demonstrated superior predictive performance in terms of discrimination, calibration, and clinical applicability. These models are reliable tools for predicting risk of ED visits among patients with T2D. They can estimate future ED demand and assist clinicians in identifying critical factors associated with ED utilization, enabling early interventions to reduce such visits. The top five important features were age, the difference between visitation gaps, visitation gaps, R10 or abdominal and pelvic pain, and the Index of Concentration at the Extremes (ICE) for income. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.08984v1-abstract-full').style.display = 'none'; document.getElementById('2412.08984v1-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 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">This manuscript has been accepted and presented at AI-PHSS 2024: The 2024 International Workshop on AI Applications in Public Health and Social Services in conjunction with the 22nd International Conference of Artificial Intelligence in Medicine (AIME 2024)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.06936">arXiv:2412.06936</a> <span> [<a href="https://arxiv.org/pdf/2412.06936">pdf</a>, <a href="https://arxiv.org/format/2412.06936">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> <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"> Creating a Cooperative AI Policymaking Platform through Open Source Collaboration </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Lewington%2C+A">Aiden Lewington</a>, <a href="/search/cs?searchtype=author&query=Vittalam%2C+A">Alekhya Vittalam</a>, <a href="/search/cs?searchtype=author&query=Singh%2C+A">Anshumaan Singh</a>, <a href="/search/cs?searchtype=author&query=Uppuluri%2C+A">Anuja Uppuluri</a>, <a href="/search/cs?searchtype=author&query=Ashok%2C+A">Arjun Ashok</a>, <a href="/search/cs?searchtype=author&query=Athmaram%2C+A+M">Ashrith Mandayam Athmaram</a>, <a href="/search/cs?searchtype=author&query=Milt%2C+A">Austin Milt</a>, <a href="/search/cs?searchtype=author&query=Smith%2C+B">Benjamin Smith</a>, <a href="/search/cs?searchtype=author&query=Weinberger%2C+C">Charlie Weinberger</a>, <a href="/search/cs?searchtype=author&query=Sarin%2C+C">Chatanya Sarin</a>, <a href="/search/cs?searchtype=author&query=Bergmeir%2C+C">Christoph Bergmeir</a>, <a href="/search/cs?searchtype=author&query=Chang%2C+C">Cliff Chang</a>, <a href="/search/cs?searchtype=author&query=Patel%2C+D">Daivik Patel</a>, <a href="/search/cs?searchtype=author&query=Li%2C+D">Daniel Li</a>, <a href="/search/cs?searchtype=author&query=Bell%2C+D">David Bell</a>, <a href="/search/cs?searchtype=author&query=Cao%2C+D">Defu Cao</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+D">Donghwa Shin</a>, <a href="/search/cs?searchtype=author&query=Kang%2C+E">Edward Kang</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+E">Edwin Zhang</a>, <a href="/search/cs?searchtype=author&query=Li%2C+E">Enhui Li</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+F">Felix Chen</a>, <a href="/search/cs?searchtype=author&query=Smithline%2C+G">Gabe Smithline</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+H">Haipeng Chen</a>, <a href="/search/cs?searchtype=author&query=Gasztowtt%2C+H">Henry Gasztowtt</a>, <a href="/search/cs?searchtype=author&query=Shin%2C+H">Hoon Shin</a> , et al. (26 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2412.06936v1-abstract-short" style="display: inline;"> Advances in artificial intelligence (AI) present significant risks and opportunities, requiring improved governance to mitigate societal harms and promote equitable benefits. Current incentive structures and regulatory delays may hinder responsible AI development and deployment, particularly in light of the transformative potential of large language models (LLMs). To address these challenges, we p… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.06936v1-abstract-full').style.display = 'inline'; document.getElementById('2412.06936v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.06936v1-abstract-full" style="display: none;"> Advances in artificial intelligence (AI) present significant risks and opportunities, requiring improved governance to mitigate societal harms and promote equitable benefits. Current incentive structures and regulatory delays may hinder responsible AI development and deployment, particularly in light of the transformative potential of large language models (LLMs). To address these challenges, we propose developing the following three contributions: (1) a large multimodal text and economic-timeseries foundation model that integrates economic and natural language policy data for enhanced forecasting and decision-making, (2) algorithmic mechanisms for eliciting diverse and representative perspectives, enabling the creation of data-driven public policy recommendations, and (3) an AI-driven web platform for supporting transparent, inclusive, and data-driven policymaking. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.06936v1-abstract-full').style.display = 'none'; document.getElementById('2412.06936v1-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 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.06927">arXiv:2412.06927</a> <span> [<a href="https://arxiv.org/pdf/2412.06927">pdf</a>, <a href="https://arxiv.org/ps/2412.06927">ps</a>, <a href="https://arxiv.org/format/2412.06927">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="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Gradient-based facial encoding for key generation to encrypt and decrypt multimedia data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Patel%2C+A+K">Ankit Kumar Patel</a>, <a href="/search/cs?searchtype=author&query=Paul%2C+D">Dewanshi Paul</a>, <a href="/search/cs?searchtype=author&query=Giri%2C+S">Sarthak Giri</a>, <a href="/search/cs?searchtype=author&query=Chaudhary%2C+S">Sneha Chaudhary</a>, <a href="/search/cs?searchtype=author&query=Gautam%2C+B">Bikalpa Gautam</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="2412.06927v2-abstract-short" style="display: inline;"> Security systems relying on passwords are vulnerable to being forgotten, guessed, or breached. Likewise, biometric systems that operate independently are at risk of template spoofing and replay incidents. This paper introduces a biocryptosystem utilizing face recognition techniques to address these issues, allowing for the encryption and decryption of various file types through the Advanced Encryp… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.06927v2-abstract-full').style.display = 'inline'; document.getElementById('2412.06927v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.06927v2-abstract-full" style="display: none;"> Security systems relying on passwords are vulnerable to being forgotten, guessed, or breached. Likewise, biometric systems that operate independently are at risk of template spoofing and replay incidents. This paper introduces a biocryptosystem utilizing face recognition techniques to address these issues, allowing for the encryption and decryption of various file types through the Advanced Encryption Standard (AES). The proposed system creates a distinct 32-bit encryption key derived from facial features identified by Histogram of Oriented Gradients (HOG) and categorized using Support Vector Machines (SVM). HOG efficiently identifies edge-aligned facial features, even in dim lighting, ensuring that reliable biometric keys can be generated. This key is then used with AES to encrypt and decrypt a variety of data formats, such as text, audio, and video files. This encryption key, derived from an individual's distinctive facial traits, is exceedingly challenging for adversaries to reproduce or guess. The security and performance of the system have been validated through experiments using several metrics, including correlation analysis, Shannon entropy, normalized Hamming distance, and the avalanche effect on 25 different file types. Potential uses for the proposed system include secure file sharing, online transactions, and data archiving, making it a strong and trustworthy approach to safeguarding sensitive information by integrating the uniqueness of facial biometrics with the established security of AES encryption. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.06927v2-abstract-full').style.display = 'none'; document.getElementById('2412.06927v2-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 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 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">12 pages, 2 figures, This work has been submitted to the IEEE for possible publication</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.03537">arXiv:2412.03537</a> <span> [<a href="https://arxiv.org/pdf/2412.03537">pdf</a>, <a href="https://arxiv.org/format/2412.03537">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Evaluating Gender Bias Transfer between Pre-trained and Prompt-Adapted Language Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Mackraz%2C+N">Natalie Mackraz</a>, <a href="/search/cs?searchtype=author&query=Sivakumar%2C+N">Nivedha Sivakumar</a>, <a href="/search/cs?searchtype=author&query=Khorshidi%2C+S">Samira Khorshidi</a>, <a href="/search/cs?searchtype=author&query=Patel%2C+K">Krishna Patel</a>, <a href="/search/cs?searchtype=author&query=Theobald%2C+B">Barry-John Theobald</a>, <a href="/search/cs?searchtype=author&query=Zappella%2C+L">Luca Zappella</a>, <a href="/search/cs?searchtype=author&query=Apostoloff%2C+N">Nicholas Apostoloff</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="2412.03537v1-abstract-short" style="display: inline;"> Large language models (LLMs) are increasingly being adapted to achieve task-specificity for deployment in real-world decision systems. Several previous works have investigated the bias transfer hypothesis (BTH) by studying the effect of the fine-tuning adaptation strategy on model fairness to find that fairness in pre-trained masked language models have limited effect on the fairness of models whe… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.03537v1-abstract-full').style.display = 'inline'; document.getElementById('2412.03537v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.03537v1-abstract-full" style="display: none;"> Large language models (LLMs) are increasingly being adapted to achieve task-specificity for deployment in real-world decision systems. Several previous works have investigated the bias transfer hypothesis (BTH) by studying the effect of the fine-tuning adaptation strategy on model fairness to find that fairness in pre-trained masked language models have limited effect on the fairness of models when adapted using fine-tuning. In this work, we expand the study of BTH to causal models under prompt adaptations, as prompting is an accessible, and compute-efficient way to deploy models in real-world systems. In contrast to previous works, we establish that intrinsic biases in pre-trained Mistral, Falcon and Llama models are strongly correlated (rho >= 0.94) with biases when the same models are zero- and few-shot prompted, using a pronoun co-reference resolution task. Further, we find that bias transfer remains strongly correlated even when LLMs are specifically prompted to exhibit fair or biased behavior (rho >= 0.92), and few-shot length and stereotypical composition are varied (rho >= 0.97). Our findings highlight the importance of ensuring fairness in pre-trained LLMs, especially when they are later used to perform downstream tasks via prompt adaptation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.03537v1-abstract-full').style.display = 'none'; document.getElementById('2412.03537v1-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 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.17713">arXiv:2411.17713</a> <span> [<a href="https://arxiv.org/pdf/2411.17713">pdf</a>, <a href="https://arxiv.org/format/2411.17713">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Llama Guard 3-1B-INT4: Compact and Efficient Safeguard for Human-AI Conversations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Fedorov%2C+I">Igor Fedorov</a>, <a href="/search/cs?searchtype=author&query=Plawiak%2C+K">Kate Plawiak</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+L">Lemeng Wu</a>, <a href="/search/cs?searchtype=author&query=Elgamal%2C+T">Tarek Elgamal</a>, <a href="/search/cs?searchtype=author&query=Suda%2C+N">Naveen Suda</a>, <a href="/search/cs?searchtype=author&query=Smith%2C+E">Eric Smith</a>, <a href="/search/cs?searchtype=author&query=Zhan%2C+H">Hongyuan Zhan</a>, <a href="/search/cs?searchtype=author&query=Chi%2C+J">Jianfeng Chi</a>, <a href="/search/cs?searchtype=author&query=Hulovatyy%2C+Y">Yuriy Hulovatyy</a>, <a href="/search/cs?searchtype=author&query=Patel%2C+K">Kimish Patel</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+Z">Zechun Liu</a>, <a href="/search/cs?searchtype=author&query=Zhao%2C+C">Changsheng Zhao</a>, <a href="/search/cs?searchtype=author&query=Shi%2C+Y">Yangyang Shi</a>, <a href="/search/cs?searchtype=author&query=Blankevoort%2C+T">Tijmen Blankevoort</a>, <a href="/search/cs?searchtype=author&query=Pasupuleti%2C+M">Mahesh Pasupuleti</a>, <a href="/search/cs?searchtype=author&query=Soran%2C+B">Bilge Soran</a>, <a href="/search/cs?searchtype=author&query=Coudert%2C+Z+D">Zacharie Delpierre Coudert</a>, <a href="/search/cs?searchtype=author&query=Alao%2C+R">Rachad Alao</a>, <a href="/search/cs?searchtype=author&query=Krishnamoorthi%2C+R">Raghuraman Krishnamoorthi</a>, <a href="/search/cs?searchtype=author&query=Chandra%2C+V">Vikas Chandra</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="2411.17713v1-abstract-short" style="display: inline;"> This paper presents Llama Guard 3-1B-INT4, a compact and efficient Llama Guard model, which has been open-sourced to the community during Meta Connect 2024. We demonstrate that Llama Guard 3-1B-INT4 can be deployed on resource-constrained devices, achieving a throughput of at least 30 tokens per second and a time-to-first-token of 2.5 seconds or less on a commodity Android mobile CPU. Notably, our… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.17713v1-abstract-full').style.display = 'inline'; document.getElementById('2411.17713v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.17713v1-abstract-full" style="display: none;"> This paper presents Llama Guard 3-1B-INT4, a compact and efficient Llama Guard model, which has been open-sourced to the community during Meta Connect 2024. We demonstrate that Llama Guard 3-1B-INT4 can be deployed on resource-constrained devices, achieving a throughput of at least 30 tokens per second and a time-to-first-token of 2.5 seconds or less on a commodity Android mobile CPU. Notably, our experiments show that Llama Guard 3-1B-INT4 attains comparable or superior safety moderation scores to its larger counterpart, Llama Guard 3-1B, despite being approximately 7 times smaller in size (440MB). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.17713v1-abstract-full').style.display = 'none'; document.getElementById('2411.17713v1-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 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.14433">arXiv:2411.14433</a> <span> [<a href="https://arxiv.org/pdf/2411.14433">pdf</a>, <a href="https://arxiv.org/format/2411.14433">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> <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="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> </div> </div> <p class="title is-5 mathjax"> Transforming Engineering Education Using Generative AI and Digital Twin Technologies </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Lin%2C+Y">Yu-Zheng Lin</a>, <a href="/search/cs?searchtype=author&query=Alhamadah%2C+A+H+J">Ahmed Hussain J Alhamadah</a>, <a href="/search/cs?searchtype=author&query=Redondo%2C+M+W">Matthew William Redondo</a>, <a href="/search/cs?searchtype=author&query=Patel%2C+K+H">Karan Himanshu Patel</a>, <a href="/search/cs?searchtype=author&query=Ghimire%2C+S">Sujan Ghimire</a>, <a href="/search/cs?searchtype=author&query=Latibari%2C+B+S">Banafsheh Saber Latibari</a>, <a href="/search/cs?searchtype=author&query=Salehi%2C+S">Soheil Salehi</a>, <a href="/search/cs?searchtype=author&query=Satam%2C+P">Pratik Satam</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="2411.14433v1-abstract-short" style="display: inline;"> Digital twin technology, traditionally used in industry, is increasingly recognized for its potential to enhance educational experiences. This study investigates the application of industrial digital twins (DTs) in education, focusing on how DT models of varying fidelity can support different stages of Bloom's taxonomy in the cognitive domain. We align Bloom's six cognitive stages with educational… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.14433v1-abstract-full').style.display = 'inline'; document.getElementById('2411.14433v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.14433v1-abstract-full" style="display: none;"> Digital twin technology, traditionally used in industry, is increasingly recognized for its potential to enhance educational experiences. This study investigates the application of industrial digital twins (DTs) in education, focusing on how DT models of varying fidelity can support different stages of Bloom's taxonomy in the cognitive domain. We align Bloom's six cognitive stages with educational levels: undergraduate studies for "Remember" and "Understand," master's level for "Apply" and "Analyze," and doctoral level for "Evaluate" and "Create." Low-fidelity DTs aid essential knowledge acquisition and skill training, providing a low-risk environment for grasping fundamental concepts. Medium-fidelity DTs offer more detailed and dynamic simulations, enhancing application skills and problem-solving. High-fidelity DTs support advanced learners by replicating physical phenomena, allowing for innovative design and complex experiments. Within this framework, large language models (LLMs) serve as mentors, assessing progress, filling knowledge gaps, and assisting with DT interactions, parameter setting, and debugging. We evaluate the educational impact using the Kirkpatrick Model, examining how each DT model's fidelity influences learning outcomes. This framework helps educators make informed decisions on integrating DTs and LLMs to meet specific learning objectives. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.14433v1-abstract-full').style.display = 'none'; document.getElementById('2411.14433v1-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 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">8 pages, 7 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.11858">arXiv:2411.11858</a> <span> [<a href="https://arxiv.org/pdf/2411.11858">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Engineering, Finance, and Science">cs.CE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Numerical Analysis">math.NA</span> </div> </div> <p class="title is-5 mathjax"> Assessing AI-Enhanced Single-Sweep Approximations for Problems with Forward-Peaked Scattering in Slab Geometry </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Patel%2C+J+K">Japan K. Patel</a>, <a href="/search/cs?searchtype=author&query=Schmidt%2C+M+C">Matthew C. Schmidt</a>, <a href="/search/cs?searchtype=author&query=Magliari%2C+A">Anthony Magliari</a>, <a href="/search/cs?searchtype=author&query=Wareing%2C+T+A">Todd A. Wareing</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="2411.11858v1-abstract-short" style="display: inline;"> While the Boltzmann transport equation can accurately model transport problems with highly forward-peaked scattering, obtaining its solution can become arbitrarily slow due to near-unity spectral radius associated with source iteration. Standard acceleration techniques like diffusion synthetic acceleration and nonlinear diffusion acceleration obtain merely one order of magnitude speedups compared… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11858v1-abstract-full').style.display = 'inline'; document.getElementById('2411.11858v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.11858v1-abstract-full" style="display: none;"> While the Boltzmann transport equation can accurately model transport problems with highly forward-peaked scattering, obtaining its solution can become arbitrarily slow due to near-unity spectral radius associated with source iteration. Standard acceleration techniques like diffusion synthetic acceleration and nonlinear diffusion acceleration obtain merely one order of magnitude speedups compared to source iteration due to slowly decaying error moments. Additionally, converging approximations to the Boltzmann equation like Fokker-Planck and Boltzmann Fokker Planck run into similar problems with slow convergence. In this paper we assess the feasibility of using Fourier neural operators to obtain AI-enhanced low order, and single-sweep solutions for the transport equation in slab geometry using a predictor-corrector framework. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11858v1-abstract-full').style.display = 'none'; document.getElementById('2411.11858v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 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">Paper draft has been submitted to M&C 2025</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.01008">arXiv:2411.01008</a> <span> [<a href="https://arxiv.org/pdf/2411.01008">pdf</a>, <a href="https://arxiv.org/format/2411.01008">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="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Neural and Evolutionary Computing">cs.NE</span> </div> </div> <p class="title is-5 mathjax"> AI-Guided Codesign Framework for Novel Material and Device Design applied to MTJ-based True Random Number Generators </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Patel%2C+K+P">Karan P. Patel</a>, <a href="/search/cs?searchtype=author&query=Maicke%2C+A">Andrew Maicke</a>, <a href="/search/cs?searchtype=author&query=Arzate%2C+J">Jared Arzate</a>, <a href="/search/cs?searchtype=author&query=Kwon%2C+J">Jaesuk Kwon</a>, <a href="/search/cs?searchtype=author&query=Smith%2C+J+D">J. Darby Smith</a>, <a href="/search/cs?searchtype=author&query=Aimone%2C+J+B">James B. Aimone</a>, <a href="/search/cs?searchtype=author&query=Incorvia%2C+J+A+C">Jean Anne C. Incorvia</a>, <a href="/search/cs?searchtype=author&query=Cardwell%2C+S+G">Suma G. Cardwell</a>, <a href="/search/cs?searchtype=author&query=Schuman%2C+C+D">Catherine D. Schuman</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="2411.01008v1-abstract-short" style="display: inline;"> Novel devices and novel computing paradigms are key for energy efficient, performant future computing systems. However, designing devices for new applications is often time consuming and tedious. Here, we investigate the design and optimization of spin orbit torque and spin transfer torque magnetic tunnel junction models as the probabilistic devices for true random number generation. We leverage r… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.01008v1-abstract-full').style.display = 'inline'; document.getElementById('2411.01008v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.01008v1-abstract-full" style="display: none;"> Novel devices and novel computing paradigms are key for energy efficient, performant future computing systems. However, designing devices for new applications is often time consuming and tedious. Here, we investigate the design and optimization of spin orbit torque and spin transfer torque magnetic tunnel junction models as the probabilistic devices for true random number generation. We leverage reinforcement learning and evolutionary optimization to vary key device and material properties of the various device models for stochastic operation. Our AI guided codesign methods generated different candidate devices capable of generating stochastic samples for a desired probability distribution, while also minimizing energy usage for the devices. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.01008v1-abstract-full').style.display = 'none'; document.getElementById('2411.01008v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.13893">arXiv:2410.13893</a> <span> [<a href="https://arxiv.org/pdf/2410.13893">pdf</a>, <a href="https://arxiv.org/format/2410.13893">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"> Can LLMs be Scammed? A Baseline Measurement Study </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sehwag%2C+U+M">Udari Madhushani Sehwag</a>, <a href="/search/cs?searchtype=author&query=Patel%2C+K">Kelly Patel</a>, <a href="/search/cs?searchtype=author&query=Mosca%2C+F">Francesca Mosca</a>, <a href="/search/cs?searchtype=author&query=Ravi%2C+V">Vineeth Ravi</a>, <a href="/search/cs?searchtype=author&query=Staddon%2C+J">Jessica Staddon</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="2410.13893v1-abstract-short" style="display: inline;"> Despite the importance of developing generative AI models that can effectively resist scams, current literature lacks a structured framework for evaluating their vulnerability to such threats. In this work, we address this gap by constructing a benchmark based on the FINRA taxonomy and systematically assessing Large Language Models' (LLMs') vulnerability to a variety of scam tactics. First, we inc… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.13893v1-abstract-full').style.display = 'inline'; document.getElementById('2410.13893v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.13893v1-abstract-full" style="display: none;"> Despite the importance of developing generative AI models that can effectively resist scams, current literature lacks a structured framework for evaluating their vulnerability to such threats. In this work, we address this gap by constructing a benchmark based on the FINRA taxonomy and systematically assessing Large Language Models' (LLMs') vulnerability to a variety of scam tactics. First, we incorporate 37 well-defined base scam scenarios reflecting the diverse scam categories identified by FINRA taxonomy, providing a focused evaluation of LLMs' scam detection capabilities. Second, we utilize representative proprietary (GPT-3.5, GPT-4) and open-source (Llama) models to analyze their performance in scam detection. Third, our research provides critical insights into which scam tactics are most effective against LLMs and how varying persona traits and persuasive techniques influence these vulnerabilities. We reveal distinct susceptibility patterns across different models and scenarios, underscoring the need for targeted enhancements in LLM design and deployment. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.13893v1-abstract-full').style.display = 'none'; document.getElementById('2410.13893v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.09140">arXiv:2409.09140</a> <span> [<a href="https://arxiv.org/pdf/2409.09140">pdf</a>, <a href="https://arxiv.org/format/2409.09140">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> ResPilot: Teleoperated Finger Gaiting via Gaussian Process Residual Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Naughton%2C+P">Patrick Naughton</a>, <a href="/search/cs?searchtype=author&query=Cui%2C+J">Jinda Cui</a>, <a href="/search/cs?searchtype=author&query=Patel%2C+K">Karankumar Patel</a>, <a href="/search/cs?searchtype=author&query=Iba%2C+S">Soshi Iba</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="2409.09140v1-abstract-short" style="display: inline;"> Dexterous robot hand teleoperation allows for long-range transfer of human manipulation expertise, and could simultaneously provide a way for humans to teach these skills to robots. However, current methods struggle to reproduce the functional workspace of the human hand, often limiting them to simple grasping tasks. We present a novel method for finger-gaited manipulation with multi-fingered robo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.09140v1-abstract-full').style.display = 'inline'; document.getElementById('2409.09140v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.09140v1-abstract-full" style="display: none;"> Dexterous robot hand teleoperation allows for long-range transfer of human manipulation expertise, and could simultaneously provide a way for humans to teach these skills to robots. However, current methods struggle to reproduce the functional workspace of the human hand, often limiting them to simple grasping tasks. We present a novel method for finger-gaited manipulation with multi-fingered robot hands. Our method provides the operator enhanced flexibility in making contacts by expanding the reachable workspace of the robot hand through residual Gaussian Process learning. We also assist the operator in maintaining stable contacts with the object by allowing them to constrain fingertips of the hand to move in concert. Extensive quantitative evaluations show that our method significantly increases the reachable workspace of the robot hand and enables the completion of novel dexterous finger gaiting tasks. Project website: http://respilot-hri.github.io <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.09140v1-abstract-full').style.display = 'none'; document.getElementById('2409.09140v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <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 to CoRL 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.07709">arXiv:2409.07709</a> <span> [<a href="https://arxiv.org/pdf/2409.07709">pdf</a>, <a href="https://arxiv.org/format/2409.07709">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="Information Retrieval">cs.IR</span> </div> </div> <p class="title is-5 mathjax"> Harnessing TI Feeds for Exploitation Detection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Patel%2C+K">Kajal Patel</a>, <a href="/search/cs?searchtype=author&query=Shafiq%2C+Z">Zubair Shafiq</a>, <a href="/search/cs?searchtype=author&query=Nogueira%2C+M">Mateus Nogueira</a>, <a href="/search/cs?searchtype=author&query=Menasch%C3%A9%2C+D+S">Daniel Sadoc Menasch茅</a>, <a href="/search/cs?searchtype=author&query=Lovat%2C+E">Enrico Lovat</a>, <a href="/search/cs?searchtype=author&query=Kashif%2C+T">Taimur Kashif</a>, <a href="/search/cs?searchtype=author&query=Woiwood%2C+A">Ashton Woiwood</a>, <a href="/search/cs?searchtype=author&query=Martins%2C+M">Matheus Martins</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="2409.07709v1-abstract-short" style="display: inline;"> Many organizations rely on Threat Intelligence (TI) feeds to assess the risk associated with security threats. Due to the volume and heterogeneity of data, it is prohibitive to manually analyze the threat information available in different loosely structured TI feeds. Thus, there is a need to develop automated methods to vet and extract actionable information from TI feeds. To this end, we present… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.07709v1-abstract-full').style.display = 'inline'; document.getElementById('2409.07709v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.07709v1-abstract-full" style="display: none;"> Many organizations rely on Threat Intelligence (TI) feeds to assess the risk associated with security threats. Due to the volume and heterogeneity of data, it is prohibitive to manually analyze the threat information available in different loosely structured TI feeds. Thus, there is a need to develop automated methods to vet and extract actionable information from TI feeds. To this end, we present a machine learning pipeline to automatically detect vulnerability exploitation from TI feeds. We first model threat vocabulary in loosely structured TI feeds using state-of-the-art embedding techniques (Doc2Vec and BERT) and then use it to train a supervised machine learning classifier to detect exploitation of security vulnerabilities. We use our approach to identify exploitation events in 191 different TI feeds. Our longitudinal evaluation shows that it is able to accurately identify exploitation events from TI feeds only using past data for training and even on TI feeds withheld from training. Our proposed approach is useful for a variety of downstream tasks such as data-driven vulnerability risk assessment. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.07709v1-abstract-full').style.display = 'none'; document.getElementById('2409.07709v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">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">This paper appears at IEEE International Conference on Cyber Security and Resilience (IEEE CSR 2024)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.11804">arXiv:2408.11804</a> <span> [<a href="https://arxiv.org/pdf/2408.11804">pdf</a>, <a href="https://arxiv.org/format/2408.11804">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"> Approaching Deep Learning through the Spectral Dynamics of Weights </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Yunis%2C+D">David Yunis</a>, <a href="/search/cs?searchtype=author&query=Patel%2C+K+K">Kumar Kshitij Patel</a>, <a href="/search/cs?searchtype=author&query=Wheeler%2C+S">Samuel Wheeler</a>, <a href="/search/cs?searchtype=author&query=Savarese%2C+P">Pedro Savarese</a>, <a href="/search/cs?searchtype=author&query=Vardi%2C+G">Gal Vardi</a>, <a href="/search/cs?searchtype=author&query=Livescu%2C+K">Karen Livescu</a>, <a href="/search/cs?searchtype=author&query=Maire%2C+M">Michael Maire</a>, <a href="/search/cs?searchtype=author&query=Walter%2C+M+R">Matthew R. Walter</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.11804v1-abstract-short" style="display: inline;"> We propose an empirical approach centered on the spectral dynamics of weights -- the behavior of singular values and vectors during optimization -- to unify and clarify several phenomena in deep learning. We identify a consistent bias in optimization across various experiments, from small-scale ``grokking'' to large-scale tasks like image classification with ConvNets, image generation with UNets,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.11804v1-abstract-full').style.display = 'inline'; document.getElementById('2408.11804v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.11804v1-abstract-full" style="display: none;"> We propose an empirical approach centered on the spectral dynamics of weights -- the behavior of singular values and vectors during optimization -- to unify and clarify several phenomena in deep learning. We identify a consistent bias in optimization across various experiments, from small-scale ``grokking'' to large-scale tasks like image classification with ConvNets, image generation with UNets, speech recognition with LSTMs, and language modeling with Transformers. We also demonstrate that weight decay enhances this bias beyond its role as a norm regularizer, even in practical systems. Moreover, we show that these spectral dynamics distinguish memorizing networks from generalizing ones, offering a novel perspective on this longstanding conundrum. Additionally, we leverage spectral dynamics to explore the emergence of well-performing sparse subnetworks (lottery tickets) and the structure of the loss surface through linear mode connectivity. Our findings suggest that spectral dynamics provide a coherent framework to better understand the behavior of neural networks across diverse settings. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.11804v1-abstract-full').style.display = 'none'; document.getElementById('2408.11804v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.08499">arXiv:2408.08499</a> <span> [<a href="https://arxiv.org/pdf/2408.08499">pdf</a>, <a href="https://arxiv.org/ps/2408.08499">ps</a>, <a href="https://arxiv.org/format/2408.08499">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="Computer Science and Game Theory">cs.GT</span> </div> </div> <p class="title is-5 mathjax"> The Limitations of Model Retraining in the Face of Performativity </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kabra%2C+A">Anmol Kabra</a>, <a href="/search/cs?searchtype=author&query=Patel%2C+K+K">Kumar Kshitij Patel</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.08499v1-abstract-short" style="display: inline;"> We study stochastic optimization in the context of performative shifts, where the data distribution changes in response to the deployed model. We demonstrate that naive retraining can be provably suboptimal even for simple distribution shifts. The issue worsens when models are retrained given a finite number of samples at each retraining step. We show that adding regularization to retraining corre… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.08499v1-abstract-full').style.display = 'inline'; document.getElementById('2408.08499v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.08499v1-abstract-full" style="display: none;"> We study stochastic optimization in the context of performative shifts, where the data distribution changes in response to the deployed model. We demonstrate that naive retraining can be provably suboptimal even for simple distribution shifts. The issue worsens when models are retrained given a finite number of samples at each retraining step. We show that adding regularization to retraining corrects both of these issues, attaining provably optimal models in the face of distribution shifts. Our work advocates rethinking how machine learning models are retrained in the presence of performative effects. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.08499v1-abstract-full').style.display = 'none'; document.getElementById('2408.08499v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted to 2024 ICML Workshop on Humans, Algorithmic Decision-Making and Society</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.19970">arXiv:2407.19970</a> <span> [<a href="https://arxiv.org/pdf/2407.19970">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Graphics">cs.GR</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="Emerging Technologies">cs.ET</span> </div> </div> <p class="title is-5 mathjax"> From Flat to Spatial: Comparison of 4 methods constructing 3D, 2 and 1/2D Models from 2D Plans with neural networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sam%2C+J">Jacob Sam</a>, <a href="/search/cs?searchtype=author&query=Patel%2C+K">Karan Patel</a>, <a href="/search/cs?searchtype=author&query=Saad%2C+M">Mike Saad</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.19970v1-abstract-short" style="display: inline;"> In the field of architecture, the conversion of single images into 2 and 1/2D and 3D meshes is a promising technology that enhances design visualization and efficiency. This paper evaluates four innovative methods: "One-2-3-45," "CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction Model," "Instant Mesh," and "Image-to-Mesh." These methods are at the forefront of this technology… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.19970v1-abstract-full').style.display = 'inline'; document.getElementById('2407.19970v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.19970v1-abstract-full" style="display: none;"> In the field of architecture, the conversion of single images into 2 and 1/2D and 3D meshes is a promising technology that enhances design visualization and efficiency. This paper evaluates four innovative methods: "One-2-3-45," "CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction Model," "Instant Mesh," and "Image-to-Mesh." These methods are at the forefront of this technology, focusing on their applicability in architectural design and visualization. They streamline the creation of 3D architectural models, enabling rapid prototyping and detailed visualization from minimal initial inputs, such as photographs or simple sketches.One-2-3-45 leverages a diffusion-based approach to generate multi-view reconstructions, ensuring high geometric fidelity and texture quality. CRM utilizes a convolutional network to integrate geometric priors into its architecture, producing detailed and textured meshes quickly and efficiently. Instant Mesh combines the strengths of multi-view diffusion and sparse-view models to offer speed and scalability, suitable for diverse architectural projects. Image-to-Mesh leverages a generative adversarial network (GAN) to produce 3D meshes from single images, focusing on maintaining high texture fidelity and geometric accuracy by incorporating image and depth map data into its training process. It uses a hybrid approach that combines voxel-based representations with surface reconstruction techniques to ensure detailed and realistic 3D models.This comparative study highlights each method's contribution to reducing design cycle times, improving accuracy, and enabling flexible adaptations to various architectural styles and requirements. By providing architects with powerful tools for rapid visualization and iteration, these advancements in 3D mesh generation are set to revolutionize architectural practices. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.19970v1-abstract-full').style.display = 'none'; document.getElementById('2407.19970v1-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 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.18207">arXiv:2407.18207</a> <span> [<a href="https://arxiv.org/pdf/2407.18207">pdf</a>, <a href="https://arxiv.org/format/2407.18207">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Geometry Fidelity for Spherical Images </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Christensen%2C+A">Anders Christensen</a>, <a href="/search/cs?searchtype=author&query=Mojab%2C+N">Nooshin Mojab</a>, <a href="/search/cs?searchtype=author&query=Patel%2C+K">Khushman Patel</a>, <a href="/search/cs?searchtype=author&query=Ahuja%2C+K">Karan Ahuja</a>, <a href="/search/cs?searchtype=author&query=Akata%2C+Z">Zeynep Akata</a>, <a href="/search/cs?searchtype=author&query=Winther%2C+O">Ole Winther</a>, <a href="/search/cs?searchtype=author&query=Gonzalez-Franco%2C+M">Mar Gonzalez-Franco</a>, <a href="/search/cs?searchtype=author&query=Colaco%2C+A">Andrea Colaco</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.18207v1-abstract-short" style="display: inline;"> Spherical or omni-directional images offer an immersive visual format appealing to a wide range of computer vision applications. However, geometric properties of spherical images pose a major challenge for models and metrics designed for ordinary 2D images. Here, we show that direct application of Fr茅chet Inception Distance (FID) is insufficient for quantifying geometric fidelity in spherical imag… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.18207v1-abstract-full').style.display = 'inline'; document.getElementById('2407.18207v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.18207v1-abstract-full" style="display: none;"> Spherical or omni-directional images offer an immersive visual format appealing to a wide range of computer vision applications. However, geometric properties of spherical images pose a major challenge for models and metrics designed for ordinary 2D images. Here, we show that direct application of Fr茅chet Inception Distance (FID) is insufficient for quantifying geometric fidelity in spherical images. We introduce two quantitative metrics accounting for geometric constraints, namely Omnidirectional FID (OmniFID) and Discontinuity Score (DS). OmniFID is an extension of FID tailored to additionally capture field-of-view requirements of the spherical format by leveraging cubemap projections. DS is a kernel-based seam alignment score of continuity across borders of 2D representations of spherical images. In experiments, OmniFID and DS quantify geometry fidelity issues that are undetected by FID. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.18207v1-abstract-full').style.display = 'none'; document.getElementById('2407.18207v1-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 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted at ECCV 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.06237">arXiv:2407.06237</a> <span> [<a href="https://arxiv.org/pdf/2407.06237">pdf</a>, <a href="https://arxiv.org/ps/2407.06237">ps</a>, <a href="https://arxiv.org/format/2407.06237">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> </div> </div> <p class="title is-5 mathjax"> Discounted Pseudocosts in MILP </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Patel%2C+K+K">Krunal Kishor Patel</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.06237v1-abstract-short" style="display: inline;"> In this article, we introduce the concept of discounted pseudocosts, inspired by discounted total reward in reinforcement learning, and explore their application in mixed-integer linear programming (MILP). Traditional pseudocosts estimate changes in the objective function due to variable bound changes during the branch-and-bound process. By integrating reinforcement learning concepts, we propose a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.06237v1-abstract-full').style.display = 'inline'; document.getElementById('2407.06237v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.06237v1-abstract-full" style="display: none;"> In this article, we introduce the concept of discounted pseudocosts, inspired by discounted total reward in reinforcement learning, and explore their application in mixed-integer linear programming (MILP). Traditional pseudocosts estimate changes in the objective function due to variable bound changes during the branch-and-bound process. By integrating reinforcement learning concepts, we propose a novel approach incorporating a forward-looking perspective into pseudocost estimation. We present the motivation behind discounted pseudocosts and discuss how they represent the anticipated reward for branching after one level of exploration in the MILP problem space. Initial experiments on MIPLIB 2017 benchmark instances demonstrate the potential of discounted pseudocosts to enhance branching strategies and accelerate the solution process for challenging MILP problems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.06237v1-abstract-full').style.display = 'none'; document.getElementById('2407.06237v1-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 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 90C11 (Primary); 90C10; 90-08 (Secondary) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.00101">arXiv:2407.00101</a> <span> [<a href="https://arxiv.org/pdf/2407.00101">pdf</a>, <a href="https://arxiv.org/format/2407.00101">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Complexity">cs.CC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Neural and Evolutionary Computing">cs.NE</span> </div> </div> <p class="title is-5 mathjax"> Hybrid Approach to Parallel Stochastic Gradient Descent </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Vora%2C+A+S">Aakash Sudhirbhai Vora</a>, <a href="/search/cs?searchtype=author&query=Joshi%2C+D+C">Dhrumil Chetankumar Joshi</a>, <a href="/search/cs?searchtype=author&query=Patel%2C+A+K">Aksh Kantibhai Patel</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.00101v1-abstract-short" style="display: inline;"> Stochastic Gradient Descent is used for large datasets to train models to reduce the training time. On top of that data parallelism is widely used as a method to efficiently train neural networks using multiple worker nodes in parallel. Synchronous and asynchronous approach to data parallelism is used by most systems to train the model in parallel. However, both of them have their drawbacks. We pr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.00101v1-abstract-full').style.display = 'inline'; document.getElementById('2407.00101v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.00101v1-abstract-full" style="display: none;"> Stochastic Gradient Descent is used for large datasets to train models to reduce the training time. On top of that data parallelism is widely used as a method to efficiently train neural networks using multiple worker nodes in parallel. Synchronous and asynchronous approach to data parallelism is used by most systems to train the model in parallel. However, both of them have their drawbacks. We propose a third approach to data parallelism which is a hybrid between synchronous and asynchronous approaches, using both approaches to train the neural network. When the threshold function is selected appropriately to gradually shift all parameter aggregation from asynchronous to synchronous, we show that in a given time period our hybrid approach outperforms both asynchronous and synchronous approaches. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.00101v1-abstract-full').style.display = 'none'; document.getElementById('2407.00101v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.07693">arXiv:2406.07693</a> <span> [<a href="https://arxiv.org/pdf/2406.07693">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> </div> </div> <p class="title is-5 mathjax"> A Labelled Dataset for Sentiment Analysis of Videos on YouTube, TikTok, and Other Sources about the 2024 Outbreak of Measles </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Thakur%2C+N">Nirmalya Thakur</a>, <a href="/search/cs?searchtype=author&query=Su%2C+V">Vanessa Su</a>, <a href="/search/cs?searchtype=author&query=Shao%2C+M">Mingchen Shao</a>, <a href="/search/cs?searchtype=author&query=Patel%2C+K+A">Kesha A. Patel</a>, <a href="/search/cs?searchtype=author&query=Jeong%2C+H">Hongseok Jeong</a>, <a href="/search/cs?searchtype=author&query=Knieling%2C+V">Victoria Knieling</a>, <a href="/search/cs?searchtype=author&query=Bian%2C+A">Andrew Bian</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.07693v3-abstract-short" style="display: inline;"> The work of this paper presents a dataset that contains the data of 4011 videos about the ongoing outbreak of measles published on 264 websites on the internet between January 1, 2024, and May 31, 2024. The dataset is available at https://dx.doi.org/10.21227/40s8-xf63. These websites primarily include YouTube and TikTok, which account for 48.6% and 15.2% of the videos, respectively. The remainder… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.07693v3-abstract-full').style.display = 'inline'; document.getElementById('2406.07693v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.07693v3-abstract-full" style="display: none;"> The work of this paper presents a dataset that contains the data of 4011 videos about the ongoing outbreak of measles published on 264 websites on the internet between January 1, 2024, and May 31, 2024. The dataset is available at https://dx.doi.org/10.21227/40s8-xf63. These websites primarily include YouTube and TikTok, which account for 48.6% and 15.2% of the videos, respectively. The remainder of the websites include Instagram and Facebook as well as the websites of various global and local news organizations. For each of these videos, the URL of the video, title of the post, description of the post, and the date of publication of the video are presented as separate attributes in the dataset. After developing this dataset, sentiment analysis (using VADER), subjectivity analysis (using TextBlob), and fine-grain sentiment analysis (using DistilRoBERTa-base) of the video titles and video descriptions were performed. This included classifying each video title and video description into (i) one of the sentiment classes i.e. positive, negative, or neutral, (ii) one of the subjectivity classes i.e. highly opinionated, neutral opinionated, or least opinionated, and (iii) one of the fine-grain sentiment classes i.e. fear, surprise, joy, sadness, anger, disgust, or neutral. These results are presented as separate attributes in the dataset for the training and testing of machine learning algorithms for performing sentiment analysis or subjectivity analysis in this field as well as for other applications. Finally, this paper also presents a list of open research questions that may be investigated using this dataset. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.07693v3-abstract-full').style.display = 'none'; document.getElementById('2406.07693v3-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, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 11 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">19 pages</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.7; I.2.8; I.5.4; K.4.2; H.2.8; I.2.6 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.11667">arXiv:2405.11667</a> <span> [<a href="https://arxiv.org/pdf/2405.11667">pdf</a>, <a href="https://arxiv.org/format/2405.11667">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> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Patel%2C+K+K">Kumar Kshitij Patel</a>, <a href="/search/cs?searchtype=author&query=Glasgow%2C+M">Margalit Glasgow</a>, <a href="/search/cs?searchtype=author&query=Zindari%2C+A">Ali Zindari</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+L">Lingxiao Wang</a>, <a href="/search/cs?searchtype=author&query=Stich%2C+S+U">Sebastian U. Stich</a>, <a href="/search/cs?searchtype=author&query=Cheng%2C+Z">Ziheng Cheng</a>, <a href="/search/cs?searchtype=author&query=Joshi%2C+N">Nirmit Joshi</a>, <a href="/search/cs?searchtype=author&query=Srebro%2C+N">Nathan Srebro</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="2405.11667v1-abstract-short" style="display: inline;"> Local SGD is a popular optimization method in distributed learning, often outperforming other algorithms in practice, including mini-batch SGD. Despite this success, theoretically proving the dominance of local SGD in settings with reasonable data heterogeneity has been difficult, creating a significant gap between theory and practice. In this paper, we provide new lower bounds for local SGD under… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.11667v1-abstract-full').style.display = 'inline'; document.getElementById('2405.11667v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.11667v1-abstract-full" style="display: none;"> Local SGD is a popular optimization method in distributed learning, often outperforming other algorithms in practice, including mini-batch SGD. Despite this success, theoretically proving the dominance of local SGD in settings with reasonable data heterogeneity has been difficult, creating a significant gap between theory and practice. In this paper, we provide new lower bounds for local SGD under existing first-order data heterogeneity assumptions, showing that these assumptions are insufficient to prove the effectiveness of local update steps. Furthermore, under these same assumptions, we demonstrate the min-max optimality of accelerated mini-batch SGD, which fully resolves our understanding of distributed optimization for several problem classes. Our results emphasize the need for better models of data heterogeneity to understand the effectiveness of local SGD in practice. Towards this end, we consider higher-order smoothness and heterogeneity assumptions, providing new upper bounds that imply the dominance of local SGD over mini-batch SGD when data heterogeneity is low. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.11667v1-abstract-full').style.display = 'none'; document.getElementById('2405.11667v1-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 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.02425">arXiv:2405.02425</a> <span> [<a href="https://arxiv.org/pdf/2405.02425">pdf</a>, <a href="https://arxiv.org/format/2405.02425">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> <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"> Learning Robot Soccer from Egocentric Vision with Deep Reinforcement Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Tirumala%2C+D">Dhruva Tirumala</a>, <a href="/search/cs?searchtype=author&query=Wulfmeier%2C+M">Markus Wulfmeier</a>, <a href="/search/cs?searchtype=author&query=Moran%2C+B">Ben Moran</a>, <a href="/search/cs?searchtype=author&query=Huang%2C+S">Sandy Huang</a>, <a href="/search/cs?searchtype=author&query=Humplik%2C+J">Jan Humplik</a>, <a href="/search/cs?searchtype=author&query=Lever%2C+G">Guy Lever</a>, <a href="/search/cs?searchtype=author&query=Haarnoja%2C+T">Tuomas Haarnoja</a>, <a href="/search/cs?searchtype=author&query=Hasenclever%2C+L">Leonard Hasenclever</a>, <a href="/search/cs?searchtype=author&query=Byravan%2C+A">Arunkumar Byravan</a>, <a href="/search/cs?searchtype=author&query=Batchelor%2C+N">Nathan Batchelor</a>, <a href="/search/cs?searchtype=author&query=Sreendra%2C+N">Neil Sreendra</a>, <a href="/search/cs?searchtype=author&query=Patel%2C+K">Kushal Patel</a>, <a href="/search/cs?searchtype=author&query=Gwira%2C+M">Marlon Gwira</a>, <a href="/search/cs?searchtype=author&query=Nori%2C+F">Francesco Nori</a>, <a href="/search/cs?searchtype=author&query=Riedmiller%2C+M">Martin Riedmiller</a>, <a href="/search/cs?searchtype=author&query=Heess%2C+N">Nicolas Heess</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="2405.02425v1-abstract-short" style="display: inline;"> We apply multi-agent deep reinforcement learning (RL) to train end-to-end robot soccer policies with fully onboard computation and sensing via egocentric RGB vision. This setting reflects many challenges of real-world robotics, including active perception, agile full-body control, and long-horizon planning in a dynamic, partially-observable, multi-agent domain. We rely on large-scale, simulation-b… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.02425v1-abstract-full').style.display = 'inline'; document.getElementById('2405.02425v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.02425v1-abstract-full" style="display: none;"> We apply multi-agent deep reinforcement learning (RL) to train end-to-end robot soccer policies with fully onboard computation and sensing via egocentric RGB vision. This setting reflects many challenges of real-world robotics, including active perception, agile full-body control, and long-horizon planning in a dynamic, partially-observable, multi-agent domain. We rely on large-scale, simulation-based data generation to obtain complex behaviors from egocentric vision which can be successfully transferred to physical robots using low-cost sensors. To achieve adequate visual realism, our simulation combines rigid-body physics with learned, realistic rendering via multiple Neural Radiance Fields (NeRFs). We combine teacher-based multi-agent RL and cross-experiment data reuse to enable the discovery of sophisticated soccer strategies. We analyze active-perception behaviors including object tracking and ball seeking that emerge when simply optimizing perception-agnostic soccer play. The agents display equivalent levels of performance and agility as policies with access to privileged, ground-truth state. To our knowledge, this paper constitutes a first demonstration of end-to-end training for multi-agent robot soccer, mapping raw pixel observations to joint-level actions, that can be deployed in the real world. Videos of the game-play and analyses can be seen on our website https://sites.google.com/view/vision-soccer . <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.02425v1-abstract-full').style.display = 'none'; document.getElementById('2405.02425v1-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 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.19770">arXiv:2403.19770</a> <span> [<a href="https://arxiv.org/pdf/2403.19770">pdf</a>, <a href="https://arxiv.org/format/2403.19770">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> <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"> Hierarchical Deep Learning for Intention Estimation of Teleoperation Manipulation in Assembly Tasks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Cai%2C+M">Mingyu Cai</a>, <a href="/search/cs?searchtype=author&query=Patel%2C+K">Karankumar Patel</a>, <a href="/search/cs?searchtype=author&query=Iba%2C+S">Soshi Iba</a>, <a href="/search/cs?searchtype=author&query=Li%2C+S">Songpo Li</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.19770v1-abstract-short" style="display: inline;"> In human-robot collaboration, shared control presents an opportunity to teleoperate robotic manipulation to improve the efficiency of manufacturing and assembly processes. Robots are expected to assist in executing the user's intentions. To this end, robust and prompt intention estimation is needed, relying on behavioral observations. The framework presents an intention estimation technique at hie… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.19770v1-abstract-full').style.display = 'inline'; document.getElementById('2403.19770v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.19770v1-abstract-full" style="display: none;"> In human-robot collaboration, shared control presents an opportunity to teleoperate robotic manipulation to improve the efficiency of manufacturing and assembly processes. Robots are expected to assist in executing the user's intentions. To this end, robust and prompt intention estimation is needed, relying on behavioral observations. The framework presents an intention estimation technique at hierarchical levels i.e., low-level actions and high-level tasks, by incorporating multi-scale hierarchical information in neural networks. Technically, we employ hierarchical dependency loss to boost overall accuracy. Furthermore, we propose a multi-window method that assigns proper hierarchical prediction windows of input data. An analysis of the predictive power with various inputs demonstrates the predominance of the deep hierarchical model in the sense of prediction accuracy and early intention identification. We implement the algorithm on a virtual reality (VR) setup to teleoperate robotic hands in a simulation with various assembly tasks to show the effectiveness of online estimation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.19770v1-abstract-full').style.display = 'none'; document.getElementById('2403.19770v1-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 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">ICRA 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.18180">arXiv:2403.18180</a> <span> [<a href="https://arxiv.org/pdf/2403.18180">pdf</a>, <a href="https://arxiv.org/format/2403.18180">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Multi-Layer Dense Attention Decoder for Polyp Segmentation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Patel%2C+K">Krushi Patel</a>, <a href="/search/cs?searchtype=author&query=Li%2C+F">Fengjun Li</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+G">Guanghui Wang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.18180v1-abstract-short" style="display: inline;"> Detecting and segmenting polyps is crucial for expediting the diagnosis of colon cancer. This is a challenging task due to the large variations of polyps in color, texture, and lighting conditions, along with subtle differences between the polyp and its surrounding area. Recently, vision Transformers have shown robust abilities in modeling global context for polyp segmentation. However, they face… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.18180v1-abstract-full').style.display = 'inline'; document.getElementById('2403.18180v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.18180v1-abstract-full" style="display: none;"> Detecting and segmenting polyps is crucial for expediting the diagnosis of colon cancer. This is a challenging task due to the large variations of polyps in color, texture, and lighting conditions, along with subtle differences between the polyp and its surrounding area. Recently, vision Transformers have shown robust abilities in modeling global context for polyp segmentation. However, they face two major limitations: the inability to learn local relations among multi-level layers and inadequate feature aggregation in the decoder. To address these issues, we propose a novel decoder architecture aimed at hierarchically aggregating locally enhanced multi-level dense features. Specifically, we introduce a novel module named Dense Attention Gate (DAG), which adaptively fuses all previous layers' features to establish local feature relations among all layers. Furthermore, we propose a novel nested decoder architecture that hierarchically aggregates decoder features, thereby enhancing semantic features. We incorporate our novel dense decoder with the PVT backbone network and conduct evaluations on five polyp segmentation datasets: Kvasir, CVC-300, CVC-ColonDB, CVC-ClinicDB, and ETIS. Our experiments and comparisons with nine competing segmentation models demonstrate that the proposed architecture achieves state-of-the-art performance and outperforms the previous models on four datasets. The source code is available at: https://github.com/krushi1992/Dense-Decoder. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.18180v1-abstract-full').style.display = 'none'; document.getElementById('2403.18180v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.05530">arXiv:2403.05530</a> <span> [<a href="https://arxiv.org/pdf/2403.05530">pdf</a>, <a href="https://arxiv.org/format/2403.05530">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Gemini+Team"> Gemini Team</a>, <a href="/search/cs?searchtype=author&query=Georgiev%2C+P">Petko Georgiev</a>, <a href="/search/cs?searchtype=author&query=Lei%2C+V+I">Ving Ian Lei</a>, <a href="/search/cs?searchtype=author&query=Burnell%2C+R">Ryan Burnell</a>, <a href="/search/cs?searchtype=author&query=Bai%2C+L">Libin Bai</a>, <a href="/search/cs?searchtype=author&query=Gulati%2C+A">Anmol Gulati</a>, <a href="/search/cs?searchtype=author&query=Tanzer%2C+G">Garrett Tanzer</a>, <a href="/search/cs?searchtype=author&query=Vincent%2C+D">Damien Vincent</a>, <a href="/search/cs?searchtype=author&query=Pan%2C+Z">Zhufeng Pan</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+S">Shibo Wang</a>, <a href="/search/cs?searchtype=author&query=Mariooryad%2C+S">Soroosh Mariooryad</a>, <a href="/search/cs?searchtype=author&query=Ding%2C+Y">Yifan Ding</a>, <a href="/search/cs?searchtype=author&query=Geng%2C+X">Xinyang Geng</a>, <a href="/search/cs?searchtype=author&query=Alcober%2C+F">Fred Alcober</a>, <a href="/search/cs?searchtype=author&query=Frostig%2C+R">Roy Frostig</a>, <a href="/search/cs?searchtype=author&query=Omernick%2C+M">Mark Omernick</a>, <a href="/search/cs?searchtype=author&query=Walker%2C+L">Lexi Walker</a>, <a href="/search/cs?searchtype=author&query=Paduraru%2C+C">Cosmin Paduraru</a>, <a href="/search/cs?searchtype=author&query=Sorokin%2C+C">Christina Sorokin</a>, <a href="/search/cs?searchtype=author&query=Tacchetti%2C+A">Andrea Tacchetti</a>, <a href="/search/cs?searchtype=author&query=Gaffney%2C+C">Colin Gaffney</a>, <a href="/search/cs?searchtype=author&query=Daruki%2C+S">Samira Daruki</a>, <a href="/search/cs?searchtype=author&query=Sercinoglu%2C+O">Olcan Sercinoglu</a>, <a href="/search/cs?searchtype=author&query=Gleicher%2C+Z">Zach Gleicher</a>, <a href="/search/cs?searchtype=author&query=Love%2C+J">Juliette Love</a> , et al. (1112 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.05530v5-abstract-short" style="display: inline;"> In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio. The family includes two new models: (1) an updated Gemini 1.5 Pro, which exceeds the February… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.05530v5-abstract-full').style.display = 'inline'; document.getElementById('2403.05530v5-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.05530v5-abstract-full" style="display: none;"> In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio. The family includes two new models: (1) an updated Gemini 1.5 Pro, which exceeds the February version on the great majority of capabilities and benchmarks; (2) Gemini 1.5 Flash, a more lightweight variant designed for efficiency with minimal regression in quality. Gemini 1.5 models achieve near-perfect recall on long-context retrieval tasks across modalities, improve the state-of-the-art in long-document QA, long-video QA and long-context ASR, and match or surpass Gemini 1.0 Ultra's state-of-the-art performance across a broad set of benchmarks. Studying the limits of Gemini 1.5's long-context ability, we find continued improvement in next-token prediction and near-perfect retrieval (>99%) up to at least 10M tokens, a generational leap over existing models such as Claude 3.0 (200k) and GPT-4 Turbo (128k). Finally, we highlight real-world use cases, such as Gemini 1.5 collaborating with professionals on completing their tasks achieving 26 to 75% time savings across 10 different job categories, as well as surprising new capabilities of large language models at the frontier; when given a grammar manual for Kalamang, a language with fewer than 200 speakers worldwide, the model learns to translate English to Kalamang at a similar level to a person who learned from the same content. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.05530v5-abstract-full').style.display = 'none'; document.getElementById('2403.05530v5-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 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 8 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.10797">arXiv:2402.10797</a> <span> [<a href="https://arxiv.org/pdf/2402.10797">pdf</a>, <a href="https://arxiv.org/format/2402.10797">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Mathematical Software">cs.MS</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="Computation">stat.CO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> BlackJAX: Composable Bayesian inference in JAX </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Cabezas%2C+A">Alberto Cabezas</a>, <a href="/search/cs?searchtype=author&query=Corenflos%2C+A">Adrien Corenflos</a>, <a href="/search/cs?searchtype=author&query=Lao%2C+J">Junpeng Lao</a>, <a href="/search/cs?searchtype=author&query=Louf%2C+R">R茅mi Louf</a>, <a href="/search/cs?searchtype=author&query=Carnec%2C+A">Antoine Carnec</a>, <a href="/search/cs?searchtype=author&query=Chaudhari%2C+K">Kaustubh Chaudhari</a>, <a href="/search/cs?searchtype=author&query=Cohn-Gordon%2C+R">Reuben Cohn-Gordon</a>, <a href="/search/cs?searchtype=author&query=Coullon%2C+J">Jeremie Coullon</a>, <a href="/search/cs?searchtype=author&query=Deng%2C+W">Wei Deng</a>, <a href="/search/cs?searchtype=author&query=Duffield%2C+S">Sam Duffield</a>, <a href="/search/cs?searchtype=author&query=Dur%C3%A1n-Mart%C3%ADn%2C+G">Gerardo Dur谩n-Mart铆n</a>, <a href="/search/cs?searchtype=author&query=Elantkowski%2C+M">Marcin Elantkowski</a>, <a href="/search/cs?searchtype=author&query=Foreman-Mackey%2C+D">Dan Foreman-Mackey</a>, <a href="/search/cs?searchtype=author&query=Gregori%2C+M">Michele Gregori</a>, <a href="/search/cs?searchtype=author&query=Iguaran%2C+C">Carlos Iguaran</a>, <a href="/search/cs?searchtype=author&query=Kumar%2C+R">Ravin Kumar</a>, <a href="/search/cs?searchtype=author&query=Lysy%2C+M">Martin Lysy</a>, <a href="/search/cs?searchtype=author&query=Murphy%2C+K">Kevin Murphy</a>, <a href="/search/cs?searchtype=author&query=Orduz%2C+J+C">Juan Camilo Orduz</a>, <a href="/search/cs?searchtype=author&query=Patel%2C+K">Karm Patel</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+X">Xi Wang</a>, <a href="/search/cs?searchtype=author&query=Zinkov%2C+R">Rob Zinkov</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="2402.10797v2-abstract-short" style="display: inline;"> BlackJAX is a library implementing sampling and variational inference algorithms commonly used in Bayesian computation. It is designed for ease of use, speed, and modularity by taking a functional approach to the algorithms' implementation. BlackJAX is written in Python, using JAX to compile and run NumpPy-like samplers and variational methods on CPUs, GPUs, and TPUs. The library integrates well w… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.10797v2-abstract-full').style.display = 'inline'; document.getElementById('2402.10797v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.10797v2-abstract-full" style="display: none;"> BlackJAX is a library implementing sampling and variational inference algorithms commonly used in Bayesian computation. It is designed for ease of use, speed, and modularity by taking a functional approach to the algorithms' implementation. BlackJAX is written in Python, using JAX to compile and run NumpPy-like samplers and variational methods on CPUs, GPUs, and TPUs. The library integrates well with probabilistic programming languages by working directly with the (un-normalized) target log density function. BlackJAX is intended as a collection of low-level, composable implementations of basic statistical 'atoms' that can be combined to perform well-defined Bayesian inference, but also provides high-level routines for ease of use. It is designed for users who need cutting-edge methods, researchers who want to create complex sampling methods, and people who want to learn how these work. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.10797v2-abstract-full').style.display = 'none'; document.getElementById('2402.10797v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 16 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Companion paper for the library https://github.com/blackjax-devs/blackjax Update: minor changes and updated the list of authors to include technical contributors</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.17643">arXiv:2312.17643</a> <span> [<a href="https://arxiv.org/pdf/2312.17643">pdf</a>, <a href="https://arxiv.org/format/2312.17643">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> b-it-bots RoboCup@Work Team Description Paper 2023 </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Patel%2C+K">Kevin Patel</a>, <a href="/search/cs?searchtype=author&query=Kalagaturu%2C+V">Vamsi Kalagaturu</a>, <a href="/search/cs?searchtype=author&query=Mannava%2C+V">Vivek Mannava</a>, <a href="/search/cs?searchtype=author&query=Selvaraju%2C+R">Ravisankar Selvaraju</a>, <a href="/search/cs?searchtype=author&query=Shinde%2C+S">Shubham Shinde</a>, <a href="/search/cs?searchtype=author&query=Bakaraniya%2C+D">Dharmin Bakaraniya</a>, <a href="/search/cs?searchtype=author&query=Nair%2C+D">Deebul Nair</a>, <a href="/search/cs?searchtype=author&query=Wasil%2C+M">Mohammad Wasil</a>, <a href="/search/cs?searchtype=author&query=Thoduka%2C+S">Santosh Thoduka</a>, <a href="/search/cs?searchtype=author&query=Awaad%2C+I">Iman Awaad</a>, <a href="/search/cs?searchtype=author&query=Schneider%2C+S">Sven Schneider</a>, <a href="/search/cs?searchtype=author&query=Hochgeschwender%2C+N">Nico Hochgeschwender</a>, <a href="/search/cs?searchtype=author&query=Pl%C3%B6ger%2C+P+G">Paul G. Pl枚ger</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="2312.17643v1-abstract-short" style="display: inline;"> This paper presents the b-it-bots RoboCup@Work team and its current hardware and functional architecture for the KUKA youBot robot. We describe the underlying software framework and the developed capabilities required for operating in industrial environments including features such as reliable and precise navigation, flexible manipulation, robust object recognition and task planning. New developme… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.17643v1-abstract-full').style.display = 'inline'; document.getElementById('2312.17643v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.17643v1-abstract-full" style="display: none;"> This paper presents the b-it-bots RoboCup@Work team and its current hardware and functional architecture for the KUKA youBot robot. We describe the underlying software framework and the developed capabilities required for operating in industrial environments including features such as reliable and precise navigation, flexible manipulation, robust object recognition and task planning. New developments include an approach to grasp vertical objects, placement of objects by considering the empty space on a workstation, and the process of porting our code to ROS2. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.17643v1-abstract-full').style.display = 'none'; document.getElementById('2312.17643v1-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 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.11885">arXiv:2312.11885</a> <span> [<a href="https://arxiv.org/pdf/2312.11885">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Physics and Society">physics.soc-ph</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.3390/data8110163">10.3390/data8110163 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> A Large-Scale Dataset of Search Interests Related to Disease X Originating from Different Geographic Regions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Thakur%2C+N">Nirmalya Thakur</a>, <a href="/search/cs?searchtype=author&query=Cui%2C+S">Shuqi Cui</a>, <a href="/search/cs?searchtype=author&query=Patel%2C+K+A">Kesha A. Patel</a>, <a href="/search/cs?searchtype=author&query=Hall%2C+I">Isabella Hall</a>, <a href="/search/cs?searchtype=author&query=Duggal%2C+Y+N">Yuvraj Nihal Duggal</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="2312.11885v1-abstract-short" style="display: inline;"> The World Health Organization added Disease X to their shortlist of blueprint priority diseases to represent a hypothetical, unknown pathogen that could cause a future epidemic. During different virus outbreaks of the past, such as COVID-19, Influenza, Lyme Disease, and Zika virus, researchers from various disciplines utilized Google Trends to mine multimodal components of web behavior to study, i… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.11885v1-abstract-full').style.display = 'inline'; document.getElementById('2312.11885v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.11885v1-abstract-full" style="display: none;"> The World Health Organization added Disease X to their shortlist of blueprint priority diseases to represent a hypothetical, unknown pathogen that could cause a future epidemic. During different virus outbreaks of the past, such as COVID-19, Influenza, Lyme Disease, and Zika virus, researchers from various disciplines utilized Google Trends to mine multimodal components of web behavior to study, investigate, and analyze the global awareness, preparedness, and response associated with these respective virus outbreaks. As the world prepares for Disease X, a dataset on web behavior related to Disease X would be crucial to contribute towards the timely advancement of research in this field. Furthermore, none of the prior works in this field have focused on the development of a dataset to compile relevant web behavior data, which would help to prepare for Disease X. To address these research challenges, this work presents a dataset of web behavior related to Disease X, which emerged from different geographic regions of the world, between February 2018 and August 2023. Specifically, this dataset presents the search interests related to Disease X from 94 geographic regions. The dataset was developed by collecting data using Google Trends. The relevant search interests for all these regions for each month in this time range are available in this dataset. This paper also discusses the compliance of this dataset with the FAIR principles of scientific data management. Finally, an analysis of this dataset is presented to uphold the applicability, relevance, and usefulness of this dataset for the investigation of different research questions in the interrelated fields of Big Data, Data Mining, Healthcare, Epidemiology, and Data Analysis with a specific focus on Disease X. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.11885v1-abstract-full').style.display = 'none'; document.getElementById('2312.11885v1-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, 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/2311.17586">arXiv:2311.17586</a> <span> [<a href="https://arxiv.org/pdf/2311.17586">pdf</a>, <a href="https://arxiv.org/format/2311.17586">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="Optimization and Control">math.OC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Federated Online and Bandit Convex Optimization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Patel%2C+K+K">Kumar Kshitij Patel</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+L">Lingxiao Wang</a>, <a href="/search/cs?searchtype=author&query=Saha%2C+A">Aadirupa Saha</a>, <a href="/search/cs?searchtype=author&query=Sebro%2C+N">Nati Sebro</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="2311.17586v1-abstract-short" style="display: inline;"> We study the problems of distributed online and bandit convex optimization against an adaptive adversary. We aim to minimize the average regret on $M$ machines working in parallel over $T$ rounds with $R$ intermittent communications. Assuming the underlying cost functions are convex and can be generated adaptively, our results show that collaboration is not beneficial when the machines have access… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.17586v1-abstract-full').style.display = 'inline'; document.getElementById('2311.17586v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.17586v1-abstract-full" style="display: none;"> We study the problems of distributed online and bandit convex optimization against an adaptive adversary. We aim to minimize the average regret on $M$ machines working in parallel over $T$ rounds with $R$ intermittent communications. Assuming the underlying cost functions are convex and can be generated adaptively, our results show that collaboration is not beneficial when the machines have access to the first-order gradient information at the queried points. This is in contrast to the case for stochastic functions, where each machine samples the cost functions from a fixed distribution. Furthermore, we delve into the more challenging setting of federated online optimization with bandit (zeroth-order) feedback, where the machines can only access values of the cost functions at the queried points. The key finding here is identifying the high-dimensional regime where collaboration is beneficial and may even lead to a linear speedup in the number of machines. We further illustrate our findings through federated adversarial linear bandits by developing novel distributed single and two-point feedback algorithms. Our work is the first attempt towards a systematic understanding of federated online optimization with limited feedback, and it attains tight regret bounds in the intermittent communication setting for both first and zeroth-order feedback. Our results thus bridge the gap between stochastic and adaptive settings in federated online optimization. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.17586v1-abstract-full').style.display = 'none'; document.getElementById('2311.17586v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.16766">arXiv:2311.16766</a> <span> [<a href="https://arxiv.org/pdf/2311.16766">pdf</a>, <a href="https://arxiv.org/format/2311.16766">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Rescuing referral failures during automated diagnosis of domain-shifted medical images </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Srivastava%2C+A">Anuj Srivastava</a>, <a href="/search/cs?searchtype=author&query=Patel%2C+K">Karm Patel</a>, <a href="/search/cs?searchtype=author&query=Shenoy%2C+P">Pradeep Shenoy</a>, <a href="/search/cs?searchtype=author&query=Sridharan%2C+D">Devarajan Sridharan</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="2311.16766v1-abstract-short" style="display: inline;"> The success of deep learning models deployed in the real world depends critically on their ability to generalize well across diverse data domains. Here, we address a fundamental challenge with selective classification during automated diagnosis with domain-shifted medical images. In this scenario, models must learn to avoid making predictions when label confidence is low, especially when tested wi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.16766v1-abstract-full').style.display = 'inline'; document.getElementById('2311.16766v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.16766v1-abstract-full" style="display: none;"> The success of deep learning models deployed in the real world depends critically on their ability to generalize well across diverse data domains. Here, we address a fundamental challenge with selective classification during automated diagnosis with domain-shifted medical images. In this scenario, models must learn to avoid making predictions when label confidence is low, especially when tested with samples far removed from the training set (covariate shift). Such uncertain cases are typically referred to the clinician for further analysis and evaluation. Yet, we show that even state-of-the-art domain generalization approaches fail severely during referral when tested on medical images acquired from a different demographic or using a different technology. We examine two benchmark diagnostic medical imaging datasets exhibiting strong covariate shifts: i) diabetic retinopathy prediction with retinal fundus images and ii) multilabel disease prediction with chest X-ray images. We show that predictive uncertainty estimates do not generalize well under covariate shifts leading to non-monotonic referral curves, and severe drops in performance (up to 50%) at high referral rates (>70%). We evaluate novel combinations of robust generalization and post hoc referral approaches, that rescue these failures and achieve significant performance improvements, typically >10%, over baseline methods. Our study identifies a critical challenge with referral in domain-shifted medical images and finds key applications in reliable, automated disease diagnosis. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.16766v1-abstract-full').style.display = 'none'; document.getElementById('2311.16766v1-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 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/2311.16459">arXiv:2311.16459</a> <span> [<a href="https://arxiv.org/pdf/2311.16459">pdf</a>, <a href="https://arxiv.org/format/2311.16459">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> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Science and Game Theory">cs.GT</span> </div> </div> <p class="title is-5 mathjax"> On the Effect of Defections in Federated Learning and How to Prevent Them </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Han%2C+M">Minbiao Han</a>, <a href="/search/cs?searchtype=author&query=Patel%2C+K+K">Kumar Kshitij Patel</a>, <a href="/search/cs?searchtype=author&query=Shao%2C+H">Han Shao</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+L">Lingxiao Wang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2311.16459v1-abstract-short" style="display: inline;"> Federated learning is a machine learning protocol that enables a large population of agents to collaborate over multiple rounds to produce a single consensus model. There are several federated learning applications where agents may choose to defect permanently$-$essentially withdrawing from the collaboration$-$if they are content with their instantaneous model in that round. This work demonstrates… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.16459v1-abstract-full').style.display = 'inline'; document.getElementById('2311.16459v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.16459v1-abstract-full" style="display: none;"> Federated learning is a machine learning protocol that enables a large population of agents to collaborate over multiple rounds to produce a single consensus model. There are several federated learning applications where agents may choose to defect permanently$-$essentially withdrawing from the collaboration$-$if they are content with their instantaneous model in that round. This work demonstrates the detrimental impact of such defections on the final model's robustness and ability to generalize. We also show that current federated optimization algorithms fail to disincentivize these harmful defections. We introduce a novel optimization algorithm with theoretical guarantees to prevent defections while ensuring asymptotic convergence to an effective solution for all participating agents. We also provide numerical experiments to corroborate our findings and demonstrate the effectiveness of our algorithm. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.16459v1-abstract-full').style.display = 'none'; document.getElementById('2311.16459v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 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/2310.10026">arXiv:2310.10026</a> <span> [<a href="https://arxiv.org/pdf/2310.10026">pdf</a>, <a href="https://arxiv.org/format/2310.10026">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> </div> </div> <p class="title is-5 mathjax"> Real-time Speech Enhancement and Separation with a Unified Deep Neural Network for Single/Dual Talker Scenarios </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Patel%2C+K">Kashyap Patel</a>, <a href="/search/cs?searchtype=author&query=Kovalyov%2C+A">Anton Kovalyov</a>, <a href="/search/cs?searchtype=author&query=Panahi%2C+I">Issa Panahi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.10026v1-abstract-short" style="display: inline;"> This paper introduces a practical approach for leveraging a real-time deep learning model to alternate between speech enhancement and joint speech enhancement and separation depending on whether the input mixture contains one or two active speakers. Scale-invariant signal-to-distortion ratio (SI-SDR) has shown to be a highly effective training measure in time-domain speech separation. However, the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.10026v1-abstract-full').style.display = 'inline'; document.getElementById('2310.10026v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.10026v1-abstract-full" style="display: none;"> This paper introduces a practical approach for leveraging a real-time deep learning model to alternate between speech enhancement and joint speech enhancement and separation depending on whether the input mixture contains one or two active speakers. Scale-invariant signal-to-distortion ratio (SI-SDR) has shown to be a highly effective training measure in time-domain speech separation. However, the SI-SDR metric is ill-defined for zero-energy target signals, which is a problem when training a speech separation model using utterances with varying numbers of talkers. Unlike existing solutions that focus on modifying the loss function to accommodate zero-energy target signals, the proposed approach circumvents this problem by training the model to extract speech on both its output channels regardless if the input is a single or dual-talker mixture. A lightweight speaker overlap detection (SOD) module is also introduced to differentiate between single and dual-talker segments in real-time. The proposed module takes advantage of the new formulation by operating directly on the separated masks, given by the separation model, instead of the original mixture, thus effectively simplifying the detection task. Experimental results show that the proposed training approach outperforms existing solutions, and the SOD module exhibits high accuracy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.10026v1-abstract-full').style.display = 'none'; document.getElementById('2310.10026v1-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, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">6 Pages, Accepted at IEEE Asilomar</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.04546">arXiv:2310.04546</a> <span> [<a href="https://arxiv.org/pdf/2310.04546">pdf</a>, <a href="https://arxiv.org/format/2310.04546">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> </div> </div> <p class="title is-5 mathjax"> Privacy-Preserving Financial Anomaly Detection via Federated Learning & Multi-Party Computation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Arora%2C+S">Sunpreet Arora</a>, <a href="/search/cs?searchtype=author&query=Beams%2C+A">Andrew Beams</a>, <a href="/search/cs?searchtype=author&query=Chatzigiannis%2C+P">Panagiotis Chatzigiannis</a>, <a href="/search/cs?searchtype=author&query=Meiser%2C+S">Sebastian Meiser</a>, <a href="/search/cs?searchtype=author&query=Patel%2C+K">Karan Patel</a>, <a href="/search/cs?searchtype=author&query=Raghuraman%2C+S">Srinivasan Raghuraman</a>, <a href="/search/cs?searchtype=author&query=Rindal%2C+P">Peter Rindal</a>, <a href="/search/cs?searchtype=author&query=Shah%2C+H">Harshal Shah</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+Y">Yizhen Wang</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+Y">Yuhang Wu</a>, <a href="/search/cs?searchtype=author&query=Yang%2C+H">Hao Yang</a>, <a href="/search/cs?searchtype=author&query=Zamani%2C+M">Mahdi Zamani</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.04546v1-abstract-short" style="display: inline;"> One of the main goals of financial institutions (FIs) today is combating fraud and financial crime. To this end, FIs use sophisticated machine-learning models trained using data collected from their customers. The output of machine learning models may be manually reviewed for critical use cases, e.g., determining the likelihood of a transaction being anomalous and the subsequent course of action.… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.04546v1-abstract-full').style.display = 'inline'; document.getElementById('2310.04546v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.04546v1-abstract-full" style="display: none;"> One of the main goals of financial institutions (FIs) today is combating fraud and financial crime. To this end, FIs use sophisticated machine-learning models trained using data collected from their customers. The output of machine learning models may be manually reviewed for critical use cases, e.g., determining the likelihood of a transaction being anomalous and the subsequent course of action. While advanced machine learning models greatly aid an FI in anomaly detection, model performance could be significantly improved using additional customer data from other FIs. In practice, however, an FI may not have appropriate consent from customers to share their data with other FIs. Additionally, data privacy regulations may prohibit FIs from sharing clients' sensitive data in certain geographies. Combining customer data to jointly train highly accurate anomaly detection models is therefore challenging for FIs in operational settings. In this paper, we describe a privacy-preserving framework that allows FIs to jointly train highly accurate anomaly detection models. The framework combines the concept of federated learning with efficient multi-party computation and noisy aggregates inspired by differential privacy. The presented framework was submitted as a winning entry to the financial crime detection track of the US/UK PETs Challenge. The challenge considered an architecture where banks hold customer data and execute transactions through a central network. We show that our solution enables the network to train a highly accurate anomaly detection model while preserving privacy of customer data. Experimental results demonstrate that use of additional customer data using the proposed approach results in improvement of our anomaly detection model's AUPRC from 0.6 to 0.7. We discuss how our framework, can be generalized to other similar scenarios. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.04546v1-abstract-full').style.display = 'none'; document.getElementById('2310.04546v1-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 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">12 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/2308.11477">arXiv:2308.11477</a> <span> [<a href="https://arxiv.org/pdf/2308.11477">pdf</a>, <a href="https://arxiv.org/ps/2308.11477">ps</a>, <a href="https://arxiv.org/format/2308.11477">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> </div> </div> <p class="title is-5 mathjax"> An improved column-generation-based matheuristic for learning classification trees </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Patel%2C+K+K">Krunal Kishor Patel</a>, <a href="/search/cs?searchtype=author&query=Desaulniers%2C+G">Guy Desaulniers</a>, <a href="/search/cs?searchtype=author&query=Lodi%2C+A">Andrea Lodi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2308.11477v2-abstract-short" style="display: inline;"> Decision trees are highly interpretable models for solving classification problems in machine learning (ML). The standard ML algorithms for training decision trees are fast but generate suboptimal trees in terms of accuracy. Other discrete optimization models in the literature address the optimality problem but only work well on relatively small datasets. \cite{firat2020column} proposed a column-g… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.11477v2-abstract-full').style.display = 'inline'; document.getElementById('2308.11477v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2308.11477v2-abstract-full" style="display: none;"> Decision trees are highly interpretable models for solving classification problems in machine learning (ML). The standard ML algorithms for training decision trees are fast but generate suboptimal trees in terms of accuracy. Other discrete optimization models in the literature address the optimality problem but only work well on relatively small datasets. \cite{firat2020column} proposed a column-generation-based heuristic approach for learning decision trees. This approach improves scalability and can work with large datasets. In this paper, we describe improvements to this column generation approach. First, we modify the subproblem model to significantly reduce the number of subproblems in multiclass classification instances. Next, we show that the data-dependent constraints in the master problem are implied, and use them as cutting planes. Furthermore, we describe a separation model to generate data points for which the linear programming relaxation solution violates their corresponding constraints. We conclude by presenting computational results that show that these modifications result in better scalability. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.11477v2-abstract-full').style.display = 'none'; document.getElementById('2308.11477v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 22 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Submitted to Computers and Operations Research journal</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.09109">arXiv:2306.09109</a> <span> [<a href="https://arxiv.org/pdf/2306.09109">pdf</a>, <a href="https://arxiv.org/format/2306.09109">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> NAVI: Category-Agnostic Image Collections with High-Quality 3D Shape and Pose Annotations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jampani%2C+V">Varun Jampani</a>, <a href="/search/cs?searchtype=author&query=Maninis%2C+K">Kevis-Kokitsi Maninis</a>, <a href="/search/cs?searchtype=author&query=Engelhardt%2C+A">Andreas Engelhardt</a>, <a href="/search/cs?searchtype=author&query=Karpur%2C+A">Arjun Karpur</a>, <a href="/search/cs?searchtype=author&query=Truong%2C+K">Karen Truong</a>, <a href="/search/cs?searchtype=author&query=Sargent%2C+K">Kyle Sargent</a>, <a href="/search/cs?searchtype=author&query=Popov%2C+S">Stefan Popov</a>, <a href="/search/cs?searchtype=author&query=Araujo%2C+A">Andr茅 Araujo</a>, <a href="/search/cs?searchtype=author&query=Martin-Brualla%2C+R">Ricardo Martin-Brualla</a>, <a href="/search/cs?searchtype=author&query=Patel%2C+K">Kaushal Patel</a>, <a href="/search/cs?searchtype=author&query=Vlasic%2C+D">Daniel Vlasic</a>, <a href="/search/cs?searchtype=author&query=Ferrari%2C+V">Vittorio Ferrari</a>, <a href="/search/cs?searchtype=author&query=Makadia%2C+A">Ameesh Makadia</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+C">Ce Liu</a>, <a href="/search/cs?searchtype=author&query=Li%2C+Y">Yuanzhen Li</a>, <a href="/search/cs?searchtype=author&query=Zhou%2C+H">Howard Zhou</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2306.09109v2-abstract-short" style="display: inline;"> Recent advances in neural reconstruction enable high-quality 3D object reconstruction from casually captured image collections. Current techniques mostly analyze their progress on relatively simple image collections where Structure-from-Motion (SfM) techniques can provide ground-truth (GT) camera poses. We note that SfM techniques tend to fail on in-the-wild image collections such as image search… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.09109v2-abstract-full').style.display = 'inline'; document.getElementById('2306.09109v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.09109v2-abstract-full" style="display: none;"> Recent advances in neural reconstruction enable high-quality 3D object reconstruction from casually captured image collections. Current techniques mostly analyze their progress on relatively simple image collections where Structure-from-Motion (SfM) techniques can provide ground-truth (GT) camera poses. We note that SfM techniques tend to fail on in-the-wild image collections such as image search results with varying backgrounds and illuminations. To enable systematic research progress on 3D reconstruction from casual image captures, we propose NAVI: a new dataset of category-agnostic image collections of objects with high-quality 3D scans along with per-image 2D-3D alignments providing near-perfect GT camera parameters. These 2D-3D alignments allow us to extract accurate derivative annotations such as dense pixel correspondences, depth and segmentation maps. We demonstrate the use of NAVI image collections on different problem settings and show that NAVI enables more thorough evaluations that were not possible with existing datasets. We believe NAVI is beneficial for systematic research progress on 3D reconstruction and correspondence estimation. Project page: https://navidataset.github.io <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.09109v2-abstract-full').style.display = 'none'; document.getElementById('2306.09109v2-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 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">NeurIPS 2023 camera ready. Project page: https://navidataset.github.io</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.05630">arXiv:2305.05630</a> <span> [<a href="https://arxiv.org/pdf/2305.05630">pdf</a>, <a href="https://arxiv.org/format/2305.05630">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> </div> </div> <p class="title is-5 mathjax"> Accurate Real-Time Estimation of 2-Dimensional Direction of Arrival using a 3-Microphone Array </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kovalyov%2C+A">Anton Kovalyov</a>, <a href="/search/cs?searchtype=author&query=Patel%2C+K">Kashyap Patel</a>, <a href="/search/cs?searchtype=author&query=Panahi%2C+I">Issa Panahi</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="2305.05630v1-abstract-short" style="display: inline;"> This paper presents a method for real-time estimation of 2-dimensional direction of arrival (2D-DOA) of one or more sound sources using a nonlinear array of three microphones. 2D-DOA is estimated employing frame-level time difference of arrival (TDOA) measurements. Unlike conventional methods, which infer location parameters from TDOAs using a theoretical model, we propose a more practical approac… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.05630v1-abstract-full').style.display = 'inline'; document.getElementById('2305.05630v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.05630v1-abstract-full" style="display: none;"> This paper presents a method for real-time estimation of 2-dimensional direction of arrival (2D-DOA) of one or more sound sources using a nonlinear array of three microphones. 2D-DOA is estimated employing frame-level time difference of arrival (TDOA) measurements. Unlike conventional methods, which infer location parameters from TDOAs using a theoretical model, we propose a more practical approach based on supervised learning. The proposed model employs nearest neighbor search (NNS) applied to a spherical Fibonacci lattice consisting of TDOA to 2D-DOA mappings learned directly in the field. Filtering and clustering post-processors are also introduced for improved source detection and localization robustness. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.05630v1-abstract-full').style.display = 'none'; document.getElementById('2305.05630v1-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 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">5 pages, 6 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2304.13653">arXiv:2304.13653</a> <span> [<a href="https://arxiv.org/pdf/2304.13653">pdf</a>, <a href="https://arxiv.org/format/2304.13653">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> <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 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/10.1126/scirobotics.adi8022">10.1126/scirobotics.adi8022 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Haarnoja%2C+T">Tuomas Haarnoja</a>, <a href="/search/cs?searchtype=author&query=Moran%2C+B">Ben Moran</a>, <a href="/search/cs?searchtype=author&query=Lever%2C+G">Guy Lever</a>, <a href="/search/cs?searchtype=author&query=Huang%2C+S+H">Sandy H. Huang</a>, <a href="/search/cs?searchtype=author&query=Tirumala%2C+D">Dhruva Tirumala</a>, <a href="/search/cs?searchtype=author&query=Humplik%2C+J">Jan Humplik</a>, <a href="/search/cs?searchtype=author&query=Wulfmeier%2C+M">Markus Wulfmeier</a>, <a href="/search/cs?searchtype=author&query=Tunyasuvunakool%2C+S">Saran Tunyasuvunakool</a>, <a href="/search/cs?searchtype=author&query=Siegel%2C+N+Y">Noah Y. Siegel</a>, <a href="/search/cs?searchtype=author&query=Hafner%2C+R">Roland Hafner</a>, <a href="/search/cs?searchtype=author&query=Bloesch%2C+M">Michael Bloesch</a>, <a href="/search/cs?searchtype=author&query=Hartikainen%2C+K">Kristian Hartikainen</a>, <a href="/search/cs?searchtype=author&query=Byravan%2C+A">Arunkumar Byravan</a>, <a href="/search/cs?searchtype=author&query=Hasenclever%2C+L">Leonard Hasenclever</a>, <a href="/search/cs?searchtype=author&query=Tassa%2C+Y">Yuval Tassa</a>, <a href="/search/cs?searchtype=author&query=Sadeghi%2C+F">Fereshteh Sadeghi</a>, <a href="/search/cs?searchtype=author&query=Batchelor%2C+N">Nathan Batchelor</a>, <a href="/search/cs?searchtype=author&query=Casarini%2C+F">Federico Casarini</a>, <a href="/search/cs?searchtype=author&query=Saliceti%2C+S">Stefano Saliceti</a>, <a href="/search/cs?searchtype=author&query=Game%2C+C">Charles Game</a>, <a href="/search/cs?searchtype=author&query=Sreendra%2C+N">Neil Sreendra</a>, <a href="/search/cs?searchtype=author&query=Patel%2C+K">Kushal Patel</a>, <a href="/search/cs?searchtype=author&query=Gwira%2C+M">Marlon Gwira</a>, <a href="/search/cs?searchtype=author&query=Huber%2C+A">Andrea Huber</a>, <a href="/search/cs?searchtype=author&query=Hurley%2C+N">Nicole Hurley</a> , et al. (3 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2304.13653v2-abstract-short" style="display: inline;"> We investigate whether Deep Reinforcement Learning (Deep RL) is able to synthesize sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be composed into complex behavioral strategies in dynamic environments. We used Deep RL to train a humanoid robot with 20 actuated joints to play a simplified one-versus-one (1v1) soccer game. The resulting agent exhibits robust… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.13653v2-abstract-full').style.display = 'inline'; document.getElementById('2304.13653v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2304.13653v2-abstract-full" style="display: none;"> We investigate whether Deep Reinforcement Learning (Deep RL) is able to synthesize sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be composed into complex behavioral strategies in dynamic environments. We used Deep RL to train a humanoid robot with 20 actuated joints to play a simplified one-versus-one (1v1) soccer game. The resulting agent exhibits robust and dynamic movement skills such as rapid fall recovery, walking, turning, kicking and more; and it transitions between them in a smooth, stable, and efficient manner. The agent's locomotion and tactical behavior adapts to specific game contexts in a way that would be impractical to manually design. The agent also developed a basic strategic understanding of the game, and learned, for instance, to anticipate ball movements and to block opponent shots. Our agent was trained in simulation and transferred to real robots zero-shot. We found that a combination of sufficiently high-frequency control, targeted dynamics randomization, and perturbations during training in simulation enabled good-quality transfer. Although the robots are inherently fragile, basic regularization of the behavior during training led the robots to learn safe and effective movements while still performing in a dynamic and agile way -- well beyond what is intuitively expected from the robot. Indeed, in experiments, they walked 181% faster, turned 302% faster, took 63% less time to get up, and kicked a ball 34% faster than a scripted baseline, while efficiently combining the skills to achieve the longer term objectives. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.13653v2-abstract-full').style.display = 'none'; document.getElementById('2304.13653v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 26 April, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Project website: https://sites.google.com/view/op3-soccer</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2302.13407">arXiv:2302.13407</a> <span> [<a href="https://arxiv.org/pdf/2302.13407">pdf</a>, <a href="https://arxiv.org/format/2302.13407">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> </div> </div> <p class="title is-5 mathjax"> DFSNet: A Steerable Neural Beamformer Invariant to Microphone Array Configuration for Real-Time, Low-Latency Speech Enhancement </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kovalyov%2C+A">Anton Kovalyov</a>, <a href="/search/cs?searchtype=author&query=Patel%2C+K">Kashyap Patel</a>, <a href="/search/cs?searchtype=author&query=Panahi%2C+I">Issa Panahi</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="2302.13407v1-abstract-short" style="display: inline;"> Invariance to microphone array configuration is a rare attribute in neural beamformers. Filter-and-sum (FS) methods in this class define the target signal with respect to a reference channel. However, this not only complicates formulation in reverberant conditions but also the network, which must have a mechanism to infer what the reference channel is. To address these issues, this study presents… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.13407v1-abstract-full').style.display = 'inline'; document.getElementById('2302.13407v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2302.13407v1-abstract-full" style="display: none;"> Invariance to microphone array configuration is a rare attribute in neural beamformers. Filter-and-sum (FS) methods in this class define the target signal with respect to a reference channel. However, this not only complicates formulation in reverberant conditions but also the network, which must have a mechanism to infer what the reference channel is. To address these issues, this study presents Delay Filter-and-Sum Network (DFSNet), a steerable neural beamformer invariant to microphone number and array geometry for causal speech enhancement. In DFSNet, acquired signals are first steered toward the speech source direction prior to the FS operation, which simplifies the task into the estimation of delay-and-summed reverberant clean speech. The proposed model is designed to incur low latency, distortion, and memory and computational burden, giving rise to high potential in hearing aid applications. Simulation results reveal comparable performance to noncausal state-of-the-art. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.13407v1-abstract-full').style.display = 'none'; document.getElementById('2302.13407v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 February, 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">5 pages, 1 figure, 2 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2302.03222">arXiv:2302.03222</a> <span> [<a href="https://arxiv.org/pdf/2302.03222">pdf</a>, <a href="https://arxiv.org/format/2302.03222">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"> Bringing the State-of-the-Art to Customers: A Neural Agent Assistant Framework for Customer Service Support </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Obadinma%2C+S">Stephen Obadinma</a>, <a href="/search/cs?searchtype=author&query=Khattak%2C+F+K">Faiza Khan Khattak</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+S">Shirley Wang</a>, <a href="/search/cs?searchtype=author&query=Sidhom%2C+T">Tania Sidhom</a>, <a href="/search/cs?searchtype=author&query=Lau%2C+E">Elaine Lau</a>, <a href="/search/cs?searchtype=author&query=Robertson%2C+S">Sean Robertson</a>, <a href="/search/cs?searchtype=author&query=Niu%2C+J">Jingcheng Niu</a>, <a href="/search/cs?searchtype=author&query=Au%2C+W">Winnie Au</a>, <a href="/search/cs?searchtype=author&query=Munim%2C+A">Alif Munim</a>, <a href="/search/cs?searchtype=author&query=Bhaskar%2C+K+R+K">Karthik Raja K. Bhaskar</a>, <a href="/search/cs?searchtype=author&query=Wei%2C+B">Bencheng Wei</a>, <a href="/search/cs?searchtype=author&query=Ren%2C+I">Iris Ren</a>, <a href="/search/cs?searchtype=author&query=Muhammad%2C+W">Waqar Muhammad</a>, <a href="/search/cs?searchtype=author&query=Li%2C+E">Erin Li</a>, <a href="/search/cs?searchtype=author&query=Ishola%2C+B">Bukola Ishola</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+M">Michael Wang</a>, <a href="/search/cs?searchtype=author&query=Tanner%2C+G">Griffin Tanner</a>, <a href="/search/cs?searchtype=author&query=Shiah%2C+Y">Yu-Jia Shiah</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+S+X">Sean X. Zhang</a>, <a href="/search/cs?searchtype=author&query=Apponsah%2C+K+P">Kwesi P. Apponsah</a>, <a href="/search/cs?searchtype=author&query=Patel%2C+K">Kanishk Patel</a>, <a href="/search/cs?searchtype=author&query=Narain%2C+J">Jaswinder Narain</a>, <a href="/search/cs?searchtype=author&query=Pandya%2C+D">Deval Pandya</a>, <a href="/search/cs?searchtype=author&query=Zhu%2C+X">Xiaodan Zhu</a>, <a href="/search/cs?searchtype=author&query=Rudzicz%2C+F">Frank Rudzicz</a> , et al. (1 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2302.03222v1-abstract-short" style="display: inline;"> Building Agent Assistants that can help improve customer service support requires inputs from industry users and their customers, as well as knowledge about state-of-the-art Natural Language Processing (NLP) technology. We combine expertise from academia and industry to bridge the gap and build task/domain-specific Neural Agent Assistants (NAA) with three high-level components for: (1) Intent Iden… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.03222v1-abstract-full').style.display = 'inline'; document.getElementById('2302.03222v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2302.03222v1-abstract-full" style="display: none;"> Building Agent Assistants that can help improve customer service support requires inputs from industry users and their customers, as well as knowledge about state-of-the-art Natural Language Processing (NLP) technology. We combine expertise from academia and industry to bridge the gap and build task/domain-specific Neural Agent Assistants (NAA) with three high-level components for: (1) Intent Identification, (2) Context Retrieval, and (3) Response Generation. In this paper, we outline the pipeline of the NAA's core system and also present three case studies in which three industry partners successfully adapt the framework to find solutions to their unique challenges. Our findings suggest that a collaborative process is instrumental in spurring the development of emerging NLP models for Conversational AI tasks in industry. The full reference implementation code and results are available at \url{https://github.com/VectorInstitute/NAA} <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.03222v1-abstract-full').style.display = 'none'; document.getElementById('2302.03222v1-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 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">Camera Ready Version of Paper Published in EMNLP 2022 Industry Track</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2212.02346">arXiv:2212.02346</a> <span> [<a href="https://arxiv.org/pdf/2212.02346">pdf</a>, <a href="https://arxiv.org/format/2212.02346">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"> Accu-Help: A Machine Learning based Smart Healthcare Framework for Accurate Detection of Obsessive Compulsive Disorder </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Patel%2C+K">Kabita Patel</a>, <a href="/search/cs?searchtype=author&query=Tripathy%2C+A+K">Ajaya Kumar Tripathy</a>, <a href="/search/cs?searchtype=author&query=Padhy%2C+L+N">Laxmi Narayan Padhy</a>, <a href="/search/cs?searchtype=author&query=Kar%2C+S+K">Sujita Kumar Kar</a>, <a href="/search/cs?searchtype=author&query=Padhy%2C+S+K">Susanta Kumar Padhy</a>, <a href="/search/cs?searchtype=author&query=Mohanty%2C+S+P">Saraju Prasad Mohanty</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="2212.02346v1-abstract-short" style="display: inline;"> In recent years the importance of Smart Healthcare cannot be overstated. The current work proposed to expand the state-of-art of smart healthcare in integrating solutions for Obsessive Compulsive Disorder (OCD). Identification of OCD from oxidative stress biomarkers (OSBs) using machine learning is an important development in the study of OCD. However, this process involves the collection of OCD c… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.02346v1-abstract-full').style.display = 'inline'; document.getElementById('2212.02346v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2212.02346v1-abstract-full" style="display: none;"> In recent years the importance of Smart Healthcare cannot be overstated. The current work proposed to expand the state-of-art of smart healthcare in integrating solutions for Obsessive Compulsive Disorder (OCD). Identification of OCD from oxidative stress biomarkers (OSBs) using machine learning is an important development in the study of OCD. However, this process involves the collection of OCD class labels from hospitals, collection of corresponding OSBs from biochemical laboratories, integrated and labeled dataset creation, use of suitable machine learning algorithm for designing OCD prediction model, and making these prediction models available for different biochemical laboratories for OCD prediction for unlabeled OSBs. Further, from time to time, with significant growth in the volume of the dataset with labeled samples, redesigning the prediction model is required for further use. The whole process requires distributed data collection, data integration, coordination between the hospital and biochemical laboratory, dynamic machine learning OCD prediction mode design using a suitable machine learning algorithm, and making the machine learning model available for the biochemical laboratories. Keeping all these things in mind, Accu-Help a fully automated, smart, and accurate OCD detection conceptual model is proposed to help the biochemical laboratories for efficient detection of OCD from OSBs. OSBs are classified into three classes: Healthy Individual (HI), OCD Affected Individual (OAI), and Genetically Affected Individual (GAI). The main component of this proposed framework is the machine learning OCD prediction model design. In this Accu-Help, a neural network-based approach is presented with an OCD prediction accuracy of 86 percent. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.02346v1-abstract-full').style.display = 'none'; document.getElementById('2212.02346v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 December, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2212.00625">arXiv:2212.00625</a> <span> [<a href="https://arxiv.org/pdf/2212.00625">pdf</a>, <a href="https://arxiv.org/format/2212.00625">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"> Probabilistic Neural Circuits leveraging AI-Enhanced Codesign for Random Number Generation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Cardwell%2C+S+G">Suma G. Cardwell</a>, <a href="/search/cs?searchtype=author&query=Schuman%2C+C+D">Catherine D. Schuman</a>, <a href="/search/cs?searchtype=author&query=Smith%2C+J+D">J. Darby Smith</a>, <a href="/search/cs?searchtype=author&query=Patel%2C+K">Karan Patel</a>, <a href="/search/cs?searchtype=author&query=Kwon%2C+J">Jaesuk Kwon</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+S">Samuel Liu</a>, <a href="/search/cs?searchtype=author&query=Allemang%2C+C">Christopher Allemang</a>, <a href="/search/cs?searchtype=author&query=Misra%2C+S">Shashank Misra</a>, <a href="/search/cs?searchtype=author&query=Incorvia%2C+J+A">Jean Anne Incorvia</a>, <a href="/search/cs?searchtype=author&query=Aimone%2C+J+B">James B. Aimone</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="2212.00625v1-abstract-short" style="display: inline;"> Stochasticity is ubiquitous in the world around us. However, our predominant computing paradigm is deterministic. Random number generation (RNG) can be a computationally inefficient operation in this system especially for larger workloads. Our work leverages the underlying physics of emerging devices to develop probabilistic neural circuits for RNGs from a given distribution. However, codesign for… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.00625v1-abstract-full').style.display = 'inline'; document.getElementById('2212.00625v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2212.00625v1-abstract-full" style="display: none;"> Stochasticity is ubiquitous in the world around us. However, our predominant computing paradigm is deterministic. Random number generation (RNG) can be a computationally inefficient operation in this system especially for larger workloads. Our work leverages the underlying physics of emerging devices to develop probabilistic neural circuits for RNGs from a given distribution. However, codesign for novel circuits and systems that leverage inherent device stochasticity is a hard problem. This is mostly due to the large design space and complexity of doing so. It requires concurrent input from multiple areas in the design stack from algorithms, architectures, circuits, to devices. In this paper, we present examples of optimal circuits developed leveraging AI-enhanced codesign techniques using constraints from emerging devices and algorithms. Our AI-enhanced codesign approach accelerated design and enabled interactions between experts from different areas of the microelectronics design stack including theory, algorithms, circuits, and devices. We demonstrate optimal probabilistic neural circuits using magnetic tunnel junction and tunnel diode devices that generate an RNG from a given distribution. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.00625v1-abstract-full').style.display = 'none'; document.getElementById('2212.00625v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 December, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> SAND2022-16607 C </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2210.15822">arXiv:2210.15822</a> <span> [<a href="https://arxiv.org/pdf/2210.15822">pdf</a>, <a href="https://arxiv.org/format/2210.15822">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> </div> </div> <p class="title is-5 mathjax"> UX-NET: Filter-and-Process-based Improved U-Net for Real-time Time-domain Audio Separation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Patel%2C+K">Kashyap Patel</a>, <a href="/search/cs?searchtype=author&query=Kovalyov%2C+A">Anton Kovalyov</a>, <a href="/search/cs?searchtype=author&query=Panahi%2C+I">Issa Panahi</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="2210.15822v1-abstract-short" style="display: inline;"> This study presents UX-Net, a time-domain audio separation network (TasNet) based on a modified U-Net architecture. The proposed UX-Net works in real-time and handles either single or multi-microphone input. Inspired by the filter-and-process-based human auditory behavior, the proposed system introduces novel mixer and separation modules, which result in cost and memory efficient modeling of speec… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.15822v1-abstract-full').style.display = 'inline'; document.getElementById('2210.15822v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2210.15822v1-abstract-full" style="display: none;"> This study presents UX-Net, a time-domain audio separation network (TasNet) based on a modified U-Net architecture. The proposed UX-Net works in real-time and handles either single or multi-microphone input. Inspired by the filter-and-process-based human auditory behavior, the proposed system introduces novel mixer and separation modules, which result in cost and memory efficient modeling of speech sources. The mixer module combines encoded input in a latent feature space and outputs a desired number of output streams. Then, in the separation module, a modified U-Net (UX) block is applied. The UX block first filters the encoded input at various resolutions followed by aggregating the filtered information and applying recurrent processing to estimate masks of separated sources. The letter 'X' in UX-Net is a name placeholder for the type of recurrent layer employed in the UX block. Empirical findings on the WSJ0-2mix benchmark dataset show that one of the UX-Net configurations outperforms the state-of-the-art Conv-TasNet system by 0.85 dB SI-SNR while using only 16% of the model parameters, 58% fewer computations, and maintaining low latency. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.15822v1-abstract-full').style.display = 'none'; document.getElementById('2210.15822v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 October, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 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 ICASSP 2023</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2210.12061">arXiv:2210.12061</a> <span> [<a href="https://arxiv.org/pdf/2210.12061">pdf</a>, <a href="https://arxiv.org/format/2210.12061">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="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Validation of Composite Systems by Discrepancy Propagation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Reeb%2C+D">David Reeb</a>, <a href="/search/cs?searchtype=author&query=Patel%2C+K">Kanil Patel</a>, <a href="/search/cs?searchtype=author&query=Barsim%2C+K">Karim Barsim</a>, <a href="/search/cs?searchtype=author&query=Schiegg%2C+M">Martin Schiegg</a>, <a href="/search/cs?searchtype=author&query=Gerwinn%2C+S">Sebastian Gerwinn</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="2210.12061v2-abstract-short" style="display: inline;"> Assessing the validity of a real-world system with respect to given quality criteria is a common yet costly task in industrial applications due to the vast number of required real-world tests. Validating such systems by means of simulation offers a promising and less expensive alternative, but requires an assessment of the simulation accuracy and therefore end-to-end measurements. Additionally, co… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.12061v2-abstract-full').style.display = 'inline'; document.getElementById('2210.12061v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2210.12061v2-abstract-full" style="display: none;"> Assessing the validity of a real-world system with respect to given quality criteria is a common yet costly task in industrial applications due to the vast number of required real-world tests. Validating such systems by means of simulation offers a promising and less expensive alternative, but requires an assessment of the simulation accuracy and therefore end-to-end measurements. Additionally, covariate shifts between simulations and actual usage can cause difficulties for estimating the reliability of such systems. In this work, we present a validation method that propagates bounds on distributional discrepancy measures through a composite system, thereby allowing us to derive an upper bound on the failure probability of the real system from potentially inaccurate simulations. Each propagation step entails an optimization problem, where -- for measures such as maximum mean discrepancy (MMD) -- we develop tight convex relaxations based on semidefinite programs. We demonstrate that our propagation method yields valid and useful bounds for composite systems exhibiting a variety of realistic effects. In particular, we show that the proposed method can successfully account for data shifts within the experimental design as well as model inaccuracies within the simulation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.12061v2-abstract-full').style.display = 'none'; document.getElementById('2210.12061v2-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 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 21 October, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 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">21 pages incl. 11 pages appendix; camera-ready version at UAI 2023</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI 2023), PMLR 216:1730-1740, 2023, URL: https://proceedings.mlr.press/v216/reeb23a.html </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2208.12466">arXiv:2208.12466</a> <span> [<a href="https://arxiv.org/pdf/2208.12466">pdf</a>, <a href="https://arxiv.org/format/2208.12466">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"> An approach to implement Reinforcement Learning for Heterogeneous Vehicular Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Peshavaria%2C+B">Bhavya Peshavaria</a>, <a href="/search/cs?searchtype=author&query=Kavaiya%2C+S">Sagar Kavaiya</a>, <a href="/search/cs?searchtype=author&query=Patel%2C+D+K">Dhaval K. Patel</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="2208.12466v1-abstract-short" style="display: inline;"> This paper presents the extension of the idea of spectrum sharing in the vehicular networks towards the Heterogeneous Vehicular Network(HetVNET) based on multi-agent reinforcement learning. Here, the multiple vehicle-to-vehicle(V2V) links reuse the spectrum of other vehicle-to-interface(V2I) and also those of other networks. The fast-changing environment in vehicular networks limits the idea of ce… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.12466v1-abstract-full').style.display = 'inline'; document.getElementById('2208.12466v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2208.12466v1-abstract-full" style="display: none;"> This paper presents the extension of the idea of spectrum sharing in the vehicular networks towards the Heterogeneous Vehicular Network(HetVNET) based on multi-agent reinforcement learning. Here, the multiple vehicle-to-vehicle(V2V) links reuse the spectrum of other vehicle-to-interface(V2I) and also those of other networks. The fast-changing environment in vehicular networks limits the idea of centralizing the CSI and allocate the channels. So, the idea of implementing ML-based methods is used here so that it can be implemented in a distributed manner in all vehicles. Here each On-Board Unit(OBU) can sense the signals in the channel and based on that information runs the RL to decide which channel to autonomously take up. Here, each V2V link will be an agent in MARL. The idea is to train the RL model in such a way that these agents will collaborate rather than compete. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.12466v1-abstract-full').style.display = 'none'; document.getElementById('2208.12466v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 August, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2208.06640">arXiv:2208.06640</a> <span> [<a href="https://arxiv.org/pdf/2208.06640">pdf</a>, <a href="https://arxiv.org/format/2208.06640">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Software Engineering">cs.SE</span> </div> </div> <p class="title is-5 mathjax"> The Sense of Logging in the Linux Kernel </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Patel%2C+K">Keyur Patel</a>, <a href="/search/cs?searchtype=author&query=Faccin%2C+J">Joao Faccin</a>, <a href="/search/cs?searchtype=author&query=Hamou-Lhadj%2C+A">Abdelwahab Hamou-Lhadj</a>, <a href="/search/cs?searchtype=author&query=Nunes%2C+I">Ingrid Nunes</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="2208.06640v1-abstract-short" style="display: inline;"> Logging plays a crucial role in software engineering because it is key to perform various tasks including debugging, performance analysis, and detection of anomalies. Despite the importance of log data, the practice of logging still suffers from the lack of common guidelines and best practices. Recent studies investigated logging in C/C++ and Java open-source systems. In this paper, we complement… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.06640v1-abstract-full').style.display = 'inline'; document.getElementById('2208.06640v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2208.06640v1-abstract-full" style="display: none;"> Logging plays a crucial role in software engineering because it is key to perform various tasks including debugging, performance analysis, and detection of anomalies. Despite the importance of log data, the practice of logging still suffers from the lack of common guidelines and best practices. Recent studies investigated logging in C/C++ and Java open-source systems. In this paper, we complement these studies by conducting the first empirical study on logging practices in the Linux kernel, one of the most elaborate open-source development projects in the computer industry. We analyze 22 Linux releases with a focus on three main aspects: the pervasiveness of logging in Linux, the types of changes made to logging statements, and the rationale behind these changes. Our findings show that logging code accounts for 3.73% of the total source code in the Linux kernel, distributed across 72.36% of Linux files. We also found that the distribution of logging statements across Linux subsystems and their components vary significantly with no apparent reasons, suggesting that developers use different criteria when logging. In addition, we observed a slow decrease in the use of logging-reduction of 9.27% between versions v4.3 and v5.3. The majority of changes in logging code are made to fix language issues, modify log levels, and upgrade logging code to use new logging libraries, with the overall goal of improving the precision and consistency of the log output. Many recommendations are derived from our findings such as the use of static analysis tools to detect log-related issues, the adoption of common writing styles to improve the quality of log messages, the development of conventions to guide developers when selecting log levels, the establishment of review sessions to review logging code, and so on. [...] <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.06640v1-abstract-full').style.display = 'none'; document.getElementById('2208.06640v1-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 August, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 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 Empirical Software Engineering journal</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2208.04955">arXiv:2208.04955</a> <span> [<a href="https://arxiv.org/pdf/2208.04955">pdf</a>, <a href="https://arxiv.org/format/2208.04955">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> </div> </div> <p class="title is-5 mathjax"> Explainable prediction of Qcodes for NOTAMs using column generation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Patel%2C+K+K">Krunal Kishor Patel</a>, <a href="/search/cs?searchtype=author&query=Desaulniers%2C+G">Guy Desaulniers</a>, <a href="/search/cs?searchtype=author&query=Lodi%2C+A">Andrea Lodi</a>, <a href="/search/cs?searchtype=author&query=Lecue%2C+F">Freddy Lecue</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="2208.04955v2-abstract-short" style="display: inline;"> A NOtice To AirMen (NOTAM) contains important flight route related information. To search and filter them, NOTAMs are grouped into categories called QCodes. In this paper, we develop a tool to predict, with some explanations, a Qcode for a NOTAM. We present a way to extend the interpretable binary classification using column generation proposed in Dash, Gunluk, and Wei (2018) to a multiclass text… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.04955v2-abstract-full').style.display = 'inline'; document.getElementById('2208.04955v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2208.04955v2-abstract-full" style="display: none;"> A NOtice To AirMen (NOTAM) contains important flight route related information. To search and filter them, NOTAMs are grouped into categories called QCodes. In this paper, we develop a tool to predict, with some explanations, a Qcode for a NOTAM. We present a way to extend the interpretable binary classification using column generation proposed in Dash, Gunluk, and Wei (2018) to a multiclass text classification method. We describe the techniques used to tackle the issues related to one vs-rest classification, such as multiple outputs and class imbalances. Furthermore, we introduce some heuristics, including the use of a CP-SAT solver for the subproblems, to reduce the training time. Finally, we show that our approach compares favorably with state-of-the-art machine learning algorithms like Linear SVM and small neural networks while adding the needed interpretability component. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.04955v2-abstract-full').style.display = 'none'; document.getElementById('2208.04955v2-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 January, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 August, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2202.09664">arXiv:2202.09664</a> <span> [<a href="https://arxiv.org/pdf/2202.09664">pdf</a>, <a href="https://arxiv.org/format/2202.09664">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="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Accurate Prediction and Uncertainty Estimation using Decoupled Prediction Interval Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Patel%2C+K">Kinjal Patel</a>, <a href="/search/cs?searchtype=author&query=Waslander%2C+S">Steven Waslander</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="2202.09664v1-abstract-short" style="display: inline;"> We propose a network architecture capable of reliably estimating uncertainty of regression based predictions without sacrificing accuracy. The current state-of-the-art uncertainty algorithms either fall short of achieving prediction accuracy comparable to the mean square error optimization or underestimate the variance of network predictions. We propose a decoupled network architecture that is cap… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.09664v1-abstract-full').style.display = 'inline'; document.getElementById('2202.09664v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2202.09664v1-abstract-full" style="display: none;"> We propose a network architecture capable of reliably estimating uncertainty of regression based predictions without sacrificing accuracy. The current state-of-the-art uncertainty algorithms either fall short of achieving prediction accuracy comparable to the mean square error optimization or underestimate the variance of network predictions. We propose a decoupled network architecture that is capable of accomplishing both at the same time. We achieve this by breaking down the learning of prediction and prediction interval (PI) estimations into a two-stage training process. We use a custom loss function for learning a PI range around optimized mean estimation with a desired coverage of a proportion of the target labels within the PI range. We compare the proposed method with current state-of-the-art uncertainty quantification algorithms on synthetic datasets and UCI benchmarks, reducing the error in the predictions by 23 to 34% while maintaining 95% Prediction Interval Coverage Probability (PICP) for 7 out of 9 UCI benchmark datasets. We also examine the quality of our predictive uncertainty by evaluating on Active Learning and demonstrating 17 to 36% error reduction on UCI benchmarks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.09664v1-abstract-full').style.display = 'none'; document.getElementById('2202.09664v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 February, 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/2202.02950">arXiv:2202.02950</a> <span> [<a href="https://arxiv.org/pdf/2202.02950">pdf</a>, <a href="https://arxiv.org/format/2202.02950">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> <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 class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1145/3491102.3502004">10.1145/3491102.3502004 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Jury Learning: Integrating Dissenting Voices into Machine Learning Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Gordon%2C+M+L">Mitchell L. Gordon</a>, <a href="/search/cs?searchtype=author&query=Lam%2C+M+S">Michelle S. Lam</a>, <a href="/search/cs?searchtype=author&query=Park%2C+J+S">Joon Sung Park</a>, <a href="/search/cs?searchtype=author&query=Patel%2C+K">Kayur Patel</a>, <a href="/search/cs?searchtype=author&query=Hancock%2C+J+T">Jeffrey T. Hancock</a>, <a href="/search/cs?searchtype=author&query=Hashimoto%2C+T">Tatsunori Hashimoto</a>, <a href="/search/cs?searchtype=author&query=Bernstein%2C+M+S">Michael S. Bernstein</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="2202.02950v1-abstract-short" style="display: inline;"> Whose labels should a machine learning (ML) algorithm learn to emulate? For ML tasks ranging from online comment toxicity to misinformation detection to medical diagnosis, different groups in society may have irreconcilable disagreements about ground truth labels. Supervised ML today resolves these label disagreements implicitly using majority vote, which overrides minority groups' labels. We intr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.02950v1-abstract-full').style.display = 'inline'; document.getElementById('2202.02950v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2202.02950v1-abstract-full" style="display: none;"> Whose labels should a machine learning (ML) algorithm learn to emulate? For ML tasks ranging from online comment toxicity to misinformation detection to medical diagnosis, different groups in society may have irreconcilable disagreements about ground truth labels. Supervised ML today resolves these label disagreements implicitly using majority vote, which overrides minority groups' labels. We introduce jury learning, a supervised ML approach that resolves these disagreements explicitly through the metaphor of a jury: defining which people or groups, in what proportion, determine the classifier's prediction. For example, a jury learning model for online toxicity might centrally feature women and Black jurors, who are commonly targets of online harassment. To enable jury learning, we contribute a deep learning architecture that models every annotator in a dataset, samples from annotators' models to populate the jury, then runs inference to classify. Our architecture enables juries that dynamically adapt their composition, explore counterfactuals, and visualize dissent. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.02950v1-abstract-full').style.display = 'none'; document.getElementById('2202.02950v1-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 February, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 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">To appear at CHI 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/2201.12903">arXiv:2201.12903</a> <span> [<a href="https://arxiv.org/pdf/2201.12903">pdf</a>, <a href="https://arxiv.org/format/2201.12903">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Aggregating Global Features into Local Vision Transformer </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Patel%2C+K">Krushi Patel</a>, <a href="/search/cs?searchtype=author&query=Bur%2C+A+M">Andres M. Bur</a>, <a href="/search/cs?searchtype=author&query=Li%2C+F">Fengjun Li</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+G">Guanghui Wang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2201.12903v1-abstract-short" style="display: inline;"> Local Transformer-based classification models have recently achieved promising results with relatively low computational costs. However, the effect of aggregating spatial global information of local Transformer-based architecture is not clear. This work investigates the outcome of applying a global attention-based module named multi-resolution overlapped attention (MOA) in the local window-based t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.12903v1-abstract-full').style.display = 'inline'; document.getElementById('2201.12903v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2201.12903v1-abstract-full" style="display: none;"> Local Transformer-based classification models have recently achieved promising results with relatively low computational costs. However, the effect of aggregating spatial global information of local Transformer-based architecture is not clear. This work investigates the outcome of applying a global attention-based module named multi-resolution overlapped attention (MOA) in the local window-based transformer after each stage. The proposed MOA employs slightly larger and overlapped patches in the key to enable neighborhood pixel information transmission, which leads to significant performance gain. In addition, we thoroughly investigate the effect of the dimension of essential architecture components through extensive experiments and discover an optimum architecture design. Extensive experimental results CIFAR-10, CIFAR-100, and ImageNet-1K datasets demonstrate that the proposed approach outperforms previous vision Transformers with a comparatively fewer number of parameters. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.12903v1-abstract-full').style.display = 'none'; document.getElementById('2201.12903v1-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 January, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2201.09242">arXiv:2201.09242</a> <span> [<a href="https://arxiv.org/pdf/2201.09242">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1007/978-981-15-7533-4_35">10.1007/978-981-15-7533-4_35 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Task Scheduling in Cloud Computing Using Hybrid Meta-heuristic: A Review </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Patel%2C+S+K">Sandeep Kumar Patel</a>, <a href="/search/cs?searchtype=author&query=Singh%2C+A">Avtar Singh</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="2201.09242v1-abstract-short" style="display: inline;"> In recent years with the advent of high bandwidth internet access availability, the cloud computing applications have boomed. With more and more applications being run over the cloud and an increase in the overall user base of the different cloud platforms, the need for highly efficient job scheduling techniques has also increased. The task of a conventional job scheduling algorithm is to determin… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.09242v1-abstract-full').style.display = 'inline'; document.getElementById('2201.09242v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2201.09242v1-abstract-full" style="display: none;"> In recent years with the advent of high bandwidth internet access availability, the cloud computing applications have boomed. With more and more applications being run over the cloud and an increase in the overall user base of the different cloud platforms, the need for highly efficient job scheduling techniques has also increased. The task of a conventional job scheduling algorithm is to determine a sequence of execution for the jobs, which uses the least resources like time, processing, memory, etc. Generally, the user requires more services and very high efficiency. An efficient scheduling technique helps in proper utilization of the resources. In this research realm, the hybrid meta-heuristic algorithms have proven to be very effective in optimizing the task scheduling by providing better cost efficiency than when singly employed. This study presents a systematic and extensive analysis of task scheduling techniques in cloud computing using the various hybrid variants of meta-heuristic methods, like Genetic Algorithm, Tabu Search, Harmony Search, Artificial Bee Colony, Particle Swarm Optimization, etc. In this research review, a separate section discusses the use of various performance evaluation metrics throughout the literature. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.09242v1-abstract-full').style.display = 'none'; document.getElementById('2201.09242v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 January, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2022. </p> </li> </ol> <nav class="pagination is-small is-centered breathe-horizontal" role="navigation" aria-label="pagination"> <a href="" 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