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is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.08938">arXiv:2410.08938</a> <span> [<a href="https://arxiv.org/pdf/2410.08938">pdf</a>, <a href="https://arxiv.org/format/2410.08938">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"> KinDEL: DNA-Encoded Library Dataset for Kinase Inhibitors </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chen%2C+B">Benson Chen</a>, <a href="/search/cs?searchtype=author&query=Danel%2C+T">Tomasz Danel</a>, <a href="/search/cs?searchtype=author&query=McEnaney%2C+P+J">Patrick J. McEnaney</a>, <a href="/search/cs?searchtype=author&query=Jain%2C+N">Nikhil Jain</a>, <a href="/search/cs?searchtype=author&query=Novikov%2C+K">Kirill Novikov</a>, <a href="/search/cs?searchtype=author&query=Akki%2C+S+U">Spurti Umesh Akki</a>, <a href="/search/cs?searchtype=author&query=Turnbull%2C+J+L">Joshua L. Turnbull</a>, <a href="/search/cs?searchtype=author&query=Pandya%2C+V+A">Virja Atul Pandya</a>, <a href="/search/cs?searchtype=author&query=Belotserkovskii%2C+B+P">Boris P. Belotserkovskii</a>, <a href="/search/cs?searchtype=author&query=Weaver%2C+J+B">Jared Bryce Weaver</a>, <a href="/search/cs?searchtype=author&query=Biswas%2C+A">Ankita Biswas</a>, <a href="/search/cs?searchtype=author&query=Nguyen%2C+D">Dat Nguyen</a>, <a href="/search/cs?searchtype=author&query=Dreiman%2C+G+H+S">Gabriel H. S. Dreiman</a>, <a href="/search/cs?searchtype=author&query=Sultan%2C+M">Mohammad Sultan</a>, <a href="/search/cs?searchtype=author&query=Stanley%2C+N">Nathaniel Stanley</a>, <a href="/search/cs?searchtype=author&query=Whalen%2C+D+M">Daniel M Whalen</a>, <a href="/search/cs?searchtype=author&query=Kanichar%2C+D">Divya Kanichar</a>, <a href="/search/cs?searchtype=author&query=Klein%2C+C">Christoph Klein</a>, <a href="/search/cs?searchtype=author&query=Fox%2C+E">Emily Fox</a>, <a href="/search/cs?searchtype=author&query=Watts%2C+R+E">R. Edward Watts</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="2410.08938v1-abstract-short" style="display: inline;"> DNA-Encoded Libraries (DEL) are combinatorial small molecule libraries that offer an efficient way to characterize diverse chemical spaces. Selection experiments using DELs are pivotal to drug discovery efforts, enabling high-throughput screens for hit finding. However, limited availability of public DEL datasets hinders the advancement of computational techniques designed to process such data. To… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.08938v1-abstract-full').style.display = 'inline'; document.getElementById('2410.08938v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.08938v1-abstract-full" style="display: none;"> DNA-Encoded Libraries (DEL) are combinatorial small molecule libraries that offer an efficient way to characterize diverse chemical spaces. Selection experiments using DELs are pivotal to drug discovery efforts, enabling high-throughput screens for hit finding. However, limited availability of public DEL datasets hinders the advancement of computational techniques designed to process such data. To bridge this gap, we present KinDEL, one of the first large, publicly available DEL datasets on two kinases: Mitogen-Activated Protein Kinase 14 (MAPK14) and Discoidin Domain Receptor Tyrosine Kinase 1 (DDR1). Interest in this data modality is growing due to its ability to generate extensive supervised chemical data that densely samples around select molecular structures. Demonstrating one such application of the data, we benchmark different machine learning techniques to develop predictive models for hit identification; in particular, we highlight recent structure-based probabilistic approaches. Finally, we provide biophysical assay data, both on- and off-DNA, to validate our models on a smaller subset of molecules. Data and code for our benchmarks can be found at: https://github.com/insitro/kindel. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.08938v1-abstract-full').style.display = 'none'; document.getElementById('2410.08938v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.02800">arXiv:2410.02800</a> <span> [<a href="https://arxiv.org/pdf/2410.02800">pdf</a>, <a href="https://arxiv.org/format/2410.02800">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Estimating Body Volume and Height Using 3D Data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sonar%2C+V+G">Vivek Ganesh Sonar</a>, <a href="/search/cs?searchtype=author&query=Jan%2C+M+T">Muhammad Tanveer Jan</a>, <a href="/search/cs?searchtype=author&query=Wells%2C+M">Mike Wells</a>, <a href="/search/cs?searchtype=author&query=Pandya%2C+A">Abhijit Pandya</a>, <a href="/search/cs?searchtype=author&query=Engstrom%2C+G">Gabriela Engstrom</a>, <a href="/search/cs?searchtype=author&query=Shih%2C+R">Richard Shih</a>, <a href="/search/cs?searchtype=author&query=Furht%2C+B">Borko Furht</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="2410.02800v1-abstract-short" style="display: inline;"> Accurate body weight estimation is critical in emergency medicine for proper dosing of weight-based medications, yet direct measurement is often impractical in urgent situations. This paper presents a non-invasive method for estimating body weight by calculating total body volume and height using 3D imaging technology. A RealSense D415 camera is employed to capture high-resolution depth maps of th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.02800v1-abstract-full').style.display = 'inline'; document.getElementById('2410.02800v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.02800v1-abstract-full" style="display: none;"> Accurate body weight estimation is critical in emergency medicine for proper dosing of weight-based medications, yet direct measurement is often impractical in urgent situations. This paper presents a non-invasive method for estimating body weight by calculating total body volume and height using 3D imaging technology. A RealSense D415 camera is employed to capture high-resolution depth maps of the patient, from which 3D models are generated. The Convex Hull Algorithm is then applied to calculate the total body volume, with enhanced accuracy achieved by segmenting the point cloud data into multiple sections and summing their individual volumes. The height is derived from the 3D model by identifying the distance between key points on the body. This combined approach provides an accurate estimate of body weight, improving the reliability of medical interventions where precise weight data is unavailable. The proposed method demonstrates significant potential to enhance patient safety and treatment outcomes in emergency settings. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.02800v1-abstract-full').style.display = 'none'; document.getElementById('2410.02800v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">6 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/2408.01584">arXiv:2408.01584</a> <span> [<a href="https://arxiv.org/pdf/2408.01584">pdf</a>, <a href="https://arxiv.org/format/2408.01584">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="Hardware Architecture">cs.AR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Graphics">cs.GR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Performance">cs.PF</span> </div> </div> <p class="title is-5 mathjax"> GPUDrive: Data-driven, multi-agent driving simulation at 1 million FPS </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kazemkhani%2C+S">Saman Kazemkhani</a>, <a href="/search/cs?searchtype=author&query=Pandya%2C+A">Aarav Pandya</a>, <a href="/search/cs?searchtype=author&query=Cornelisse%2C+D">Daphne Cornelisse</a>, <a href="/search/cs?searchtype=author&query=Shacklett%2C+B">Brennan Shacklett</a>, <a href="/search/cs?searchtype=author&query=Vinitsky%2C+E">Eugene Vinitsky</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="2408.01584v2-abstract-short" style="display: inline;"> Multi-agent learning algorithms have been successful at generating superhuman planning in various games but have had limited impact on the design of deployed multi-agent planners. A key bottleneck in applying these techniques to multi-agent planning is that they require billions of steps of experience. To enable the study of multi-agent planning at scale, we present GPUDrive, a GPU-accelerated, mu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.01584v2-abstract-full').style.display = 'inline'; document.getElementById('2408.01584v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.01584v2-abstract-full" style="display: none;"> Multi-agent learning algorithms have been successful at generating superhuman planning in various games but have had limited impact on the design of deployed multi-agent planners. A key bottleneck in applying these techniques to multi-agent planning is that they require billions of steps of experience. To enable the study of multi-agent planning at scale, we present GPUDrive, a GPU-accelerated, multi-agent simulator built on top of the Madrona Game Engine that can generate over a million simulation steps per second. Observation, reward, and dynamics functions are written directly in C++, allowing users to define complex, heterogeneous agent behaviors that are lowered to high-performance CUDA. We show that using GPUDrive we can effectively train reinforcement learning agents over many scenes in the Waymo Open Motion Dataset, yielding highly effective goal-reaching agents in minutes for individual scenes and enabling agents to navigate thousands of scenarios within hours. The code base with pre-trained agents is available at \url{https://github.com/Emerge-Lab/gpudrive}. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.01584v2-abstract-full').style.display = 'none'; document.getElementById('2408.01584v2-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 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 2 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.07911">arXiv:2403.07911</a> <span> [<a href="https://arxiv.org/pdf/2403.07911">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> </div> </div> <p class="title is-5 mathjax"> Standing on FURM ground -- A framework for evaluating Fair, Useful, and Reliable AI Models in healthcare systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Callahan%2C+A">Alison Callahan</a>, <a href="/search/cs?searchtype=author&query=McElfresh%2C+D">Duncan McElfresh</a>, <a href="/search/cs?searchtype=author&query=Banda%2C+J+M">Juan M. Banda</a>, <a href="/search/cs?searchtype=author&query=Bunney%2C+G">Gabrielle Bunney</a>, <a href="/search/cs?searchtype=author&query=Char%2C+D">Danton Char</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+J">Jonathan Chen</a>, <a href="/search/cs?searchtype=author&query=Corbin%2C+C+K">Conor K. Corbin</a>, <a href="/search/cs?searchtype=author&query=Dash%2C+D">Debadutta Dash</a>, <a href="/search/cs?searchtype=author&query=Downing%2C+N+L">Norman L. Downing</a>, <a href="/search/cs?searchtype=author&query=Jain%2C+S+S">Sneha S. Jain</a>, <a href="/search/cs?searchtype=author&query=Kotecha%2C+N">Nikesh Kotecha</a>, <a href="/search/cs?searchtype=author&query=Masterson%2C+J">Jonathan Masterson</a>, <a href="/search/cs?searchtype=author&query=Mello%2C+M+M">Michelle M. Mello</a>, <a href="/search/cs?searchtype=author&query=Morse%2C+K">Keith Morse</a>, <a href="/search/cs?searchtype=author&query=Nallan%2C+S">Srikar Nallan</a>, <a href="/search/cs?searchtype=author&query=Pandya%2C+A">Abby Pandya</a>, <a href="/search/cs?searchtype=author&query=Revri%2C+A">Anurang Revri</a>, <a href="/search/cs?searchtype=author&query=Sharma%2C+A">Aditya Sharma</a>, <a href="/search/cs?searchtype=author&query=Sharp%2C+C">Christopher Sharp</a>, <a href="/search/cs?searchtype=author&query=Thapa%2C+R">Rahul Thapa</a>, <a href="/search/cs?searchtype=author&query=Wornow%2C+M">Michael Wornow</a>, <a href="/search/cs?searchtype=author&query=Youssef%2C+A">Alaa Youssef</a>, <a href="/search/cs?searchtype=author&query=Pfeffer%2C+M+A">Michael A. Pfeffer</a>, <a href="/search/cs?searchtype=author&query=Shah%2C+N+H">Nigam H. 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="2403.07911v2-abstract-short" style="display: inline;"> The impact of using artificial intelligence (AI) to guide patient care or operational processes is an interplay of the AI model's output, the decision-making protocol based on that output, and the capacity of the stakeholders involved to take the necessary subsequent action. Estimating the effects of this interplay before deployment, and studying it in real time afterwards, are essential to bridge… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.07911v2-abstract-full').style.display = 'inline'; document.getElementById('2403.07911v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.07911v2-abstract-full" style="display: none;"> The impact of using artificial intelligence (AI) to guide patient care or operational processes is an interplay of the AI model's output, the decision-making protocol based on that output, and the capacity of the stakeholders involved to take the necessary subsequent action. Estimating the effects of this interplay before deployment, and studying it in real time afterwards, are essential to bridge the chasm between AI model development and achievable benefit. To accomplish this, the Data Science team at Stanford Health Care has developed a Testing and Evaluation (T&E) mechanism to identify fair, useful and reliable AI models (FURM) by conducting an ethical review to identify potential value mismatches, simulations to estimate usefulness, financial projections to assess sustainability, as well as analyses to determine IT feasibility, design a deployment strategy, and recommend a prospective monitoring and evaluation plan. We report on FURM assessments done to evaluate six AI guided solutions for potential adoption, spanning clinical and operational settings, each with the potential to impact from several dozen to tens of thousands of patients each year. We describe the assessment process, summarize the six assessments, and share our framework to enable others to conduct similar assessments. Of the six solutions we assessed, two have moved into a planning and implementation phase. Our novel contributions - usefulness estimates by simulation, financial projections to quantify sustainability, and a process to do ethical assessments - as well as their underlying methods and open source tools, are available for other healthcare systems to conduct actionable evaluations of candidate AI solutions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.07911v2-abstract-full').style.display = 'none'; document.getElementById('2403.07911v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 26 February, 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/2307.13173">arXiv:2307.13173</a> <span> [<a href="https://arxiv.org/pdf/2307.13173">pdf</a>, <a href="https://arxiv.org/format/2307.13173">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"> Opinion Mining Using Population-tuned Generative Language Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Susaiyah%2C+A">Allmin Susaiyah</a>, <a href="/search/cs?searchtype=author&query=Pandya%2C+A">Abhinay Pandya</a>, <a href="/search/cs?searchtype=author&query=H%C3%A4rm%C3%A4%2C+A">Aki H盲rm盲</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2307.13173v1-abstract-short" style="display: inline;"> We present a novel method for mining opinions from text collections using generative language models trained on data collected from different populations. We describe the basic definitions, methodology and a generic algorithm for opinion insight mining. We demonstrate the performance of our method in an experiment where a pre-trained generative model is fine-tuned using specifically tailored conte… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.13173v1-abstract-full').style.display = 'inline'; document.getElementById('2307.13173v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2307.13173v1-abstract-full" style="display: none;"> We present a novel method for mining opinions from text collections using generative language models trained on data collected from different populations. We describe the basic definitions, methodology and a generic algorithm for opinion insight mining. We demonstrate the performance of our method in an experiment where a pre-trained generative model is fine-tuned using specifically tailored content with unnatural and fully annotated opinions. We show that our approach can learn and transfer the opinions to the semantic classes while maintaining the proportion of polarisation. Finally, we demonstrate the usage of an insight mining system to scale up the discovery of opinion insights from a real text corpus. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.13173v1-abstract-full').style.display = 'none'; document.getElementById('2307.13173v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 July, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2201.04742">arXiv:2201.04742</a> <span> [<a href="https://arxiv.org/pdf/2201.04742">pdf</a>, <a href="https://arxiv.org/format/2201.04742">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"> nuReality: A VR environment for research of pedestrian and autonomous vehicle interactions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Schmitt%2C+P">Paul Schmitt</a>, <a href="/search/cs?searchtype=author&query=Britten%2C+N">Nicholas Britten</a>, <a href="/search/cs?searchtype=author&query=Jeong%2C+J">JiHyun Jeong</a>, <a href="/search/cs?searchtype=author&query=Coffey%2C+A">Amelia Coffey</a>, <a href="/search/cs?searchtype=author&query=Clark%2C+K">Kevin Clark</a>, <a href="/search/cs?searchtype=author&query=Kothawade%2C+S+S">Shweta Sunil Kothawade</a>, <a href="/search/cs?searchtype=author&query=Grigore%2C+E+C">Elena Corina Grigore</a>, <a href="/search/cs?searchtype=author&query=Khaw%2C+A">Adam Khaw</a>, <a href="/search/cs?searchtype=author&query=Konopka%2C+C">Christopher Konopka</a>, <a href="/search/cs?searchtype=author&query=Pham%2C+L">Linh Pham</a>, <a href="/search/cs?searchtype=author&query=Ryan%2C+K">Kim Ryan</a>, <a href="/search/cs?searchtype=author&query=Schmitt%2C+C">Christopher Schmitt</a>, <a href="/search/cs?searchtype=author&query=Pandya%2C+A">Aryaman Pandya</a>, <a href="/search/cs?searchtype=author&query=Frazzoli%2C+E">Emilio Frazzoli</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="2201.04742v1-abstract-short" style="display: inline;"> We present nuReality, a virtual reality 'VR' environment designed to test the efficacy of vehicular behaviors to communicate intent during interactions between autonomous vehicles 'AVs' and pedestrians at urban intersections. In this project we focus on expressive behaviors as a means for pedestrians to readily recognize the underlying intent of the AV's movements. VR is an ideal tool to use to te… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.04742v1-abstract-full').style.display = 'inline'; document.getElementById('2201.04742v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2201.04742v1-abstract-full" style="display: none;"> We present nuReality, a virtual reality 'VR' environment designed to test the efficacy of vehicular behaviors to communicate intent during interactions between autonomous vehicles 'AVs' and pedestrians at urban intersections. In this project we focus on expressive behaviors as a means for pedestrians to readily recognize the underlying intent of the AV's movements. VR is an ideal tool to use to test these situations as it can be immersive and place subjects into these potentially dangerous scenarios without risk. nuReality provides a novel and immersive virtual reality environment that includes numerous visual details (road and building texturing, parked cars, swaying tree limbs) as well as auditory details (birds chirping, cars honking in the distance, people talking). In these files we present the nuReality environment, its 10 unique vehicle behavior scenarios, and the Unreal Engine and Autodesk Maya source files for each scenario. The files are publicly released as open source at www.nuReality.org, to support the academic community studying the critical AV-pedestrian interaction. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.04742v1-abstract-full').style.display = 'none'; document.getElementById('2201.04742v1-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 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/2112.09866">arXiv:2112.09866</a> <span> [<a href="https://arxiv.org/pdf/2112.09866">pdf</a>, <a href="https://arxiv.org/format/2112.09866">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="Human-Computer Interaction">cs.HC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Pandya%2C+H+A">Hariom A. Pandya</a>, <a href="/search/cs?searchtype=author&query=Ardeshna%2C+B">Bhavik Ardeshna</a>, <a href="/search/cs?searchtype=author&query=Bhatt%2C+B+S">Brijesh S. Bhatt</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="2112.09866v1-abstract-short" style="display: inline;"> Transformer based architectures have shown notable results on many down streaming tasks including question answering. The availability of data, on the other hand, impedes obtaining legitimate performance for low-resource languages. In this paper, we investigate the applicability of pre-trained multilingual models to improve the performance of question answering in low-resource languages. We tested… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.09866v1-abstract-full').style.display = 'inline'; document.getElementById('2112.09866v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2112.09866v1-abstract-full" style="display: none;"> Transformer based architectures have shown notable results on many down streaming tasks including question answering. The availability of data, on the other hand, impedes obtaining legitimate performance for low-resource languages. In this paper, we investigate the applicability of pre-trained multilingual models to improve the performance of question answering in low-resource languages. We tested four combinations of language and task adapters using multilingual transformer architectures on seven languages similar to MLQA dataset. Additionally, we have also proposed zero-shot transfer learning of low-resource question answering using language and task adapters. We observed that stacking the language and the task adapters improves the multilingual transformer models' performance significantly for low-resource languages. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.09866v1-abstract-full').style.display = 'none'; document.getElementById('2112.09866v1-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 December, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2112.03572">arXiv:2112.03572</a> <span> [<a href="https://arxiv.org/pdf/2112.03572">pdf</a>, <a href="https://arxiv.org/format/2112.03572">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="Human-Computer Interaction">cs.HC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Question Answering Survey: Directions, Challenges, Datasets, Evaluation Matrices </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Pandya%2C+H+A">Hariom A. Pandya</a>, <a href="/search/cs?searchtype=author&query=Bhatt%2C+B+S">Brijesh S. Bhatt</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="2112.03572v1-abstract-short" style="display: inline;"> The usage and amount of information available on the internet increase over the past decade. This digitization leads to the need for automated answering system to extract fruitful information from redundant and transitional knowledge sources. Such systems are designed to cater the most prominent answer from this giant knowledge source to the user query using natural language understanding (NLU) an… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.03572v1-abstract-full').style.display = 'inline'; document.getElementById('2112.03572v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2112.03572v1-abstract-full" style="display: none;"> The usage and amount of information available on the internet increase over the past decade. This digitization leads to the need for automated answering system to extract fruitful information from redundant and transitional knowledge sources. Such systems are designed to cater the most prominent answer from this giant knowledge source to the user query using natural language understanding (NLU) and thus eminently depends on the Question-answering(QA) field. Question answering involves but not limited to the steps like mapping of user question to pertinent query, retrieval of relevant information, finding the best suitable answer from the retrieved information etc. The current improvement of deep learning models evince compelling performance improvement in all these tasks. In this review work, the research directions of QA field are analyzed based on the type of question, answer type, source of evidence-answer, and modeling approach. This detailing followed by open challenges of the field like automatic question generation, similarity detection and, low resource availability for a language. In the end, a survey of available datasets and evaluation measures is presented. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.03572v1-abstract-full').style.display = 'none'; document.getElementById('2112.03572v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 December, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2009.06115">arXiv:2009.06115</a> <span> [<a href="https://arxiv.org/pdf/2009.06115">pdf</a>, <a href="https://arxiv.org/format/2009.06115">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-channel MRI Embedding: An EffectiveStrategy for Enhancement of Human Brain WholeTumor Segmentation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Pandya%2C+A">Apurva Pandya</a>, <a href="/search/cs?searchtype=author&query=Samuel%2C+C">Catherine Samuel</a>, <a href="/search/cs?searchtype=author&query=Patel%2C+N">Nisargkumar Patel</a>, <a href="/search/cs?searchtype=author&query=Patel%2C+V">Vaibhavkumar Patel</a>, <a href="/search/cs?searchtype=author&query=Akilan%2C+T">Thangarajah Akilan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2009.06115v1-abstract-short" style="display: inline;"> One of the most important tasks in medical image processing is the brain's whole tumor segmentation. It assists in quicker clinical assessment and early detection of brain tumors, which is crucial for lifesaving treatment procedures of patients. Because, brain tumors often can be malignant or benign, if they are detected at an early stage. A brain tumor is a collection or a mass of abnormal cells… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2009.06115v1-abstract-full').style.display = 'inline'; document.getElementById('2009.06115v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2009.06115v1-abstract-full" style="display: none;"> One of the most important tasks in medical image processing is the brain's whole tumor segmentation. It assists in quicker clinical assessment and early detection of brain tumors, which is crucial for lifesaving treatment procedures of patients. Because, brain tumors often can be malignant or benign, if they are detected at an early stage. A brain tumor is a collection or a mass of abnormal cells in the brain. The human skull encloses the brain very rigidly and any growth inside this restricted place can cause severe health issues. The detection of brain tumors requires careful and intricate analysis for surgical planning and treatment. Most physicians employ Magnetic Resonance Imaging (MRI) to diagnose such tumors. A manual diagnosis of the tumors using MRI is known to be time-consuming; approximately, it takes up to eighteen hours per sample. Thus, the automatic segmentation of tumors has become an optimal solution for this problem. Studies have shown that this technique provides better accuracy and it is faster than manual analysis resulting in patients receiving the treatment at the right time. Our research introduces an efficient strategy called Multi-channel MRI embedding to improve the result of deep learning-based tumor segmentation. The experimental analysis on the Brats-2019 dataset wrt the U-Net encoder-decoder (EnDec) model shows significant improvement. The embedding strategy surmounts the state-of-the-art approaches with an improvement of 2% without any timing overheads. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2009.06115v1-abstract-full').style.display = 'none'; document.getElementById('2009.06115v1-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, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1611.05136">arXiv:1611.05136</a> <span> [<a href="https://arxiv.org/pdf/1611.05136">pdf</a>, <a href="https://arxiv.org/format/1611.05136">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"> Machine Learning Approach for Skill Evaluation in Robotic-Assisted Surgery </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Fard%2C+M+J">Mahtab J. Fard</a>, <a href="/search/cs?searchtype=author&query=Ameri%2C+S">Sattar Ameri</a>, <a href="/search/cs?searchtype=author&query=Chinnam%2C+R+B">Ratna B. Chinnam</a>, <a href="/search/cs?searchtype=author&query=Pandya%2C+A+K">Abhilash K. Pandya</a>, <a href="/search/cs?searchtype=author&query=Klein%2C+M+D">Michael D. Klein</a>, <a href="/search/cs?searchtype=author&query=Ellis%2C+R+D">R. Darin Ellis</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="1611.05136v1-abstract-short" style="display: inline;"> Evaluating surgeon skill has predominantly been a subjective task. Development of objective methods for surgical skill assessment are of increased interest. Recently, with technological advances such as robotic-assisted minimally invasive surgery (RMIS), new opportunities for objective and automated assessment frameworks have arisen. In this paper, we applied machine learning methods to automatica… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1611.05136v1-abstract-full').style.display = 'inline'; document.getElementById('1611.05136v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1611.05136v1-abstract-full" style="display: none;"> Evaluating surgeon skill has predominantly been a subjective task. Development of objective methods for surgical skill assessment are of increased interest. Recently, with technological advances such as robotic-assisted minimally invasive surgery (RMIS), new opportunities for objective and automated assessment frameworks have arisen. In this paper, we applied machine learning methods to automatically evaluate performance of the surgeon in RMIS. Six important movement features were used in the evaluation including completion time, path length, depth perception, speed, smoothness and curvature. Different classification methods applied to discriminate expert and novice surgeons. We test our method on real surgical data for suturing task and compare the classification result with the ground truth data (obtained by manual labeling). The experimental results show that the proposed framework can classify surgical skill level with relatively high accuracy of 85.7%. This study demonstrates the ability of machine learning methods to automatically classify expert and novice surgeons using movement features for different RMIS tasks. Due to the simplicity and generalizability of the introduced classification method, it is easy to implement in existing trainers. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1611.05136v1-abstract-full').style.display = 'none'; document.getElementById('1611.05136v1-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 November, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2016. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Lecture Notes in Engineering and Computer Science: Proceedings of The World Congress on Engineering and Computer Science 2016, 19-21 October, 2016, San Francisco, USA </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a 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