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JARVIS-Leaderboard:a large scale benchmark of materials design methods

npj Computational Materials 2024年第1期10卷 2280-2296页

作者： Kamal Choudhary Daniel Wines Kangming Li Kevin F.Garrity Vishu Gupta Aldo H.Romero Jaron T.Krogel Kayahan Saritas Addis Fuhr Panchapakesan Ganesh Paul R.C.Kent Keqiang Yan Yuchao Lin Shuiwang Ji Ben Blaiszik Patrick Reiser Pascal Friederich Ankit Agrawal Pratyush Tiwary Eric Beyerle Peter Minch Trevor David Rhone Ichiro Takeuchi Robert B.Wexler Arun Mannodi-Kanakkithodi Elif Ertekin Avanish Mishra Nithin Mathew Mitchell Wood Andrew Dale Rohskopf Jason Hattrick-Simpers Shih-Han Wang Luke E.K.Achenie Hongliang Xin Maureen Williams Adam J.Biacchi Francesca Tavazza Material Measurement Laboratory National Institute of Standards and TechnologyGaithersburgMD20899USA Department of Materials Science and Engineering University of Toronto27 King’s College CirTorontoONCanada Department of Electrical and Computer Engineering Northwestern UniversityEvanstonIL 60208USA Lewis-Sigler Institute for Integrative Genomics Princeton UniversityPrincetonNJ 08544USA Ludwig Institute for Cancer Research Princeton UniversityPrincetonNJ 08544USA Department of Physics and Astronomy West Virginia UniversityMorgantownWV 26506USA Materials Science and Technology Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Center for Nanophase Materials Science Oak Ridge National LaboratoryOak RidgeTN 37831USA Computational Sciences and Engineering Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Department of Computer Science and Engineering Texas A&M UniversityCollege StationTX 77843USA Globus University of ChicagoChicagoIL 60637USA Data Science and Learning Division Argonne National LabLemontIL 60439USA Institute of Nanotechnology Karlsruhe Institute of TechnologyKaiserstraße 1276131 KarlsruheGermany Institute of Theoretical Informatics Karlsruhe Institute of TechnologyKaiserstraße 1276131 KarlsruheGermany Department of Chemistry and Biochemistry and Institute for Physical Science and Technology University of MarylandCollege ParkMD 20742USA Department of Physics Applied Physics and AstronomyRensselaer Polytechnic InstituteTroyNY12180USA Department of Materials Science and Engineering University of MarylandCollege ParkMD20742USA Department of Chemistry and Institute of Materials Science and Engineering Washington University in St.LouisSt.LouisMO63130USA School of Materials Engineering Purdue UniversityWest LafayetteIN47907USA Department of Mechanical Science and Engineering University of Illinois Urbana-ChampaignUrbanaIllinois 61801USA Materials Research Laboratory University of Illinois Urbana-ChampaignUrbanaIL 61801USA Theore

Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many *** science,in particular,encompasses a variety of experimental and theoretical approaches that require careful *** efforts have been developed previously to mitigate these ***,a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still *** work introduces JARVIS-Leaderboard,an open-source and community-driven platform that facilitates benchmarking and enhances *** platform allows users to set up benchmarks with customtasks and enables contributions in the form of dataset,code,and meta-data *** cover the following materials design categories:Artificial Intelligence(AI),Electronic Structure(ES).

关键词： rigorous perfect enable

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Thermohydraulic analysis of fuel assembly of the WWER-1000 reactors at reduced power 4

Thermohydraulic analysis of fuel assembly of the WWER-1000 r...

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4th International Youth Conference on Radio Electronics, Electrical and Power engineering, REEPE 2022

作者： Singh, Mehakdeep Xu, Yubin Aljasar, Shojaa A. Institute of Applied Mathematics and Computer Science Tomsk State University Tomsk Russia School of Nuclear Science and Engineering Tomsk Polytechnic University Tomsk Russia

ISBN: (纸本)9781665414340

It is important to make a safety assessment of different hypothetical scenarios in actual nuclear power plants (NPPs). Some of the common scenarios analyzed are coolant primary tube rupture. In this paper, instantaneous coolant loss occurred at 75% and 50%. The event was simulated under abnormal conditions using the TRETON code, which is used to treat light water reactor (WWER) fuel assemblies. It is based on an algorithm to solve the heat transfer function at multiple levels and develop a porous body. In the study, the operating modes of the main circulation pump on an incomplete number of fuel rods provided acceptable levels of uneven flow and heating;The temperature of the coolant, cladding and fuel did not exceed the limits. © 2022 IEEE.

关键词： Coolants

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Effective Summarization Techniques for Legal Document Analysis 4

Effective Summarization Techniques for Legal Document Analys...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Gurrapu, Neelima Krishna, J S V Gopala Ram Pavan Kumar, V.T.M. Dhumpati, Raghu Mantena, Jeevana Sujitha Reddy Penubaka, Kiran Kumar Sr University Department of Computer Science and Artificial Intelligence Warangal India Sir C R Reddy College of Engineering Department of Cse Eluru India Kakaraparti Bhavanarayana College Department of Computer Science Vijayawada India Bahrain Polytechnic College Department of Cse Bahrain Srkr Engineering College Department of Cse Bhimavaram India Mlr Institute of Technology Department of CSE-AIML Hyderabad India

ISBN: (纸本)9798331523923

Summarizing legal texts is a critical task in natural language processing (NLP), which addresses the growing demands for efficient processing of large legal documents. Due to the complexity and structured nature of legal texts, the development of models capable of generating accurate and concise summaries presents significant challenges. This study examines the use of three state-of-the-art transformer-based models for summarizing legal texts. Each model is evaluated for its ability to capture nuanced semantics and preserve the contextual integrity of legal documents. The research uses a specialized dataset containing different types of legal documents, including summaries of case law, statutes and regulatory texts. Preprocessing steps include tokenization, normalization, and removal of non-informative content, ensuring that model inputs reflect the structure and verbosity contained in legal texts. Performance benchmarking is done using standard metrics such as ROUGE, BLEU, and BERT Score, along with domain-specific criteria to assess factual consistency and relevance. Experimental consequences exhibit that BART-base excels in generating fluent and human-like summaries, LED-base proves effective for longer documents due to its prolonged context-handling ability, and T5 showcases its versatility in domain edition. A specified analysis famous the strengths and limitations of every version in dealing with domain-unique demanding situations consisting of felony terminology, quotation references, and logical reasoning. © 2025 IEEE.

关键词： Abstractive Summarization BART BLEU CLS Tokenization Distil BART LED Legal Text Summarization ROUGE T5 Transformer Models

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Thermal Ablation Therapy Control with Tissue Necrosis-driven Temperature Feedback Enabled by Neural State Space Model with Extended Kalman Filter

Thermal Ablation Therapy Control with Tissue Necrosis-driven...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Ryo Murakami Satoshi Mori Haichong K. Zhang Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA The United States Department of Biomedical Engineering Robotics Engineering and Computer Science Engineering Worcester Polytechnic Institute Worcester MA The United States

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Thermal ablation therapy is a major minimally invasive treatment. One of the challenges is that the targeted region and therapeutic progression are often invisible to clinicians, requiring feedback provided in numerical information or imaging. Several emerging imaging modalities offer visualization of the ablation-induced necrosis formation; however, relying solely on necrosis monitoring can result in tissue overheating and endangering patients. Some of the necrosis monitoring modalities are known for their capabilities in temperature sensing, but the principles on which they are based have several limitations, such as sensitivity to the tissue motion and their environment. In this study, we propose a necrosis progression-based temperature estimation technique as an added safety feature for avoiding overheating. This model-based method does not require additional sensing hardware. It is designed to work as an independent estimator or a complimentary estimation component with other thermometers for improved robustness. For this objective, the Neural State Space model is used to approximate the ablation therapy, whose theoretical models involve nonlinear partial differential equations. Then, the Extended Kalman Filter is designed based on the model. The simulation study shows the estimation module robustly estimates the tissue temperature under several types of noise. The maximum estimation error observed before terminating ablation was around 1 °C, and the desired safety feature was successfully demonstrated. The estimator is expected to be used in a variety of necrosis monitoring modalities to guarantee more precise and safer treatment. More ambitiously, the architecture with the Neural State Space model and Extended Kalman Filter is generalizable to other medical/biological procedures involving nonlinear and patient/environment-specific physics and even to procedures having no reliable theoretical models.

关键词： Thermometers Estimation Medical treatment Mathematical models Robustness Sensors Safety Temperature control Kalman filters Monitoring

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Artificial intelligence technique based performance estimation of solid oxide fuel cells

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Materials Today: Proceedings 2023年 80卷 2573-2576页

作者： Subramanian, Yathavan Veena, R. Muhammed Ali, S.A. Kumar, Avinash Gubediran, Ramesh Kumar Dhanasekaran, Anitha Gurusamy, Dhanasekaran Muniandi, Kalpana Department of Mechanical Engineering Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai India Faculty of Integrated Technologies Universiti Brunei Darussalam GadongBE1410 Brunei Fuel Cell Institute Universiti Kebangsaan Malaysia Selangor Bangi43600 Malaysia Arni India Department of Mechanical Engineering Vellore Polytechnic College Vellore India Department of Computer Science and Engineering Government College of Engineering Salem India

In General, a larger number of experimental data would be required for optimizing the performance efficiency of the electrodes in solid oxide fuel cells (SOFC), as it was likely to involve many complex chemical and physical reactions. Therefore, in this study, we have attempted the artificial intelligence (AI) technique for analyzing the performance of the anode and cathode electrodes of SOFC under various key parameters that normally influences the efficiency of the electrodes. Since the cost of SOFC elements is expensive, computational trials with the aid of AI would not only be curtailing the preparation cost but also ensures less time consumption. Among the variety of AI methods available in the literature, the Support Vector Machine (SVM) appears to be one of the excellent and effective machine learning techniques. Hence, we have developed a SVM model and predicted the maximum current density and power density of Nickel oxide-samarium-doped ceria (NiO-SDC) composite anode and La0.6Sr0.4Co0.2Fe0.8O3-δ (LSCF) cathode electrodes of SOFC. The input parameters such as stack temperature, supply voltage were supplied to the SVM model and obtained maximum current density and power density as output and it decides the working performance of the electrodes. We have also predicted that the maximum current density and power density of 1160 mA cm2 and 225 mW cm2 respectively at 800 °C. To validate the results as predicted from SVM, we have experimented using Yttrium stabilized zirconia electrolyte-supported single cell with our prepared NiO-SDC anode and LSCF cathode which exhibited a maximum current and power density of 1170 mA cm2 and 227 mW cm2 at 800 °C using H2 as fuel. It has been found that that the theoretical predicted data current and power densities of the electrodes from the SVM approach reasonably agrees well with the experimental results. © 2021

关键词： Nickel oxide

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DeepSC-Edge: Scientific Corrosion Segmentation with Edge-Guided and Class-Balanced Losses

DeepSC-Edge: Scientific Corrosion Segmentation with Edge-Gui...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Biao Yin Nicholas Josselyn Thomas Considine John Kelley Berend Rinderspacher Robert Jensen James Snyder Ziming Zhang Elke Rundensteiner Department of Data Science Worcester Polytechnic Institute Worcester MA Department of Computer Science Worcester Polytechnic Institute Worcester MA Department of Electrical and Computer Engineering Worcester Polytechnic Institute Worcester MA

Corrosion is a prevalent issue in numerous industrial fields, causing expenses nearing $3 trillion or 4% of the GDP annually with safety threats and environmental pollution. To timely qualify and validate new corrosion-inhibiting materials on a large scale, accurate and efficient corrosion assessment is crucial. Yet it is hindered by a lack of automatic tools for expert-level corrosion segmentation of material science experimental images. Developing such tools is challenging due to limited domain-valid data, image artifacts visually similar to corrosion, various corrosion morphology, strong class imbalance, and millimeter-precision corrosion boundaries. To help the community address these challenges, we curate the first expert-level segmentation annotations for a real-world image dataset [1] for scientific corrosion segmentation. In addition, we design a deep learning based model, called DeepSC-Edge that achieves guidance of ground-truth edge learning by adopting a novel loss that avoids over-fitting to edges. It also is enriched by integrating a class-balanced loss that improves segmentation with small area but crucial edges of interest for scientific corrosion assessment. Our dataset and methods pave the way to advanced deep-learning models for corrosion assessment and generation – promoting new research to connect computer vision and material science discovery. Once the appropriate approvals have been cleared, we expect to release the code and data at: https://***/

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A Fair Multi-Partner Profit Allocation for Islanded Micro-grid 2

A Fair Multi-Partner Profit Allocation for Islanded Micro-gr...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Oladejo, Ismaheel O. Folly, Komla A. Brahma, Biswajit Ajagbe, Sunday Adeola Bandyopadhyay, Anjan Awotunde, Joseph Bamidele Department of Electrical Engineering The Polytechnic Ibadan Nigeria Department of Electrical Engineering University of Cape Town South Africa McKesson Corporation 32559 Lake Bridgeport St FremontCA94555 United States Department of Computer Engineering First Technical University Ibadan200255 Nigeria School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Nigeria

A micro-grid (MG) is regarded as a localized small-scale power supply system designed for providing electrical power for a remote community, which may be connected to the grid. The paper considers six participants' sites in a remote community. The peak energy consumption patterns for these participants must be different to ensure coalition for mutual benefits and this would facilitate efficient utilization of energy resources. This community concerned may include rural communities having participants such as schools, housing estates, industrial sites, etc. The propose approach would ensure that many participants and their needs are captured and optimized profit of the participants would ensure mutual benefits. In islanded MG, most of the cost/profit distributions in energy trading problems are done based on the self-interest of the participants. Some consider a fair (acceptable or reasonable) cost/profit distribution using the cooperative game theory (CGT) based on the Nash bargaining solution (NBS). However, this method leads to a high degree of dissatisfaction. To improve the fairness and mutual benefits for all MG participants, a new flexible approach is developed. Specifically, CGT which uses a generalized NBS (GNBS) has been proposed, which relaxes certain axiom of the NBS to ensure that fairness of game theory is achieved. The approach allows the profit of the MG participants to be shared using a mechanism based on the negotiation power (i.e. weighted fairness) so that the MG would attain an improved economic outcome. Empirical evaluation of the approach indicates that overall profits using CGT is higher when compared with an independent approach. Moreover, different in negotiation power is achieved in which higher profit is allocated to any participant based on participant's mutual agreement in achieving fair profit distribution. A robust optimization algorithm called Teaching-Learning-Based-Optimization (TLBO) is developed to obtain optimal results. The heur

关键词： Profitability

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Deep Mapper: A Multi-Channel Single-Cycle Near-Sensor DNN Accelerator 8

Deep Mapper: A Multi-Channel Single-Cycle Near-Sensor DNN Ac...

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8th IEEE International Conference on Rebooting Computing, ICRC 2023

作者： Morsali, Mehrdad Tabrizchi, Sepehr Liehr, Maximilian Cady, Nathaniel Imani, Mohsen Roohi, Arman Angizi, Shaahin New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ United States University of Nebraska-Lincoln School of Computing LincolnNE United States Suny Polytechnic Institute College of Nanoscale Science and Engineering AlbanyNY United States University of California Irvine Department of Computer Science IrvineCA United States

ISBN: (纸本)9798350382044

This work proposes the Deep Mapper, as a new near-sensor resistive accelerator architecture for Deep Neural Networks (DNN) inference that co-integrates the sensing and computing phases of resource-constrained edge devices. Deep Mapper is developed to intrinsically realize highly parallelized multi-channel processing of input frames supported by a new dense hardware-friendly mapping methodology. Our circuit-to-application simulation results on the DNN acceleration task show that Deep Mapper reaches an efficiency of 4.71 TOp/s/W outperforming state-of-the-art near-/in-sensor accelerators. © 2023 IEEE.

关键词： Deep neural networks

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Enhancing Track Management Systems with Vehicle-To-Vehicle Enabled Sensor Fusion

Enhancing Track Management Systems with Vehicle-To-Vehicle E...

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IEEE Conference on Vehicular Technology (VTC)

作者： Thomas Billington Ansh Gwash Aadi Kothari Lucas Izquierdo Timothy Talty Department of Computer Science Virginia Polytechnic Institute and State University Blacksburg VA USA Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University Blacksburg VA USA

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

In the rapidly advancing landscape of connected and automated vehicles (CAV), the integration of Vehicle-to-Everything (V2X) communication in traditional fusion systems presents a promising avenue for enhancing vehicle perception. Addressing current limitations with vehicle sensing, this paper proposes a novel Vehicle-to-Vehicle (V2V) enabled track management system that leverages the synergy between V2V signals and detections from radar and camera sensors. The core innovation lies in the creation of independent priority track lists, consisting of fused detections validated through V2V communication. This approach enables more flexible and resilient thresholds for track management, particularly in scenarios with numerous occlusions where the tracked objects move outside the field of view of the perception sensors. The proposed system considers the implications of falsification of V2X signals which is combated through an initial vehicle identification process using detection from perception sensors. Presented are the fusion algorithm, simulated environments, and validation mechanisms. Experimental results demonstrate the improved accuracy and robustness of the proposed system in common driving scenarios, highlighting its potential to advance the reliability and efficiency of autonomous vehicles.

关键词： Measurement Target tracking Error analysis Vehicular ad hoc networks Radar Sensor fusion Radar tracking Sensor systems Sensors Vehicle-to-everything

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Evaluating importance of AI in Robotics and Automation in Healthcare for Different age Groups 1

Evaluating importance of AI in Robotics and Automation in He...

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1st International Conference on Healthcare Innovations, Software and engineering Technologies, HISET 2024

作者： Abu Eid, Aws Ismail Pethkar, Rajendra Huda, Bintul Patil, Jaykar Bhaskar Gatla, Ranjith Kumar Kumbhar, Ramchandra Arab Open University Faculty of Computing Studies Amman Jordan Jaywant College of Engineering and Polytechnic Department of Mechanical Engineering Karad India Sharda University Department of Computer Science & Technology Srinagar India Jaywant Institute of Management Karad India Institute of Aeronautical Engineering Department of Electrical and Electronics Engineering Hyderabad India Department of Radio-Diagnosis Karad India

ISBN: (纸本)9798350360707

Nowadays robotics is mostly used in the world. There are many industries which used robots for reducing time and to have high precision of work. The use of artificial intelligence is now increasing which helps in automations and processing of machines. Thus, the focus of study is to find the different applications of automation of robotics (i.e. robotic process automation) in the field of healthcare and its impact on the employment of medical field. Four major categories can be used to group AI applications in healthcare: expressive, analytical, prognostic, and prescriptive. The only way to close the skills gap in the healthcare industry is to increase the application of artificial intelligence. Hospitals are developing mental commit robots to treat their patients. Through physical contact, these robots can have an impact on humans' psyche, physiology, and social interactions. Thus, in coming time most of the healthcare industry will be with the robots which will reduces the human interventions with the patients through one of the host servers. © 2024 IEEE.

关键词： Medical robotics

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