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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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An Efficient Shortest Path Optimized Algorithm for Vehicle Adhoc Networks System 7

An Efficient Shortest Path Optimized Algorithm for Vehicle A...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Shalini, K. Reddy, K. Harinath Punati, Kondalarao Kavin, K.S. Veena, P. Pushpalatha, K. Vardhaman College of Engineering Department of Artificial Intelligence and Data Science Shamshabad India Annamacharya Institute of Technology and Sciences Department of Electrical and Electronics Engineering Rajampet India Hyderabad Institute of Technology and Management Faculty of Electronics and Communication Engineering Hyderabad500090 India Government College of Engineering Department of Electrical and Electronics Engineering Tamilnadu Tirunelveli India N. S. S. Polytechnic College Department of Computer Engineering Pandalam Pathanamthitta India Coimbatore Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore641109 India

ISBN: (纸本)9798350372816

VANET (Vehicular ad-hoc network) is a network of advanced automobiles designed to solve the problem of mobility and, as a result, reduce the number of accidents. Because VANET is a type of MANET (mobile ad hoc network) surrounded by human-driven nodes, the social concept can be used to explain VANET routing decisions. This motivates academics to look into the possibilities of using Social Network Analysis (SNA) to create routing strategies. The most critical problem in any system is parcel drop. Social characteristics-based architectures encounter comparable challenges, resulting in inefficient message delivery. In this research, the dragonfly method is used to the shortest path methodology for fixed line VANET with the purpose of reducing package drop and increasing network reliability. This is made achievable by sculpted system of values centralization analysis, which improves the decision via dragonfly optimization methodology. The behavior of the using nodes capability then be determined by the importance computation. In this study, the sustainability of the optimized shortest path system is measured in terms of End to End delay, Energy Consumption, Packet Loss, and Throughput. The dragonfly algorithm's throughput evaluation is 650 kbps as compared to the traditional way. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Enhanced GAN-Assisted Intrusion Detection Model for Addressing data Imbalance Issues

Enhanced GAN-Assisted Intrusion Detection Model for Addressi...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Raghu Dhumpati Nirmala Reddy Sankati Ramakrishna Posimsetti Naveen Kumar Sheo Kumar Homera Durani Department of Computer Science and Engineering Bahrain Polytechnic Bahrain Department of Computer Science and Engineering IES College of Technology Bhopal India Department of Computer Science and Engineering-Data Science KKR & KSR Institute of Technology and Sciences Guntur India VLSI System Design Department of Computer Science and Engineering CMR Engineering College Hyderabad India Department of CSE School of Engineering RK University Rajkot India

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

With the introduction of IIoT systems, industry breakthroughs and linked smart objects revolutionize the world. The enemies are finding a lot of room to launch a number of devastating cyber assaults thanks to all this connection and dependability. However, implementing IDS systems may be tough due to the absence of dataset for training as well as testing. This makes it difficult to detect likely cyberattacks with the required accuracy. The method of detection might not be able to learn perfectly and detect cyberattacks significantly higher precision if there aren't enough records related to every threat category in the dataset. In addition, the detection of breaches procedure is becoming more complicated due to unbalanced datasets. In this light, our research suggests a more effective GAN-based IDS model for creating simulated data samples that can fool and evade security systems before directing the real-life network assault. To be more specific, it creates fictitious datasets and trains IDS on these in conjunction with the actual data in order to boost the system's efficiency. The specifics of the Improved Generative Adversarial Network-based Intrusion Detection System (IGAN-IDS) model that is suggested in this study are described in this section. With an accuracy of 99.6 percent, the suggested IGAN-IDS model surpasses all of its competitors, including BSA-IDS, GAN, MLP-GAN, Synthetic Minority Over-sampling Technique (SMOTE), and ADASYN. This proves that it outperforms all other approaches when it comes to classification.

关键词： Training Accuracy Intrusion detection Generative adversarial networks Rendering (computer graphics) data models Security computer crime Industrial Internet of Things Testing

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A 0.18 μm CMOS Integrated Optical Wireless Power Transfer System for Implantable Biomedical Devices

A 0.18 μm CMOS Integrated Optical Wireless Power Transfer S...

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International Conference on Mixed Design of Integrated Circuits and Systems (MIXDES)

作者： Guido Di Patrizio Stanchieri Andrea De Marcellis Marco Faccio Elia Palange Ulkuhan Guler Department of Information Engineering Computer Science and Mathematics (DISIM) University of L’Aquila L’Aquila Italy Department of Electrical and Computer Engineering (ECE) Worcester Polytechnic Institute Worcester Massachusetts USA

This paper reports on a fully integrated optical wireless power transfer system designed for implantable biomedical devices. If compared with the solutions based on radiative and non-radiative methodologies, like the capacitive, inductive, magnetic couplings and radio frequency techniques, the optical systems reach the highest electromagnetic compatibility respect to the simultaneous presence of other electronic instrumentations. A further advantage common to all the radio frequencies, is the capability to transfer power to implantable and wearable biomedical devices also from long distances using environmental visible and near infrared light and/or LED-based lamps. The proposed solution has been designed at transistor level in TSMC 0.18 μm CMOS technology using inverter stages with the aim to maximize the output voltage value starting from low input voltages coming from a Si photodiode, also included in the overall layout of the system that requires a total Si area of about 0.466 mm 2 . By performing post-layout simulations, considering a minimum input voltage as low as 0.5 V, the achieved output voltage is higher than 2 V that increases up to 3.4 V for an input voltage of 0.7 V, with a best power conversion efficiency of about 70 %. The comparison of the main characteristics of the proposed solution with the State-of-Art demonstrates that, starting from the lower input voltages, the proposed system is capable to work with a reduced clock frequency achieving higher output voltages to supply external loads, satisfactory output currents and good power conversion efficiencies, requiring a reduced Si area.

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Multi-Task Learning with LLMs for Implicit Sentiment Analysis: data-level and Task-level Automatic Weight Learning

arXiv

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arXiv 2024年

作者： Lai, Wenna Xie, Haoran Xu, Guandong Li, Qing The Department of Computing Hong Kong Polytechnic University Hong Kong The School of Data Science Lingnan University Hong Kong The School of Computer Science The Data Science Institute University of Technology Sydney SydneyNSW2007 Australia The Education University of Hong Kong Hong Kong

Implicit sentiment analysis (ISA) presents significant challenges due to the absence of salient cue words. Previous methods have struggled with insufficient data and limited reasoning capabilities to infer underlying opinions. Integrating multi-task learning (MTL) with large language models (LLMs) offers the potential to enable models of varying sizes to reliably perceive and recognize genuine opinions in ISA. However, existing MTL approaches are constrained by two sources of uncertainty: data-level uncertainty, arising from hallucination problems in LLM-generated contextual information, and task-level uncertainty, stemming from the varying capacities of models to process contextual information. To handle these uncertainties, we introduce MT-ISA, a novel MTL framework that enhances ISA by leveraging the generation and reasoning capabilities of LLMs through automatic MTL. Specifically, MT-ISA constructs auxiliary tasks using generative LLMs to supplement sentiment elements and incorporates automatic MTL to fully exploit auxiliary data. We introduce data-level and task-level automatic weight learning (AWL), which dynamically identifies relationships and prioritizes more reliable data and critical tasks, enabling models of varying sizes to adaptively learn fine-grained weights based on their reasoning capabilities. We investigate three strategies for data-level AWL, while also introducing homoscedastic uncertainty for task-level AWL. Extensive experiments reveal that models of varying sizes achieve an optimal balance between primary prediction and auxiliary tasks in MT-ISA. This underscores the effectiveness and adaptability of our approach. © 2024, CC BY-NC-SA.

关键词： Multi-task learning

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Dimension Estimation Using Autoencoders with Applications to Financial Market Analysis 19

Dimension Estimation Using Autoencoders with Applications to...

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19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020

作者： Bahadur, Nitish Paffenroth, Randy Worcester Polytechnic Institute Data Science WorcesterMA United States

ISBN: (纸本)9781728184708

Dimension Estimation (DE) and Dimension Reduction (DR) are two closely related topics, but with quite different goals. In DR, one attempts to project a random vector, either linearly or non-linearly, to a lower dimensional space that preserves the information contained in the original higher dimensional space. However, in DE, one attempts to estimate the intrinsic dimensionality or number of latent variables in a set of measurements of a random vector. DE and DR are closely linked because reducing the dimension to a smaller value than suggested by DE will likely lead to information loss. In particular, when considering linear methods, such as Principal Component Analysis (PCA), DE and DR are often accomplished simultaneously. However, in this paper we will focus on a particular class of deep neural networks called autoencoders which are used extensively for DR but are less well studied for DE. We show that several important questions arise when using AEs for DE, above and beyond those that arise for more classic DR/DE techniques such as PCA. We address AE architectural choices and regularization techniques that allow one to transform AE latent layer representations into estimates of intrinsic dimension. We demonstrate the effectiveness of our techniques on synthetic, image processing benchmark problems, and, most importantly, the analysis of financial markets. © 2020 IEEE.

关键词： Principal component analysis

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Clique covers of complete graphs and piercing multitrack intervals

arXiv

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arXiv 2024年

作者： Barát, János Gyárfás, András Sárközy, Gábor N. Alfréd Rényi Institute of Mathematics Budapest P.O. Box 127 BudapestH-1364 Hungary University of Pannonia Hungary Computer Science Department Worcester Polytechnic Institute WorcesterMA United States

Assume that R1, R2, . . ., Rt are disjoint parallel lines in the plane. A t-interval (or t-track interval) is a set that can be written as the union of t closed intervals, each on a different line. It is known that pairwise intersecting 2-intervals can be pierced by two points, one from each line. However, it is not true that every set of pairwise intersecting 3-intervals can be pierced by three points, one from each line. For k ≥ 3, Kaiser and Rabinovich asked whether k-wise intersecting t-intervals can be pierced by t points, one from each line. Our main result provides a positive answer in an asymptotic sense: in any set S1, . . ., Sn of k-wise intersecting t-intervals, at least kk+1−1n can be pierced by t points, one from each line. We prove this in a more general form, replacing intervals by subtrees of a tree. This leads to questions and results on covering vertices of edge-colored complete graphs by vertices of monochromatic cliques having distinct colors, where the colorings are chordal, or more generally induced C4-free graphs. For instance, we show that if the edges of a complete graph Kn are colored with red or blue so that both color classes are induced C4-free, then at least 45n vertices can be covered by a red and a blue clique, andMSC Codes 05C62, 05D10 Copyright © 2024, The Authors. All rights reserved.

关键词： Piercing

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Can AI Fix Buggy Code? Exploring the Use of Large Language Models in Automated Program Repair

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computer 2025年第7期58卷 122-128页

作者： Lan Zhang Anoop Singhal Qingtian Zou Xiaoyan Sun Peng Liu Hsiao-Ying Lin Northern Arizona University Flagstaff AZ USA National Institute of Standards and Technology Gaithersburg MD USA University of Texas Southwestern Medical Center Dallas TX USA Department of Computer Science Worcester Polytechnic Institute Worcester MA USA The Pennsylvania State University State College PA USA Huawei France France

This article reviews the current human–large language models collaboration approach to bug fixing and points out the research directions toward (the development of) autonomous program repair artificial intelligence a... 详细信息

关键词： Software Development Language Model Intelligence Agencies Software Engineering Human Experts Complete Coding Artificial Intelligence Systems Autonomous Agents Software Defect Complex Software Code Review Bug Fixes Programming Language Application Programming Interface Understanding Of Context Debugging Artificial Intelligence Models Part Of Code Memory Management Programming Tasks

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Dimension Estimation using Second Order data in Finance

Dimension Estimation using Second Order data in Finance

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IEEE International Conference on Big data

作者： Nitish Bahadur Randy Paffenroth Data Science Worcester Polytechnic Institute Worcester MA

ISBN: (纸本)9781665445993

Estimating intrinsic dimension of financial markets informs the number of assets in portfolio selection models and can help archetypal Mean-Variance (MV) single portfolio optimization model pioneered by Markowitz, especially when intrinsic dimension is introduced as a cardinality constraint in the model. The balance between portfolio concentration risk and portfolio diversification benefit is a quintessential active portfolio management problem. Portfolio variance-covariance matrix, a second order dataset derived from first order portfolio constituents return time series, is an essential tool to quantify risk in a portfolio. Our goal in this paper is to estimate financial markets non-linear intrinsic dimension using second order inputs such as distances (dissimilarity) between asset returns. Second- order data is widely used in financial applications such as portfolio optimization, hierarchical clustering, and risk metrics. We demonstrate the effectiveness of our techniques on synthetic data, benchmark image problems, and, most importantly, financial markets.

关键词： COVID-19 Training Time series analysis Estimation Euclidean distance Big data Resource management

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InfantMotion2Vec: Unlabeled data-Driven Infant Pose Estimation Using a Single Chest IMU

InfantMotion2Vec: Unlabeled Data-Driven Infant Pose Estimati...

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International Workshop on Wearable and Implantable Body Sensor Networks (BSN)

作者： Mohammad Nur Hossain Khan Nancy L. McElwain Mark Hasegawa-Johnson Bashima Islam Department of Electrical and Computer Engineering Worcester Polytechnic Institute Department of Human Development and Family Studies University of Illinois Beckrnan Institute for Advanced Science and Technology University of Illinois Department of Electrical and Computer Engineering University of Illinois

ISBN: (数字)9798331530143

ISBN: (纸本)9798331530150

Early identification of neuro-developmental risks in infants is crucial for timely intervention and improved quality of life. Current screening methods are costly, intrusive, and limited by artificial environments or require the infant to wear multiple sensors. To address these challenges, we propose a novel approach leveraging inertial measurement units (IMUs) to monitor infants' spontaneous motor abilities in natural settings. Our method introduces a hierarchical semi-supervised classifier and the InfantMotion2Vec embedding to capture detailed motion patterns, accommodating a wide age range (up to 36 months) while minimizing reliance on labeled data and cumbersome sensor setups. We collected labeled IMU data from 25 families and unlabeled data from 42 families using a single wearable sensor. Pretraining an embedding network using unlabeled data with a hierarchical pose estimator resulted in a 26% increase in F1-score and a 77.7% increase in Cohen's Kappa score compared to using only labeled data. The InfantMotion2Vec embedding adequately handles highly unbalanced labeled data, demonstrating its effectiveness in infant posture classification.

关键词： Body sensor networks Measurement units Pose estimation Inertial navigation Motors Wearable sensors Monitoring

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