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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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A THEORY OF INITIALISATION’S IMPACT ON SPECIALISATION

arXiv

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arXiv 2025年

作者： Jarvis, Devon Lee, Sebastian Dominé, Clémentine Carla Juliette Saxe, Andrew M. Mannelli, Stefano Sarao School of Computer Science and Applied Mathematics University of the Witwatersrand South Africa Center for Computational Neuroscience Flatiron Institute Simons Foundation Gatsby Computational Neuroscience Unit Sainsbury Wellcome Centre UCL United Kingdom Data Science and AI Computer Science and Engineering Chalmers University of Technology University of Gothenburg Sweden Machine Intelligence and Neural Discovery Institute University of the Witwatersrand South Africa CIFAR Azrieli Global Scholar CIFAR

Prior work has demonstrated a consistent tendency in neural networks engaged in continual learning tasks, wherein intermediate task similarity results in the highest levels of catastrophic interference. This phenomenon is attributed to the network’s tendency to reuse learned features across tasks. However, this explanation heavily relies on the premise that neuron specialisation occurs, i.e. the emergence of localised representations. Our investigation challenges the validity of this assumption. Using theoretical frameworks for the analysis of neural networks, we show a strong dependence of specialisation on the initial condition. More precisely, we show that weight imbalance and high weight entropy can favour specialised solutions. We then apply these insights in the context of continual learning, first showing the emergence of a monotonic relation between task-similarity and forgetting in non-specialised networks. Finally, we show that specialization by weight imbalance is beneficial on the commonly employed elastic weight consolidation regularisation technique. Copyright © 2025, The Authors. All rights reserved.

关键词： Neural networks

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Trapping-influenced photoluminescence intensity decay in semiconductor nanoplatelets 6

Trapping-influenced photoluminescence intensity decay in sem...

引用

6th International Conference on Metamaterials and Nanophotonics, METANANO 2021

作者： Kurilovich, A.A. Mantsevich, V.N. Stevenson, K.J. Chechkin, A.V. Palyulin, V.V. Center for Energy Science and Technology Skolkovo Institute of Science and Technology Moscow121205 Russia Chair of Semiconductors and Cryoelectronics and Quantum Technology Center Physics department Lomonosov Moscow State University Moscow119991 Russia Institute for Physics and Astronomy University of Potsdam Potsdam-GolmD-14476 Germany Akhiezer Institute for Theoretical Physics National Science Center Kharkov Institute of Physics and Technology" Kharkov61108 Ukraine Center for Computational and Data-intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow121205 Russia

We present a diffusion-based simulation model for explanation of long time power-law decay of photoluminescence (PL) emission intensity in semiconductor nanoplatelets. In our model the shape of emission curves is an outcome of interplay of recombination, diffusion and trapping of excitons. At short times the excitons diffuse freely following the normal diffusion behaviour. The emission decay is purely exponential and is defined by recombination. At long times the transition into the subdiffusive motion happens and the emission occurs due to the release of excitons from surface traps. A power-law tail for intensity is a consequence of the release. The crossover from onelimit to another is controlled by diffusion properties. The approach reproduces the properties of experimental curves measured for different nanoplatelet systems. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Excitons

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Measurement of the ^(232)Th(n,f)cross section in the 1-200 MeV range at the CSNS Back-n

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Nuclear science and Techniques 2023年第8期34卷 142-153页

作者： Zhi-Zhou Ren Yi-Wei Yang Yong-Hao Chen Rong Liu Bang-Jiao Ye Jie Wen Hai-Rui Guo Zi-Jie Han Qi-Ping Chen Zhong-Wei Wen Wei-Li Sun Han Yi Xing-Yan Liu Tao Ye Jiang-Bo Bai Qi An Jie Bao Yu Bao Ping Cao Hao-Lei Chen Zhen Chen Zeng-Qi Cui Rui-Rui Fan Chang-Qing Feng Ke-Qing Gao Xiao-Long Gao Min-Hao Gu Chang-Cai Han Guo-Zhu He Yong-Cheng He Yang Hong Yi-Wei Hu Han-Xiong Huang Xi-Ru Huang Hao-Yu Jiang Wei Jiang Zhi-Jie Jiang Han-Tao Jing Ling Kang Bo Li Chao Li Jia-Wen Li Qiang Li Xiao Li Yang Li Jie Liu Shu-Bin Liu Ze Long Guang-Yuan Luan Chang-Jun Ning Meng-Chen Niu Bin-Bin Qi Jie Ren Xi-Chao Ruan Zhao-Hui Song Kang Sun Zhi-Jia Sun Zhi-Xin Tan Jing-Yu Tang Xin-Yi Tang Bin-Bin Tian Li-Jiao Wang Peng-Cheng Wang Zhao-Hui Wang Xiao-Guang Wu Xuan Wu Li-Kun Xie Xiao-Yun Yang Li Yu Tao Yu Yong-Ji Yu Guo-Hui Zhang Lin-Hao Zhang Qi-Wei Zhang Xian-Peng Zhang Yu-Liang Zhang Zhi-Yong Zhang Lu-Ping Zhou Zhi-Hao Zhou Ke-Jun Zhu Department of Modern Physics University of Science and Technology of ChinaHefei 230026China Institute of Nuclear Physics and Chemistry China Academy of Engineering PhysicsMianyang 621900China Institute of High Energy Physics Chinese Academy of Sciences(CAS)Beijing 100049China Spallation Neutron Source Science Center Dongguan 523803China Institute of Applied Physics and Computational Mathematics Beijing 100088China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of ChinaHefei 230027China Key Laboratory of Nuclear Data China Institute of Atomic EnergyBeijing 102413China School of Nuclear Science and Technology University of Science and Technology of ChinaHefei 230027China State Key Laboratory of Nuclear Physics and Technology School of PhysicsPeking UniversityBeijing 100871China State Key Laboratory of Particle Detection and Electronics Institute of High Energy PhysicsChinese Academy of SciencesBeijingChina Northwest Institute of Nuclear Technology Xi’an 710024China University of Chinese Academy of Sciences Beijing 100049China Department of Engineering and Applied Physics University of Science and Technology of ChinaHefei 230026China

The ^(232)Th(n,f)cross section is very important in basic nuclear physics and applications based on the Th/U fuel *** the time-of-flight method and a multi-cell fast-fission ionization chamber,a novel measurement of the^(232)Th(n,f)cross sec-tion relative to^(235)U in the 1–200 MeV range was performed at the China Spallation Neutron Source Back-n white neutron source(Back-n).The fission event-neutron energy spectra of^(232)Th and^(235)U fission cells were measured in the single-bunch *** 232Th/235U fission cross-sectional ratios were obtained,and the measurement uncertainties were 2.5–3.7%for energies in the 2–20 MeV range and 3.6–6.2%for energies in the 20–200 MeV ***^(232)Th(n,f)cross section was obtained by introducing the standard cross section of^(235)U(n,f).The results were compared with those of previous theoreti-cal calculations,measurements,and *** measured 232Th fission cross section agreed with the main evaluation results in terms of the experimental uncertainty,and 232Th fission resonances were observed in the 1–3 MeV *** present results provide^(232)Th(n,f)cross-sectional data for the evaluation and design of Th/U cycle nuclear systems.

关键词： 232Th(n f)cross section Fast-fission ionization chamber Back-n white neutron source

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Easy Variational Inference for Categorical Models via an Independent Binary Approximation

arXiv

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arXiv 2022年

作者： Wojnowicz, Michael T. Aeron, Shuchin Miller, Eric L. Hughes, Michael C. Tufts University MedfordMA United States Data Intensive Studies Center Dept. of Electrical and Computer Engineering Dept. of Computer Science

We pursue tractable Bayesian analysis of generalized linear models (GLMs) for categorical data. Thus far, GLMs are difficult to scale to more than a few dozen categories due to non-conjugacy or strong posterior dependencies when using conjugate auxiliary variable methods. We define a new class of GLMs for categorical data called categorical-from-binary (CB) models. Each CB model has a likelihood that is bounded by the product of binary likelihoods, suggesting a natural posterior approximation. This approximation makes inference straightforward and fast;using well-known auxiliary variables for probit or logistic regression, the product of binary models admits conjugate closed-form variational inference that is embarrassingly parallel across categories and invariant to category ordering. Moreover, an independent binary model simultaneously approximates multiple CB models. Bayesian model averaging over these can improve the quality of the approximation for any given dataset. We show that our approach scales to thousands of categories, outperforming posterior estimation competitors like Automatic Differentiation Variational Inference (ADVI) and No U-Turn Sampling (NUTS) in the time required to achieve fixed prediction quality. © 2022, CC BY.

关键词： Bayesian networks

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Meter-scale variation within a single transect demands attention to taxon accumulation curves in riverine microbiome studies

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Frontiers of Environmental science & engineering 2022年第5期16卷 115-125页

作者： Bingdi Liu Lin Zhang Jason H.Knouft Fangqiong Ling Department of Energy Environmental and Chemical EngineeringWashington University in St.LouisSt.LouisMO 63130USA Department of Biology Saint Louis UniversitySt.LouisMO 63103USA National Great Rivers Research and Education Center East AltonMO 62035USA Department of Computer Science and Engineering Washington University in St.LouisSt.LouisMO 63130USA Division of Biological and Biomedical Sciences Washington University in St.LouisSt.LouisMO 63110USA Division of Computational and Data Science Washington University in St.LouisSt.LouisMO 63130USA

Microbial communities inhabiting river ecosystems play crucial roles in global biogeochemical cycling and pollution *** variations in local microbial assemblages are important for detailed understanding of community assembly and developing robust biodiversity sampling ***,we intensely analyzed twenty water samples collected from a one-meter spaced transect from the near-shore to the near-center in the Meramec River in eastern Missouri,USA and examined the microbial community composition with 16S rRNA gene amplicon *** microbiomes across the transect exhibited extremely high similarity,with Pearson’s correlation coefficients above 0.9 for all pairwise community composition ***,despite the high similarity,PERMANOVA revealed significant spatial differences between near-shore and nearcenter communities(p=0.001).Sloan’s neutral model simulations revealed that within-transect community composition variation was largely explained by demographic stochasticity(R^(2)=0.89).Despite being primarily explained by neutral processes,LefSe analyses also revealed taxa from ten families of which relative abundances differed directionally from the bank to the river center,indicating an additional role of environmental ***,the local variations within a river transect can have profound impacts on the documentation of alpha ***-accumulation curves indicated that even twenty samples did not fully saturate the sampling effort at the genus level,yet four,six and seven samples were able to capture 80%of the phylum-level,family-level,and genus-level diversity,*** study for the first time reveals hyperlocal variations in riverine microbiomes and their assembly mechanisms,demanding attention to more robust sampling strategies for documenting microbial diversity in riverine systems.

关键词： Microbiome Freshwater Taxon accumulation curve Community assembly

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Topological Characteristics of 5d Spatially Dynamic Brain Networks in Schizophrenia

Topological Characteristics of 5d Spatially Dynamic Brain Ne...

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IEEE International Symposium on Biomedical Imaging

作者： Mustafa S. Salman Armin Iraji Noah Lewis Vince D. Calhoun School of Electrical and Computer Engineering Georgia Institute of Technology Tri-Institutional Center for Translational Research in Neuroimaging & Data Science (TReNDS) Georgia Institute of Technology Georgia State University & Emory University School of Computational Science and Engineering Georgia Institute of Technology Atlanta GA USA

The last decade of rich data-driven research on functional magnetic resonance imaging (fMRI) has provided novel insights into human brain function and aberrant behavior in brain disorders. Independent component analysis (ICA) is a widely-used technique for data-driven analysis of fMRI data. Spatial ICA is the most prominent variation of ICA and provides replicable and interpretable intrinsic connectivity network (ICN). It assumes common spatial activation across time. However, very recent studies indicate that there is utility in adopting a dynamic spatial activation modeling approach. Characterizing dynamics for both temporal and spatial domains means we have a multitude of decompositions of the already high-dimensional, multi-dataset, and multi-subject fMRI data. Hence making sense of the derived data becomes a significant issue. Here we use topological data analysis (TDA) to identify topological descriptors of the windowed spatially dynamic components of fMRI data. We discover and summarize differences in the spatial dynamics of controls and schizophrenia patients (SZs). We discover that SZs generally have lower Betti numbers and higher Wasserstein distance between spatiotemporal brain states, which provide intuitive summaries of the reduced dynamism SZs exhibit in resting-state fMRI studies.

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Time Series Analysis and Forecasting of Air Quality Index of Dhaka City of Bangladesh

Time Series Analysis and Forecasting of Air Quality Index of...

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AI IoT Congress (AIIoT), World

作者： Sheikh Rahmatulla Sakib Kamarun Nahar Sara Md. Tahmid Hossain Rasel Md. Masudul Islam Asif Md. Aynul Hasan Nahid Md. Saifur Rahman M. F. Mridha Ashraful Islam Department of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science American International University-Bangladesh Dhaka Bangladesh Center for Computational and Data Sciences Independent University Bangladesh Dhaka Bangladesh

In Dhaka, the capital city of Bangladesh, various sources including vehicle emissions, industrial activities, brick kilns, building sites, and open rubbish burning contribute to the air pollution problem. To assess the air quality, the Air Quality Index (AQI) is utilized, which categorizes air quality based on pollutant concentration. In this study, we have built ARIMA, Auto-ARIMA, SARIMAX, and VAR models to predict the air quality of Dhaka. Unlike previous studies, we have utilized hourly air pollutants factors such as PM 2.5 , PM 10 , SO 2 , CO, NO 2 , and O 3 to forecast air quality. Our novel approach enables us to predict the monthly and weekly air quality of Dhaka city. Our analysis reveals that the SARIMAX model, which takes into account seasonal patterns, trends, and external factors, is the most accurate in predicting Dhaka city’s air quality. The model’s prediction performance is assessed using statistical indicators such as mean absolute percentage error and root mean square error. The study highlights that the SARIMAX model could aid policymakers in evaluating the efficacy of air pollution control measures.

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Topology Optimizer for Inverse Design and Fabrication of Passive Photonic Integrated Components UsingWeighted Target

Topology Optimizer for Inverse Design and Fabrication of Pas...

引用

Advanced Solid State Lasers, ASSL 2021 - Part of Laser Congress, LAC 2021

作者： Minin, Iurii Kazakov, Ivan Kontorov, Sergey Shipulin, Arkady Matveev, Sergey Fedorov, Maxim Skoltech Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Bolshoy Boulevard 30 bld.1 Moscow Russia Skoltech Center for Photonics and Quantum Materials Skolkovo Institute of Science and Technology Bolshoy Boulevard 30 building 1 Moscow Russia Faculty of Computational Mathematics and Cybernetics Lomonosov Moscow State University Leninskie Gory 1-52 2nd educational building 6th floor room 663 Moscow Russia

We propose a novel approach optimizing passive photonic integrated component topology computations. It is based on Green's Function Integral Equation method, utilizes weighted optimization methods and fast Toeplit... 详细信息

ISBN: (纸本)9781557528209

关键词： Integral equations

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Quantitative Analysis of IITs’ Research Growth and SDG Contributions

arXiv

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arXiv 2024年

作者： Sharma, Kiran Nagori, Akshat Manya Dubey, Mehul Khurana, Parul School of Engineering & Technology BML Munjal University Haryana Gurugram122413 India Center for Advanced Data and Computational Science BML Munjal University Haryana Gurugram122413 India School of Computer Applications Lovely Professional University Punjab Phagwara144411 India

The Indian Institutes of Technology (IITs) are vital to India’s research ecosystem, advancing technology and engineering for industrial and societal benefits. This study reviews the research performance of top IITs—Bombay, Delhi, Madras, Kharagpur, and Kanpur based on Scopus-indexed publications (1952–2024). Research output has grown exponentially, supported by increased funding and collaborations. IIT-Kanpur excels in research impact, while IIT-Bombay and IIT-Madras are highly productive but show slightly lower per-paper impact. Internationally, IITs collaborate robustly with the USA, Germany, and the UK, alongside Asian nations like Japan and South Korea, with IIT-Madras leading inter-IIT partnerships. Research priorities align with SDG 3 (Health), SDG 7 (Clean Energy), and SDG 11 (Sustainable Cities). Despite strengths in fields like energy, fluid dynamics, and materials science, challenges persist, including limited collaboration with newer IITs and gaps in emerging fields. Strengthening specialization and partnerships is crucial for addressing global challenges and advancing sustainable development. © 2024, CC BY.

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