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SRBench++: Principled Benchmarking of Symbolic Regression With Domain-Expert Interpretation

IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： de Franca, F.O. Virgolin, M. Kommenda, M. Majumder, M.S. Cranmer, M. Espada, G. Ingelse, L. Fonseca, A. Landajuela, M. Petersen, B. Glatt, R. Mundhenk, N. Lee, C.S. Hochhalter, J.D. Randall, D.L. Kamienny, P. Zhang, H. Dick, G. Simon, A. Burlacu, B. Kasak, Jaan Machado, Meera Wilstrup, Casper Cavaz, W.G.L. Federal University of ABC Santo Andre Brazil Evolutionary Intelligence group Centrum Wiskunde and Informatica Science Park Amsterdam Netherlands University of Applied Sciences Upper Austria Hagenberg Austria Computational Health Informatics Program Boston Children’s Hospital Harvard Medical School Boston USA Center for Computational Astrophysics Flatiron Institute and Department of Astrophysical Sciences of Princeton University USA LASIGE Faculdade de Ciências Universidade de Lisboa Lisboa Portugal Computational Engineering Division Lawrence Livermore National Laboratory Livermore USA Department of Mechanical Engineering University of Utah Utah USA Meta FAIR France Victoria University of Wellington School of Engineering and Computer Science New Zealand University of otago Department of Information Science New Zealand Institut für Angewandte Physik Universität TübingenMax Planck Institute for Intelligent Systems Tübingen Germany Abzu AI Orient Plads 1 Nordhavn Denmark

Symbolic regression searches for analytic expressions that accurately describe studied phenomena. The main promise of this approach is that it may return an interpretable model that can be insightful to users, while maintaining high accuracy. The current standard for benchmarking these algorithms is SRBench, which evaluates methods on hundreds of datasets that are a mix of real-world and simulated processes spanning multiple domains. At present, the ability of SRBench to evaluate interpretability is limited to measuring the size of expressions on real-world data, and the exactness of model forms on synthetic data. In practice, model size is only one of many factors used by subject experts to determine how interpretable a model truly is. Furthermore, SRBench does not characterize algorithm performance on specific, challenging sub-tasks of regression such as feature selection and evasion of local minima. In this work, we propose and evaluate an approach to benchmarking SR algorithms that addresses these limitations of SRBench by 1) incorporating expert evaluations of interpretability on a domain-specific task, and 2) evaluating algorithms over distinct properties of data science tasks. We evaluate 12 modern symbolic regression algorithms on these benchmarks and present an in-depth analysis of the results, discuss current challenges of symbolic regression algorithms and highlight possible improvements for the benchmark itself. Authors

关键词： Benchmarking

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Incorporating ethics education into an electrical and computer engineering undergraduate program 125

Incorporating ethics education into an electrical and comput...

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125th ASEE Annual Conference and Exposition

作者： Ghorbani, Mahsa Maciejewski, Anthony A. Siller, Thomas J. Chong, Edwin K.P. Omur-Ozbek, Pinar Atadero, Rebecca A. Colorado State University Program of Systems Engineering United States Colorado State University Department of Electrical and Computer Engineering United States Colorado State University Department in Civil and Environmental Engineering United States Colorado State University Department of Electrical and Computer Engineering and of Mathematics United States Colorado State University Civil and Environmental Engineering Department United States Colorado State University Department of Civil and Environmental Engineering United States

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Quantum transient heat transport in the hyperparametric oscillator

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Physical Review A 2021年第5期104卷 052220-052220页

作者： JungYun Han Daniel Leykam Dimitris G. Angelakis Juzar Thingna Center for Theoretical Physics of Complex Systems Institute for Basic Science (IBS) Daejeon 34126 Republic of Korea Basic Science Program University of Science and Technology (UST) Daejeon 34113 Republic of Korea Centre for Quantum Technologies National University of Singapore 3 Science Drive 2 Singapore 117543 School of Electrical and Computer Engineering Technical University of Crete Chania Crete 73100 Greece

We explore nonequilibrium quantum heat transport in nonlinear bosonic systems in the presence of a non-Kerr-type interaction governed by hyperparametric oscillation due to two-photon hopping between the two cavities. We estimate the thermodynamic response analytically by constructing the su(2) algebra of the nonlinear Hamiltonian and predict that the system exhibits a negative excitation mode. Consequently, this specific form of interaction enables the cooling of the system by inducing a ground-state transition when the number of particles increases, even though the interaction strength is small. We demonstrate a transition of the heat current numerically in the presence of symmetric coupling between the system and the bath and show long relaxation times in the cooling phase. We compare with the Kerr-type Bose-Hubbard form of interaction induced via cross-phase modulation, which does not exhibit any such transition. We further compute the nonequilibrium heat current in the presence of two baths at different temperatures and observe that the cooling effect for the non-Kerr-type interaction persists. Our findings may help in the manipulation of quantum states using the system's interactions to induce cooling.

关键词： Quantum optics Quantum thermodynamics Quantum transport

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Valley-polarized Exitonic Mott Insulator in WS2/WSe2 Moiré Superlattice

arXiv

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arXiv 2023年

作者： Lian, Zhen Meng, Yuze Ma, Lei Maity, Indrajit Yan, Li Wu, Qiran Huang, Xiong Chen, Dongxue Chen, Xiaotong Chen, Xinyue Blei, Mark Taniguchi, Takashi Watanabe, Kenji Tongay, Sefaattin Lischner, Johannes Cui, Yong-Tao Shi, Su-Fei Department of Chemical and Biological Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Departments of Materials The Thomas Young Centre for Theory and Simulation of Materials Imperial College London United Kingdom Department of Physics and Astronomy University of California RiversideCA92521 United States Department of Materials Science and Engineering University of California RiversideCA92521 United States Materials Science and Engineering Program School for Engineering of Matter Transport and Energy Arizona State University TempeAZ85287 United States International Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Research Center for Functional Materials National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Department of Electrical Computer & Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

Strongly enhanced electron-electron interaction in semiconducting moiré superlattices formed by transition metal dichalcogenides (TMDCs) heterobilayers has led to a plethora of intriguing fermionic correlated states1–7. Meanwhile, interlayer excitons in a type-II aligned TMDC heterobilayer moiré superlattice, with electrons and holes separated in different layers8,9, inherit this enhanced interaction and strongly interact with each other, promising for realizing tunable correlated bosonic quasiparticles10,11 with valley degree of freedom. We employ photoluminescence spectroscopy to investigate the strong repulsion between interlayer excitons and correlated electrons in a WS2/WSe2 moiré superlattice and combine with theoretical calculations to reveal the spatial extent of interlayer excitons and the band hierarchy of correlated states. We further find that an excitonic Mott insulator state emerges when one interlayer exciton occupies one moiré cell, evidenced by emerging photoluminescence peaks under increased optical excitation power. Double occupancy of excitons in one unit cell requires overcoming the energy cost of exciton-exciton repulsion of about 30-40 meV, depending on the stacking configuration of the WS2/WSe2 heterobilayer. Further, the valley polarization of the excitonic Mott insulator state is enhanced by nearly one order of magnitude. Our study demonstrates the WS2/WSe2 moiré superlattice as a promising platform for engineering and exploring new correlated states of fermion, bosons, and a mixture of both12 © 2023, CC BY.

关键词： Excitons

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Implementation of Generate and Test Algorithm for Junior High School Zoning System in Malang 4

Implementation of Generate and Test Algorithm for Junior Hig...

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4th International Conference on Vocational Education and Training, ICOVET 2020

作者： Ratnasar, Novia Agustin Mawarni, Inggar Tri Nur Handayani, Anik Wibawa, Aji Prasetya Hammad, Jehad A. H Wibowotomo, Budi Sugandi, MacHmud Universitas Negeri Malang Vocational Education Program Department Indonesia Universitas Negeri Malang Electrical Engineering Dept Indonesia Al-Quds Open University Faculty of Technology and Applied Sciences Department of Computer Information Systems Palestine Universitas Negeri Malang Industrial Technology Department Malang65145 Indonesia Universitas Negeri Malang Mechanical ENgineering Department Malang65145 Indonesia

ISBN: (纸本)9781728181318

Admission of new students in 2019 through zoning still faces many obstacles, one of which is the readiness of the application to determine the distance of a student's house from the nearest recommended school. The purpose of this study is to provide better recommendations regarding the shortest and most accurate distance in the zoning system for admission of junior high school students in Malang by using the Generate and Test algorithm without having to do it manually or using other map applications. The results of this study indicate that the distance value obtained from the haversine formula corresponds to the original distance measured through a zoning application that applies the Generate and Test algorithm to measure the closest distance from the student's location. The recommended distance is given through a process of repetition and comparison of nodes. By using the generate and test algorithm for the search process the shortest distance from the user to the destination school has an average time of 6.2MB, the level of accuracy is the same as the Haversine calculation. © 2020 IEEE.

关键词： Students

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Experimental Investigation of Lord Kelvin’s Isentropic Cooling and Heating Expression in Tensile Bars for Two engineering Alloys

arXiv

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arXiv 2023年

作者： Burns, S.J. Pratt, Christine E. Carrock, Joseph Gagnon, Jean-Philippe Sefkow, A.B. Materials Science Program College of Arts Science and Engineering University of Rochester RochesterNY14627 United States Department of Mechanical Engineering University of Rochester 236 Hopeman Hall RochesterNY14627 United States Field Application Engineering Telops GenevaNY14456 United States Systems Engineering Telops 100-2600 St-Jean-Baptiste Ave. QuébecQCG2E 6J5 Canada Laboratory for Laser Energetics University of Rochester 250 E. River Rd. RochesterNY14623 United States Department of Physics and Astronomy University of Rochester 206 Bausch and Lomb Hall RochesterNY14627 United States Department of Computer Science University of Rochester 2513 Wegmans Hall RochesterNY14627 United States

Solids when rapidly and elastically stressed change temperature, the effect proposed by Lord Kelvin is adiabatic thermo-elastic cooling or heating depending on the sign of the stress. A fast sensitive IR camera has measured temperature both decreasing and increasing. Temperature measurements made from the reversible, elastic part of the stress-strain curve during fast uniaxial tensile loading have been investigated. The isentropic temperature cooling from the loading curve is recovered by heating after the specimen fractures when the load is released. These measurements establish for the first time isentropic thermal recovery in two engineering alloys. The materials tested are an AISI 4340 steel and an aluminum 2024 alloy. Measurements of the isentropic thermo-elastic stress cooling are -0.61 K/GPa for steel and -1.7 K/GPa for aluminum alloy. The isentropic thermo-elastic stress heating is -1.16 K/GPa for steel and -1.6 K/GPa for aluminum alloy. These values are compared to Lord Kelvin’s theoretical thermodynamic prediction. Comparisons are also to equations of state tables. The experimental values are slightly below theoretical predictions. The isentropic, elastic part of the temperature is fully recoverable even after extensive plastic deformation upon fracture. Copyright © 2023, The Authors. All rights reserved.

关键词： Cooling

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Machine learning for improved current-density reconstruction from two-dimensional vector magnetic images

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Physical Review Applied 2025年第3期23卷 034035-034035页

作者： Niko R. Reed Danyal Bhutto Matthew J. Turner Declan M. Daly Sean M. Oliver Jiashen Tang Kevin S. Olsson Nicholas Langellier Mark J.H. Ku Department of Physics University of Maryland College Park Maryland USA Quantum Technology Center University of Maryland College Park Maryland USA Joint Quantum Institute University of Maryland College Park Maryland USA Department of Biomedical Engineering Boston University Boston Massachusetts USA A. A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown Massachusetts USA Department of Electrical and Computer Engineering University of Maryland College Park Maryland USA The MITRE Corporation McLean Virginia 22102 USA Intelligence Community Postdoctoral Research Fellowship Program University of Maryland College Park Maryland 20742 USA Department of Physics Harvard University Cambridge Massachusetts USA Present address: VideaHealth Inc. Boston MA. Department of Materials Science and Engineering University of Delaware Newark Delaware 19716 USA Department of Physics and Astronomy University of Delaware Newark Delaware 19716 USA Present address: Northrop Grumman Mission Systems Linthicum Maryland 21090 USA.

The reconstruction of electrical current densities from magnetic field measurements is an important technique with applications in materials science, circuit design, quality control, plasma physics, and biology. Analytic reconstruction methods exist for planar currents, but break down in the presence of high-spatial-frequency noise or large standoff distance, restricting the types of systems that can be studied. Here, we demonstrate the use of a deep convolutional neural network for current density reconstruction from two-dimensional images of vector magnetic fields acquired by a quantum diamond microscope . Trained network performance significantly exceeds analytic reconstruction for data with high noise or large standoff distances. This machine learning technique can perform quality inversions on lower-signal-to-noise-ratio data, significantly reducing the data collection time and permitting reconstructions of weaker and three-dimensional current sources.

关键词： Signal to noise ratio

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Periodicity Scoring of Time Series Encodes Dynamical Behavior of the Tumor Suppressor p53 ⁎ ⁎

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IFAC-PapersOnLine 2021年第9期54卷 488-495页

作者： Caroline Moosmüller Christopher J. Tralie Mahdi Kooshkbaghi Zehor Belkhatir Maryam Pouryahya José Reyes Joseph O. Deasy Allen R. Tannenbaum Ioannis G. Kevrekidis C. Moosmüller and C. Tralie contributed equally to this work Department of Mathematics University of California San Diego La Jolla CA 92093 USA Department of Medical Physics Memorial Sloan-Kettering Cancer Center NY USA Department of Chemical and Biomolecular Engineering Johns Hopkins University Baltimore MD 21218 USA Department of Mathematics and Computer Science Ursinus College Collegeville PA USA Program in Applied and Computational Mathematics Princeton University NJ USA School of Engineering and Sustainable Development De Montfort University Leicester UK Cancer Biology and Genetics Program and Computational and Systems Biology Program Memorial Sloan-Kettering Cancer Center New York NY 10065 USA and Department of Systems Biology Harvard Medical School Boston MA 02115 USA Departments of Computer Science and Applied Mathematics & Statistics Stony Brook University NY USA

In this paper we analyze the dynamical behavior of the tumor suppressor protein p53, an essential player in the cellular stress response, which prevents a cell from dividing if severe DNA damage is present. When this response system is malfunctioning, e.g. due to mutations in p53, uncontrolled cell proliferation may lead to the development of cancer. Understanding the behavior of p53 is thus crucial to prevent its failing. It has been shown in various experiments that periodicity of the p53 signal is one of the main descriptors of its dynamics, and that its pulsing behavior (regular vs. spontaneous) indicates the level and type of cellular stress. In the present work, we introduce an algorithm to score the local periodicity of a given time series (such as the p53 signal), which we call Detrended Autocorrelation Periodicity Scoring (DAPS). It applies pitch detection (via autocorrelation) on sliding windows of the entire time series to describe the overall periodicity by a distribution of localized pitch scores. We apply DAPS to the p53 time series obtained from single cell experiments and establish a correlation between the periodicity scoring of a cell’s p53 signal and the number of cell division events. In particular, we show that high periodicity scoring of p53 is correlated to a low number of cell divisions and vice versa. We show similar results with a more computationally intensive state-of-the-art periodicity scoring algorithm based on topology known as Sw1PerS. This correlation has two major implications: It demonstrates that periodicity scoring of the p53 signal is a good descriptor for cellular stress, and it connects the high variability of p53 periodicity observed in cell populations to the variability in the number of cell division events.

关键词： systems biology cellular systems time series modeling bioinformatics computational biology

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Estimation of obesity levels based on computational intelligence

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Informatics in Medicine Unlocked 2020年 21卷

作者： Cervantes, Rodolfo Cañas Palacio, Ubaldo Martinez Department of Computer Science and Electronics Universidad de la Costa CUC. Faculty Teacher Systems Engineering Program Colombia

Obesity is a worldwide disease that affects people of all ages and gender;in consequence, researchers have made great efforts to identify factors that cause it early. In this study, an intelligent method is created, based on supervised and unsupervised techniques of data mining such as Simple K-Means, Decision Trees (DT), and Support Vector Machines (SVM) to detect obesity levels and help people and health professionals to have a healthier lifestyle against this global epidemic. In this research the primary source of collection was from students 18 and 25 years old at institutions in the countries of Colombia, Mexico, and Peru. The study takes a dataset relating to the main causes of obesity, based on the aim to reference high caloric intake, a decrease of energy expenditure due to the lack of physical activity, alimentary disorders, genetics, socioeconomic factors, and/or anxiety and depression. In the selected dataset, 178 students participated in the study, 81 male and 97 female. Using algorithms including Decision Tree, Support Vector Machine (SVM), and Simple K-Means, the results show a relevant tool to perform a comparative analysis among the mentioned algorithms. © 2020 The Authors

关键词： Decision trees Obesity Simple k-means Support vector machines

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Radar Occupancy Prediction with Lidar Supervision while Preserving Long-Range Sensing and Penetrating Capabilities

arXiv

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arXiv 2021年

作者： Kung, Pou-Chun Wang, Chieh-Chih Lin, Wen-Chieh The Graduate Degree Program of Robotics National Yang Ming Chiao Tung University Hsinchu Taiwan The Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Taiwan The Mechanical and Mechatronics Systems Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan The Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan

Radar shows great potential for autonomous driving by accomplishing long-range sensing under diverse weather conditions. But radar is also a particularly challenging sensing modality due to the radar noises. Recent works have made enormous progress in classifying free and occupied spaces in radar images by leveraging lidar label supervision. However, there are still several unsolved issues. Firstly, the sensing distance of the results is limited by the sensing range of lidar. Secondly, the performance of the results is degenerated by lidar due to the physical sensing discrepancies between the two sensors. For example, some objects visible to lidar are invisible to radar, and some objects occluded in lidar scans are visible in radar images because of the radar's penetrating capability. These sensing differences cause false positive and penetrating capability degeneration, respectively. In this paper, we propose training data preprocessing and polar sliding window inference to solve the issues. The data preprocessing aims to reduce the effect caused by radar-invisible measurements in lidar scans. The polar sliding window inference aims to solve the limited sensing range issue by applying a near-range trained network to the long-range region. Instead of using common Cartesian representation, we propose to use polar representation to reduce the shape dissimilarity between long-range and near-range data. We find that extending a near-range trained network to long-range region inference in the polar space has 4.2 times better IoU than in Cartesian space. Besides, the polar sliding window inference can preserve the radar penetrating capability by changing the viewpoint of the inference region, which makes some occluded measurements seem non-occluded for a pretrained network. Copyright © 2021, The Authors. All rights reserved.

关键词： Optical radar

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