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arXiv 2025年

作者： Jacobs, Ryan Morgan, Dane Attarian, Siamak Meng, Jun Shen, Chen Wu, Zhenghao Xie, Clare Yijia Yang, Julia H. Artrith, Nongnuch Blaiszik, Ben Ceder, Gerbrand Choudhary, Kamal Csanyi, Gabor Cubuk, Ekin Dogus Deng, Bowen Drautz, Ralf Fu, Xiang Godwin, Jonathan Honavar, Vasant Isayev, Olexandr Johansson, Anders Kozinsky, Boris Martiniani, Stefano Ong, Shyue Ping Poltavsky, Igor Schmidt, K.J. Takamoto, So Thompson, Aidan Westermayr, Julia Wood, Brandon M. Department of Materials Science and Engineering University of Wisconsin-Madison MadisonWI55705 United States Harvard University Center for the Environment Harvard University CambridgeMA02138 United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Materials Chemistry and Catalysis Debye Institute for Nanomaterials Science Utrecht University Utrecht3584 CG Netherlands Globus University of Chicago ChicagoIL United States Data Science and Learning Division Argonne National Laboratory LemontIL United States Department of Materials Science and Engineering University of California BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Laboratory CA94720 United States Material Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom Google DeepMind Mountain ViewCA United States Ruhr-Universität Bochum Bochum44780 Germany Meta United States Orbital Materials London United Kingdom Department of Computer Science and Engineering The Pennsylvania State University University ParkPA United States College of Information Sciences and Technology The Pennsylvania State University University ParkPA United States Artificial Intelligence Research Laboratory The Pennsylvania State University University ParkPA United States Center for Artificial Intelligence Foundations and Scientific Applications The Pennsylvania State University University ParkPA United States Department of Chemistry Mellon College of Science Carnegie Mellon University PittsburghPA15213 United States Computational Biology Department School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Courant Institute of Mathematical Sciences New York University New YorkNY10003 United States Center for Soft Matter Research Department of P

The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not experts but wish to use these tools. The spirit of this review is to help such researchers by serving as a practical, accessible guide to the state-of-the-art in MLIPs. This review paper covers a broad range of topics related to MLIPs, including (i) central aspects of how and why MLIPs are enablers of many exciting advancements in molecular modeling, (ii) the main underpinnings of different types of MLIPs, including their basic structure and formalism, (iii) the potentially transformative impact of universal MLIPs for both organic and inorganic systems, including an overview of the most recent advances, capabilities, downsides, and potential applications of this nascent class of MLIPs, (iv) a practical guide for estimating and understanding the execution speed of MLIPs, including guidance for users based on hardware availability, type of MLIP used, and prospective simulation size and time, (v) a manual for what MLIP a user should choose for a given application by considering hardware resources, speed requirements, energy and force accuracy requirements, as well as guidance for choosing pre-trained potentials or fitting a new potential from scratch, (vi) discussion around MLIP infrastructure, including sources of training data, pre-trained potentials, and hardware resources for training, (vii) summary of some key limitations of present MLIPs and current approaches to mitigate such limitations, including methods of including long-range interactions, handling magnetic systems, and treatment of excited states, and finally (viii) we finish with some more speculative thoughts on what the future holds for the development and application of MLIPs over the next 3-10+ years. © 2025, CC BY.

关键词： Digital storage

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How to Best Predict the Daily Number of New Infections of Covid-19

arXiv

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arXiv 2020年

作者： Skiera, Bernd Jürgensmeier, Lukas Stowe, Kevin Gurevych, Iryna Board of EFL Data Science Institute Goethe University Frankfurt Theodor-W.-Adorno-Platz 4 Frankfurt60629 Germany Deakin University Australia Graduate School of Economics Finance and Management Goethe University Frankfurt Frankfurt60629 Germany Ubiquitous Processing Lab Computer Science Department Technical University of Darmstadt Hochschulstrasse 10 Darmstadt64289 Germany

Knowledge about the daily number of new infections of Covid-19 is important because it is the basis for political decisions resulting in lockdowns and urgent health care measures. We use Germany as an example to illustrate shortcomings of official numbers, which are, at least in Germany, disclosed only with several days of delay and severely underreported on weekends (more than 40%). These shortcomings outline an urgent need for alternative data sources. The other widely cited source provided by the Center for Systems science and Engineering at Johns Hopkins University (JHU) also deviates for Germany on average by 79% from the official numbers. We argue that Google Search and Twitter data should complement official numbers. They predict even better than the original values from Johns Hopkins University and do so several days ahead. These two data sources could also be used in parts of the world where official numbers do not exist or are perceived to be unreliable. Copyright © 2020, The Authors. All rights reserved.

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Ambient heat and human sleep

arXiv

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arXiv 2020年

作者： Minor, Kelton Bjerre-Nielsen, Andreas Jónasdóttir, Sigrídur Svala Lehmann, Sune Obradovich, Nick Copenhagen Center for Social Data Science University of Copenhagen Denmark Department of the Built Environment University of Aalborg Denmark Global Policy Laboratory University of California Berkeley United States Department of Economics University of Copenhagen Denmark Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Center for Humans and Machines Max Planck Institute for Human Development Germany

Ambient temperatures are rising globally, with the greatest increases recorded at night. Concurrently, the prevalence of insufficient sleep is increasing in many populations, with substantial costs to human health and well-being. Even though nearly a third of the human lifespan is spent asleep, it remains unknown whether temperature and weather impact objective measures of sleep in real-world settings, globally. Here we link billions of sleep measurements from wearable devices comprising over 7 million nighttime sleep records across 68 countries to local daily meteorological data from 2015 to 2017. Rising nighttime temperatures shorten within-person sleep duration primarily through delayed onset, increasing the probability of insufficient sleep. The effect of temperature on sleep loss is substantially larger for residents from lower income countries and older adults, and females are affected more than are males. Nighttime temperature increases inflict the greatest sleep loss during summer and fall months, and we do not find evidence of short-term acclimatization. Coupling historical behavioral measurements with output from climate models, we project that climate change will further erode human sleep, producing substantial geographic inequalities. Our findings have significant implications for adaptation planning and illuminate a pathway through which rising temperatures may globally impact public health. © 2020, CC-BY-NC-ND.

关键词： Sleep research

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TOAN: Target-Oriented Alignment Network for fine-grained image categorization with few labeled samples

arXiv

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arXiv 2020年

作者： Huang, Huaxi Zhang, Junjie Yu, Litao Zhang, Jian Wu, Qiang Xu, Chang The Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW2007 Australia The Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute of Advanced Communication and Data Science Shanghai University Shanghai200444 China The School of Computer Science University of Sydney SydneyNSW2006 Australia

In this paper, we study the fine-grained categorization problem under the few-shot setting, i.e., each fine-grained class only contains a few labeled examples, termed Fine-Grained Few-Shot classification (FGFS). The core predicament in FGFS is the high intra-class variance yet low inter-class fluctuations in the dataset. In traditional fine-grained classification, the high intra-class variance can be somewhat relieved by conducting the supervised training on the abundant labeled samples. However, with few labeled examples, it is hard for the FGFS model to learn a robust class representation with the significantly higher intra-class variance. Moreover, the inter- and intra-class variance are closely related. The significant intra-class variance in FGFS often aggravates the low inter-class variance issue. To address the above challenges, we propose a Target-Oriented Alignment Network (TOAN) to tackle the FGFS problem from both intra- and inter-class perspective. To reduce the intra-class variance, we propose a target-oriented matching mechanism to reformulate the spatial features of each support image to match the query ones in the embedding space. To enhance the inter-class discrimination, we devise discriminative fine-grained features by integrating local compositional concept representations with the global second-order pooling. We conducted extensive experiments on four public datasets for fine-grained categorization, and the results show the proposed TOAN obtains the state-of-the-art. © 2020, CC BY-NC-SA.

关键词： Image classification

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The effectiveness of backward contact tracing in networks

arXiv

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arXiv 2020年

作者： Kojaku, Sadamori Hébert-Dufresne, Laurent Mones, Enys Lehmann, Sune Ahn, Yong-Yeol Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Vermont Complex Systems Center University of Vermont BurlingtonVT05405 United States Department of Computer Science University of Vermont BurlingtonVT05405 United States DTU Compute Technical University of Denmark Kgs. Lyngby 2800 Denmark Center for Social Data Science University of Copenhagen Copenhagen K1353 Denmark Indiana University Network Science Institute Indiana University BloomingtonIN47408 United States

Discovering and isolating infected individuals is a cornerstone of epidemic control1-7. Because many infectious diseases spread through close contacts, contact tracing is a key tool for case discovery and control8-15. However, although contact tracing has been performed widely, the mathematical understanding of contact tracing has not been fully established and it has not been clearly understood what determines the efficacy of contact tracing. Here, we reveal that, compared with "forward" tracing-tracing to whom disease spreads, "backward" tracing-tracing from whom disease spreads-is profoundly more effective. The effectiveness of backward tracing is due to simple but overlooked biases arising from the heterogeneity in contacts. Using simulations on both synthetic and high-resolution empirical contact datasets, we show that even at a small probability of detecting infected individuals, strategically executed contact tracing can prevent a significant fraction of further transmissions. We also show that-in terms of the number of prevented transmissions per isolation-case isolation combined with a small amount of contact tracing is more efficient than case isolation alone. By demonstrating that backward contact tracing is highly effective at discovering super-spreading events, we argue that the potential effectiveness of contact tracing has been underestimated. Therefore, there is a critical need for revisiting current contact tracing strategies so that they leverage all forms of biases. Our results also have important consequences for digital contact tracing because it will be crucial to incorporate the capability for backward and deep tracing while adhering to the privacy-preserving requirements of these new platforms. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

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Crystalline phase discriminating neutron tomography using advanced reconstruction methods

arXiv

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arXiv 2021年

作者： Ametova, Evelina Burca, Genoveva Chilingaryan, Suren Fardell, Gemma Jørgensen, Jakob S. Papoutsellis, Evangelos Pasca, Edoardo Warr, Ryan Turner, Martin Lionheart, William R.B. Withers, Philip J. Henry Royce Institute Department of Materials The University of Manchester M13 9PL United Kingdom Laboratory for Application of Synchrotron Radiation Karlsruhe Institute of Technology Germany ISIS Pulsed Neutron and Muon Source STFC UKRI Rutherford Appleton Laboratory United Kingdom Department of Mathematics The University of Manchester M13 9PL United Kingdom Institute for Data Processing and Electronics Karlsruhe Institute of Technology Germany Scientific Computing Department STFC UKRI Rutherford Appleton Laboratory United Kingdom Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Research IT Services The University of Manchester M13 9PL United Kingdom

Time-of-flight neutron imaging offers complementary attenuation contrast to X-ray computed tomography (CT), coupled with the ability to extract additional information from the variation in attenuation as a function of neutron energy (time of flight) at every point (voxel) in the image. In particular Bragg edge positions provide crystallographic information and therefore enable the identification of crystalline phases directly. Here we demonstrate Bragg edge tomography with high spatial and spectral resolution. We propose a new iterative tomographic reconstruction method with a tailored regularisation term to achieve high quality reconstruction from low-count data, where conventional filtered back-projection (FBP) fails. The regularisation acts in a separated mode for spatial and spectral dimensions and favours characteristic piece-wise constant and piecewise smooth behaviour in the respective dimensions. The proposed method is compared against FBP and a state-of-the-art regulariser for multi-channel tomography on a multi-material phantom. The proposed new regulariser which accommodates specific image properties outperforms both conventional and state-of-the-art methods and therefore facilitates Bragg edge fitting at the voxel level. The proposed method requires significantly shorter exposure to retrieve features of interest. This in turn facilitates more efficient usage of expensive neutron beamline time and enables the full utilisation of state-of-the-art high resolution detectors. © 2021, CC BY.

关键词： Iterative methods

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Opportunistic Scheduling Revisited Using Restless Bandits: Indexability and Index Policy

Opportunistic Scheduling Revisited Using Restless Bandits: I...

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作者： Wang, Kehao Yu, Jihong Chen, Lin Zhou, Pan Ge, Xiaohu Win, Moe Z. Hubei Key Laboratory of Broadband Wireless Communication and Sensor Networks Wuhan University of Technology Wuhan430070 China School of Information and Electronics Beijing Institute of Technology Beijing100081 China School of Data Computer Science Sun Yat-sen University Guangzhou510275 China School of Electrical and Information Communications Huazhong University of Science and Technology Wuhan430074 China Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States

We revisit the opportunistic scheduling problem in which a server opportunistically serves multiple classes of users under time-varying multi-state Markovian channels. The aim of the server is to find an optimal policy minimizing the average waiting cost of those users. Mathematically, the problem can be recast to a restless multiarmed bandit one, and a pivot to solve restless bandit by the Whittle index approach is to establish indexability. Despite the theoretical and practical importance of the Whittle index policy, the indexability is still open for opportunistic scheduling in the heterogeneous multi-state channel case. To fill this gap, we mathematically identify a set of sufficient conditions on a channel state transition matrix under which the indexability is guaranteed and consequently, the Whittle index policy is feasible. Furthermore, we obtain the closed-form Whittle index by exploiting the structural property of the channel state transition matrix. For a generic channel state transition matrix, we propose an eigenvalue-arithmetic-mean scheme to obtain the corresponding approximate matrix which satisfies the sufficient conditions, and consequently can get an approximate Whittle index. This paper constitutes a small step toward solving the opportunistic scheduling problem in its generic form involving multi-state Markovian channels and multi-class users. © 2002-2012 IEEE.

关键词： Eigenvalues and eigenfunctions

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Cryptanalysis of Internet of Things (IoT) Wireless Technology

Cryptanalysis of Internet of Things (IoT) Wireless Technolog...

引用

International Conference on Radio Electronics & Info Communications (UkrMiCo)

作者： Lela Mirtskhulava Larysa Globa Nugzar Meshveliani Nana Gulua Department of Computer Science Iv. Javakhishvili Tbilisi State University Tbilisi Georgia Department Information-telecommunication Networks Institute of Telecommunication Systems National Technical University of Ukraine Kiev Ukraine Department of Computer Science Faculty of Mathematics and Computer Science Sokhumi State University Tbilisi Georgia

ISBN: (数字)9781728126920

ISBN: (纸本)9781728126937

In the given paper, we discuss the lattice-based open source public-key NTRU cryptosystem that can be used in IoT (Internet of Things). IoT spans users' personal sensitive information and many crucial aspects of person's private life and therefore IoT requires a special protection. IoT security involves the network security and the devices security they are connected to. Cryptography technologies are the best solution to withstand communication attacks. NTRU can implement the NTRUEncrypt public key encryption algorithm capable of withstanding the attacks from the quantum computers. Therefore, NTRU is significantly faster than the other public-key cryptosystems such as RSA or ECC, allowing long-term cryptanalysis. NTRU can use one-off keys making possible to change keys in a few seconds what makes difficult for an attacker to crack the encryption trying to obtain more than thousands of keys for doing that.

关键词： Wireless fidelity Encryption Protocols Internet of Things Communication system security

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Generative neural network based spectrum sharing using linear sum assignment problems

arXiv

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arXiv 2019年

作者： Zaky, Ahmed B. Huang, Joshua Zhexue Wu, Kaishun ElHalawany, Basem M. Big data Institute School of Computer Science Shenzhen University China Benha University Egypt Shenzhen University China PCL Research Center of Networks and Communications Peng Cheng Laboratory China

Spectrum management and resource allocation (RA) problems are challenging and critical in a vast number of research areas such as wireless communications and computer networks. The traditional approaches for solving such problems usually consume time and memory, especially for large-size problems. Recently different machine learning approaches have been considered as potential promising techniques for combinatorial optimization problems, especially the generative model of the deep neural networks. In this work, we propose a resource allocation deep autoencoder network, as one of the promising generative models, for enabling spectrum sharing in underlay device-to-device (D2D) communication by solving linear sum assignment problems (LSAPs). Specifically, we investigate the performance of three different architectures for the conditional variational autoencoders (CVAE). The three proposed architecture are the convolutional neural network (CVAE-CNN) autoencoder, the feed-forward neural network (CVAE-FNN) autoencoder, and the hybrid (H-CVAE) autoencoder. The simulation results show that the proposed approach could be used as a replacement of the conventional RA techniques, such as the Hungarian algorithm, due to its ability to find solutions of LASPs of different sizes with high accuracy and very fast execution time. Moreover, the simulation results reveal that the accuracy of the proposed hybrid autoencoder architecture outperforms the other proposed architectures and the state-of-the-art DNN techniques. Copyright © 2019, The Authors. All rights reserved.

关键词： Combinatorial optimization

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Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients

arXiv

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arXiv 2021年

作者： Khozeimeh, Fahime Sharifrazi, Danial Izadi, Navid Hoseini Joloudari, Javad Hassannataj Shoeibi, Afshin Alizadehsani, Roohallah Gorriz, Juan M. Hussain, Sadiq Sani, Zahra Alizadeh Moosaei, Hossein Khosravi, Abbas Nahavandi, Saeid Islam, Sheikh Mohammed Shariful Deakin University Geelong Australia Department of Computer Engineering School of Technical and Engineering Shiraz Branch Islamic Azad University Shiraz Iran Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran Department of Computer Engineering Faculty of Engineering University of Birjand Birjand Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Faculty of Electrical and Computer Engineering Biomedical Data Acquisition Lab K. N. Toosi University of Technology Tehran Iran Department of Signal Theory Networking and Communications Universidad de Granada Department of Psychiatry University of Cambridge Cambridge United Kingdom Dibrugarh University Assam 786004 India Omid Hospital Iran University of Medical Sciences Tehran Iran Department of Mathematics Faculty of Science University of Bojnord Iran Institute for Physical Activity and Nutrition School of Exercise and Nutrition Sciences Deakin University GeelongVIC3220 Australia Cardiovascular Division The George Institute for Global Health Newtown Australia Sydney Medical School University of Sydney Camperdown Australia

COVID-19 has caused many deaths worldwide. The automation of the diagnosis of this virus is highly desired. Convolutional neural networks (CNNs) have shown outstanding classification performance on image datasets. To date, it appears that COVID computer-aided diagnosis systems based on CNNs and clinical information have not yet been analysed or explored. We propose a novel method, named the CNN-AE, to predict the survival chance of COVID-19 patients using a CNN trained with clinical information. Notably, the required resources to prepare CT images are expensive and limited compared to those required to collect clinical data, such as blood pressure, liver disease, etc. We evaluated our method using a publicly available clinical dataset that we collected. The dataset properties were carefully analysed to extract important features and compute the correlations of features. A data augmentation procedure based on autoencoders (AEs) was proposed to balance the dataset. The experimental results revealed that the average accuracy of the CNN-AE (96.05%) was higher than that of the CNN (92.49%). To demonstrate the generality of our augmentation method, we trained some existing mortality risk prediction methods on our dataset (with and without data augmentation) and compared their performances. We also evaluated our method using another dataset for further generality verification. To show that clinical data can be used for COVID-19 survival chance prediction, the CNN-AE was compared with multiple pre-trained deep models that were tuned based on CT images. Copyright © 2021, The Authors. All rights reserved.

关键词： Forecasting

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