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SSRN 2022年

作者： Zhou, XiaoJing Wang, Hao Xu, Chuang Zhao, Zixuan Xu, Wei Deng, Zhaoju Department of Information and Computing Science Heilongjiang Bayi Agricultural University No. 5 Xinyang Road Heilongjiang Province Daqing163319 China Heilongjiang Provincial Key Laboratory of Prevention and Control of Bovine Diseases College of Animal Science and Veterinary Medicine Heilongjiang Bayi Agricultural University No. 5 Xinyang Road Heilongjiang Province Daqing163319 China Animal Husbandry and Veterinary Branch of Heilongjiang Academy of Agricultural Science Heilongjiang Province Qiqihar161005 China Department of Biosystems Division of Animal and Human Health Engineering KU Leuven Oude Markt 13 3000 Belgium College of Veterinary Medicine China Agricultural University Haidian District Tsinghua East road No.17 Beijing10010 China

In commercial dairy farms, mastitis is associated with potentially increased use of antimicrobials and associated resistance, thereby, affecting the welfare of dairy cows and increasing the rate of culling and/or death. Therefore, accurate and efficient mastitis prediction is valuable for timely intervention. Machine learning algorithms have been successfully used to solve many biological problems such as predicting or detecting subclinical mastitis using high-dimensional and noisy data obtained from sensors used on the farm. Therefore, this study aimed to develop sensor-based prediction models for bovine mastitis using variables related to daily activity and rumination time generated from collars, coupled with daily composite milk yield, and milk electrical conductivity from rotary milking systems from five commercial farms in Northeast China. We employed nine machine learning algorithms to analyze original data of 447 diseased and 2146 healthy cows obtained over 2.5 years and the corresponding Z-standardized dataset of four specific stages of lactation to train and test the prediction models for recognizing and predicting naturally occurring mastitis in these dairy cows. The sensitivity, specificity, accuracy, precision, AUC, and Matthews’s correlation coefficient of these models were calculated to evaluate the model performance. For Z-standardized data of cows in the 0-28 DIM and 29-100 DIM subgroups, the multi-layer artificial neural net (MNET) algorithm showed the best performance, with the sensitivity, specificity and accuracy of 91.89%, 93.69%, 93.24% for data of cows in 0-28 DIM, and 91.84%, 96.73, 95.44% for that in 29-100 DIM, respectively. For data of the subgroups 101–200 DIM and 201–305 DIM, random forest performed best on both the non-standardized and the Z-standardized datasets. Results of the Z-standardized dataset for all four subgroups were better than those of the non-standardized data. We also calculated the peak milk yield (PMY) and days in milk

关键词： Forecasting

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A data-driven approach for discovering stochastic dynamical systems with non-Gaussian Lévy noise

arXiv

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arXiv 2020年

作者： Li, Yang Duan, Jinqiao State Key Laboratory of Mechanics and Control of Mechanical Structures College of Aerospace Engineering Nanjing University of Aeronautics and Astronautics 29 Yudao Street Nanjing210016 China Department of Applied Mathematics College of Computing Illinois Institute of Technology ChicagoIL60616 United States

With the rapid increase of valuable observational, experimental and simulating data for complex systems, great efforts are being devoted to discovering governing laws underlying the evolution of these systems. However, the existing techniques are limited to extract governing laws from data as either deterministic differential equations or stochastic differential equations with Gaussian noise. In the present work, we develop a new data-driven approach to extract stochastic dynamical systems with non-Gaussian symmetric Lévy noise, as well as Gaussian noise. First, we establish a feasible theoretical framework, by expressing the drift coefficient, diffusion coefficient and jump measure (i.e., anomalous diffusion) for the underlying stochastic dynamical system in terms of sample paths data. We then design a numerical algorithm to compute the drift, diffusion coefficient and jump measure, and thus extract a governing stochastic differential equation with Gaussian and non-Gaussian noise. Finally, we demonstrate the efficacy and accuracy of our approach by applying to several prototypical one-, two- and three-dimensional systems. This new approach will become a tool in discovering governing dynamical laws from noisy data sets, from observing or simulating complex phenomena, such as rare events triggered by random fluctuations with heavy as well as light tail statistical features. Copyright © 2020, The Authors. All rights reserved.

关键词： Gaussian noise (electronic)

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HAWK: Rapid Android malware detection through heterogeneous graph attention networks

arXiv

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arXiv 2021年

作者： Hei, Yiming Yang, Renyu Peng, Hao Wang, Lihong Xu, Xiaolin Liu, Jianwei Liu, Hong Xu, Jie Sun, Lichao The School of Cyber Science and Technology Beihang University Beijing100083 China The School of Computing University of Leeds LeedsLS2 9JT United Kingdom The National Computer Network Emergency Response Technical Team Coordination Center of China Beijing100029 China The School of Computer Science and Software Engineering East China Normal University Shanghai Trusted Industrial Control Platform Co. Ltd. Shanghai200062 China The Department of Computer Science and Engineering Lehigh University Bethlehem United States

Android is undergoing unprecedented malicious threats daily, but the existing methods for malware detection often fail to cope with evolving camouflage in malware. To address this issue, we present HAWK, a new malware detection framework for evolutionary Android applications. We model Android entities and behavioural relationships as a heterogeneous information network (HIN), exploiting its rich semantic meta-structures for specifying implicit higher-order relationships. An incremental learning model is created to handle the applications that manifest dynamically, without the need for re-constructing the whole HIN and the subsequent embedding model. The model can pinpoint rapidly the proximity between a new application and existing in-sample applications and aggregate their numerical embeddings under various semantics. Our experiments examine more than 80,860 malicious and 100,375 benign applications developed over a period of seven years, showing that HAWK achieves the highest detection accuracy against baselines and takes only 3.5ms on average to detect an out-of-sample application, with the accelerated training time of 50x faster than the existing approach. Copyright © 2021, The Authors. All rights reserved.

关键词： Android (operating system)

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Study of Asymptotic Free Scalar Field Theories from Adaptive Perturbation Method

arXiv

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arXiv 2023年

作者： Ma, Chen-Te Zhang, Hui Department of Physics and Astronomy Iowa State University AmesIA50010 United States Asia Pacific Center for Theoretical Physics Pohang University of Science and Technology Gyeongsangbuk-do Pohang37673 Korea Republic of Guangdong Provincial Key Laboratory of Nuclear Science Institute of Quantum Matter South China Normal University Guangdong Guangzhou510006 China School of Physics and Telecommunication Engineering South China Normal University Guangdong Guangzhou510006 China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Southern Nuclear Science Computing Center South China Normal University Guangdong Guangzhou510006 China The Laboratory for Quantum Gravity and Strings Department of Mathematics and Applied Mathematics University of Cape Town Private Bag Rondebosch7700 South Africa Key Laboratory of Atomic and Subatomic Structure and Quantum Control Institute of Quantum Matter South China Normal University Guangdong Guangzhou510006 China Physics Department Center for Exploration of Energy and Matter Indiana University BloomingtonIN47408 United States

We focus on the behavior of (2+1)d λ4 and (5+1)d λ3 or λ||3 theories in different regimes and compare the results obtained from the adaptive perturbation method with those obtained from lattice simulation. These theories are simple models that exhibit asymptotic freedom, which is a property that is also observed in more complex theories such as QCD, which describes the strong interaction between quarks and gluons. Asymptotic freedom is an important feature of these theories because it allows for a perturbative treatment of interactions at high energies. However, the standard perturbation scheme breaks down in the presence of strong interactions, and the adaptive perturbation method, which involves resuming the Feynman diagrams, is more suitable for studying these interactions. Our research involves comparing the perturbation result to lattice simulation. In the case of the 3 theory, there is no stable vacuum, so we explore evidence from the ||3 theory instead. Our results appear to show that resummation improves the strong coupling result for both the λ4 and λ||3 theories. Additionally, we improve the resummation method for the three-point coupling vertex and study the RG flow to analyze the resummation contribution and theoretical properties. Copyright © 2023, The Authors. All rights reserved.

关键词： Lattice theory

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Integrate and Fire Neuron Implementation using CMOS Predictive Technology Model for 32nm

Integrate and Fire Neuron Implementation using CMOS Predicti...

引用

Microelectronics Technology and Devices (SBMicro)

作者： Gabriel Maranhão Janaina G. Guimarães Department of Electrical Engineering Universidade de Brasília Brasília Brazil Department of Control Automation and Computing Engineering Universidade Federal de Santa Catarina Blumenau Brazil

The propose of this work is to evolve the studies on neuromorphic circuits by simulating transistors models that look the most with the ones used in commercial process. Since most of data about company models contain a restricted access, some universities provide predictions models to reproduce the real ones. In this paper we present a silicon analog integrate and fire neuron (I&F), proposed by G. Indiveri as a part of a neuromorphic device. Using 32nm CMOS technology simulated in LTspice with BSIM4v4 and applying predictive parameters provides by Predictive Technology Model (PTM), we were able to reduce the source power, to 0.9V, and chip size of the latest design which was implemented using a 180nm CMOS process at 1.8 power supply.

关键词： Neurons Predictive models Logic gates Semiconductor device modeling Transistors Mathematical model Computational modeling

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Binary stochastic filtering: A method for Neural network size minimization and supervised feature selection

arXiv

引用

arXiv 2019年

作者： Trelin, A. Procházka, A. Department of Solid State Engineering University of Chemistry and Technology Prague16628 Czech Republic Department of Computing and Control Engineering University of Chemistry and Technology Prague16628 Czech Republic

Binary Stochastic Filtering (BSF), the algorithm for feature selection and neuron pruning is proposed in this work. The method defines filtering layer which penalizes amount of the information involved in the training process. This information could be the input data or output of the previous layer, which directly lead to the feature selection or neuron pruning respectively, producing ad hoc subset of features or selecting optimal number of neurons in each layer. Filtering layer stochastically passes or drops features based on individual weights, which are tuned with standard backpropagation algorithm during the training process. Multifold decrease of neural network size has been achieved in the experiments. Besides, the method was able to select minimal number of features, surpassing literature references by the accuracy/dimensionality ratio. Ready-to-use implementation of the BSF layer is provided. Copyright © 2019, The Authors. All rights reserved.

关键词： Neural networks

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Lagrangian generic second order traffic flow models for node

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Journal of Traffic and Transportation engineering(English Edition) 2018年第1期5卷 14-27页

作者： Asma Khelifi Habib Haj-Salem Jean-Patrick Lebacque Lotfi Nabli Engineering of Surface Transportation Networks and Advanced Computing Laboratory IFSTTAR/GRETTIA Department of Electrical Engineering Research Laboratory of Control Signal Processing and Imaging National Engineering School of Monastir

This study sheds light on higher order macroscopic traffic flow modeling on road networks, thanks to the generic second order models （GSOM family） which embeds a myriad of traffic models. It has been demonstrated that such higher order models are easily solved in Lagrangian coordinates which are compatible with both microscopic and macroscopic descriptions. The generalized GSOM model is reformulated in the Lagrangian coordinate system to develop a more efficient numerical method. The difficulty in applying this approach on networks basically resides in dealing with node dynamics. Traffic flow characteristics at node are different from that on homogeneous links. Different geometry features can lead to different critical research issues. For instance, discontinuity in traffic stream can be an important issue for traffic signal operations, while capacity drop may be crucial for lane-merges. The current paper aims to establish and analyze a new adapted node model for macroscopic traffic flow models by applying upstream and downstream boundary conditions on the Lagrangian coordinates in order to perform simulations on networks of roads, and accompanying numerical method. The internal node dynamics between upstream and downstream links are taken into account of the node model. Therefore, a numerical example is provided to underscore the efficiency of this approach. Simulations show that the discretized node model yields accurate results. Additional kinematic waves and contact discontinuities are induced by the variation of the driver attribute.

关键词： Macroscopic Traffic flow modeling GSOM family Lagrangian Node

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A Non-Stop Double Buffering Mechanism for Dataflow Architecture

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Journal of Computer Science & Technology 2018年第1期33卷 145-157页

作者： Xu Tan Xiao-Wei Shen Xiao-Chun Ye Da Wang Dong-Rui Fan Lunkai Zhang Wen-Ming Li Zhi-Min Zhang Zhi-Min Tang State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 100049 China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125 China Department of Computer Science The University of Chicago Chicago IL 60637 U.S.A.

Double buffering is an effective mechanism to hide the latency of data transfers between on-chip and off-chip memory. However, in dataflow architecture, the swapping of two buffers during the execution of many tiles decreases the performance because of repetitive filling and draining of the dataflow accelerator. In this work, we propose a non-stop double buffering mechanism for dataflow architecture. The proposed non-stop mechanism assigns tiles to the processing element array without stopping the execution of processing elements through optimizing control logic in dataflow architecture. Moreover, we propose a work-flow program to cooperate with the non-stop double buffering mechanism. After optimizations both on control logic and on work-flow program, the filling and draining of the array needs to be done only once across the execution of all tiles belonging to the same dataflow graph. Experimental results show that the proposed double buffering mechanism for dataftow architecture achieves a 16.2% average efficiency improvement over that without the optimization.

关键词： non-stop double buffering dataflow architecture high-performance computing

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Mathematical modeling of the synthesis process of vinyl acetate 5

Mathematical modeling of the synthesis process of vinyl acet...

引用

5th International Scientific and Practical Conference on Virtual Simulation, Prototyping and Industrial Design 2018, VSPID 2018

作者： Arapov, D. Tikhomirov, S. Podvalny, S. Denisenko, V. Avcinov, I. Higher Mathematics and Information Technology Voronezh State University of Engineering Technologies 19 Revolution Avenue Voronezh394036 Russia Information and Control Systems Voronezh State University of Engineering Technologies 19 Revolution Avenue Voronezh394036 Russia Head of the Department of Automated and Computing Systems Voronezh State Technical University 14 Moskovsky Avenue Voronezh394026 Russia

A complete mathematical modeling of the technological process of manufacturing vinyl acetate monomer by vapor-phase method has been implemented. The model is partial differential equations of the material and energy balance and catalyst aging. An algorithm for an integrated equation based on the second-order Gregg-Bulirsh-Shter rectangle symbol has been developed. The error in modeling the process is equal (CH3COOCH=CH2) ± 5.4% rel. for the target matter, and (CO2) ± 6.4% rel. for the secondary substance The kinetic coefficients of differential equations, namely the pre-exponential factors and the activation energy have been refined. The obtained data of the modeling of the vapor-phase manufacturing process of vinyl acetate are shown in the form of tables and graphic three-dimensional dependencies of quality indicators on technological parameters. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： Activation energy

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Front Cover: Micrometer-Sized Liposome-Based Systems: A Hierarchical Breakdown (ChemSystemsChem 3/2025)

ChemSystemsChem

引用

ChemSystemsChem 2025年第3期7卷

作者： Prof. Dr. Shogo Hamada Dr. Hironori Sugiyama Prof. Dr. Yiting Zhang Prof. Dr. Shoji Iwabuchi Soichiro Hiroi Toshiki Maruyama Yuktesh Balaji Sota Kumagai Prof. Dr. Satoshi Murata Prof. Dr. Taro Toyota Department of Computer Science School of Computing & Department of Systems and Control Engineering School of Engineering Institute of Science Tokyo 4259 Nagatsuta-cho Midori-ku Yokohama-city Kanagawa 226-8501 Japan These authors contributed equally. Department of Applied Chemistry Graduate School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan Department of Chemistry College of Science Rikkyo University 3-34-1 Nishi-Ikebukuro Toshima-ku Tokyo 171-8501 Japan Department of Biotechnology and Life Science Tokyo University of Agriculture and Technology 2-24-16 Naka-cho Koganei-shi Tokyo 185-8588 Japan Department of Basic Science Graduate School of Arts and Sciences The University of Tokyo 3-8-1 Komaba Meguro-ku Tokyo 153-8902 Japan Department of Robotics Graduate School of Engineering Tohoku University 6-6-01 Aramaki-Aza Aoba Aoba-ku Sendai Miyagi 980-8579 Japan Universal Biology Institute The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan

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