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The Journal of machine learning Research 2021年第1期22卷 9024-9090页

作者： Jinshan Zeng Shao-Bo Lin Yuan Yao Ding-Xuan Zhou School of Computer and Information Engineering Jiangxi Normal University Nanchang China and Liu Bie Ju Centre for Mathematical Sciences City University of Hong Kong Hong Kong and Department of Mathematics Hong Kong University of Science and Technology Hong Kong Center of Intelligent Decision-Making and Machine Learning School of Management Xi'an Jiaotong University Xi'an China Department of Mathematics Hong Kong University of Science and Technology Hong Kong School of Data Science and Department of Mathematics City University of Hong Kong Hong Kong

In this paper, we develop an alternating direction method of multipliers (ADMM) for deep neural networks training with sigmoid-type activation functions (called sigmoid-ADMM pair), mainly motivated by the gradient-free nature of ADMM in avoiding the saturation of sigmoid-type activations and the advantages of deep neural networks with sigmoid-type activations (called deep sigmoid nets) over their rectified linear unit (ReLU) counterparts (called deep ReLU nets) in terms of approximation. In particular, we prove that the approximation capability of deep sigmoid nets is not worse than that of deep ReLU nets by showing that ReLU activation function can be well approximated by deep sigmoid nets with two hidden layers and finitely many free parameters but not vice-verse. We also establish the global convergence of the proposed ADMM for the nonlinearly constrained formulation of the deep sigmoid nets training from arbitrary initial points to a Karush-Kuhn-Tucker (KKT) point at a rate of order O(1/k). Besides sigmoid activation, such a convergence theorem holds for a general class of smooth activations. Compared with the widely used stochastic gradient descent (SGD) algorithm for the deep ReLU nets training (called ReLU-SGD pair), the proposed sigmoid-ADMM pair is practically stable with respect to the algorithmic hyperparameters including the learning rate, initial schemes and the pro-processing of the input data. Moreover, we find that to approximate and learn simple but important functions the proposed sigmoid-ADMM pair numerically outperforms the ReLU-SGD pair.

关键词： deep learning ADMM sigmoid global convergence saturation avoidance

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Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces

arXiv

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arXiv 2022年

作者： Chormai, Pattarawat Herrmann, Jan Müller, Klaus-Robert Montavon, Grégoire Berlin10587 Germany The Max Planck School of Cognition Max Planck Institute for Human Cognitive and Brain Sciences Leipzig04103 Germany Darmstadt64289 Germany BASF SE Statistics and Machine Learning Carl-Bosch-Straße 38 Ludwigshafen am Rhein67056 Germany The Department of Artificial Intelligence Korea University Seoul136-713 Korea Republic of The Max Planck Institute for Informatics Saarbrücken66123 Germany BIFOLD Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany Berlin14195 Germany

Explainable AI aims to overcome the black-box nature of complex ML models like neural networks by generating explanations for their predictions. Explanations often take the form of a heatmap identifying input features (e.g. pixels) that are relevant to the model's decision. These explanations, however, entangle the potentially multiple factors that enter into the overall complex decision strategy. We propose to disentangle explanations by extracting at some intermediate layer of a neural network, subspaces that capture the multiple and distinct activation patterns (e.g. visual concepts) that are relevant to the prediction. To automatically extract these subspaces, we propose two new analyses, extending principles found in PCA or ICA to explanations. These novel analyses, which we call principal relevant component analysis (PRCA) and disentangled relevant subspace analysis (DRSA), maximize relevance instead of e.g. variance or kurtosis. This allows for a much stronger focus of the analysis on what the ML model actually uses for predicting, ignoring activations or concepts to which the model is invariant. Our approach is general enough to work alongside common attribution techniques such as Shapley Value, Integrated Gradients, or LRP. Our proposed methods show to be practically useful and compare favorably to the state of the art as demonstrated on benchmarks and three use cases. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Unintended consequences: Factors influencing oil palm plantation expansion 3

Unintended consequences: Factors influencing oil palm planta...

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3rd Life and Environmental sciences Academics Forum, LEAF 2019

作者： Wongsai, S. Keson, J. Wongsai, N. Thammasat University Research Unit in Data Learning Division of Mathematics and Statistics Faculty of Science and Technology Thammasat University Pathumthani12120 Thailand Faculty of Technology and Environment Prince of Songkla University Phuket Campus Phuket83120 Thailand Department of Mathematics and Computer Science Faculty of Science and Technology Prince of Songkla University Pattani Campus Pattani94000 Thailand

We investigated the landscape variables affecting the current dramatic expansion of oil palm plantations in Lam Thap district Krabi Province Thailand. THEOS satellite data was used to map land use classifications using the support vector machine method. Seven land use classes were consolidated into oil palm plantations and other rural uses. A logistic regression model was then applied to search for the relationship between two land use classes and the landscape variables of slope (three classes) and soil drainage (four classes). Overall, slope and drainage were statistically significant in explaining oil palm plantation expansion. Flat areas with poor drainage were the strongest factors. This contradicts the known optimal agronomic requirements for oil palms of well drainage. We suggest that what is observed is an unintended consequence of government oil palm support policies. © Published under licence by IOP Publishing Ltd.

关键词： Land use

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Nanoscale chemical imaging with structured X-ray illumination

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Proceedings of the National Academy of sciences of the United States of America 2023年第49期120卷 e2314542120-e2314542120页

作者： Li, Jizhou Chen, Si Ratner, Daniel Blu, Thierry Pianetta, Piero Liu, Yijin Stanford Synchrotron Radiation Lightsource SLAC National Accelerator Laboratory Menlo Park 94025 CA United States School of Data Science City University of HongKong Hong Kong X-ray Science Division Argonne National Laboratory Lemont 60439 IL United States Machine Learning Initiative SLAC National Accelerator Laboratory Menlo Park 94025 CA United States Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Walker Department of Mechanical Engineering The University of Texas at Austin Austin 78705 TX United States

High-resolution imaging with compositional and chemical sensitivity is crucial for a wide range of scientific and engineering disciplines. Although synchrotron X-ray imaging through spectromicroscopy has been tremendously successful and broadly applied, it encounters challenges in achieving enhanced detection sensitivity, satisfactory spatial resolution, and high experimental throughput simultaneously. In this work, based on structured illumination, we develop a single-pixel X-ray imaging approach coupled with a generative image reconstruction model for mapping the compositional heterogeneity with nanoscale resolvability. This method integrates a full-field transmission X-ray microscope with an X-ray fluorescence detector and eliminates the need for nanoscale X-ray focusing and raster scanning. We experimentally demonstrate the effectiveness of our approach by imaging a battery sample composed of mixed cathode materials and successfully retrieving the compositional variations of the imaged cathode particles. Bridging the gap between structural and chemical characterizations using X-rays, this technique opens up vast opportunities in the fields of biology, environmental, and materials science, especially for radiation-sensitive samples. © 2023 the Author(s).

关键词： image reconstruction synchrotron X-rays transmission X-ray microscopy X-ray chemical imaging X-ray fluorescence

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Estimate the efficiency of multiprocessor's cash memory work algorithms

arXiv

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arXiv 2021年

作者： Hamada, Mohamed A. Abdallah, Abdelrahman International Information Technology University Almaty Almaty050000 Kazakhstan Department of Machine Learning & Data Science Satbayev University Almaty Almaty050013 Kazakhstan National Open Research Laboratory for Information and Space Technologies Satbayev University Almaty Almaty050013 Kazakhstan

Many computer systems for calculating the proper organization of memory are among the most critical issues. Using a tier cache memory (along with branching prediction) is an effective means of increasing modern multi-core processors' performance. Designing high-performance processors is a complex task and requires preliminary verification and analysis of the model level, usually used in analytical and simulation modeling. The refinement of extreme programming is an unfortunate challenge. Few experts disagree with the synthesis of access points. This article demonstrates that Internet QoS and 16-bit architectures are always incompatible, but it's the same situation for write-back caches. The solution to this problem can be implemented by analyzing simulation models of different complexity in combination with the analytical evaluation of individual algorithms. This work is devoted to designing a multi-parameter simulation model of a multi-process for evaluating the performance of cache memory algorithms and the optimality of the structure. Optimization of the structures and algorithms of the cache memory allows you to accelerate the interaction of the memory process and improve the performance of the entire system. © 2021, CC BY.

关键词： Cache memory

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Dynamics, statistics, and Predictability of Rossby Waves, Heat Waves, and Spatially Compounding Extreme Events

Dynamics, Statistics, and Predictability of Rossby Waves, He...

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作者： Lembo, Valerio Bordoni, Simona Bevacqua, Emanuele Domeisen, Daniela I.V. Franzke, Christian L.E. Galfi, Vera M. Garfinkel, Chaim I. Grams, Christian M. Hochman, Assaf Jha, Roshan Kornhuber, Kai Kwasniok, Frank Lucarini, Valerio Messori, Gabriele Pappert, Duncan Perez-Fernandez, Iago Riboldi, Jacopo Russo, Emmanuele Shaw, Tiffany A. Strigunova, Iana Strnad, Felix Yiou, Pascal Zagar, Nedjeljka Bologna Italy University of Trento Trento Italy Department of Compound Environmental Risks Helmholtz Centre for Environmental Research – UFZ Leipzig Germany University of Lausanne Lausanne Switzerland Institute for Atmospheric and Climate Science ETH Zurich Zurich Switzerland Center for Climate Physics Institute for Basic Science Busan Korea Republic of Pusan National University Busan Korea Republic of Institute for Environmental Studies Vrije Universiteit Amsterdam Amsterdam Netherlands Fredy and Nadine Herrmann Institute of Earth Sciences The Hebrew University of Jerusalem Jerusalem Israel Federal Office of Meteorology and Climatology MeteoSwiss Zurich-Airport Switzerland IDP in Climate Studies Indian Institute of Technology Bombay Mumbai India International Institute of Applied Systems Analysis Laxenburg Austria Potsdam Institute for Climate impact Research Potsdam Germany Lamont-Doherty Earth Observatory Columbia University New YorkNY United States Department of Mathematics and Statistics University of Exeter Exeter United Kingdom School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Department of Earth Sciences Uppsala University Uppsala Sweden Uppsala University Uppsala Sweden Department of Meteorology Bolin Centre for Climate Research Stockholm University Stockholm Sweden Institute of Geography University of Bern Bern Switzerland University of Bern Bern Switzerland Departamento de Ciencias de la atmósfera y Física de los Océanos Facultad de Ciencias Universidad de la República Montevideo Uruguay Department of the Geophysical Sciences The University of Chicago ChicagoIL United States Machine Learning in Climate Science University of Tübingen Tübingen Germany Laboratoire des Sciences du Climat et de’Environnement UMR8212 CEA-CNRS-UVSQ IPSL & Université Paris-Saclay Gif-sur-Yvette France Meteorological Institute CEN Universität Hamburg Hamburg Germany

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Drone flight data reveal energy and greenhouse gas emissions savings for small package delivery

arXiv

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arXiv 2021年

作者： Rodrigues, Thiago A. Patrikar, Jay Oliveira, Natalia L. Scott Matthews, H. Scherer, Sebastian Samaras, Constantine Department of Civil and Environmental Engineering Carnegie Mellon University 5000 Forbes Avenue PittsburghPA15213 United States Robotics Institute Carnegie Mellon University 5000 Forbes Avenue Pittsburgh15213 United States Department of Statistics and Data Science Carnegie Mellon University 5000 Forbes Avenue Pittsburgh15213 United States Machine Learning Department Carnegie Mellon University 5000 Forbes Avenue Pittsburgh15213 United States

The adoption of Uncrewed Aerial Vehicles (UAVs) for last-mile deliveries will affect the energy productivity of package delivery and require new methods to understand the associated energy consumption and greenhouse gas (GHG) emissions. Here we combine empirical testing of 187 quadcopter flights with first principles analysis to develop a usable energy model for drone package delivery. We develop a machine learning algorithm to assess energy use across three different flight regimes: takeoff, cruise, and landing. Our model shows that, in the US, a small electric quadcopter drone with a payload of 1 kg would consume approximately 0.05 MJ/km and result in 41 g of CO2e per package. The energy per package delivered by drones (0.19 MJ/package) can be up to 96% lower than conventional transportation modes. Our open model and generalizable coefficients can assist stakeholders in understanding and improving the energy use of drone package delivery. © 2021, CC BY-NC-SA.

关键词： Energy utilization

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Case study: Predictive fairness to reduce misdemeanor recidivism through social service interventions

arXiv

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arXiv 2020年

作者： Rodolfa, Kit T. Mendieta, Iván Higuera Salomon, Erika Larson, Jamie Haynes, Lauren Ghani, Rayid Machine Learning Department Carnegie Mellon University Ctr for Data Science & Public Policy University of Chicago Attorney’s Office Los Angeles City Machine Learning Department Heinz College of Information Systems & Public Policy Carnegie Mellon University

The criminal justice system is currently ill-equipped to improve outcomes of individuals who cycle in and out of the system with a series of misdemeanor offenses. Often due to constraints of caseload and poor record linkage, prior interactions with an individual may not be considered when an individual comes back into the system, let alone in a proactive manner through the application of diversion programs. The Los Angeles City Attorney’s Office recently created a new Recidivism Reduction and Drug Diversion unit (R2D2) tasked with reducing recidivism in this population. Here we describe a collaboration with this new unit as a case study for the incorporation of predictive equity into machine learning based decision making in a resource-constrained setting. The program seeks to improve outcomes by developing individually-tailored social service interventions (i.e., diversions, conditional plea agreements, stayed sentencing, or other favorable case disposition based on appropriate social service linkage rather than traditional sentencing methods) for individuals likely to experience subsequent interactions with the criminal justice system, a time and resource-intensive undertaking that necessitates an ability to focus resources on individuals most likely to be involved in a future case. Seeking to achieve both efficiency (through predictive accuracy) and equity (improving outcomes in traditionally under-served communities and working to mitigate existing disparities in criminal justice outcomes), we discuss the equity outcomes we seek to achieve, describe the corresponding choice of a metric for measuring predictive fairness in this context, and explore a set of options for balancing equity and efficiency when building and selecting machine learning models in an operational public policy setting. Copyright © 2020, The Authors. All rights reserved.

关键词： Balancing

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Adaptive distributed honeypot detection network for enhanced cybersecurity against DoS and DDoS attacks

Results in Engineering

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Results in Engineering 2025年 26卷

作者： V. Selva Kumar K.R. Mohan Raj S. Gopalakrishnan G. Vennila D. Dhinakaran P. Kavitha Department of Computer Science and Engineering P.S.R. Engineering College Sivakasi India Department of Information Technology Velammal Engineering College Chennai India Department of Computer Science & Engineering (Data Science) Madanapalle Institute of Technology & Science Andhra Pradesh India Department of Artificial Intelligence and Machine Learning School of Computing Mohan Babu University Tirupati India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering R.M.K. Engineering college Chennai India

The increasing prevalence and sophistication of Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks present significant challenges in ensuring the security and stability of modern networked systems. These attacks, characterized by their ability to disrupt services and compromise resources, require innovative and robust detection mechanisms to safeguard highly interactive environments such as honeypot systems. Traditional detection techniques often fall short in addressing the complexities posed by dynamic traffic patterns, diverse attack types, and real-time processing demands. This study introduces the Adaptive Distributed Honeypot Detection Network (ADHDN), a novel framework that leverages deep learning and probabilistic modeling to address the limitations of existing solutions. ADHDN employs a combination of Deep Generative Adversarial Networks (DGANs) and Discrete Hidden Markov Models (DHMMs) to achieve superior detection precision across various DoS attack types, including application-level, protocol-level, and data volume attacks. Implemented in a highly interactive honeypot environment with distributed server and virtual machine configurations, ADHDN demonstrates remarkable adaptability and resilience. Performance evaluation using the IoTID20 dataset reveals that ADHDN consistently outperforms contemporary models, such as RBMD, BNDH, and AHDL. ADHDN achieves a true positive rate of 99.7% for protocol-level attacks, 99.4% for application-level attacks, and 97.5% for data volume attacks under low attack volumes, maintaining robust performance even as attack intensity scales. These results underscore ADHDN’s potential to redefine DoS detection in dynamic and high-interaction environments, offering a scalable and efficient solution to contemporary cybersecurity challenges.

关键词： Denial of service Distributed denial of service Intrusion detection systems Generative adversarial networks Real-time attack detection Deep learning

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Spatio-temporal methods for estimating subsurface ocean thermal response to tropical cyclones

arXiv

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arXiv 2020年

作者： Hu, Addison J. Kuusela, Mikael Lee, Ann B. Giglio, Donata Wood, Kimberly M. Department of Statistics & Data Science Carnegie Mellon University United States Department of Atmospheric and Oceanic Sciences University of Colorado Boulder United States Department of Hydrology and Atmospheric Sciences University of Arizona United States Machine Learning Department Carnegie Mellon University United States

Tropical cyclones (TCs), driven by heat exchange between the air and sea, pose a substantial risk to many communities around the world. Accurate characterization of the subsurface ocean thermal response to TC passage is crucial for accurate TC intensity forecasts and for an understanding of the role that TCs play in the global climate system. However, that characterization is complicated by the high-noise ocean environment, correlations inherent in spatio-temporal data, relative scarcity of in situ observations, and the entanglement of the TC-induced signal with seasonal signals. We present a general methodological framework that addresses these difficulties, integrating existing techniques in seasonal mean field estimation, Gaussian process modeling, and nonparametric regression into an ANOVA decomposition model. Importantly, we improve upon past work by properly handling seasonality, providing rigorous uncertainty quantification, and treating time as a continuous variable, rather than producing estimates that are binned in time. This ANOVA model is estimated using in situ subsurface temperature profiles from the Argo fleet of autonomous floats through a multi-step procedure, which (1) characterizes the upper ocean seasonal shift during the TC season;(2) models the variability in the temperature observations;(3) fits a thin-plate spline using the variability estimates to account for heteroskedasticity and correlation between the observations. This spline fit reveals the ocean thermal response to TC passage. Through this framework, we obtain new scientific insights into the interaction between TCs and the ocean on a global scale, including a three-dimensional characterization of the near-surface and subsurface cooling along the TC storm track and the mixing-induced subsurface warming on the track's right side. Copyright © 2020, The Authors. All rights reserved.

关键词： Tropical cyclone

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