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arXiv 2025年

作者： Partin, Alexander Vasanthakumari, Priyanka Narykov, Oleksandr Wilke, Andreas Koussa, Natasha Jones, Sara E. Zhu, Yitan Overbeek, Jamie C. Jain, Rajeev Fernando, Gayara Demini Sanchez-Villalobos, Cesar Garcia-Cardona, Cristina Mohd-Yusof, Jamaludin Chia, Nicholas Wozniak, Justin M. Ghosh, Souparno Pal, Ranadip Brettin, Thomas S. Weil, M. Ryan Stevens, Rick L. Division of Data Science and Learning Argonne National Laboratory LemontIL United States Frederick National Laboratory for Cancer Research Cancer Data Science Initiatives Cancer Research Technology Program FrederickMD United States Department of Statistics University of Nebraska–Lincoln LincolnNE United States Department of Electrical & Computer Engineering Texas Tech University LubbockTX United States Division of Computer Computational and Statistical Sciences Los Alamos National Laboratory Los AlamosNM United States Department of Computer Science The University of Chicago ChicagoIL United States

Deep learning (DL) and machine learning (ML) models have shown promise in drug response prediction (DRP), yet their ability to generalize across datasets remains an open question, raising concerns about their real-world applicability. Due to the lack of standardized benchmarking approaches, model evaluations and comparisons often rely on inconsistent datasets and evaluation criteria, making it difficult to assess true predictive capabilities. In this work, we introduce a benchmarking framework for evaluating cross-dataset prediction generalization in DRP models. Our framework incorporates five publicly available drug screening datasets, six standardized DRP models, and a scalable workflow for systematic evaluation. To assess model generalization, we introduce a set of evaluation metrics that quantify both absolute performance (e.g., predictive accuracy across datasets) and relative performance (e.g., performance drop compared to within-dataset results), enabling a more comprehensive assessment of model transferability. Our results reveal substantial performance drops when models are tested on unseen datasets, underscoring the importance of rigorous generalization assessments. While several models demonstrate relatively strong cross-dataset generalization, no single model consistently outperforms across all datasets. Furthermore, we identify CTRPv2 as the most effective source dataset for training, yielding higher generalization scores across target datasets. By sharing this standardized evaluation framework with the community, our study aims to establish a rigorous foundation for model comparison, and accelerate the development of robust DRP models for real-world applications. © 2025, CC BY.

关键词： Prediction models

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Proactive Energy Management for Renewable-Integrated Smart Grids with Real-Time Optimization using a Novel Hybrid PSO-GA Algorithm

Proactive Energy Management for Renewable-Integrated Smart G...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Divya Nimma V.Kanpur Rani Shamim Ahmad Khan Amit Kumar Mishra Mithun D'Souza I Infant Raj Computational Science University of Southern Mississippi Data Analyst in UMMC USA Department of Electrical and Electronics Engineering Hindustan Institute of Technology and Science India Glocal School of Science & Technology Glocal University U.P India Department of CSE Graphic Era Hill University Dehradun Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science St. Joseph's University Bangalore India Department of CSE K.Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

The increasing integration of renewable energy sources (RES) into smart grids presents significant challenges related to variability, stability, and energy management. This research proposes a novel hybrid PSO-GA algorithm for real-time decision-making and optimization in smart grid systems. The proposed PSO-GA combines the exploration capabilities of Particle Swarm Optimization (PSO) with the capabilities of Genetic Algorithm (GA), enabling it to effectively address the dynamic and challenging nature of smart grid environments. The hybrid PSO-GA algorithm demonstrates significant improvements in transmission and distribution losses, reducing them by up to 6%. Additionally, it enhances energy efficiency, achieving an increase of 88% compared to traditional and non-hybrid optimized systems. The proposed system offers lower operational costs and improved power system stability, facilitating the integration of high levels of renewable power. Furthermore, the system's ability to respond to real-time demands and proactively address dynamic grid situations ensures reliable and efficient power delivery. This research contributes to the development of advanced energy management strategies for smart grids, promoting the increased adoption of renewable energy and paving the way for a more sustainable and efficient energy future.

关键词： Renewable energy sources Heuristic algorithms Power system dynamics Power system stability Real-time systems Stability analysis Smart grids Reliability Optimization Genetic algorithms

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The Berkeley Single Cell computational Microscopy (BSCCM) dataset

arXiv

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arXiv 2024年

作者： Pinkard, Henry Liu, Cherry Nyatigo, Fanice Fletcher, Daniel A. Waller, Laura Department of Electrical Engineering and Computer Sciences University of California Berkeley United States Berkeley Institute for Data Science United States University of California San Francisco Bakar Computational Health Sciences Institute United States Department of Bioengineering University of California Berkeley United States

computational microscopy, in which hardware and algorithms of an imaging system are jointly designed, shows promise for making imaging systems that cost less, perform more robustly, and collect new types of information. Often, the performance of computational imaging systems, especially those that incorporate machine learning, is sample-dependent. Thus, standardized datasets are an essential tool for comparing the performance of different approaches. Here, we introduce the Berkeley Single Cell computational Microscopy (BSCCM) dataset, which contains over 12,000,000 images of 400,000 of individual white blood cells. The dataset contains images captured with multiple illumination patterns on an LED array microscope and fluorescent measurements of the abundance of surface proteins that mark different cell types. We hope this dataset will provide a valuable resource for the development and testing of new algorithms in computational microscopy and computer vision with practical biomedical applications. © 2024, CC BY.

关键词： Imaging systems

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Mean-field theory for double-well systems on degree-heterogeneous networks

arXiv

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arXiv 2022年

作者： Kundu, Prosenjit MacLaren, Neil G. Kori, Hiroshi Masuda, Naoki Department of Mathematics State University of New York BuffaloNY14260-2900 United States Department of Complexity Science and Engineering The University of Tokyo Chiba277-8561 Japan Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY14260-5030 United States

Many complex dynamical systems in the real world, including ecological, climate, financial, and power-grid systems, often show critical transitions, or tipping points, in which the system’s dynamics suddenly transit into a qualitatively different state. In mathematical models, tipping points happen as a control parameter gradually changes and crosses a certain threshold. Tipping elements in such systems may interact with each other as a network, and understanding the behavior of interacting tipping elements is a challenge because of the high dimensionality originating from the network. Here we develop a degree-based mean-field theory for a prototypical double-well system coupled on a network with the aim of understanding coupled tipping dynamics with a low-dimensional description. The method approximates both the onset of the tipping point and the position of equilibria with a reasonable accuracy. Based on the developed theory and numerical simulations, we also provide evidence for multistage tipping point transitions in networks of double-well systems. © 2022, CC BY.

关键词： Mean field theory

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On Computing Probabilistic Explanations for Decision Trees

arXiv

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arXiv 2022年

作者： Arenas, Marcelo Barceló, Pablo Romero, Miguel Roa, Bernardo Anibal Subercaseaux Department of Computer Science PUC Chile Institute for Mathematical and Computational Engineering PUC Chile Faculty of Engineering and Science UAI Chile Millenium Institute for Foundational Research on Data Chile CENIA Chile Carnegie Mellon University Pittsburgh United States

Formal XAI (explainable AI) is a growing area that focuses on computing explanations with mathematical guarantees for the decisions made by ML models. Inside formal XAI, one of the most studied cases is that of explaining the choices taken by decision trees, as they are traditionally deemed as one of the most interpretable classes of models. Recent work has focused on studying the computation of sufficient reasons, a kind of explanation in which given a decision tree T and an instance x, one explains the decision T(x) by providing a subset y of the features of x such that for any other instance z compatible with y, it holds that T(z) = T(x), intuitively meaning that the features in y are already enough to fully justify the classification of x by T. It has been argued, however, that sufficient reasons constitute a restrictive notion of explanation. For such a reason, the community has started to study their probabilistic counterpart, in which one requires that the probability of T(z) = T(x) must be at least some value δ ∈ (0, 1], where z is a random instance that is compatible with y. Our paper settles the computational complexity of δ-sufficient-reasons over decision trees, showing that both (1) finding δ-sufficient-reasons that are minimal in size, and (2) finding δ-sufficient-reasons that are minimal inclusion-wise, do not admit polynomial-time algorithms (unless PTIME = NP). This is in stark contrast with the deterministic case (δ = 1) where inclusion-wise minimal sufficient-reasons are easy to compute. By doing this, we answer two open problems originally raised by Izza et al., and extend the hardness of explanations for Boolean circuits presented by Wäldchen et al. to the more restricted case of decision trees. On the positive side, we identify structural restrictions of decision trees that make the problem tractable, and show how SAT solvers might be able to tackle these problems in practical settings. © 2022, CC BY.

关键词： Decision trees

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Weisfeiler and Leman go machine learning: the story so far

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 15865-15923页

作者： Christopher Morris Yaron Lipman Haggai Maron Bastian Rieck Nils M. Kriege Martin Grohe Matthias Fey Karsten Borgwardt Department of Computer Science RWTH Aachen University Aachen Germany Meta AI Research Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot Israel NVIDIA Research Tel Aviv Israel AIDOS Lab Institute of AI for Health Helmholtz Zentrum München and Technical University of Munich Munich Germany Faculty of Computer Science and Research Network Data Science University of Vienna Vienna Austria Kumo.AI Mountain View CA Machine Learning & Computational Biology Lab Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland and Swiss Institute of Bioinformatics Lausanne Switzerland

In recent years, algorithms and neural architectures based on the Weisfeiler-Leman algorithm, a well-known heuristic for the graph isomorphism problem, have emerged as a powerful tool for machine learning with graphs and relational data. Here, we give a comprehensive overview of the algorithm's use in a machine-learning setting, focusing on the supervised regime. We discuss the theoretical background, show how to use it for supervised graph and node representation learning, discuss recent extensions, and outline the algorithm's connection to (permutation-)equivariant neural architectures. Moreover, we give an overview of current applications and future directions to stimulate further research.

关键词： machine learning for graphs graph neural networks Weisfeiler-Leman algorithm expressivity equivariance

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IMPROVED ANALYSIS OF SPARSE LINEAR REGRESSION IN LOCAL DIFFERENTIAL PRIVACY MODEL

arXiv

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arXiv 2023年

作者： Zhu, Liyang Ding, Meng Aggarwal, Vaneet Xu, Jinhui Wang, Di Saudi Arabia Department of Computer Science and Engineering The State University of New York Buffalo United States School of IE ECE Purdue University United States Computational Bioscience Research Center Japan SDAIA-KAUST Center of Excellence in Data Science and Artificial Intelligence Saudi Arabia

In this paper, we revisit the problem of sparse linear regression in the local differential privacy (LDP) model. Existing research in the non-interactive and sequentially local models has focused on obtaining the lower bounds for the case where the underlying parameter is 1-sparse, and extending such bounds to the more general k-sparse case has proven to be challenging. Moreover, it is unclear whether efficient non-interactive LDP (NLDP) algorithms exist. To address these issues, we first consider the problem in the ∊ non-interactive LDP model and provide a lower bound of (formula presented) on the 2-norm estimation error for sub-Gaussian data, where n is the sample size and d is the dimension of the space. We propose an innovative NLDP algorithm, the very first of its kind for the problem. As a remarkable outcome, this algorithm also yields a novel and_ highly efficient estimator as a valuable by-product. Our algorithm achieves an upper bound of (formula presented) for the estimation error when the data is sub-Gaussian, which can be further improved by a factor of O(√d) if the server has additional public but_ unlabeled data. For the sequentially interactive LDP model, we show a similar lower bound of (formula presented). As for the upper bound, we rectify a previous method and show that it is possible to achieve a bound of (formula presented). Our findings reveal fundamental differences between the non-private case, central DP model, and local DP model in the sparse linear regression problem. © 2023, CC BY.

关键词： Linear regression

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Early Warnings for Multistage Transitions in Dynamics on Networks

arXiv

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arXiv 2022年

作者： MacLaren, Neil G. Kundu, Prosenjit Masuda, Naoki Department of Mathematics State University of New York BuffaloNY14260-2900 United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY14260-5030 United States

Successfully anticipating sudden major changes in complex systems is a practical concern. Such complex systems often form a heterogeneous network, which may show multistage transitions in which some nodes experience a regime shift earlier than others as an environment gradually changes. Here we investigate early warning signals for networked systems undergoing a multistage transition. We found that knowledge of both the ongoing multistage transition and network structure enables us to calculate effective early warning signals for multistage transitions. Furthermore, we found that small subsets of nodes could anticipate transitions as well as or even better than using all the nodes. Even if we fix the network and dynamical system, no single best subset of nodes provides good early warning signals, and a good choice of sentinel nodes depends on the tipping direction and the current stage of the dynamics within a multistage transition, which we systematically characterize. © 2022, CC BY.

关键词： Dynamics

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Gnn-Mgrpool: Enhanced Graph Neural Networks with Multi-Granularity Pooling for Graph Classification

SSRN

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SSRN 2023年

作者： Sun, Haichao Wang, Guoyin Liu, Qun Guo, Yike Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China Department of Computer Science and Engineering The Hong Kong University of Science and Technology 999077 Hong Kong School of Computer Engineering and Science Shanghai University Shanghai200444 China

Graph neural networks (GNNs) have gained significant attention and have been applied in various domain tasks. Currently, numerous pooling approaches have been proposed to aggregate node features and obtain node embeddings. However, current GNNs are almost black-box models that typically use a flat or single pooling step to aggregate nodes by only considering the similarity between nodes within the cluster. These approaches ignore the influence of relationships within and between clusters. To address this issue, we propose a novel multi-granular pooling method to aggregate nodes by simultaneously considering density and relationships among nodes and clusters. This method allows to obtain multi-granular node embedding clusters. The clusters in the current layer are built upon the previous layer, and these clusters change from fine to coarse as the number of clusters decreases, which is achieved by using multi-granular pooling (MgrPool for short). Additionally, the node representation of each layer is established through the ratio of node distance within clusters to that of between clusters. Finally, we conduct several experiments on node and graph classification tasks by this pooling approach. The results demonstrate that our GNN-MgrPool model outperforms the state-of-the-art similar algorithms and largely improves the interpretability of the learning process. © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Non-adaptive and two-stage coding over the Z-channel

arXiv

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arXiv 2022年

作者： Lebedev, Alexey Lebedev, Vladimir Vorobyev, Ilya Deppe, Christian Kharkevich Institute for Information Transmission Problems Russian Academy of Sciences Moscow Russia Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow Russia Institute for Communications Engineering Department of Electrical and Computer Engineering Technical University of Munich Munich Germany

In this paper, we developed new coding strategies for the Z-channel. In particular, we look at the case with two-stage encoding. In this case, the encoder uses noiseless feedback once and adjusts the further encoding strategy based on the previous partial output of the channel. Nevertheless, the developed codes improve the known results with full feedback for small length and 1 error. A tool for the two-stage strategy is the development of a new optimality condition for non-adaptive codes. Copyright © 2022, The Authors. All rights reserved.

关键词： Signal encoding

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