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arXiv 2025年

作者： Xu, Ziyu Wang, Catherine Wasserman, Larry Roeder, Kathryn Ramdas, Aaditya Department of Statistics and Data Science United States Machine Learning Department Germany Computational Biology Department Carnegie Mellon University United States

Researchers often lack the resources to test every hypothesis of interest directly or compute test statistics comprehensively, but often possess auxiliary data from which we can compute an estimate of the experimental outcome. We introduce a novel approach for selecting which hypotheses to query a statistic (i.e., run an experiment, perform expensive computation, etc.) in a hypothesis testing setup by leveraging estimates (e.g., from experts, machine learning models, previous experiments, etc.) to compute proxy statistics. Our framework allows a scientist to propose a a proxy statistic, and then query the true statistic with some probability based on the value of the proxy. We make no assumptions about how the proxy is derived and it can be arbitrarily dependent with the true statistic. If the true statistic is not queried, the proxy is used in its place. We characterize "active" methods that produce valid p-values and e-values in this setting and utilize this framework in the multiple testing setting to create procedures with false discovery rate (FDR) control. Through simulations and real data analysis of causal effects in scCRISPR screen experiments, we empirically demonstrate that our proxy framework has both high power and low resource usage when our proxies are accurate estimates of the respective true statistics. Copyright © 2025, The Authors. All rights reserved.

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Joint Multi-Scale Contextual and Noise Suppression for Group Emotion Recognition

Joint Multi-Scale Contextual and Noise Suppression for Group...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Guo, Wangdong Zhu, Qing Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications China Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China

ISBN: (纸本)9798350368741

Group Emotion Recognition (GER) seeks to identify emotional states within multi-person groups. The complexity of the environment and the reliance on a single group emotion label often result in noisy individuals with inconsistent emotional expressions, hampering accurate emotion classification. Mainstream GER networks attempt to downweight noisy individuals, but when their numbers are high and over-relying on a single group label, the negative impact of noise on individuals remains significant, further complicating classification and diminishing accuracy. To address these limitations, we propose the Multi-Scale Contextual and Noise Suppression Model (MCon-NSM), a novel framework that enhances GER by capturing fine-grained interaction contexts and collaboratively suppressing noise at both the individual and group label levels. Extensive experiments on the GAF series datasets demonstrate that our method achieves results comparable to state-of-the-art techniques, validating its effectiveness in mitigating noise in GER. © 2025 IEEE.

关键词： Emotion analysis Group emotion recognition Multi-Scale context learning Noise suppression

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An a-posteriori analysis of co-kurtosis PCA based dimensionality reduction using a neural ODE solver

arXiv

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arXiv 2025年

作者： Sai, Tadikonda Shiva Kolla, Hemanth Aditya, Konduri Department of Computational and Data Sciences Indian Institute of Science KA Bengaluru560012 India Sandia National Laboratories LivermoreCA94550 United States

A low-dimensional representation of thermochemical scalars based on cokurtosis principal component analysis (CoK-PCA) has been shown to effectively capture stiff chemical dynamics in reacting flows relative to the widely used principal component analysis (PCA). The effectiveness of the reduced manifold was evaluated in a priori analyses using both linear and nonlinear reconstructions of thermochemical scalars from aggressively truncated principal components (PCs). In this study, we demonstrate the efficacy of a CoK-PCA-based reduced manifold using a posteriori analysis. Simulations of spontaneous ignition in a homogeneous reactor that pose a challenge in accurately capturing the ignition delay time as well as the scalar profiles within the reaction zone are considered. The governing ordinary differential equations (ODEs) in the PC space were evolved from the initial conditions using two ODE solvers. First, a standard ODE solver that uses a pre-trained artificial neural network (ANN) to estimate the source terms and integrates the solution in time. Second, a neural ODE solver that incorporates the time integration of PCs into the ANN training. The time-evolved profiles of the PCs and reconstructed thermochemical scalars demonstrate the robustness of the CoK-PCA-based low-dimensional manifold in accurately capturing the ignition process. Furthermore, we observed that the neural ODE solver minimized propagation errors across time steps and provided more accurate results than the standard ODE solver. The results of this study demonstrate the potential of CoK-PCA-based manifolds to be implemented in massively parallel reacting flow solvers. © 2025, CC BY.

关键词： Principal component analysis

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Automated Quantum Algorithm Synthesis

arXiv

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arXiv 2025年

作者： Rouillard, Amy Lourens, Matt Petruccione, Francesco Department of Physics Stellenbosch University South Africa School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa Stellenbosch South Africa

We present a computational method to automatically design the n-qubit realisations of quantum algorithms. Our approach leverages a domain-specific language (DSL) that enables the construction of quantum circuits via modular building blocks, making it well-suited for evolutionary search. In this DSL quantum circuits are abstracted beyond the usual gate-sequence description and scale automatically to any problem size. This enables us to learn the algorithm structure rather than a specific unitary implementation. We demonstrate our method by automatically designing three known quantum algorithms—the Quantum Fourier Transform, the Deutsch-Jozsa algorithm, and Grover’s search. Remarkably, we were able to learn the general implementation of each algorithm by considering examples of circuits containing at most 5-qubits. Our method proves robust, as it maintains performance across increasingly large search spaces. Convergence to the relevant algorithm is achieved with high probability and with moderate computational resources. © 2025, CC BY-NC-SA.

关键词： Qubits

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Information Geometric Approaches for Patient-Specific Test-Time Adaptation of Deep Learning Models for Semantic Segmentation

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Ravishankar, Hariharan Paluru, Naveen Sudhakar, Prasad Yalavarthy, Phaneendra K. Indian Institute of Science Department of Computational and Data sciences Bangalore560 012 India John F. Welch Technology Centre Wipro-GE HealthCare Bangalore560066 India

The test-time adaptation (TTA) of deep-learning-based semantic segmentation models, specific to individual patient data, was addressed in this study. The existing TTA methods in medical imaging are often unconstrained, require anatomical prior information or additional neural networks built during training phase, making them less practical, and prone to performance deterioration. In this study, a novel framework based on information geometric principles was proposed to achieve generic, off-the-shelf, regularized patient-specific adaptation of models during test-time. By considering the pre-trained model and the adapted models as part of statistical neuromanifolds, test-time adaptation was treated as constrained functional regularization using information geometric measures, leading to improved generalization and patient optimality. The efficacy of the proposed approach was shown on three challenging problems: a) improving generalization of state-of-the-art models for segmenting COVID-19 anomalies in Computed Tomography (CT) images b) cross-institutional brain tumor segmentation from magnetic resonance (MR) images, c) segmentation of retinal layers in Optical Coherence Tomography (OCT) images. Further, it was demonstrated that robust patient-specific adaptation can be achieved without adding significant computational burden, making it first of its kind based on information geometric principles. © 2025 IEEE.

关键词： Semantic Segmentation

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First-principles electron-phonon interactions and polarons in the parent cuprate La2CuO4

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Physical Review Research 2025年第1期7卷 L012073-L012073页

作者： Benjamin K. Chang Iurii Timrov Jinsoo Park Jin-Jian Zhou Nicola Marzari Marco Bernardi Department of Applied Physics and Materials Science and Department of Physics California Institute of Technology Pasadena California 91125 USA PSI Center for Scientific Computing Theory and Data Paul Scherrer Institut 5232 Villigen PSI Switzerland Department of Physics Pohang University of Science and Technology Pohang 37673 Korea School of Physics Beijing Institute of Technology Beijing 100081 China Theory and Simulation of Materials (THEOS) and National Centre for Computational Design and Discovery of Novel Materials (MARVEL) École Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland

Understanding electronic interactions in high-temperature superconductors is an outstanding challenge. In the widely studied cuprate materials, experimental evidence points to strong electron-phonon (e-ph) coupling and broad photoemission spectra. Yet, the microscopic origin of this behavior is not fully understood. Here, we study e-ph interactions and polarons in a prototypical parent (undoped) cuprate, La2CuO4 (LCO), by means of first-principles calculations. Leveraging parameter-free Hubbard-corrected density functional theory, we obtain a ground state with the band gap and Cu magnetic moment in nearly exact agreement with experiments. This enables a quantitative characterization of e-ph interactions. Our calculations reveal two classes of longitudinal optical (LO) phonons with strong e-ph coupling to hole states. These modes consist of bond stretching and bond bending in the Cu-O plane as well as vibrations of apical O atoms. The hole spectral functions, obtained with a cumulant method that can capture strong e-ph coupling, exhibit broad quasiparticle peaks with a small spectral weight (Z≈0.25) and pronounced LO-phonon sidebands characteristic of polaron effects. Our calculations predict features observed in photoemission spectra, including a 40-meV peak in the e-ph coupling distribution function not explained by existing models. These results show that the universal strong e-ph coupling found experimentally in doped lanthanum cuprates is also present in the parent compound, and elucidate its microscopic origin.

关键词： High temperature superconductors

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A Multiple Criteria-Based Customer Segmentation and Recommender System for Rural Farmers 2nd

A Multiple Criteria-Based Customer Segmentation and Recommen...

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2nd International Conference on Artificial Intelligence: Towards Sustainable Intelligence, AI4S 2024

作者： Usip, Patience U. Gibson, Ubong Etop Udoh, Samuel S. Department of Computer Science University of Uyo Uyo Nigeria TETFund Center of Excellence in Computational Intelligence Research University of Uyo Uyo Nigeria Department of Data Science University of Uyo Uyo Nigeria

ISBN: (纸本)9783031813689

Agriculture plays an important role in any nation’s economy. Agricultural marketing brings producers (farmers often in rural areas) and customers (customers often in rural areas) together at various location/distribution centres. Farm produce will be distributed to the location/centres based on the customers’ needs and hence requires that customers are segmented based on the needs and nearness to distribution centres. Segmenting customers and recommending other farm produce available at a distribution centre is based on multiple criteria including user demographics, and historical behaviour. This paper is aimed at segmenting the available customers and recommending available farm produce at various distribution centres for easy access for or by the rural farmers. The K-mean Algorithm is used for customer segmentation, while the association rule apriori algorithm for farm produce recommendation. The dataset of 300 farmers in different villages in Oruk Anam Local Government Area, Akwa Ibom State were collected, followed by the model implementation using python programming language in jupyter notebook and other web technologies. The Apriori algorithm was used to find the frequent item sets based on clusters from the RFM-K Means model. The frequent itemsets for different clusters (0,1,2) were derived and minimum support of 0.03 for cluster 2, 0.02 for cluster 1 and 0.01 for cluster 0 were applied to find the frequent itemsets (The algorithm grouped buyers and sellers into three clusters). Minimum threshold of 1, minimum confidence score of 0.7 and minimum lift score of =>4, 2, 2 for cluster 2, 1, 0 respectively were applied to the association rules to achieve a better result. The minimum support threshold determines the frequency required for a cluster to be considered relevant. Confidence in this represents the reliability of the association rules. A higher confidence threshold means more reliable recommendations. This work provides the platform for accessibility to ru

关键词： customer segmentation e-agriculture Knowledge mining product recommender system

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CAN FRONTIER LLMS REPLACE ANNOTATORS IN BIOMEDICAL TEXT MINING? ANALYZING CHALLENGES AND EXPLORING SOLUTIONS

arXiv

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arXiv 2025年

作者： Zhao, Yichong Goto, Susumu Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences The University of Tokyo Japan Database Center for Life Science Joint Support-Center for Data Science Research ROIS Japan

Large language models (LLMs) can perform various natural language processing (NLP) tasks through in-context learning without relying on supervised data. However, multiple previous studies have reported suboptimal performance of LLMs in biological text mining. By analyzing failure patterns in these evaluations, we identified three primary challenges for LLMs in biomedical corpora: (1) LLMs fail to learn implicit dataset-specific nuances from supervised data, (2) The common formatting requirements of discriminative tasks limit the reasoning capabilities of LLMs particularly for LLMs that lack test-time compute, and (3) LLMs struggle to adhere to annotation guidelines and match exact schemas, which hinders their ability to understand detailed annotation requirements which is essential in biomedical annotation workflow. To address these challenges, we experimented with prompt engineering techniques targeted to the above issues, and developed a pipeline that dynamically extracts instructions from annotation guidelines. Our findings show that frontier LLMs can approach or surpass the performance of state-of-the-art (SOTA) BERT-based models with minimal reliance on manually annotated data and without fine-tuning. Furthermore, we performed model distillation on a closed-source LLM, demonstrating that a BERT model trained exclusively on synthetic data annotated by LLMs can also achieve a practical performance. Based on these results, we explored the feasibility of partially replacing manual annotation with LLMs in production scenarios for biomedical text mining. © 2025, CC BY.

关键词： Natural language processing systems

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Secured and Privacy-Preserving GPU-Based Machine Learning Inference in Trusted Execution Environment: A Comprehensive Survey

Secured and Privacy-Preserving GPU-Based Machine Learning In...

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International Communication Systems and Networks and Workshops, COMSNETS

作者： Arunava Chaudhuri Shubhi Shukla Sarani Bhattacharya Debdeep Mukhopadhyay Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur India Centre for Computational and Data Sciences Indian Institute of Technology Kharagpur Kharagpur India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

With the rapid advancement of machine learning (ML) models and their widespread application across various sectors such as intrusion detection, medical diagnosis, natural language processing, and autonomous driving, these technologies have achieved remarkable success. However, this progress has also raised significant concerns about ensuring the security of ML models and protecting both private training data and model outputs from getting exposed in a shared cloud environment. To address these challenges, researchers have proposed various methodologies to create privacy-preserving, secure, and trustworthy model execution environments to prevent adversarial attacks. This study provides a comprehensive review of Trusted Execution Environment (TEE) implementations across different hardware accelerators. It also offers an overview of modern techniques for preserving privacy and security in execution environments, while identifying critical research gaps that require attention. In essence, this survey is an important resource for researchers, providing insights into recent methodologies and guiding them to focus on pressing research challenges.

关键词： Surveys Deep learning Training Privacy data privacy Training data data models Stability analysis Security Hardware acceleration

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Clustering based Medical Image Segmentation: A Study on MRI Scans of Brain Tumors 24

Clustering based Medical Image Segmentation: A Study on MRI ...

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8th International Conference on Advances in Artificial Intelligence, ICAAI 2024

作者： Mimenbayeva, Aigul B. Aruova, Aliya A. Bekmagambetova, Gulmira K. Niyazova, Rozamgul S. Turebayeva, Rakhila D. Naizagarayeva, Akgul A. Tursumbayeva, Ainur F. Departament of Computational and Data Science Astana IT University Astana Kazakhstan Department of Computer Science Agrotechnical Research University Named after S. Seifullin Astana Kazakhstan L.N. Gumilyov Eurasian National University Department of Artificial Intelligence Technologies Astana Kazakhstan Department of Information Systems Kazakh Agrotechnical Research University Named after S. Seifullin Astana Kazakhstan

ISBN: (纸本)9798400718014

This study investigates the application of Hierarchical clustering for image segmentation, with a focus on its efficacy in analyzing medical images, particularly MRI scans of brain tumors. Image segmentation plays a pivotal role in computer vision, facilitating various applications across industries. Leveraging a systematic approach, we conduct a comprehensive review of recent literature on machine learning algorithms for image segmentation. Subsequently, utilizing a dataset comprising MRI images with and without tumors, we preprocess and analyze the data using the Histogram of Oriented Gradients (HOG) technique to extract pertinent features. These features serve as input for the Hierarchical clustering algorithm to partition the images into distinct regions of interest. For each row of vectors, the Jensen-Shenton distance was calculated. The resulting symmetric matrices are distances among the corresponding vectors, quantifying dissimilarity in cluster analysis. Our findings underscore the effectiveness of Hierarchical clustering in clustering medical images, with potential implications for advancing computational analysis in healthcare and related domains. © 2024 Copyright held by the owner/author(s).

关键词： Magnetic resonance imaging

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