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arXiv 2024年

作者： Deliu, Nina Chakraborty, Bibhas MEMOTEF Department Sapienza University of Rome Italy MRC - Biostatistics Unit University of Cambridge United Kingdom Centre for Quantitative Medicine and the Program in Health Services and Systems Research Duke-NUS Medical School Singapore Department of Statistics and Data Science National University of Singapore Singapore Department of Biostatistics and Bioinformatics Duke University United States

Precision health, increasingly supported by digital technologies, is a domain of research that broadens the paradigm of precision medicine, advancing everyday healthcare. This vision goes hand in hand with the groundbreaking advent of artificial intelligence (AI), which is reshaping the way we diagnose, treat, and monitor both clinical subjects and the general population. AI tools powered by machine learning have shown considerable improvements in a variety of healthcare domains. In particular, reinforcement learning (RL) holds great promise for sequential and dynamic problems such as dynamic treatment regimes and just-in-time adaptive interventions in digital health. In this work, we discuss the opportunity offered by AI, more specifically RL, to current trends in healthcare, providing a methodological survey of RL methods in the context of precision and digital health. Focusing on the area of adaptive interventions, we expand the methodological survey with illustrative case studies that used RL in real practice. © 2024, CC BY-NC-SA.

关键词： Electronic health record

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SGIRR: Sparse Graph Index Remapping for ReRAM Crossbar Operation Unit and Power Optimization*

SGIRR: Sparse Graph Index Remapping for ReRAM Crossbar Opera...

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IEEE International Conference on Computer-Aided Design

作者： Cheng-Yuan Wang Yao-Wen Chang Yuan-Hao Chang Data Science Degree Program National Taiwan University and Academia Sinica Taipei Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taipei Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan Institute of Information Science Academia Sinica Taipei Taiwan

Resistive Random Access Memory (ReRAM) Crossbars are a promising process-in-memory technology to reduce enormous data movement overheads of large-scale graph processing between computation and memory units. ReRAM cells can combine with crossbar arrays to effectively accelerate graph processing, and partitioning ReRAM crossbar arrays into Operation Units (OUs) can further improve computation accuracy of ReRAM crossbars. The operation unit utilization was not optimized in previous work, incurring extra cost. This paper proposes a two-stage algorithm with a crossbar OU-aware scheme for sparse graph index remapping for ReRAM (SGIRR) crossbars, mitigating the influence of graph sparsity. In particular, this paper is the first to consider the given operation unit size with the remapping index algorithm, optimizing the operation unit and power dissipation. Experimental results show that our proposed algorithm reduces the utilization of crossbar OUs by 31.4%, improves the total OU block usage by 10.6%, and saves energy consumption by 17.2%, on average.

关键词： Energy consumption Visualization Resistive RAM Clustering algorithms Benchmark testing Power dissipation Indexes

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Not all spacetime coordinates for general-relativistic ray tracing are created equal

arXiv

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arXiv 2023年

作者： Bozzola, Gabriele Chan, Chi-Kwan Paschalidis, Vasileios Department of Astronomy University of Arizona TucsonAZ United States Data Science Institute University of Arizona TucsonAZ United States Program in Applied Mathematics University of Arizona TucsonAZ United States Department of Physics University of Arizona TucsonAZ United States

Models for the observational appearance of astrophysical black holes rely critically on accurate general-relativistic ray tracing and radiation transport to compute the intensity measured by a distant observer. In this paper, we illustrate how the choice of coordinates and initial conditions affect this process. In particular, we show that propagating rays from the camera to the source leads to different solutions if the spatial part of the momentum of the photon points towards the horizon or away from it. In doing this, we also show that coordinates that are well suited for numerical General-Relativistic MagnetoHydroDynamic (GRMHD) simulations are typically not optimal for generic ray tracing. We discuss the implications for black-hole images and show that radiation transport in optimal and non-optimal spacetime coordinates lead to the same images up to numerical errors and algorithmic choices. Copyright © 2023, The Authors. All rights reserved.

关键词： Ray tracing

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A stable implementation of a data-driven scale-aware mesoscale parameterization

arXiv

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arXiv 2023年

作者： Perezhogin, Pavel Zhang, Cheng Adcroft, Alistair Fernandez-Granda, Carlos Zanna, Laure Courant Institute of Mathematical Sciences New York University New YorkNY United States Program in Atmospheric and Oceanic Sciences Princeton University PrincetonNJ08542 United States Center for Data Science New York University New YorkNY United States

Ocean mesoscale eddies are often poorly represented in climate models, and therefore, their effects on the large scale circulation must be parameterized. Traditional parameterizations, which represent the bulk effect of the unresolved eddies, can be improved with new subgrid models learned directly from data. Zanna and Bolton (2020) (ZB20) applied an equation-discovery algorithm to reveal an interpretable expression parameterizing the subgrid momentum fluxes by mesoscale eddies through the components of the velocity-gradient tensor. In this work, we implement the ZB20 parameterization into the primitive-equation GFDL MOM6 ocean model and test it in two idealized configurations with significantly different dynamical regimes and topography. The original parameterization was found to generate excessive numerical noise near the grid scale. We propose two filtering approaches to avoid the numerical issues and additionally enhance the strength of large-scale energy backscatter. The filtered ZB20 parameterizations led to improved climatological mean state and energy distributions, compared to the current state-of-the-art energy backscatter parameterizations. The filtered ZB20 parameterizations are scale-aware and, consequently, can be used with a single value of the non-dimensional scaling coefficient for a range of resolutions. The successful application of the filtered ZB20 parameterizations to parameterize mesoscale eddies in two idealized configurations offers a promising opportunity to reduce long-standing biases in global ocean simulations in future studies. © 2023, CC BY.

关键词： Parameterization

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Conditional Generative Modeling for Amorphous Multi-Element Materials

arXiv

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arXiv 2025年

作者： Li, Honglin Liu, Chuhao Guo, Yongfeng Luo, Xiaoshan Chen, Yijie Liu, Guangsheng Li, Yu Wang, Ruoyu Wang, Zhenyu Wu, Jianzhuo Ma, Cheng Xie, Zhuohang Lv, Jian Ding, Yufei Zhang, Huabin Luo, Jian Zhong, Zhicheng Li, Mufan Wang, Yanchao Li, Wan-Lu Key Laboratory of Material Simulation Methods and Software Ministry of Education College of Physics Jilin University Changchun China Aiiso Yufeng Li Family Department of Chemical and Nano Engineering University of California La Jolla San DiegoCA United States Institute of Molecular Engineering Plus College of Chemistry Fuzhou University Fuzhou China College of Chemistry and Molecular Engineering Peking University Beijing China School of Physics Nankai University Tianjin China Institute of Modern Physics Fudan University Shanghai China Program in Materials Science and Engineering University of California La Jolla San DiegoCA United States School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China International Center of Future Science Jilin University Changchun China Department of Computer Science and Engineering University of California La Jolla San DiegoCA United States Center for Renewable Energy and Storage Technologies Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Suzhou Lab Suzhou215123 China

Amorphous multi-element materials offer unprecedented tunability in composition and properties, yet their rational design remains challenging due to the lack of predictive structure-property relationships and the vast configurational space. Traditional modeling struggles to capture the intricate short-range order that dictates their stability and functionality. We here introduce ApolloX, a pioneering predictive framework for amorphous multi-element materials, establishing a new paradigm by integrating physics-informed generative modeling with particle swarm optimization, using chemical short-range order as an explicit constraint. By systematically navigating the disordered energy landscape, ApolloX enables the targeted design of thermodynamically stable amorphous configurations. It accurately predicts atomic-scale arrangements, including composition-driven metal clustering and amorphization trends, which are well-validated by experiments, while also guiding synthesis by leveraging sluggish diffusion to control elemental distribution and disorder. The resulting structural evolution, governed by composition, directly impacts catalytic performance, leading to improved activity and stability with increasing amorphization. This predictive-experimental synergy transforms the discovery of amorphous materials, unlocking new frontiers in catalysis, energy storage, and functional disordered systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Amorphization

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Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide Images

Multimodal Co-Attention Transformer for Survival Prediction ...

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International Conference on Computer Vision (ICCV)

作者： Richard J. Chen Ming Y. Lu Wei-Hung Weng Tiffany Y. Chen Drew FK. Williamson Trevor Manz Maha Shady Faisal Mahmood Brigham and Women’s Hospital Harvard Medical School Cancer Program Broad Institute of Harvard and MIT Cancer Data Science Program Dana-Farber Cancer Institute Computer Science and Artificial Intelligence Laboratory MIT

ISBN: (纸本)9781665428132

Survival outcome prediction is a challenging weakly-supervised and ordinal regression task in computational pathology that involves modeling complex interactions within the tumor microenvironment in gigapixel whole slide images (WSIs). Despite recent progress in formulating WSIs as bags for multiple instance learning (MIL), representation learning of entire WSIs remains an open and challenging problem, especially in overcoming: 1) the computational complexity of feature aggregation in large bags, and 2) the data heterogeneity gap in incorporating biological priors such as genomic measurements. In this work, we present a Multimodal Co-Attention Transformer (MCAT) framework that learns an interpretable, dense co-attention mapping between WSIs and genomic features formulated in an embedding space. Inspired by approaches in Visual Question Answering (VQA) that can attribute how word embed-dings attend to salient objects in an image when answering a question, MCAT learns how histology patches attend to genes when predicting patient survival. In addition to visualizing multimodal interactions, our co-attention trans-formation also reduces the space complexity of WSI bags, which enables the adaptation of Transformer layers as a general encoder backbone in MIL. We apply our proposed method on five different cancer datasets (4,730 WSIs, 67 million patches). Our experimental results demonstrate that the proposed method consistently achieves superior performance compared to the state-of-the-art methods.

关键词： Representation learning Visualization Histopathology Genomics Predictive models Transformers Biological information theory

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Parameter-free differential evolution algorithm for the analytic continuation of imaginary time correlation functions

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Physical Review E 2022年第2期106卷 025312-025312页

作者： Nathan S. Nichols Paul Sokol Adrian Del Maestro Data Science and Learning Division Argonne National Laboratory Argonne Illinois 60439 USA Department of Physics University of Vermont Burlington Vermont 05405 USA Materials Science Program University of Vermont Burlington Vermont 05404 USA Department of Physics Indiana University Bloomington Indiana 47408 USA Department of Physics and Astronomy University of Tennessee Knoxville Tennessee 37996 USA Min H. Kao Department of Electrical Engineering and Computer Science University of Tennessee Knoxville Tennessee 37996 USA

We report on differential evolution for analytic continuation: a parameter-free evolutionary algorithm to generate the dynamic structure factor from imaginary time correlation functions. Our approach to this long-standing problem in quantum many-body physics achieves enhanced spectral fidelity while using fewer compute (CPU) hours. The need for fine-tuning of algorithmic control parameters is eliminated by embedding them within the genome to be optimized for this evolutionary computation-based algorithm. Benchmarks are presented for models where the dynamic structure factor is known exactly and experimentally relevant results are included for quantum Monte Carlo simulations of bulk He4 below the superfluid transition temperature.

关键词： Nonequilibrium statistical mechanics Helium-4 superfluids Genetic algorithm Neutron scattering Numerical techniques Path-integral Monte Carlo Quantum Monte Carlo

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Embedding Space Augmentation for Weakly Supervised Learning in Whole-Slide Images

Embedding Space Augmentation for Weakly Supervised Learning ...

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IEEE International Symposium on Biomedical Imaging

作者： Imaad Zaffar Guillaume Jaume Nasir Rajpoot Faisal Mahmood Department of Computer Science University College London UK Department of Pathology Harvard Medical School Brigham and Women’s Hospital Boston MA USA Department of Pathology Harvard Medical School Massachusetts General Hospital Boston MA USA Cancer Program Broad Institute of Harvard and MIT Cambridge MA USA Data Science Program Dana-Farber Cancer Institute Boston MA USA Department of Computer Science Tissue Image Analytics Centre University of Warwick Coventry UK Department of Pathology University Hospitals Coventry and Warwickshire NHS Trust Coventry UK The Alan Turing Institute London UK

Multiple Instance Learning (MIL) is a widely employed framework for learning on gigapixel whole-slide images (WSIs) from WSI-level annotations. In most MIL-based analytical pipelines for WSI-level analysis, the WSIs are divided into patches, and deep features for patches (i.e., patch embeddings) are extracted prior to training to reduce the overall computational cost and cope with the GPUs’ limited RAM. Because of this bottleneck, incorporating patch-level data augmentations during training adds an extra computational burden. To overcome this limitation, we present EmbAugmenter, a data augmentation generative adversarial network (DA-GAN) that can synthesize data augmentations in the embedding space rather than in the pixel space, thereby significantly reducing the computational requirements. Experiments on the SICAPv2 dataset show that our approach outperforms MIL without augmentation and is on par with traditional patch-level augmentation for MIL training while being substantially faster.

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Random Forest vs. SVM vs. KNN in classifying Smartphone and Smartwatch sensor data using CRISP-DM

Random Forest vs. SVM vs. KNN in classifying Smartphone and ...

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2020 International Conference on data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020

作者： Saleh, Sadiq Jaffer Ali, Sayed Qasim Zeki, Ahmed M. University of Bahrain Big Data Science Analytics Master Program Bahrain University of Bahrain College of Information Technology Deptartment of Information Systems Bahrain

ISBN: (纸本)9781728196756

Nowadays IoT sensors become one of the major sources of data, starting from home automation going to daily devices. Focusing on handsets that have the most significant contribution factor on data blooming and booming. The problem at hand is data mining methods comparison, for selecting the best method that serves sensor data. Methodology used was CRISP-DM. This paper found Random Forest data mining method to work best under the described circumstances. © 2020 IEEE.

关键词： data mining

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Using Valence and Arousal-infused Bi-LSTM for Sentiment Analysis in Soeial Media Produet Reviews 33

Using Valence and Arousal-infused Bi-LSTM for Sentiment Anal...

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33rd Conference on Computational Linguistics and Speech Processing, ROCLING 2021

作者： Cheng, Yu-Ya Yeh, Wen-Chao Chen, Yan-Ming Chang, Yung-Chun Professional Master Program in Data Science Taipei Medical University Taipei Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Graduate Institute of Data Science Taipei Medical University Taipei Taiwan

ISBN: (纸本)9789869576949

With the popularity of the current Internet age, online social platforms have provided a bridge for communication between private companies, public organizations, and the public. The purpose of this research is to understand the user's experience of the product by analyzing product review data in different fields. We propose a BiLSTM-based neural network which infused rich emotional information. In addition to consider Valence and Arousal which is the smallest morpheme of emotional information, the dependence relationship between texts is also integrated into the deep learning model to analyze the sentiment. The experimental results show that this research can achieve good performance in predicting the vocabulary Valence and Arousal. In addition, the integration of VA and dependency information into the BiLSTM model can have excellent performance for social text sentiment analysis, which verifies that this model is effective in emotion recognition of social medial short text. © 2021 ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing. All rights reserved.

关键词： Social networking (online)

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