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arXiv 2022年

作者： Thongprayoon, Chanon Livi, Lorenzo Masuda, Naoki Department of Mathematics State University of New York at Buffalo BuffaloNY United States Department of Computer Science University of Manitoba WinnipegMB Canada Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY United States Faculty of Science and Engineering Waseda University Shinjuku Tokyo Japan

Temporal network data are increasingly available in various domains, and often represent highly complex systems with intricate structural and temporal evolutions. Due to the difficulty of processing such complex data, it may be useful to coarse grain temporal network data into a numeric trajectory embedded in a low-dimensional space. We refer to such a procedure as temporal network embedding, which is distinct from procedures that aim at embedding individual nodes. Temporal network embedding is a challenging task because we often have access only to discrete time-stamped events between node pairs, and, in general, the events occur with irregular intervals, making the construction of the network at a given time a nontrivial question already. We propose a method to generate trajectories of temporal networks embedded in a low-dimensional space given a sequence of time-stamped events as input. We realize this goal by combining the landmark multidimensional scaling, which is an out-of-sample extension of the well-known multidimensional scaling method, and the framework of tie-decay temporal networks. This combination enables us to obtain a continuous-time trajectory describing the evolution of temporal networks. We then study mathematical properties of the proposed temporal network embedding framework. Finally, we showcase the method with empirical data of social contacts to find temporal organization of contact events and loss of them over a single day and across different days. © 2022, CC BY.

关键词： Trajectories

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Dimension reduction of dynamical systems on networks with leading and non-leading eigenvectors of adjacency matrices

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Physical Review Research 2022年第2期4卷 023257-023257页

作者： Naoki Masuda Prosenjit Kundu Department of Mathematics State University of New York at Buffalo New York 14260-2900 USA Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo New York 14260-5030 USA

Dimension reduction techniques for dynamical systems on networks are considered to promote our understanding of the original high-dimensional dynamics. One strategy of dimension reduction is to derive a low-dimensional dynamical system whose behavior approximates the observables of the original dynamical system that are weighted linear summations of the state variables at the different nodes. Recently proposed methods use the leading eigenvector of the adjacency matrix of the network as the mixture weights to obtain such observables. In the present study, we explore performances of this type of one-dimensional reductions of dynamical systems on networks when we use non-leading eigenvectors of the adjacency matrix as the mixture weights. Our theory predicts that non-leading eigenvectors can be more efficient than the leading eigenvector and enables us to select the eigenvector minimizing the error. We numerically verify that the optimal non-leading eigenvector outperforms the leading eigenvector for some dynamical systems and networks. We also argue that, despite our theory, it is practically better to use the leading eigenvector as the mixture weights to avoid misplacing the bifurcation point too distantly and to be resistant against dynamical noise.

关键词： Network resilience

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GAITGen: Disentangled Motion-Pathology Impaired Gait Generative Model – Bringing Motion Generation to the Clinical Domain

arXiv

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arXiv 2025年

作者： Adeli, Vida Mehraban, Soroush Mirmehdi, Majid Whone, Alan Filtjens, Benjamin Dadashzadeh, Amirhossein Fasano, Alfonso Iaboni, Andrea Taati, Babak University of Toronto Computer Science Department Canada Vector Institute Canada KITE Research Institute UHN Canada University of Toronto Institute of Biomedical Engineering Canada University of Bristol School of Computer Science United Kingdom University of Bristol Translational Health Science United Kingdom University of Toronto Data Sciences Institute Canada University of Toronto Department of Medicine Division of Neurology Canada Krembil Research Institute UHN Canada Edmond J. Safra Program in Parkinson’s Disease UHN Canada University of Toronto Department of Psychiatry Canada Centre for Mental Health UHN Canada

Gait analysis is crucial for the diagnosis and monitoring of movement disorders like Parkinson’s Disease. While computer vision models have shown potential for objectively evaluating parkinsonian gait, their effectiveness is limited by scarce clinical datasets and the challenge of collecting large and well-labelled data, impacting model accuracy and risk of bias. To address these gaps, we propose GAITGen, a novel framework that generates realistic gait sequences conditioned on specified pathology severity levels. GAITGen employs a Conditional Residual Vector Quantized Variational Autoencoder to learn disentangled representations of motion dynamics and pathology-specific factors, coupled with Mask and Residual Transformers for conditioned sequence generation. GAITGen generates realistic, diverse gait sequences across severity levels, enriching datasets and enabling large-scale model training in parkinsonian gait analysis. Experiments on our new PD-GaM (real) dataset demonstrate that GAITGen outperforms adapted state-of-the-art models in both reconstruction fidelity and generation quality, accurately capturing critical pathology-specific gait features. A clinical user study confirms the realism and clinical relevance of our generated sequences. Moreover, incorporating GAITGen-generated data into downstream tasks improves parkinsonian gait severity estimation, highlighting its potential for advancing clinical gait analysis. © 2025, CC BY-NC-SA.

关键词： Pathology

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Climate velocities and species tracking in global mountain regions

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Nature 2024年第8010期629卷 114-120页

作者： Chan, Wei-Ping Lenoir, Jonathan Mai, Guan-Shuo Kuo, Hung-Chi Chen, I-Ching Shen, Sheng-Feng Biodiversity Research Center Academia Sinica Taipei Taiwan Department of Organismic and Evolutionary Biology Harvard University Cambridge MA United States Bachelor Program in Data Science and Management Taipei Medical University Taipei Taiwan Rowland Institute at Harvard University Cambridge MA United States UMR CNRS 7058 Ecologie et Dynamique des Systèmes Anthropisés (EDYSAN) Université de Picardie Jules Verne Amiens France Department of Atmospheric Sciences National Taiwan University Taipei Taiwan Department of Life Sciences National Cheng Kung University Tainan Taiwan Department of Biology Stanford University Stanford CA United States

Mountain ranges contain high concentrations of endemic species and are indispensable refugia for lowland species that are facing anthropogenic climate change1,2. Forecasting biodiversity redistribution hinges on asses... 详细信息

Mountain ranges contain high concentrations of endemic species and are indispensable refugia for lowland species that are facing anthropogenic climate change^1,2. Forecasting biodiversity redistribution hinges on assessing whether species can track shifting isotherms as the climate warms^3,4. However, a global analysis of the velocities of isotherm shifts along elevation gradients is hindered by the scarcity of weather stations in mountainous regions⁵. Here we address this issue by mapping the lapse rate of temperature (LRT) across mountain regions globally, both by using satellite data (SLRT) and by using the laws of thermodynamics to account for water vapour⁶ (that is, the moist adiabatic lapse rate (MALRT)). By dividing the rate of surface warming from 1971 to 2020 by either the SLRT or the MALRT, we provide maps of vertical isotherm shift velocities. We identify 17 mountain regions with exceptionally high vertical isotherm shift velocities (greater than 11.67 m per year for the SLRT;greater than 8.25 m per year for the MALRT), predominantly in dry areas but also in wet regions with shallow lapse rates;for example, northern Sumatra, the Brazilian highlands and southern Africa. By linking these velocities to the velocities of species range shifts, we report instances of close tracking in mountains with lower climate velocities. However, many species lag behind, suggesting that range shift dynamics would persist even if we managed to curb climate-change trajectories. Our findings are key for devising global conservation strategies, particularly in the 17 high-velocity mountain regions that we have identified. © The Author(s) 2024.

关键词： Conservation biology Macroecology

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Structural analysis based on unsupervised learning: Search for a characteristic low-dimensional space by local structures in atomistic simulations

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Physical Review B 2022年第7期105卷 075107-075107页

作者： Ryo Tamura Momo Matsuda Jianbo Lin Yasunori Futamura Tetsuya Sakurai Tsuyoshi Miyazaki International Center for Materials Nanoarchitectonics National Institute for Materials Science Tsukuba 305-0044 Japan Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba 305-0044 Japan Graduate School of Frontier Sciences The University of Tokyo Chiba 277-8568 Japan Department of Computer Science University of Tsukuba Tsukuba 305-8573 Japan Center for Artificial Intelligence University of Tsukuba Tsukuba 305-8573 Japan Master's/Doctoral Program in Life Science Innovation University of Tsukuba Tsukuba 305-8577 Japan

Owing to the advances in computational techniques and the increase in computational power, atomistic simulations of materials can simulate large systems with higher accuracy. Complex phenomena can be observed in such state-of-the-art atomistic simulations. However, it has become increasingly difficult to understand what is actually happening and mechanisms, for example, in molecular dynamics (MD) simulations. We propose an unsupervised machine learning method to analyze the local structure around a target atom. The proposed method, which uses the two-step locality preserving projections (TS-LPP), can find a low-dimensional space wherein the distributions of data points for each atom or groups of atoms can be properly captured. We demonstrate that the method is effective for analyzing the MD simulations of crystalline, liquid, and amorphous states and the melt-quench process from the perspective of local structures. The proposed method is demonstrated on a silicon single-component system, a silicon-germanium binary system, and a copper single-component system.

关键词： Amorphous materials Machine learning Molecular dynamics

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Statistical Power Analysis for Designing Bulk, Single-Cell, and Spatial Transcriptomics Experiments: Review, Tutorial, and Perspectives

arXiv

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arXiv 2023年

作者： Jeon, Hyeongseon Xie, Juan Jeon, Yeseul Jung, Kyeong Joo Gupta, Arkobrato Chang, Won Chung, Dongjun Department of Biomedical Informatics The Ohio State University ColumbusOH United States Pelotonia Institute for Immuno-Oncology The James Comprehensive Cancer Center The Ohio State University ColumbusOH43210 United States The Interdisciplinary PhD program in Biostatistics The Ohio State University ColumbusOH United States Department of Statistics and Data Science Yonsei University Seoul Korea Republic of Department of Applied Statistics Yonsei University Seoul Korea Republic of Department of Computer Science and Engineering The Ohio State University ColumbusOH United States Division of Statistics and Data Science University of Cincinnati CincinnatiOH United States

Gene expression profiling technologies have been used in various applications such as cancer biology. The development of gene expression profiling has expanded the scope of target discovery in transcriptomic studies, and each technology produces data with distinct characteristics. In order to guarantee biologically meaningful findings using transcriptomic experiments, it is important to consider various experimental factors in a systematic way through statistical power analysis. In this paper, we review and discuss the power analysis for three types of gene expression profiling technologies from a practical standpoint, including bulk RNA-seq, single-cell RNA-seq, and high-throughput spatial transcriptomics. Specifically, we describe the existing power analysis tools for each research objective for each of the bulk RNA-seq and scRNA-seq experiments, along with recommendations. On the other hand, since there are no power analysis tools for high-throughput spatial transcriptomics at this point, we instead investigate the factors that can influence power analysis. © 2023, CC BY-NC-ND.

关键词： RNA

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Tmf-Gnn: Temporal Matrix Factorization-Based Graph Neural Network for Multivariate Time Series Forecasting with Missing Values

SSRN

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SSRN 2023年

作者： Kim, Suhyeon Lee, Taek-Ho Lee, Junghye Graduate School of Data Science Kyungpook National University Daegu41566 Korea Republic of Technology Management Economics and Policy Program & Institute of Engineering Research Seoul National University Seoul08826 Korea Republic of Technology Management Economics and Policy Program & Graduate School of Engineering Practice Seoul National University Seoul08826 Korea Republic of

Missing data in multivariate time series (MTS) is a very common issue, often caused by unreliable sensors and data storage or transmission problems. Particularly, such missing data can cause some errors and biases in the MTS forecasting tasks of real-world applications, implying that proper handling of the missing data is essential. Therefore, in this study, we propose a new method for MTS forecasting with missing values, called a temporal matrix factorization-based graph neural network (TMF-GNN), to improve predictive performance outcomes. TMF-GNN basically uses the concept of TMF, which reconstructs partially observed MTS data. We newly present a data-adaptive regularization method for TMF based on graph-based and sequential deep learning algorithms to capture both the variable- and sample-wise information of MTS data affected by the concept known as missingness. We demonstrate the feasibility of the proposed method by conducting various experiments on three MTS datasets and show how it outperforms baseline methods. We believe that our study will have an impact on several MTS-related tasks and that it can be a useful alternative for handling missing values in MTS data. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Optimal Branch Location for Cost-effective Inference on Branchynet

Optimal Branch Location for Cost-effective Inference on Bran...

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IEEE International Conference on Big data

作者： Chang-Han Chiang Pangfeng Liu Da-Wei Wang Ding-Yong Hong Jan-Jan Wu Data Science Degree Program National Taiwan University Taipei Taiwan National Taiwan University Taipei Taiwan Institute of Information Science Academia Sinica Taipei Taiwan

ISBN: (纸本)9781665445993

Deep Neural Networks (DNNs) are very popular in many machine learning domains. To achieve higher accuracy, DNNs have become deeper and larger. However, the improvement in accuracy comes with the price of the longer inference time and energy consumption. The marginal cost to increase a unit of accuracy has become higher as the accuracy itself is *** Branchynet, known as early exits, is an architecture to address increasing marginal cost for improving accuracy. The Branchynet adds extra side classifiers to a DNN model. The inference on a significant portion of the samples can exit from the network earlier via these side branches if they already have high confidence in the *** Branchynet requires manually tuning the learning hyperparameters, e.g., the locations of branches and the confidence threshold for early exiting. The effectiveness of this manual tuning dramatically impacts the efficiency of the tuned networks. To the best of our knowledge, there are no efficient algorithms to find the best branch location, which is a trade-off between the accuracy and inference time on the *** propose an algorithm to find the optimal branch locations for the Branchynet. We formulate the problem of finding the optimal branch location for the branchynet as an optimization problem, and prove that the branch placement problem is an NPcomplete problem. We then derive dynamic programming that runs in pseudo-polynomial time and solves the branch placement problem *** also implement our algorithm and solve the branch placement problems on four types of VGG networks. The experiment results indicate that our dynamic programming can find the optimal branch locations for generating the maximum number of correct classifications within a given time budget. We also run the four VGG models on a GeForce RTX-3090 GPU with the branch combination found by the dynamic programming. The experiment results show that our dynamic programming accurately predicts the numbe

关键词： Costs Heuristic algorithms Neural networks Graphics processing units Manuals Big data Inference algorithms

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Genetic Perturbation of CD70/CD27 Co-Stimulation Promotes the Development of Bcl6-Driven Diffuse Large B-Cell Lymphoma

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BLOOD 2021年第Sup1期138卷 713-713页

作者： Mandato, Elisa Sun, Yanbo Shanmugam, Vignesh Choi, Il-Kyu Wright, Kyle T. Redd, Robert A. Lawton, Lee N. Neuberg, Donna S. Rodig, Scott J. Zhang, Baochun Shipp, Margaret A. Department of Medical Oncology Dana-Farber Cancer Institute Boston MA Department of Pathology Brigham and Women's Hospital Boston MA Broad Institute of MIT and Harvard Cancer Program Cambridge MA Department of Pathology University of Oklahoma Health Sciences Center Oklahoma City OK Department of Data Science Dana-Farber Cancer Institute Boston MA Department of Data Science Dana-Farber Cancer Institute Boston Department of Pathology Brigham & Women's Hospital Boston MA Department of Medical Oncology Dana Farber Cancer Institute Boston MA

Multiple co-stimulatory and co-inhibitory pathways modulate T-cell dependent anti-tumor immune responses in lymphoid malignancies. We recently defined the recurrent genetic alterations and associated substructure of diffuse large B-cell lymphoma (DLBCL), including five distinct clusters (1-5), and identified potential genetic bases for immune evasion [Nature Medicine 2018; 24:679-690]. In our series, 26% of tumors had inactivating somatic mutations or copy loss of CD70 and likely disruption of CD70/CD27 co-stimulation. CD70 and CD27 are homotrimer type II and homodimer type I transmembrane proteins and members of the TNF and TNF receptor superfamilies, respectively. CD70 is transiently expressed on certain normal B-cell and dendritic cell populations upon activation and constitutively expressed on multiple B-cell tumors. CD70 activation of CD27 + T cells promotes antigen-dependent T-cell expansion and immune surveillance of normal and malignant B cells. Patients with germline deficiencies of either CD70 or CD27 have an increased incidence of EBV-associated lymphoid malignancies, underscoring the importance of this co-stimulatory pathway. In our series, CD70 alterations were most common in Cluster 1 DLBCLs, which also exhibited recurrent BCL6 chromosomal translocations. The co-occurrence of CD70 and BCL6 genetic alterations was noteworthy because of the established role of CD8 + T-cell dependent immune surveillance in murine models of Bcl6 -driven DLBCL [Nature Medicine 2014; 20:283-290]. To assess the consequences of Cd70 deficiency and perturbed CD70/CD27 co-stimulation on Bcl6 -driven lymphomagenesis, we crossed the previously described Bcl6 tg/+ and Cd70 -/- mice to generate Cd70 -/- ; Bcl6 tg/+ animals. In the aging cohorts, Cd70 -/- ; Bcl6 tg/+ mice were more likely than Bcl6 tg/+ animals (or the Cd70 -/- or wild-type [WT] groups) to be euthanized for symptoms before the study endpoint (18 months [mo]) (5 of 18 Cd70 -/- ; Bcl6 tg/+ euthanized for symptoms prior

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Artificial Intelligence-based Decision Support Systems for Precision and Digital Health

arXiv

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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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