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arXiv 2020年

作者： Zhan, Xiu-Xiu Li, Ziyu Masuda, Naoki Holme, Petter Wang, Huijuan Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Mekelweg 4 Delft2628 CD Netherlands Department of Mathematics University at Buffalo State University of New York Buffalo New YorkNY14260-2900 United States Computational and Data-Enabled Science and Engineering Program University at Buffalo State University of New York Buffalo New YorkNY14260-2900 United States Institute of Innovative Research Tokyo Institute of Technology Yokohama226-8503 Japan

Link prediction can be used to extract missing information, identify spurious interactions as well as forecast network evolution. Network embedding is a methodology to assign coordinates to nodes in a low-dimensional vector space. By embedding nodes into vectors, the link prediction problem can be converted into a similarity comparison task. Nodes with similar embedding vectors are more likely to be connected. Classic network embedding algorithms are random-walk-based. They sample trajectory paths via random walks and generate node pairs from the trajectory paths. The node pair set is further used as the input for a Skip-Gram model, a representative language model that embeds nodes (which are regarded as words) into vectors. In the present study, we propose to replace random walk processes by a spreading process, namely the susceptible-infected (SI) model, to sample paths. Specifically, we propose two SI-spreading-based algorithms, SINE and TSINE, to embed static and temporal networks, respectively. The performance of our algorithms is evaluated by the missing link prediction task in comparison with state-of-the-art static and temporal network embedding algorithms. Results show that SINE and TSINE outperform the baselines across all six empirical datasets. We further find that the performance of SINE is mostly better than TSINE, suggesting that temporal information does not necessarily improve the embedding for missing link prediction. Moreover, we study the effect of the sampling size, quantified as the total length of the trajectory paths, on the performance of the embedding algorithms. The better performance of SINE and TSINE requires a smaller sampling size in comparison with the baseline algorithms. Hence, SI-spreading-based embedding tends to be more applicable to large-scale networks. Copyright © 2020, The Authors. All rights reserved.

关键词： Network embeddings

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Learn-Morph-Infer: a new way of solving the inverse problem for brain tumor modeling

arXiv

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arXiv 2021年

作者： Ezhov, Ivan Scibilia, Kevin Franitza, Katharina Steinbauer, Felix Shit, Suprosanna Zimmer, Lucas Lipkova, Jana Kofler, Florian Paetzold, Johannes Canalini, Luca Waldmannstetter, Diana Menten, Martin J. Metz, Marie Wiestler, Benedikt Menze, Bjoern Department of Informatics TUM Munich Germany TranslaTUM - Central Institute for Translational Cancer Research TUM Munich Germany Department of Mechanical Engineering TUM Munich Germany Department of Quantitative Biomedicine UZH Zurich Switzerland Department of Pathology Brigham and Women’s Hospital Harvard Medical School BostonMA United States Broad Institute of Harvard MIT CambridgeMA United States Data Science Program Dana-Farber Cancer Institute BostonMA United States Fraunhofer MEVIS Bremen Germany Neuroradiology Department of Klinikum Rechts der Isar TUM Munich Germany

Current treatment planning of patients diagnosed with a brain tumor, such as glioma, could significantly benefit by accessing the spatial distribution of tumor cell concentration. Existing diagnostic modalities, e.g. magnetic resonance imaging (MRI), contrast sufficiently well areas of high cell density. In gliomas, however, they do not portray areas of low cell concentration, which can often serve as a source for the secondary appearance of the tumor after treatment. To estimate tumor cell densities beyond the visible boundaries of the lesion, numerical simulations of tumor growth could complement imaging information by providing estimates of full spatial distributions of tumor cells. Over recent years a corpus of literature on medical image-based tumor modeling was published. It includes different mathematical formalisms describing the forward tumor growth model. Alongside, various parametric inference schemes were developed to perform an efficient tumor model personalization, i.e. solving the inverse problem. However, the unifying drawback of all existing approaches is the time complexity of the model personalization which prohibits a potential integration of the modeling into clinical settings. In this work, we introduce a deep learning based methodology for inferring the patient-specific spatial distribution of brain tumors from T1Gd and FLAIR MRI medical scans. Coined as Learn-Morph-Infer, the method achieves real-time performance in the order of minutes on widely available hardware and the compute time is stable across tumor models of different complexity, such as reaction-diffusion and reaction-advection-diffusion models. We believe the proposed inverse solution approach not only bridges the way for clinical translation of brain tumor personalization but can also be adopted to other scientific and engineering domains. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Free Energy Minimization Using the 2-D Cluster Variation Method: Initial Code Verification and Validation

arXiv

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arXiv 2018年

作者： Maren, Alianna J. Northwestern University School of Professional Studies Master of Science in Data Science Program

A new approach for artificial general intelligence (AGI), building on neural network deep learning architectures, can make use of one or more hidden layers that have the ability to continuously reach a free energy minimum even after input stimulus is removed, allowing for a variety of possible behaviors. One reason that this approach has not been developed until now has been the lack of a suitable free energy equation;one that would avoid some of the difficulties known in Hopfield-style neural networks. The cluster variation method (CVM) offers a means for characterizing 2-D local pattern distributions, or configuration variables, and provides a free energy formalism in terms of these configuration variables. The equilibrium distribution of these configuration variables is defined in terms of a single interaction enthalpy parameter, h, for the case of equiprobable distribution of bistate (neural/neural ensemble) units. For non-equiprobable distributions, the equilibrium distribution can be characterized by providing a fixed value for the fraction of units in the active state (x1), corresponding to the influence of a per-unit activation enthalpy, together with the pairwise interaction enthalpy parameter h. This paper provides verification and validation (V&V) for code that computes the configuration variable and thermodynamic values for 2-D CVM grids characterized by different interaction enthalpy parameters, or h-values. This means that there is now a working foundation for experimenting with a 2-D CVM-based hidden layer that can, as an alternative to responding strictly to inputs, also now independently come to its own free energy minimum. Such a system can also return to a free energy-minimized state after it has been perturbed, which will enable a range of input-independent behaviors that have not been hitherto available. A further use of this 2-D CVM grid is that by characterizing different kinds of patterns in terms of their corresponding h-values (together wit

关键词： Free energy

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Publisher Correction: How does damage control strategy influence organ's suitability for donation after major trauma? A multi-institutional study

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European journal of trauma and emergency surgery : official publication of the European Trauma Society 2024年第5期50卷 2623-2625页

作者： Michele Altomare Shir Sara Bekhor Marco Sacchi Federico Ambrogi Gabriele Infante Arturo Chieregato Federico Pozzi Tullia Maria De Feo Lorenza Nava Elisabetta Masturzo Luca Del Prete Carolina Perali Elena Manzo Paolo Bertoli Francesco Virdis Andrea Spota Stefano Piero Bernardo Cioffi Laura Benuzzi Giuliano Santolamazza Mauro Podda Andrea Mingoli Osvaldo Chiara Stefania Cimbanassi Department of Surgical Sciences Sapienza University of Rome Piazzale Aldo Moro 5 00185 Rome Italy. michele.altomare@uniroma1.it. Department of Acute Care Surgery and Trauma Team ASST GOM Niguarda Piazza Ospedale Maggiore 3 Milano 20162 Milan Italy. michele.altomare@uniroma1.it. Department of Acute Care Surgery and Trauma Team ASST GOM Niguarda Piazza Ospedale Maggiore 3 Milano 20162 Milan Italy. Department EMERGENZA URGENZA-E.A.S. SOREU Metropolitana Via Campanini 6 20124 Milan Italy. Department of Clinical Science and Community Health University of Milan Festa del Perdono 7 20122 Milan Italy. Department of Economics Management and Quantitative Methods (DEMM) University of Milan Via Festa del Perdono 7 Milan Italy. SSD Clinical Epidemiology and Trial Organization Department of Epidemiology and Data Science Fondazione IRCCS Istituto Nazionale dei Tumori via Venezian 1 Milan Italy. Neuro-intensive Care Unit ASST Niguarda Hospital Piazza Ospedale Maggiore 3 20162 Milan Italy. UOC Trapianti Lombardia - NITp Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico Milano Milan Italy. SS Coordinamento locale del prelievo di Organi e Tessuti ASST Niguarda Piazza Ospedale Maggiore 3 20162 Milan Italy. General and Liver Transplantation Surgery Unit Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico Via Francesco Sforza 28 20122 Milan Italy. ASST Fatebenefratelli Sacco Piazza Principessa Clotilde 3 20157 Milan Italy. ASST Papa Giovanni XXIII Piazza OMS - Organizzazione Mondiale della Sanità 1 20147 Bergamo Italy. Department of Surgical Sciences Sapienza University of Rome Piazzale Aldo Moro 5 00185 Rome Italy. General Surgery Residency Program University of Milan Festa del Perdono 7 20122 Milan Italy. Department of Surgical Science Emergency Surgery Unit University of Cagliari Cagliari Italy. Department of Medical-Surgical Physiopathology and Transplantation University of Milan Festa del Perdono 7 20122 Milan Italy.

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Bounded Manifold Completion

arXiv

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arXiv 2019年

作者： Gajamannage, Kelum Paffenroth, Randy Department of Mathematics and Statistics Texas A&M University–Corpus Christi Corpus ChristiTX78412 United States Department of Mathematical Sciences Department of Computer Science and Data Science Program Worcester Polytechnic Institute WorcesterMA01609 United States

Nonlinear dimensionality reduction or, equivalently, the approximation of high-dimensional data using a low-dimensional nonlinear manifold is an active area of research. In this paper, we will present a thematically different approach to detect the existence of a low-dimensional manifold of a given dimension that lies within a set of bounds derived from a given point cloud. A matrix representing the appropriately defined distances on a low-dimensional manifold is low-rank, and our method is based on current techniques for recovering a partially observed matrix from a small set of fully observed entries that can be implemented as a low-rank Matrix Completion (MC) problem. MC methods are currently used to solve challenging real-world problems, such as image inpainting and recommender systems, and we leverage extent efficient optimization techniques that use a nuclear norm convex relaxation as a surrogate for non-convex and discontinuous rank minimization. Our proposed method provides several advantages over current nonlinear dimensionality reduction techniques, with the two most important being theoretical guarantees on the detection of low-dimensional embeddings and robustness to non-uniformity in the sampling of the manifold. We validate the performance of this approach using both a theoretical analysis as well as synthetic and real-world benchmark *** Codes 68T05 Copyright © 2019, The Authors. All rights reserved.

关键词： Relaxation processes

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Predictors of Co-occurring Cardiovascular and Gastrointestinal Disorders among Elderly with Osteoarthritis

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Osteoarthritis and Cartilage Open 2021年第2期3卷 100148-100148页

作者： Patel, Jayeshkumar Ladani, Amit Sambamoorthi, Nethra LeMasters, Traci Dwibedi, Nilanjana Sambamoorthi, Usha Department of Pharmaceutical Systems and Policy West Virginia University Morgantown United States Rheumatology West Virginia University Medicine Morgantown WV United States Masters in Data Science Program School of Professional Studies Northwestern University Chicago IL United States Department of Pharmacotherapy University of North Texas Health Sciences Center College of Pharmacy United States Vashisht Professor Health Disparities Health Education Awareness & Research in Disparities Scholar Texas Center for Health Disparities HSC United States

Objective: To identify the leading predictors of co-occurring cardiovascular or gastrointestinal disorders (CV-GID) in a real-world cohort of elderly with osteoarthritis (OA). Method: An observational retrospective cohort study using data from Optum's deidentified Clinformatics® data Mart was conducted. Elderly with OA were identified in 2015 and were followed for two years to identify co-occurring CV-GID including ischemic heart disease, stroke, heart failure, dyspepsia, gastroesophageal reflux disorder, and peptic ulcer disease. Random Forest (RF) and Partial Dependence Plots (PDP) were used to identify the leading predictors of CV-GID and to examine their associations. Multivariable logistic regression was also used to examine the association of the leading predictors with CV-GID. Results: Our study cohort consisted of 45,385 elderly with OA (mean age 76.0 years). CV-GID were present in 59% of elderly. Using RF, age was found to be the strongest predictor of CV-GID followed by cardiac arrhythmia, duration of opioid use, number of orthopedist or physical therapy visits, number of intra-articular corticosteroid injections, polypharmacy, duration of non-selective nonsteroidal anti-inflammatory drugs or oral corticosteroids, and hypertension. The PDPs demonstrated that higher age, cardiac arrhythmia, longer durations of opioid or oral corticosteroids, higher number of physical therapy visits or intra-articular corticosteroid use, polypharmacy, and hypertension were associated with a higher risk of CV-GID. Conclusion: CV-GIDs are common among elderly with OA and can be predicted based on certain clinical factors. Machine learning methods with PDPs can be used to improve the interpretability and inform decision-making. © 2021 The Author(s)

关键词： Cardiovascular disease Gastrointestinal disorders Machine learning Older adults Osteoarthritis Predictive modeling Random forest

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data efficient and weakly supervised computational pathology on whole slide images

arXiv

引用

arXiv 2020年

作者： Lu, Ming Y. Williamson, Drew F.K. Chen, Tiffany Y. Chen, Richard J. Barbieri, Matteo Mahmood, Faisal Department of Pathology Brigham and Women’s Hospital Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Cancer Data Science Dana-Farber Cancer Institute BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States

The rapidly emerging field of computational pathology has the potential to enable objective diagnosis, therapeutic response prediction and identification of new morphological features of clinical relevance. However, deep learning-based computational pathology approaches either require manual annotation of gigapixel whole slide images (WSIs) in fully-supervised settings or thousands of WSIs with slide-level labels in a weakly-supervised setting. Moreover, whole slide level computational pathology methods also suffer from domain adaptation, interpretability and visualization issues. These challenges have prevented the broad adaptation of computational pathology for clinical and research purposes. Here we present CLAM - Clustering-constrained Attention Multiple instance learning (https://***/mahmoodlab/CLAM), an easy-to-use, high-throughput and interpretable WSI-level processing and learning method that only requires slide-level labels while being data efficient, adaptable and capable of handling multi-class subtyping problems. CLAM is a deep-learning based weakly-supervised method that uses attention-based learning to automatically identify sub-regions of high diagnostic value in order to accurately classify the whole slide, while also utilizing instance-level clustering over the representative regions identified to constrain and refine the feature space. In three separate analyses, we demonstrate the data efficiency and adaptability of CLAM and its superior performance over standard weakly-supervised classification. We demonstrate that CLAM models are interpretable and can be used to identify well-known and new morphological features without using any spatial labels during training. We further show that models trained using CLAM are adaptable to independent test cohorts, cell phone microscopy images, and varying slide tissue content. CLAM is a flexible, general purpose, and adaptable method that can be used for a variety of different computational pathology ta

关键词： Supervised learning

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A catalog of redshift estimates for 1366 BATSE long-duration gamma-ray bursts: Evidence for strong selection effects on the phenomenological prompt gamma-ray correlations

arXiv

引用

arXiv 2019年

作者： Shahmoradi, Amir Nemiroff, Robert J. Department of Physics Data Science Program University of Texas at Arlington ArlingtonTX76010 United States Department of Physics Michigan Technological University HoughtonMI49931 United States

We present a catalog of the redshift estimates and probability distributions for 1366 individual Long-duration Gamma-Ray Bursts (LGRBs) detected by the Burst And Transient Source Experiment (BATSE). This result is based on a careful classification and modeling of the population distribution of BATSE LGRBs in the 5-dimensional space of redshift as well as intrinsic prompt gamma-ray emission properties: The isotropic 1024ms peak luminosity (Liso), the total isotropic emission (Eiso), the spectral peak energy (Epz), as well as the intrinsic duration (T90z), while taking into account the complex detection mechanism of BATSE and sample incompleteness. The underlying assumption in our modeling approach is that LGRBs trace the Cosmic Star Formation Rate and that the joint 4-dimensional distribution of the aforementioned prompt gamma-ray emission properties follows a multivariate lognormal distribution. Our modeling approach enables us to constrain the redshifts of BATSE LGRBs to average uncertainty ranges of 0:7 and 1:7 at 50% and 90% confidence levels, respectively. We compare our predictions with the previous redshift estimates of BATSE GRBs based on the proposed phenomenological high-energy relations, including the lag-luminosity, the spectral peak energy-luminosity, and the variability-luminosity relations. Our predictions are almost entirely at odds with the previous estimates based on these phenomenological high-energy correlations, in particular with the estimates derived from the lag-luminosity and the variability-luminosity relations. There is, however, a weak but significant correlation of strength ∼ 0:26 between our predicted redshift estimates and those derived from the hardness-brightness relations. The discrepancies between the estimates can be explained by the strong inuence of sample incompleteness in shaping the phenomenologically proposed highenergy correlations in the literature. The presented catalog here can be useful for demographic studies of LGRBs a

关键词： Luminance

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Anode-Free Lithium–Sulfur Cells Enabled by Rationally Tuning Lithium Polysulfide Molecules

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Angewandte Chemie 2022年第35期134卷

作者： Dr. Yuxun Ren Amruth Bhargav Dr. Woochul Shin Hyunki Sul Prof. Arumugam Manthiram Materials Science & Engineering Program and Texas Materials Institute The University of Texas at Austin Austin TX 78721 USA Contribution: Conceptualization (equal) Data curation (lead) Formal analysis (lead) Investigation (lead) Methodology (lead) Writing - original draft (lead) Contribution: Data curation (supporting) Formal analysis (supporting) Investigation (supporting) Writing - review & editing (supporting) Contribution: Conceptualization (equal) Methodology (supporting) Project administration (lead) Resources (lead) Supervision (lead) Validation (equal) Writing - review & editing (equal)

The two major barriers of practical lithium–sulfur batteries are the poor reversibility of lithium-metal anode and sluggish kinetics of sulfur cathode. Here, we report a simple yet cogent, molecular tailoring approach for lithium polysulfides, enabling a synergistic enhancement of anode reversibility and cathode kinetics. We show that SnI 4 coordinates with lithium polysulfides to form soluble complexes, resulting in a Li 2 SnS 3 -rich anode interphase layer. As Li 2 SnS 3 is stable against parasitic reactions and has a lower ionic resistance over cycling, the Li plating/stripping efficiency is greatly improved. In addition, the formation of soluble complexes between SnI 4 and lithium polysulfides play a non-negligible role in suppressing the clustering behavior of lithium polysulfide molecules, resulting in a significant enhancement in sulfur conversion kinetics under lean electrolyte conditions. The synergistic improvement is validated in anode-free, lean-electrolyte pouch cells with a Li 2 S cathode that displays capacity retention of 78 % after 100 cycles.

关键词： Anode Solid Electrolyte Interphase Anode-Free Batteries Cathode Kinetics Electrolyte Additive Pouch Cells

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Fast Communication-efficient Spectral Clustering Over Distributed data

arXiv

引用

arXiv 2019年

作者： Yan, Donghui Wang, Yingjie Wang, Jin Wu, Guodong Wang, Honggang Department of Mathematics and Program in Data Science Department of Electrical and Computer Engineering University of Massachusetts DartmouthMA United States Lovelace Respiratory Research Institute AlbuquerqueNM United States

The last decades have seen a surge of interests in distributed computing thanks to advances in clustered computing and big data technology. Existing distributed algorithms typically assume all the data are already in one place, and divide the data and conquer on multiple machines. However, it is increasingly often that the data are located at a number of distributed sites, and one wishes to compute over all the data with low communication overhead. For spectral clustering, we propose a novel framework that enables its computation over such distributed data, with "minimal" communications while a major speedup in computation. The loss in accuracy is negligible compared to the non-distributed setting. Our approach allows local parallel computing at where the data are located, thus turns the distributed nature of the data into a blessing;the speedup is most substantial when the data are evenly distributed across sites. Experiments on synthetic and large UC Irvine datasets show almost no loss in accuracy with our approach while about 2x speedup under various settings with two distributed sites. As the transmitted data need not be in their original form, our framework readily addresses the privacy concern for data sharing in distributed computing. Copyright © 2019, The Authors. All rights reserved.

关键词： Large dataset

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