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Prediction of oral food challenge outcomes via ensemble learning

Informatics in Medicine Unlocked 2023年 36卷

作者： Zhang, Justin Lee, Deborah Jungles, Kylie Shaltis, Diane Najarian, Kayvan Ravikumar, Rajan Sanders, Georgiana Gryak, Jonathan Department of Electrical and Computer Engineering University of Michigan Ann Arbor MI United States Department of Internal Medicine University of Michigan Ann Arbor MI United States Department of Pediatrics University of Michigan Ann Arbor MI United States Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI United States Michigan Institute for Data Science University of Michigan Ann Arbor MI United States Department of Emergency Medicine University of Michigan Ann Arbor MI United States Department of Computer Science and Engineering University of Michigan Ann Arbor MI United States Max Harry Weil Institute for Critical Care Research and Innovation University of Michigan Ann Arbor MI United States Mary H. Weiser Food Allergy Center University of Michigan Ann Arbor MI United States Department of Computer Science Queens College City University of New York New York NY United States

Oral Food Challenges (OFCs) are essential to accurately diagnosing food allergy due to the limitations of existing clinical testing. However, some patients are hesitant to undergo OFCs, while those willing suffer from limited access to allergists in rural/community healthcare settings. Despite its success in predicting patient outcomes in other clinical settings, few applications of machine learning to food allergy have been developed. Thus, in this study, we seek to leverage machine learning methodologies for OFC outcome prediction. Retrospective data was gathered from 1,112 patients who collectively underwent a total of 1,284 OFCs, and consisted of clinical factors including serum-specific Immunoglobulin E (IgE), total IgE, skin prick tests (SPTs), comorbidities, sex, and age. Using these features, multiple machine learning models were constructed to predict OFC outcomes for three common allergens: peanut, egg, and milk. The best performing model for each allergen was an ensemble of random forest (egg) or Learning Using Concave and Convex Kernels (LUCCK) (peanut, milk) models, which achieved an Area under the Curve (AUC) of 0.91, 0.96, and 0.94, in predicting OFC outcomes for peanut, egg, and milk, respectively. Moreover, all such models had sensitivity and specificity values ≥89%. Model interpretation via SHapley Additive exPlanations (SHAP) indicates that specific IgE, along with wheal and flare values from SPTs, are highly predictive of OFC outcomes. The results of this analysis suggest that ensemble learning has the potential to predict OFC outcomes and reveal relevant clinical factors for further study. © 2022 The Author(s)

关键词： Ensemble learning Learning Using Concave and Convex Kernels Machine learning Oral food challenges SHAP

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Batch reverse osmosis:a new research direction in water desalination

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science Bulletin 2020年第20期65卷 1705-1708页

作者： Mingheng Li Yi Heng Jiu Luo Department of Chemical and Materials Engineering California State Polytechnic UniversityPomonaCA 91768USA School of Data and Computer Science Sun Yat-sen UniversityGuangzhou 510006China Guangdong Province Key Laboratory of Computational Science Guangzhou 510006China National Supercomputing Center in Guangzhou(NSCC-GZ) Guangzhou 510006China School of Materials Science and Engineering Sun Yat-sen UniversityGuangzhou 510275China

Water scarcity is one of the grand challenges across the world[1].Spiral wound reverse osmosis(RO)desalination is the most popular industrial technology to produce portable water from saline water *** terms of flow pattern,it falls into the category of cross-flow filtration where feed flows tangentially along the membrane surface while filtrate permeates across the membrane.

关键词： flow tangential category

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Preface

Lecture Notes in Networks and Systems

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Lecture Notes in Networks and Systems 2023年 750 LNNS卷 v-vi页

作者： Bringas, Pablo García García, Hilde Pérez de Pisón, Francisco Javier Martínez Álvarez, Francisco Martínez Lora, Alicia Troncoso Herrero, Álvaro Rolle, José Luis Calvo Quintián, Héctor Corchado, Emilio Faculty of Engineering University of Deusto Bilbao Spain School of Industrial Computer University of Leon León Spain Department of Mechanical Engineering University of La Rioja Logroño Spain Data Science and Big Data Lab Pablo de Olavide Uni versity Seville Spain Applied Computational Intelligence University of Burgos Burgos Spain Department of Industrial Engineering University of A Coruña A Coruña Spain Faculty of Science University of Salamanca Salamanca Spain

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Preface

Lecture Notes in Networks and Systems

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Lecture Notes in Networks and Systems 2023年 749 LNNS卷 v-vi页

作者： Rolle, José Luis Calvo de Pisón, Francisco Javier Martínez Bringas, Pablo García García, Hilde Pérez Álvarez, Francisco Martínez Lora, Alicia Troncoso Herrero, Álvaro Quintián, Héctor Corchado, Emilio Department of Industrial Engineering University of A Coruña A Coruña Spain Department of Mechanical Engineering University of La Rioja Logroño Spain Faculty of Engineering University of Deusto Bilbao Spain School of Industrial Computer University of Leon León Spain Data Science and Big Data Lab Pablo de Olavide University Seville Spain Applied Computational Intelligence University of Burgos Burgos Spain Faculty of Science University of Salamanca Salamanca Spain

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A practical guide to machine learning interatomic potentials – Status and future

arXiv

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arXiv 2025年

作者： Jacobs, Ryan Morgan, Dane Attarian, Siamak Meng, Jun Shen, Chen Wu, Zhenghao Xie, Clare Yijia Yang, Julia H. Artrith, Nongnuch Blaiszik, Ben Ceder, Gerbrand Choudhary, Kamal Csanyi, Gabor Cubuk, Ekin Dogus Deng, Bowen Drautz, Ralf Fu, Xiang Godwin, Jonathan Honavar, Vasant Isayev, Olexandr Johansson, Anders Kozinsky, Boris Martiniani, Stefano Ong, Shyue Ping Poltavsky, Igor Schmidt, K.J. Takamoto, So Thompson, Aidan Westermayr, Julia Wood, Brandon M. Department of Materials Science and Engineering University of Wisconsin-Madison MadisonWI55705 United States Harvard University Center for the Environment Harvard University CambridgeMA02138 United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Materials Chemistry and Catalysis Debye Institute for Nanomaterials Science Utrecht University Utrecht3584 CG Netherlands Globus University of Chicago ChicagoIL United States Data Science and Learning Division Argonne National Laboratory LemontIL United States Department of Materials Science and Engineering University of California BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Laboratory CA94720 United States Material Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom Google DeepMind Mountain ViewCA United States Ruhr-Universität Bochum Bochum44780 Germany Meta United States Orbital Materials London United Kingdom Department of Computer Science and Engineering The Pennsylvania State University University ParkPA United States College of Information Sciences and Technology The Pennsylvania State University University ParkPA United States Artificial Intelligence Research Laboratory The Pennsylvania State University University ParkPA United States Center for Artificial Intelligence Foundations and Scientific Applications The Pennsylvania State University University ParkPA United States Department of Chemistry Mellon College of Science Carnegie Mellon University PittsburghPA15213 United States Computational Biology Department School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Courant Institute of Mathematical Sciences New York University New YorkNY10003 United States Center for Soft Matter Research Department of P

The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not experts but wish to use these tools. The spirit of this review is to help such researchers by serving as a practical, accessible guide to the state-of-the-art in MLIPs. This review paper covers a broad range of topics related to MLIPs, including (i) central aspects of how and why MLIPs are enablers of many exciting advancements in molecular modeling, (ii) the main underpinnings of different types of MLIPs, including their basic structure and formalism, (iii) the potentially transformative impact of universal MLIPs for both organic and inorganic systems, including an overview of the most recent advances, capabilities, downsides, and potential applications of this nascent class of MLIPs, (iv) a practical guide for estimating and understanding the execution speed of MLIPs, including guidance for users based on hardware availability, type of MLIP used, and prospective simulation size and time, (v) a manual for what MLIP a user should choose for a given application by considering hardware resources, speed requirements, energy and force accuracy requirements, as well as guidance for choosing pre-trained potentials or fitting a new potential from scratch, (vi) discussion around MLIP infrastructure, including sources of training data, pre-trained potentials, and hardware resources for training, (vii) summary of some key limitations of present MLIPs and current approaches to mitigate such limitations, including methods of including long-range interactions, handling magnetic systems, and treatment of excited states, and finally (viii) we finish with some more speculative thoughts on what the future holds for the development and application of MLIPs over the next 3-10+ years. © 2025, CC BY.

关键词： Digital storage

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StarGAT: Star-Shaped Hierarchical Graph Attentional Network for Heterogeneous Network Representation Learning

StarGAT: Star-Shaped Hierarchical Graph Attentional Network ...

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IEEE International Conference on data Mining (ICDM)

作者： Wen-Zhi Li Ling Huang Chang-Dong Wang Yu-Xin Ye School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Province Key Laboratory of Computational Science Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China College of Mathematics and Informatics South China Agricultural University Guangzhou China Guangdong Provincial Key Laboratory of Public Finance and Taxation with Big Data Application Guangzhou China College of Computer Science and Technology Jilin University Changchun China

ISBN: (纸本)9781665423991

Many real-world graphs can be viewed as Heterogeneous Networks or Heterogeneous Information Networks (HINs) for that they comprise a diversity of node types and relation types. Due to the efficient representation ability of Graph Neural Network and the idea of random walk, many recent studies apply graph representation learning to HINs and achieve satisfactory results. However, these works either treat different node types in a metapath equally, which is inconsistent with the original graph semantic information for that different node types should have different statuses, or only consider the first-order (node-level) and second-order (metapath-level, a.k.a. link-level) information aggregation while ignoring the higher-order relations. To tackle these two problems, we propose a novel Star-Shaped Hierarchical Graph Attentional Network (StarGAT) model to boost representation learning in HINs. Specifically, we assume nodes in HINs can be categorized into a star-shaped structure including one center node type and a bunch of auxiliary node types in a specific task; and we encode node-level, link-level and motif-level attentions in a hierarchical manner to capture richer semantic information. Extensive experiments on three datasets illustrate the model effectiveness.

关键词： Representation learning Conferences Semantics Heterogeneous networks Graph neural networks data mining Task analysis

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ProxyBO: Accelerating Neural Architecture Search via Bayesian Optimization with Zero-Cost Proxies

arXiv

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arXiv 2021年

作者： Shen, Yu Li, Yang Zheng, Jian Zhang, Wentao Yao, Peng Li, Jixiang Yang, Sen Liu, Ji Cui, Bin Key Lab of High Confidence Software Technologies Peking University China Institute of Computational Social Science Peking University Qingdao China School of Computer Science and Engineering Beihang University China Kuaishou Technology China Data Platform TEG Tencent Inc. China Mila - Québec AI Institute Canada HEC Montréal Canada

Designing neural architectures requires immense manual efforts. This has promoted the development of neural architecture search (NAS) to automate the design. While previous NAS methods achieve promising results but run slowly, zero-cost proxies run extremely fast but are less promising. Therefore, it’s of great potential to accelerate NAS via those zero-cost proxies. The existing method has two limitations, which are unforeseeable reliability and one-shot usage. To address the limitations, we present ProxyBO, an efficient Bayesian optimization (BO) framework that utilizes the zero-cost proxies to accelerate neural architecture search. We apply the generalization ability measurement to estimate the fitness of proxies on the task during each iteration and design a novel acquisition function to combine BO with zero-cost proxies based on their dynamic influence. Extensive empirical studies show that ProxyBO consistently outperforms competitive baselines on five tasks from three public benchmarks. Concretely, ProxyBO achieves up to 5.41× and 3.86× speedups over the state-ofthe-art approaches REA and BRP-NAS. Copyright © 2021, The Authors. All rights reserved.

关键词： Iterative methods

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Stability of stochastic gradient descent on nonsmooth convex losses 20

Stability of stochastic gradient descent on nonsmooth convex...

引用

Proceedings of the 34th International Conference on Neural Information Processing Systems

作者： Raef Bassily Vitaly Feldman Cristóbal Guzmán Kunal Talwar Department of Computer Science & Engineering The Ohio State University Apple Pontificia Universidad Católica de Chile Institute for Mathematical and Computational Engineering ANID – Millennium Science Initiative Program Millennium Nucleus Center for the Discovery of Structures in Complex Data

ISBN: (纸本)9781713829546

Uniform stability is a notion of algorithmic stability that bounds the worst case change in the model output by the algorithm when a single data point in the dataset is replaced. An influential work of Hardt et al. [20] provides strong upper bounds on the uniform stability of the stochastic gradient descent (SGD) algorithm on sufficiently smooth convex losses. These results led to important progress in understanding of the generalization properties of SGD and several applications to differentially private convex optimization for smooth *** work is the first to address uniform stability of SGD on nonsmooth convex losses. Specifically, we provide sharp upper and lower bounds for several forms of SGD and full-batch GD on arbitrary Lipschitz nonsmooth convex losses. Our lower bounds show that, in the nonsmooth case, (S)GD can be inherently less stable than in the smooth case. On the other hand, our upper bounds show that (S)GD is sufficiently stable for deriving new and useful bounds on generalization error. Most notably, we obtain the first dimension-independent generalization bounds for multi-pass SGD in the nonsmooth case. In addition, our bounds allow us to derive a new algorithm for differentially private nonsmooth stochastic convex optimization with optimal excess population risk. Our algorithm is simpler and more efficient than the best known algorithm for the nonsmooth case [16].

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Selecting the number of components in PCA via random signflips

arXiv

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arXiv 2020年

作者： Hong, David Sheng, Yue Dobriban, Edgar Department of Electrical and Computer Engineering University of Delaware United States Graduate Group in Applied Mathematics and Computational Science University of Pennsylvania United States Department of Statistics and Data Science University of Pennsylvania United States

Principal component analysis (PCA) is a foundational tool in modern data analysis, and a crucial step in PCA is selecting the number of components to keep. However, classical selection methods (e.g., scree plots, parallel analysis, etc.) lack statistical guarantees in the increasingly common setting of large-dimensional data with heterogeneous noise, i.e., where each entry may have a different noise variance. Moreover, it turns out that these methods, which are highly effective for homogeneous noise, can fail dramatically for data with heterogeneous noise. This paper proposes a new method called signflip parallel analysis (FlipPA) for the setting of approximately symmetric noise: it compares the data singular values to those of "empirical null" matrices generated by flipping the sign of each entry randomly with probability one-half. We develop a rigorous theory for FlipPA, showing that it has nonasymptotic type I error control and that it consistently selects the correct rank for signals rising above the noise floor in the large-dimensional limit (even when the noise is heterogeneous). We also rigorously explain why classical permutation-based parallel analysis degrades under heterogeneous noise. Finally, we illustrate that FlipPA compares favorably to state-of-the art methods via numerical simulations and an illustration on data coming from astronomy. Copyright © 2020, The Authors. All rights reserved.

关键词： Principal component analysis

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Waiting-time paradox in 1922

arXiv

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arXiv 2020年

作者： Masuda, Naoki Hiraoka, Takayuki Department of Mathematics University at Buffalo State University of New York BuffaloNY14260-2900 United States Computational and Data-Enabled Science and Engineering Program University at Buffalo State University of New York BuffaloNY14260-5030 United States Department of Computer Science Aalto University Espoo00076 Finland

We present an English translation and discussion of an essay that a Japanese physicist, Torahiko Terada, wrote in 1922. In the essay, he described the waiting-time paradox, also called the bus paradox, which is a known mathematical phenomenon in queuing theory, stochastic processes, and modern temporal network analysis. He also observed and analyzed data on Tokyo City trams to verify the relevance of the waiting-time paradox to busy passengers in Tokyo at the time. This essay seems to be one of the earliest documentations of the waiting-time paradox in a sufficiently scientific manner. Copyright © 2020, The Authors. All rights reserved.

关键词： Queueing theory

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