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arXiv 2024年

作者： Chakraborty, Dibyajyoti Chung, Seung Whan Arcomano, Troy Maulik, Romit Information Sciences and Technology Pennsylvania State University University ParkPA United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA United States Environmental Science Division Argonne National Laboratory LemontIL United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL United States

Forecasting high-dimensional dynamical systems is a fundamental challenge in various fields, such as geosciences and engineering. Neural Ordinary Differential Equations (NODEs), which combine the power of neural networks and numerical solvers, have emerged as a promising algorithm for forecasting complex nonlinear dynamical systems. However, classical techniques used for NODE training are ineffective for learning chaotic dynamical systems. In this work, we propose a novel NODE-training approach that allows for robust learning of chaotic dynamical systems. Our method addresses the challenges of non-convexity and exploding gradients associated with underlying chaotic dynamics. Training data trajectories from such systems are split into multiple, non-overlapping time windows. In addition to the deviation from the training data, the optimization loss term further penalizes the discontinuities of the predicted trajectory between the time windows. The window size is selected based on the fastest Lyapunov time scale of the system. Multi-step penalty(MP) method is first demonstrated on Lorenz equation, to illustrate how it improves the loss landscape and thereby accelerates the optimization convergence. MP method can optimize chaotic systems in a manner similar to least-squares shadowing with significantly lower computational costs. Our proposed algorithm, denoted the Multistep Penalty NODE, is applied to chaotic systems such as the Kuramoto–Sivashinsky equation, the two-dimensional Kolmogorov flow, and ERA5 reanalysis data for the atmosphere. It is observed that MP-NODE provide viable performance for such chaotic systems, not only for short-term trajectory predictions but also for invariant statistics that are hallmarks of the chaotic nature of these dynamics. © 2024, CC BY-NC-SA.

关键词： Trajectories

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Pregnancy and Postpartum Dynamics Revealed by an Atlas of Millions of Lab Tests

SSRN

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SSRN 2023年

作者： Bar, Alon Mendelsohn-Cohen, Netta Korem Kohanim, Yael Mayo, Avi Toledano, Yoel Alon, Uri Department of Molecular Cell Biology Weizmann Institute of Science Rehovot Israel Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot Israel Division of Maternal Fetal Medicine Helen Schneider Women's Hospital Rabin Medical Center Petah Tikva Israel

Pregnancy involves dynamic alterations in most physiological systems. Delivery initiates a transition to postpartum recovery, which similarly exhibits widespread dynamic changes. Physiological dynamics during pregnancy and after delivery have not been systematically analyzed at high temporal resolution in a large human population. Here, we present a dynamic atlas of 78 laboratory tests with weekly time resolution, spanning the period from 20 weeks pre-conception until 80 weeks postpartum, totaling 45 million measurements from over half a million pregnancies. Tests exhibited discontinuities and rebound effects after delivery, and showed extended recovery times of up to a year postpartum. The accuracy of the data revealed effects of preconception supplements and intricate temporal responses to changes in blood volume and renal filtration rate. The dynamical data allowed discrimination between distinct mechanistic models of adaptation in the thyroid axis. These results provide a comprehensive dynamic portrait of the systems physiology of pregnancy. © 2023, The Authors. All rights reserved.

关键词： Physiology

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Seeking optimal Human/Machine collaborative practice in antisemitic terminology detection

Seeking optimal Human/Machine collaborative practice in anti...

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Digital Platforms and Societal Harms (DPSH), IEEE

作者： Wendy Melillo Jessica Emami Solene Guarinos Dhanush Kikkisetti Melanie Klein Lisa Liubovich Raza Ul Mustafa Nathalie Japkowicz Journalism Division School of Communication American University Department of Sociology College of Arts and Science American University Department of Mathematics College of Arts and Science American University Justice Law and Criminology School of Public Affairs American University Data Science for Applied Public Affairs School of Public Affairs American University Department of Computer Science College of Arts and Science American University

ISBN: (数字)9798350351309

ISBN: (纸本)9798350351316

This study is concerned with the use of antisemitic language on loosely moderated extremist alt-right social media. It compares, contrasts, and combines human- and machinebased approaches for discovering antisemitic coded or noncoded terminology in such media. Coded terminology refers to the language used in closed-knit communities to communicate beliefs and attitudes without expressing them explicitly. In noncoded discourse, the views of the writer are expressed more explicitly. The detection and analysis of antisemitic terminology in social media is an important problem that can help uncover and monitor the evolution of societal attitudes towards the Jews, and eventually, towards other minority groups once we expand our study. Given the growing prevalence of antisemitic rhetoric in online public spaces, there is a greater need for academic studies to determine its presence and identify strategies to curb its influence. Scholars have noted how intolerant expressions can threaten democratic values. Antisemitic content, like other forms of hate speech, has the potential to radicalize people who may commit violence based on their prejudicial viewpoints. The purpose of this paper is to discuss three different approaches for discovering antisemitic terminology on social media. In particular, it describes an experiment in which three groups—human, software, and mixed—"competed” on the task of retrieving antisemitic terminology over a two-month period (January-February, 2024). The results were manually evaluated for relevance and frequency of occurrence in the next month period (March 2024). The study shows that each group discovered valuable terminology and that neither the human nor the machine group could replace the other. Instead, we advocate that both groups work together using their respective knowledge to optimize the discovery of new terminology on social media.

关键词： Terminology Social networking (online) Hate speech Collaboration Media Software Rhetoric Monitoring

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Challenges of measuring spin Seebeck noise

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Physical Review B 2024年第10期109卷 104429-104429页

作者： Renjie Luo Xuanhan Zhao Tanner J. Legvold Liyang Chen Changjiang Liu Deshun Hong Anand Bhattacharya Douglas Natelson Department of Physics and Astronomy Rice University Houston Texas 77005 USA Applied Physics Program Smalley-Curl Institute Rice University Houston Texas 77005 USA Materials Science Division Argonne National Laboratory Lemont Illinois 60439 USA Department of Electrical and Computer Engineering Rice University Houston Texas 77005 USA Department of Materials Science and NanoEngineering Rice University Houston Texas 77005 USA

Just as electronic shot noise in driven conductors results from the granularity of charge and the statistical variation in the arrival times of charge carriers, there are predictions for fundamental noise in magnon currents due to angular momentum being carried by discrete excitations. The inverse spin Hall effect as a transduction mechanism to convert spin current into charge current raises the prospect of experimental investigations of such magnon shot noise. Spin Seebeck effect measurements have demonstrated the electrical detection of thermally driven magnon currents and have been suggested as an avenue for accessing spin current fluctuations. Using spin Seebeck structures made from yttrium iron garnet on gadolinium gallium garnet, we demonstrate the technical challenges inherent in such noise measurements. While there is a small increase in voltage noise in the inverse spin Hall detector at low temperatures associated with adding a magnetic field, the dependence on field orientation implies that this is not due to magnon shot noise. We describe theoretical predictions for the expected magnitude of magnon shot noise, highlighting ambiguities that exist. Further, we show that magnon shot noise detection through the standard inverse spin Hall approach is likely impossible due to geometric factors. Implications for future attempts to measure magnon shot noise are discussed.

关键词： Spin Seebeck effect Spin current Magnetic insulators Noise measurements

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Accounting for deficit and a known demand quantile in ABC-XYZ analysis

Accounting for deficit and a known demand quantile in ABC-XY...

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International Conference on Logistics and Operations Management (GOL)

作者： Zhanna Zenkova Anna Andrievskaya Sergey Tarima Institute of Applied Mathematics and Computer Science Tomsk State University Tomsk Russia Division of Biostatistics Institute for Health and Equity Medical College of Wisconsin Milwaukee USA

Product deficit typically appears as a result of inadequate planning and unforeseen supply chain disruptions. A traditional approach to demand distribution estimation does not account for possible deficit; deficit generates right censored data. If the censoring is ignored, the demand estimation becomes negatively biased. Consequently, all analytics based on the demand distribution also becomes biased. In this article, the well-known survival analysis Kaplan-Meier estimator is applied to demand data with observed deficit. In addition to the reduction of bias associated with the use of the KaplanMeier estimator, we also modify it with a known quantile of the demand distribution. Monte-Carlo simulation studies show that the use of additional information in the presence of censored observations substantially improves estimation quality. The estimators modified with quantile information led to new product grouping in the ABC-XYZ analysis. An illustrative example shows the impact of new estimators on ABC-XYZ grouping.

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EHands: Quantum Protocol for Polynomial Computation on Real-Valued Encoded States

arXiv

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arXiv 2025年

作者： Balewski, Jan Amankwah, Mercy G. Bethel, E. Wes Perciano, Talita Van Beeumen, Roel National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Mathematics Applied Mathematics and Statistics Case Western Reserve University ClevelandOH44106 United States Computer Science Department San Francisco State University San FranciscoCA94132 United States Scientific Data Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States

The novel constructive EHands protocol defines a universal set of quantum operations for multivariable polynomial transformations on quantum processors by introducing four basic subcircuits—multiplication, addition, negation, and parity flip—and by using expectation-value encoding (EVEN) to represent real numbers in quantum states. These elementary arithmetic operations can be systematically composed to compute degree-d polynomials, Pd(x), on a QPU. The resulting quantum circuit structure closely mirrors the stepwise evaluation of polynomials on a classical calculator, providing an intuitive and efficient approach to polynomial computation on quantum hardware. By enabling direct, predictable polynomial and nonlinear data transformations on a QPU, our method reduces dependence on classical post-processing in hybrid quantum-classical algorithms, enabling advancements in many quantum algorithms. The EHands quantum circuits are compact enough to deliver meaningful and accurate results on today’s noisy quantum processors. We present a detailed implementation of P4(x) and report experimental results for polynomial approximations of common functions, obtained using IBM’s Heron-class quantum processors and an ideal Qiskit simulator. Copyright © 2025, The Authors. All rights reserved.

关键词： Polynomial approximation

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Deep learning models for early and accurate diagnosis of ventilator-associated pneumonia in mechanically ventilated neonates

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computers in Biology and Medicine 2025年 189卷 109942-109942页

作者： Hsu, Jen-Fu Lin, Ying-Chih Lin, Chun-Yuan Chu, Shih-Ming Cheng, Hui-Jun Xu, Fan-Wei Huang, Hsuan-Rong Liao, Chen-Chu Fu, Rei-Huei Tsai, Ming-Horng Division of Neonatology and Pediatric Hematology/Oncology Department of Pediatrics Chang Gung Memorial Hospital Yunlin Taiwan Division of Pediatric Neonatology Department of Pediatrics Chang Gung Memorial Hospital Taoyuan Taiwan College of Medicine Chang Gung University Taoyuan Taiwan Department of Computer Science and Information Engineering Asia University Taichung Taiwan Department of Applied Mathematics Feng Chia University Taichung Taiwan Department of Artificial Intelligence Application Minth University of Science and Technology HsinChu County Taiwan

Background: Early and accurate confirmation of critically ill neonates with a suspected diagnosis of ventilator-associated pneumonia (VAP) can optimize the therapeutic strategy and avoid unnecessary use of empirical antibiotics. We aimed to examine whether deep learning (DL) methods can assist the diagnosis of VAP of intubated neonates in the neonatal intensive care unit (NICU). Methods: A total of 670 neonates with mechanical ventilation were prospectively observed in a tertiary-level NICU in Taiwan between October 2017 and March 2022, during which image data were collected. All neonates with clinically suspected VAP were enrolled, and various DL methods were used to test the prediction ability of VAP diagnosis. The accuracy, precision, sensitivity, specificity, F1-score, and area under curves (AUCs) of several DL methods were compared. Results: A total of 900 chest X-ray images derived from 670 neonates with VAP and/or bronchopulmonary dysplasia (BPD) were enrolled, including 399 images from patients with definite diagnosis of VAP based on the strict criteria and 501 images from neonates without VAP. Compared with conventional DNN models such as ResNet, VGG, DenseNet, the RegNetX80 achieved the best specificity of 0.8378, which facilitates a low false positive rate. For accurate diagnosis of neonatal VAP, a combinatorial model of ResNet50 and RegNetX80, created through ensemble learning, further enhanced the AUC to 0.8023 for neonates with VAP on mechanical ventilation. In addition, the consistent XAI results in the left-lower region of chest X-ray image provided informative feedback and increased confidence to AI-assisted doctors. Conclusions: Deep learning methods are applicable with good predictive accuracy using chest X-ray images to help diagnosis of VAP in the NICU, which can help clinicians make decisions regarding the choices of empiric antibiotics for critically ill neonates. Future prospective trials are warranted to document its clinical usefulness and

关键词： Contrastive Learning

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Optimal transport reveals dynamic gene regulatory networks via gene velocity estimation

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PLoS Computational Biology 2025年第5 May期21卷 e1012476页

作者： Zhao, Wenjun Larschan, Erica Sandstede, Björn Singh, Ritambhara Division of Applied Mathematics Brown University ProvidenceRI United States Department of Mathematics University of British Columbia Vancouver Canada Department of Molecular Biology Cell Biology and Biochemistry Center for Computational Molecular Biology Brown University ProvidenceRI United States Department of Computer Science Center for Computational Molecular Biology Brown University ProvidenceRI United States

Inferring gene regulatory networks from gene expression data is an important and challenging problem in the biology community. We propose OTVelo, a methodology that takes time-stamped single-cell gene expression data as input and predicts gene regulation across two time points. It is known that the rate of change of gene expression, which we will refer to as gene velocity, provides crucial information that enhances such inference;however, this information is not always available due to the limitations in sequencing depth. Our algorithm overcomes this limitation by estimating gene velocities using optimal transport. We then infer gene regulation using time-lagged correlation and Granger causality via regularized linear regression. Instead of providing an aggregated network across all time points, our method uncovers the underlying dynamical mechanism across time points. We validate our algorithm on 13 simulated datasets with both synthetic and curated networks and demonstrate its efficacy on 9 experimental data sets. © 2025 Zhao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

关键词： Transcription

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Comparison of the Effectiveness of Machine Learning Techniques for the Number of Road Traffic Injuries

Comparison of the Effectiveness of Machine Learning Techniqu...

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International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering (ECTI-NCON)

作者： Wirawan Puttamat Wikanda Phaphan Anuchit Jitpattanakul Chainarong Pianpailoon Sakorn Mekruksavanich Department of Mathematics Faculty of Education Chaiyaphum Rajabhat University Chaiyaphum Thailand Department of Applied Statistics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Research Group in Statistical Learning and Inference Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Curriculum and Teaching Program (Mathematics) Faculty of Education Sakon Nakhon Rajabhat University Sakon Nakhon Thailand Department of Computer Engineering School of Information and Communication University of Phayao Phayao Thailand

This article aims to compare forecasting techniques with machine learning techniques for predicting the number of people injured in road accidents using data from the Injury Information Collaboration Center, Department of Disease Control, Ministry of Public Health Outpatient Files (OPD) spanning the years 2018 to 2022. The four machine-learning techniques examined in this study include Decision Tree Regression, Random Forest Regression, Support Vector Regression, and Multiple Linear Regression. The data analysis was performed using the R programming language. The results revealed that the Decision Tree Regression technique yielded the most accurate predictions for the number of road traffic injuries, as evidenced by its lowest values of MAE, MSE, and RMSE.

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A novel electrical muscle stimulation device for neurorehabilitation applications with adaptable parameter optimization using AI algorithms

A novel electrical muscle stimulation device for neurorehabi...

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International Conference on Modern Circuits and Systems Technologies (MOCAST)

作者： Arsenios Arsenidis Alexandros Moraitopoulos Alkinoos Athanasiou Panagiotis Bamidis Petros Stefaneas Alexander Astaras Department of Physics School of Applied Mathematical and Physical Sciences NTUA Athens Greece Medical Physics and Digital Innovation Laboratory School of Medicine AUTH Thessaloniki Greece Department of Mathematics School of Applied Mathematical and Physical Sciences NTUA Athens Greece Computer Science Division of Science & Technology American College of Thessaloniki Thessaloniki Greece

The application of short trains of electric pulses, of appropriate frequency and power, to muscle or nerve tissue can induce action potentials which in turn lead to muscle contractions. Timing these action potentials appropriately can help enhance functional movements, a methodology called Functional Electrical Stimulation (FES). The motor and sensory recruitment of muscle and nerves by means of FES has been demonstrated to elicit short- and long-term neurophysiological changes in the central nervous system and thus constitutes a powerful therapeutic rehabilitation tool. The most significant signal parameters affecting the quality of muscle contraction and duration of cortical reorganization caused by Functional Electrical Stimulation Therapy (FEST) are pulse intensity (amplitude), frequency, pulse width and the time delay between pulses. While these parameters are particular to the stimulating signal waveform, there are other key factors at play which determine both the duration and quality of changes. Such factors are electrode placement and synchronization of voluntary command with the induced contraction, which partially determine the successful completion of a movement task. The work presented in this paper aims to study, calibrate, automate and optimize the adaptation process of the aforementioned FEST parameters with respect to induced muscle contraction intensity and perceived discomfort reported by the subject, using both conventional AI and machine learning methodologies.

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