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Robust generalized scalar-on-tensor regression

IISE TRANSACTIONS 2025年第2期57卷 145-157页

作者： Konyar, Elif Gahrooei, Mostafa Reisi Zhang, Ruizhi Univ Florida Dept Ind & Syst Engn Gainesville FL 32611 USA Univ Georgia Dept Stat Athens GA USA

High-dimensional (HD) data, such as images and profiles, are commonly collected from complex systems and contain significant explanatory and predictive information for effective systems monitoring and control. Therefore, developing accurate and robust predictive models based on HD data is crucial. In the literature, various methods, including linear scalar-on-tensor regression, are developed to model a complex system based on HD data. However, existing estimation techniques ignore the presence of outliers and are prone to biased estimations. This article proposes a robust scalar-on-tensor regression framework that handles multi-dimensional HD input data when the data contain outliers. Our proposed estimation method is constructed using maximum Lq-likelihood estimation instead of the classic maximum likelihood estimation. The asymptotic analysis under the Gaussian distribution assumption and the guideline on choosing the tuning parameter of our proposed method is provided. Several simulations and case studies evaluate the proposed method's efficacy compared to several benchmark methods in the literature.

关键词： Robust estimator scalar-on-tensor regression Lq-likelihood asset lifetime prediction

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Single breath-hold volumetric lung imaging at 0.55T using stack-of-spiral (SoS) out-in balanced SSFP

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MAGNETIC RESONANCE IN MEDICINE 2025年第5期93卷 1999-2007页

作者： Tian, Ye Lee, Nam G. Zhao, Ziwei Wilcox, Alison G. Nieva, Jorge J. Nayak, Krishna S. Univ Southern Calillrnia Viterbi Sch Engn Ming Hsieh Dept Elect & Comp Engn Los Angeles CA USA Univ Southern Calif Viterbi Sch Engn Dept Biomed Engn Los Angeles CA USA Univ Southern Calif Keck Sch Med Dept Radiol Los Angeles CA USA Univ Southern Calif Keck Sch Med Oncol Div Los Angeles CA USA

Purpose: To develop a robust single breath-hold approach for volumetric lung imaging at 0.55T. Method: A balanced-SSFP (bSSFP) pulse sequence with 3D stack-of-spiral (SoS) out-in trajectory for volumetric lung imaging at 0.55T was implemented. With 2.7x undersampling, the pulse sequence enables imaging during a 17-s breath-hold. Image reconstruction is performed using 3D SPIRiT and 3D l1-Wavelet regularizations. In two healthy volunteers, single breath-hold SoS out-in bSSFP was compared against stack-of-spiral UTE (spiral UTE) and half-radial dual-echo bSSFP (bSTAR), based on signal intensity (SI), blood-lung parenchyma contrast, and image quality. In six patients with pathologies including lung nodules, fibrosis, emphysema, and air trapping, single breath-hold SoS out-in and bSTAR were compared against low-dose computed tomography (LDCT). Results: SoS out-in bSSFP achieved 2-mm isotropic resolution lung imaging with a single breath-hold duration of 17 s. SoS out-in (2-mm isotropic) provided higher lung parenchyma and blood SI and blood-lung parenchyma contrast compared to spiral UTE (2.4 x 2.4 x 2.5 mm(3)) and bSTAR (1.6-mm isotropic). When comparing SI normalized by voxel size, SoS out-in has lower lung parenchyma signal, higher blood signal, and a higher blood-lung parenchyma contrast compared to bSTAR. In patients, SoS out-in bSSFP was able to identify lung fibrosis and lung nodules of size 4 and 8 mm, and breath-hold bSTAR was able to identify lung fibrosis and 8 mm nodules. Conclusion: Single breath-hold volumetric lung imaging at 0.55T with 2-mm isotropic spatial resolution is feasible using SoS out-in bSSFP. This approach could be useful for rapid lung disease screening, and in cases where free-breathing respiratory navigated approaches fail.

关键词： 0.55 tesla bSSFP lung imaging spiral out-in

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Data-dependent generalization bounds for parameterized quantum models under noise

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JOURNAL OF SUPERCOMPUTING 2025年第4期81卷 1-28页

作者： Khanal, Bikram Rivas, Pablo Baylor Univ Sch Engn & Comp Sci One Bear Pl Waco TX 76798 USA

Quantum machine learning offers a transformative approach to solving complex problems, but the inherent noise hinders its practical implementation in near-term quantum devices. This obstacle makes it challenging to understand the generalization capabilities of quantum circuit models. Designing robust quantum machine learning models under noise requires a principled understanding of complexity and generalization, extending beyond classical capacity measures. This study investigates the generalization properties of parameterized quantum machine learning models under the influence of noise. We present a data-dependent generalization bound grounded in the quantum Fisher information matrix. We leverage statistical learning theory to relate the parameter space volumes and training sizes to estimate the generalization capability of the trained model. By integrating local parameter neighborhoods and effective dimensions defined through quantum Fisher information matrix eigenvalues, we provide a structured characterization of complexity in quantum models. We analyze the tightness of the bound and discuss the trade-off between model expressiveness and generalization performance.

关键词： Quantum Machine Learning Generalization Bound Noise Channel NISQ

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A Distributed Neural Hybrid System Learning Framework in Modeling Complex Dynamical systems

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IEEE TRANSACTIONS ON NEURAL networkS AND LEARNING systems 2025年第5期36卷 9463-9473页

作者： Yang, Yejiang Wang, Tao Xiang, Weiming Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China Augusta Univ Sch Comp & Cyber Sci Augusta GA 30912 USA

In this article, a distributed neural network modeling framework including a novel neural hybrid system model is proposed for enhancing the scalability of neural network models in modeling dynamical systems. First, high-dimensional training data samples will be mapped to a low-dimensional feature space through the principal component analysis (PCA) featuring process. Following that, the feature space is bisected into multiple partitions based on the variation of the Shannon entropy under the maximum entropy (ME) bisecting process. The behavior of subsystems in the prespecified state space partitions will then be approximated using a group of shallow neural networks (SNNs) known as extreme learning machines (ELMs), and then it can further simplify the model by merging the redundant lattices based on their training error performance. The proposed modeling framework can handle high-dimensional dynamical system modeling problems with the advantages of reducing model complexity and improving model performance in training and verification. To demonstrate the effectiveness of the proposed modeling framework, examples of modeling the LASA dataset and an industrial robot are presented.

关键词： Data-driven modeling extreme learning machine (ELM) hybrid systems neural networks

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A qualitative exploration of parents and their children's uses and gratifications of ChatGPT

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FAMILY RELATIONS 2025年第0期

作者： Zhang, Shirley Li, Jennica Cagiltay, Bengisu Kirkorian, Heather Mutlu, Bilge Fawaz, Kassem Univ Wisconsin Madison Dept Comp Sci Madison WI 53706 USA Univ Wisconsin Madison Dept Human Dev & Family Studies Madison WI 53705 USA Univ Wisconsin Madison Dept Elect Engn Madison WI 53706 USA

ObjectiveThis Emerging Ideas report explores families' (parents and their children) uses and gratification for *** artificial intelligence-based conversational agents, such as ChatGPT, can be used to accomplish a variety of tasks, yet little is known about how and why parents and their children may use these *** conducted semistructured qualitative and exploratory interviews with 12 U.S.-based families that had experience sharing a ChatGPT account. Families were recruited using social media advertisements, and at least one child and one parent joined the interview. We asked families about what they used ChatGPT for and why they used the *** reported four main motivators for using ChatGPT: (a) information seeking, (b) enhancing productivity, (c) entertainment, and (d) social bonding. Potential barriers to use included concerns about (a) ChatGPT's credibility and capabilities, (b) being less familiar with using ChatGPT, (c) the platform's ethical implications, and (d) possible privacy *** use ChatGPT for various purposes, but their uses and gratifications sometimes may differ depending on their perceptions of and experiences with the *** findings suggest that with some improvements, ChatGPT has the potential to be a useful tool for both individual and shared use in families.

关键词： ChatGPT conversational agents family interactions generative artificial intelligence technology

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Graph Attention network for Lane-Wise and Topology-Invariant Intersection Traffic Simulation

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2025年第4期26卷 5082-5093页

作者： Yousefzadeh, Nooshin Sengupta, Rahul Karnati, Yashaswi Rangarajan, Anand Ranka, Sanjay Univ Florida Dept Comp & Informat Sci & Engn Gainesville FL 32611 USA

Traffic congestion poses significant economic, environmental, and social challenges. High-resolution loop detector data and signal state records from Automated Traffic Signal Performance Measures (ATSPM) offer new opportunities for traffic signal optimization at intersections. However, additional factors such as geometry, traffic volumes, Turning-Movement Counts (TMCs), and human driving behaviors complicate this task. Existing simulators (e.g., SUMO, Vissim) are computationally intensive, while machine learning models often lack lane-specific traffic flow estimation. To address these issues, we propose two computationally efficient Attentional Graph Auto-Encoder frameworks as "Digital Twins" for urban traffic intersections. Leveraging graph representations and Graph Attention networks (GAT), our models capture lane-level traffic flow dynamics at entry and exit points while remaining agnostic to intersection topology and lane configurations. Trained on over 40,000 hours of realistic traffic simulations with affordable GPU parallelization, our framework produces fine-grained traffic flow time series. This output supports critical applications such as estimating Measures of Effectiveness (MOEs), scaling to urban freeway corridors, and integrating with signal optimization frameworks for improved traffic management.

关键词： Digital twins Detectors Timing Driver behavior Traffic control Topology Estimation Computational modeling Vectors Time series analysis Traffic intersection waveform graph neural networks deep learning graph attention networks ATSPM

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Measuring and validating the Ukrainian ethnic coherence instrument

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POST-SOVIET AFFAIRS 2025年第2期41卷 142-160页

作者： Feinstein, Scott Iowa State Univ Dept Polit Sci 539 Ross 527 Farm House Ln Ames IA 50011 USA

To assess Ukrainian ethnic coherence, scholars have employed single-item scales such as language preference and national identity. In practice, these approaches can downplay the multidimensional nature of ethnic coherence. To address this concern, I propose a multidimensional theory of Ukrainian ethnic coherence and validate its measure with a survey in government-controlled Ukraine (n = 1,004). The factor analysis validated an 18-item scale composed of four robust indicators: myths of origin and fate, symbols of territory, language as a community symbol, and traditions of collective action. I then assessed the level of shared understanding of the four indicators and their relationship to demographic characteristics. Ethnic Ukrainians demonstrated a high level of shared understanding of myths of origin and fate and language as a community symbol, followed by traditions of collective action. Nevertheless, they demonstrated low agreement about which territorial boundaries represent ethnic Ukrainians. The study also presents socio-economic drivers that influence ethnic coherence.

关键词： Ethnicity cultural features scale development identity myths and symbols

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A Policy Gradient Algorithm for the Risk-Sensitive Exponential Cost MDP

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MATHEMATICS OF OPERATIONS RESEARCH 2025年第1期50卷 1-781 C2页

作者： Moharrami, Mehrdad Murthy, Yashaswini Roy, Arghyadip Srikant, R. Univ Iowa Dept Comp Sci Iowa City IA 52242 USA Univ Illinois Coordinated Sci Lab Urbana IL 61801 USA Univ Illinois Dept Elect & Comp Engn Urbana IL 61801 USA Indian Inst Technol Guwahati Mehta Family Sch Data Sci & Artificial Intelligenc Gauhati 781039 Assam India

We study the risk -sensitive exponential cost Markov decision process (MDP) formulation and develop a trajectory -based gradient algorithm to find the stationary point of the cost associated with a set of parameterized policies. We derive a formula that can be used to compute the policy gradient from (state, action, cost) information collected from sample paths of the MDP for each fixed parameterized policy. Unlike the traditional average cost problem, standard stochastic approximation theory cannot be used to exploit this formula. To address the issue, we introduce a truncated and smooth version of the risksensitive cost and show that this new cost criterion can be used to approximate the risksensitive cost and its gradient uniformly under some mild assumptions. We then develop a trajectory -based gradient algorithm to minimize the smooth truncated estimation of the risk -sensitive cost and derive conditions under which a sequence of truncations can be used to solve the original, untruncated cost problem.

关键词： risk-sensitive Markov decision processes reinforcement learning policy gradient theorem stochastic approximation

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Low-Voltage Gallium Oxide Memristor with Enhanced Cyclic Endurance, Stability, and Memory Window

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ACS APPLIED ELECTRONIC MATERIALS 2025年第8期7卷 3264-3273页

作者： Khan, Raufur Rahman Kwon, Ohik Das, Avishek Eisbrenner, Jacob S. Wang, Cheng Lu, Meng Dong, Liang Iowa State Univ Dept Elect & Comp Engn Ames IA 50011 USA Iowa State Univ Microelect Res Ctr Ames IA 50011 USA Iowa State Univ Dept Mech Engn Ames IA 50011 USA

We present an Al/Cu/GaOx/Au memristor that combines low-voltage operation (set and reset voltages of 0.48 and -0.3 V, respectively), high cyclic endurance (>13,987 cycles), a large memory window (similar to 4900), and stable multilevel resistance states. These outstanding properties are achieved by leveraging the synergistic interaction between a Cu-based electrochemical metallization mechanism and a GaOx intermediate layer. Comparative studies and atomistic simulations reveal that the low energy barrier for Cu diffusion into GaOx is pivotal in enhancing device performance, enabling superior functionality compared to previously reported GaOx-based memristors. This memristor supports reliable multilevel resistance programming via compliance current modulation and voltage-controlled filament rupture. Furthermore, its exceptional endurance, among the highest reported for GaOx memristors, was validated through rigorous current-voltage cycling tests and voltage pulse measurements. This work establishes the Al/Cu/GaOx/Au memristor as a promising configuration for advancing nonvolatile memory technologies, offering significant potential for high-performance, low-power storage, and neuromorphic computing solutions.

关键词： resistive switching nonvolatile memory galliumoxide endurance multibit storage

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A Biohydrogel-Enabled Microneedle Sensor for In Situ Monitoring of Reactive Oxygen Species in Plants

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ACS SENSORS 2025年第3期10卷 1797-1810页

作者： Singh, Nawab Zhang, Qinming Xu, Weihui Whitham, Steven A. Dong, Liang Iowa State Univ Dept Elect & Comp Engn Ames IA 50011 USA Iowa State Univ Microelect Res Ctr Ames IA 50011 USA Iowa State Univ Dept Plant Pathol Entomol & Microbiol Ames IA 50011 USA

This study introduces a plant sensor utilizing an array of microneedles to monitor hydrogen peroxide (H2O2) in tobacco and soybean plants under biotic stress response. The microneedle array features a biohydrogel layer composed of the natural biopolymer chitosan (Cs) and reduced graphene oxide (rGO), functionalized with horseradish peroxidase (HRP) (HRP/Cs-rGO). This HRP/Cs-rGO biohydrogel combines biocompatibility, hydrophilicity, porosity, and electron transfer ability, making it a suitable bioelectrode material for an electrochemical sensor. The sensor detects H2O2 through the catalytic reaction of the enzyme, either by direct attachment to the plant leaf with the inserted microneedle or by exposure to the solution extracted from plant parts such as leaves. Utilizing chronoamperometry, the sensor demonstrates high sensitivity of 14.7 mu A/mu M across a concentration range of 0.1-4500 mu M with a low detection limit of 0.06 mu M. The sensor enables rapid detection of H2O2 levels by exposing the sensor to extracted leaf solutions. For in situ measurements within the leaf, results are obtained in approximately 1 min, eliminating the need for sample preparation. H2O2 levels in leaves following bacterial pathogen inoculation are evaluated alongside results from qualitative histological staining and quantitative fluorescence-based Amplex Red Assay, validating the ability of the sensor to detect changes in H2O2 concentrations during plant defense responses. This sensor technology has the potential to function as a portable device for on-site measurement of reactive oxygen species in plants, providing a rapid and cost-effective solution for H2O2 quantification.

关键词： plant sensor plant science biohydrogel reactive oxygen species biosensor

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