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AM-ConvBLS: Adaptive Manifold Convolutional Broad Learning System for Cross-Session and Cross-Subject Emotion Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Lei, Chunyu Chen, C. L. Philip Zhang, Tong South China University of Technology Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information The School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Brain and Affective Cognitive Research Center Guangzhou510335 China Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Engineering Research Center Guangzhou China

Emotion recognition based on electroencephalography (EEG) data has gained rapid development because of its believability and accuracy. However, most existing EEG emotion recognition methods suffer from three issues: 1) underutilization of multi-scale emotion representations, 2) underexploitation of emotion labels and intrinsic geometric structures, and 3) inherent non-stationarity characteristic and individual variability of EEG signals. To this end, we propose an Adaptive Manifold Convolutional Broad Learning System (AM-ConvBLS) to capture the multi-scale distribution aligned emotion patterns in the geometric structure preserved emotion submanifold space. To begin with, a Multi-Scale Representation Learning (MSRL) module is developed to learn diverse multi-scale emotion representations. The Domain Discrepancy Elimination (DDE) module is then developed to further align feature distributions in the source and target domains. To further utilize EEG emotion labels, we devise a label Manifold Information Exploration (LMIE) module to retain the sample label consistency. In addition, a global feature importance analysis method for AM-ConvBLS based on Shapley Additive Global importancE (SAGE) is designed to investigate the EEG frequency band importance and brain neural activation patterns. Experiments on SEED, SEED-IV, and SEED-V datasets demonstrate the effectiveness and superiority of our AM-ConvBLS. © 2010-2012 IEEE.

关键词： Convolution

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On the particle approximation of lagged Feynman–Kac formulae

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Stochastic Processes and their Applications 2025年 188卷

作者： Awadelkarim, Elsiddig Caffarel, Michel Del Moral, Pierre Jasra, Ajay Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia University of Toulouse CNRS-Lab. de Chimie et Physique Quantiques Toulouse31062 France Centre de Recherche Inria Bordeaux Sud-Ouest Talence33405 France School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen China

In this paper we examine the numerical approximation of the limiting invariant measure associated with Feynman–Kac formulae. These are expressed in a discrete time formulation and are associated with a Markov chain and a potential function. The typical application considered here is the computation of eigenvalues associated with non-negative operators as found, for example, in physics or particle simulation of rare-events. We focus on a novel lagged approximation of this invariant measure, based upon the introduction of a ratio of time-averaged Feynman–Kac marginals associated with a positive operator iterated l∈N times;a lagged Feynman–Kac formula. This estimator and its approximation using Diffusion Monte Carlo (DMC) are commonly used in the physics literature. In short, DMC is an iterative algorithm involving N∈N particles or walkers simulated in parallel, that undergo sampling and resampling operations. In this work, it is shown that for the DMC approximation of the lagged Feynman–Kac formula, one has an almost sure characterization of the L1-error as the time parameter (iteration) goes to infinity and this is at most of O(exp{−κl}/N), for κ>0. In addition a non-asymptotic in time, and time uniform L1−bound is proved which is O(l/N). We also prove a novel central limit theorem to give a characterization of the exact asymptotic in time variance. This analysis demonstrates that the strategy used in physics, namely, to run DMC with N and l small and, for long time enough, is mathematically justified. Our results also suggest how one should choose N and l in practice. We emphasize that these results are not restricted to physical applications;they have broad relevance to the general problem of particle simulation of the Feynman–Kac formula, which is utilized in a great variety of scientific and engineering fields. © 2025 Elsevier B.V.

关键词： Iterative methods

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Analysis of thermal diffusion and diffusion thermo effects under the condition of pollutant concentration and bio-convection: An artificial neural network approach

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International Journal of Thermofluids 2025年 27卷

作者： Kattimani, Prateek Patil, Nagaraj Kalia, Rishiv Sahana, B. Sunitha, M. Nagaraja, K.V. Department of Studies in Mathematics Davangere University Karnataka Davangere India Karnataka Bangalore India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India Dept of Mathematics Government Engineering College Karnataka Kushalnagar-571234 India Department of Mathematics and Statistics University College for Women Koti Hyderabad India Computational Science Lab Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

The current research explores the influence of bio-convection, thermophoresis and Brownian motion (TBM) on the Casson liquid flow across a stretchable surface subjected to porous media. Additionally, the consequence of pollutant concentration, heat source/sink, melting effect, and magnetic field on the fluid motion is considered in the analysis. To evaluate the fluid flow's mass and heat transport characteristics, the Soret and Dufour effects are taken into account. Using the proper similarity transformations, the governing partial differential equations (PDEs) are converted into dimensionless ordinary differential equations (ODEs). The resultant ODEs are solved with the aid of Runge Kutta Fehlberg's fourth-fifth order (RKF-45) approach. Further, the Levenberg-Marquardt artificial neural network (LM-ANN) is employed to assess the heat transport rate for several parameters. Additionally, the values obtained by RKF-45 method are compared with the outcomes of LM-ANN approach. The graphs illustrate the effect of numerous parameters on the temperature, concentration, microorganism, and velocity profiles. The upsurge in the magnetic and porosity parameters diminishes the velocity profile. The rise in the Dufour number and heat source/sink parameter improves the thermal profile. The rise in the values of the Soret number and the pollutant external source parameter increases the concentration profile. © 2025 The Author(s)

关键词： Thermophoresis

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p-CuGaO2/β-Ga2O3 interfaces: A high-throughput approach for interface prediction and generation for power device applications

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Materials Today Advances 2025年 26卷

作者： Prasad, Chowdam Venkata Park, Jang Hyeok Hoai Ta, Qui Thanh Kim, Kyong Jae Jeon, Ho Jung Mir, Muzaffar Ali Raza, Mohsin Tri, Nguyen Ngoc Kim, Honggyun labed, Madani Rim, You Seung Department of Semiconductor Systems Engineering and Convergence Engineering for Intelligent Drone Sejong University Seoul05006 Korea Republic of Institute of Semiconductor and System IC Sejong University Seoul05006 Korea Republic of Department of Semiconductor Systems Engineering Sejong University Seoul05006 Korea Republic of Institute of Advanced Technology Vietnam Academy of Science and Technology 1A TL29 Street Thanh Loc Ward District 12 Ho Chi Minh City700000 Viet Nam Lab of Computational Chemistry and Modelling Faculty of Natural Sciences Quy Nhon University Quy Nhon City55100 Viet Nam

This study integrates a p-type copper gallium oxide (p-CuGaO2) interlayer to enhance the performance of β-Ga2O3-based power devices, addressing challenges in achieving reliable p-type doping. The p-CuGaO2 interlayer is expected to improve breakdown voltage (BV) and reduce leakage current, increasing device efficiency and reliability under high-temperature conditions. The heterojunction (HJ) system of β-Ga2O3 and p-CuGaO2 is investigated using density functional theory (DFT) via the Vienna Ab initio Simulation Package (VASP) and experimental methods, focusing on the β-Ga2O3 (001)/p-CuGaO2 (006) interface. Binding energy, electron localization, charge density difference, and electrostatic potential drop are analyzed, identifying Configuration-1 as the optimal configuration due to its high binding energy and favorable structural properties. Annealing temperature impacts the microstructure, stoichiometry, optical, electrical, and transport properties of p-CuGaO2/β-Ga2O3 HJs. Electrical measurements reveal reduced turn-on voltage (Von), on-resistance (Ron), and leakage currents, with the highest BV of 1.62 kV achieved at 800 °C compared to Pt/β-Ga2O3 Schottky barrier diode (SBD). The interface state density (NSS) is evaluated using the high-low frequency approach. Silvaco TCAD simulations explore breakdown mechanisms, demonstrating the potential of p-CuGaO2 for β-Ga2O3 HJ power devices. These findings support the development and manufacture of robust β-Ga2O3 HJ power devices. © 2025 The Authors

关键词： Heterojunctions

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Microstructural evolution and phase transitions in porous Ta/Cu alloys under high strain rates

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Scientific reports 2025年第1期15卷 19291页

作者： A Malti P Rashidfar A Kardani A Montazeri Computational Nanomaterials Lab (CNL) Faculty of Materials Science and Engineering K.N. Toosi University of Technology Tehran Iran. Department of Mechanical Engineering The University of Melbourne Melbourne VIC 3010 Australia. Computational Nanomaterials Lab (CNL) Faculty of Materials Science and Engineering K.N. Toosi University of Technology Tehran Iran. arash.kardani@email.kntu.ac.ir. Computational Nanomaterials Lab (CNL) Faculty of Materials Science and Engineering K.N. Toosi University of Technology Tehran Iran. a_montazeri@kntu.ac.ir.

Tantalum (Ta) and its alloys are widely used in electronics, dental implants, aerospace, and nuclear industries. Ta-based medical implants have also attracted considerable attention in recent years. However, challenges such as thermal conductivity, oxidation resistance, antibacterial properties, and the mismatch between bone and implant mechanical properties remain major concerns across various applications. The addition of alloying elements, such as copper (Cu), to Ta reduces bacterial adhesion in medical applications, while also improving thermal conductivity and oxidation resistance, making it beneficial for high-temperature environments. Furthermore, incorporating porosity into Ta-based materials mitigates stress shielding in implants, reduces weight, and enhances thermal dissipation in advanced engineering applications. While numerous studies have investigated the mechanical and physical properties of Ta-based alloys, the combined influence of porosity and alloying elements on the mechanical response of these biomaterials has not been systematically studied. This study employs molecular dynamics (MD) simulations to analyze the mechanical response and microstructural evolution of porous Ta/5 wt% Cu alloys under uniaxial tensile loading. Our results demonstrate that optimizing strain rates and introducing pores can modulate the mechanical characteristics of Ta/Cu alloys. Increasing the strain rate from 5 × 10 to 5 × 10 s enhances properties due to the rapid BCC-to-FCC phase transformation at high strain rates, while increasing porosity from 0 to 10% reduces yield stress and elastic modulus by 12% and 14%, respectively. Moreover, the influence of porosity on deformation-induced microstructural transformations, such as twinning behavior and crystallographic changes, is examined. The study offers insights into designing porous Ta-based alloys with improved mechanical performance and microstructural characteristics.

关键词： Dental implant Mechanical properties Molecular dynamics simulation Phase transformation Strain-induced twinning Ta/Cu porous alloy

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Qudit Dynamical Decoupling on a Superconducting Quantum Processor

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Physical Review Letters 2025年第5期134卷 050601-050601页

作者： Vinay Tripathi Noah Goss Arian Vezvaee Long B. Nguyen Irfan Siddiqi Daniel A. Lidar Department of Physics and Astronomy University of Southern California Los Angeles California 90089 USA Center for Quantum Information Science and Technology University of Southern California Los Angeles California 90089 USA Quantum Nanoelectronics Laboratory Department of Physics University of California at Berkeley Berkeley California 94720 USA Applied Math and Computational Research Division Lawrence Berkeley National Lab Berkeley California 94720 USA Department of Electrical and Computer Engineering University of Southern California Los Angeles California 90089 USA Department of Chemistry University of Southern California Los Angeles California 90089 USA

Multilevel qudit systems are increasingly being explored as alternatives to traditional qubit systems due to their denser information storage and processing potential. However, qudits are more susceptible to decoherence than qubits due to increased loss channels, noise sensitivity, and crosstalk. To address these challenges, we develop protocols for dynamical decoupling (DD) of qudit systems based on the Heisenberg-Weyl group. We implement and experimentally verify these DD protocols on a superconducting transmon processor that supports qudit operation based on qutrits (d=3) and ququarts (d=4). Specifically, we demonstrate single-qudit DD sequences to decouple qutrits and ququarts from system-bath-induced decoherence. We also introduce two-qudit DD sequences designed to suppress the detrimental cross-Kerr couplings between coupled qudits. This allows us to demonstrate a significant improvement in the fidelity of time-evolved qutrit Bell states. Our results highlight the utility of leveraging DD to enable scalable qudit-based quantum computing.

关键词： Qubits

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CFD Analysis of Geometry Configuration and Sensor Placement for Hybrid Triangular-T Bluff Body in Vortex Flowmeter

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Flow Measurement and Instrumentation 2025年

作者： Payam Margan Seyed Hassan Hashemabadi Computational Fluid Dynamics Lab. Iran University of Science and Technology School of Chemical Petroleum and Gas Engineering Tehran Iran Flow Measurement Research Center Iran University of Science and Technology Tehran Iran

Vortex flowmeters are extensively employed in industrial settings for precise fluid flow measurement. This study aims to enhance vortex generation and pressure oscillation by extending tails in various lengths downstream of a triangular bluff body. computational Fluid Dynamics (CFD) simulations were performed employing the Realizable k-ε turbulence model to study the geometry of the T-shaped bluff body and sensor placement for accurate flow measurement. A tail-to-width ratio (T/W) of 0.7 was identified as the most effective configuration for strong pressure fluctuations. Analysis of sensor placement indicated that the highest intensity of dynamic pressure oscillations was found at a distance of 7.9 times the width of the bluff body (X/W = 7.9) from the inlet. Additionally, variations in velocity, dynamic and static pressure, and turbulent kinetic energy production and dissipation were investigated along the flowmeter’s axis to confirm the ideal sensor location. Results demonstrated signal clarity and sensitivity of the flowmeter for the proposed geometry configuration and sensor placement, indicating the potential for improved measurement consistency. Rangeability assessment revealed a stable Strouhal number (0.26-0.27) over a broad range of Reynolds numbers, ensuring reliable performance across diverse flow rates compared to conventional flowmeters.

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The Value and Significance of Metagenomics of Marine Environments

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Genomics, Proteomics & Bioinformatics 2015年第5期13卷 271-274页

作者： Fangqing Zhao Vladimir B.Bajic Computational Genomics Lab Beijing Institutes of Life ScienceChinese Academy of Sciences Computational Bioscience Research Center (CBRC) ComputerElectrical and Mathematical Sciences and Engineering Division (CEMSE)King Abdullah University of Science and Technology (KAUST)

Microbes have played a fundamental role in the natural his- tory of our planet, and have done so for billions of years. They have adapted to Earth＇s many environments from the mild to the very extreme. Studying their diversity and their way of life is critical for understanding their full impact on the global ecology. Although the field of metagenomics is still young, it has unravelled a wider microbial diversity that had otherwise been completely overlooked by the traditional methods of microbiology. There are three major metagenomic themes：（a） marker metagenomics that surveys microbial community structure by targeting the highly-conserved 16S rRNA gene, （b） functional metagenomics that takes the total environmental DNA, from which it infers the metabolic potential of the microbial community, and （c） identification of novel enzymes.

关键词： The Value and Significance of Metagenomics of Marine Environments

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Towards identifying possible fault-tolerant advantage of quantum linear system algorithms in terms of space, time and energy

arXiv

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arXiv 2025年

作者： Tu, Yue Dubynskyi, Mark Mohammadisiahroudi, Mohammadhossein Riashchentceva, Ekaterina Cheng, Jinglei Ryashchentsev, Dmitry Terlaky, Tamás Liu, Junyu Department of Computer Science The University of Pittsburgh PittsburghPA15260 United States Department of Computational and Applied Mathematics The University of Chicago ChicagoIL60637 United States Mason Experimental Geometry Lab FairfaxVA22030 United States Department of Industrial and Systems Engineering Lehigh University BethlehemPA18015 United States The University of Portland PortlandOR97203 United States Charles Schwab Corporation WestlakeTX76262 United States

Quantum computing, a prominent non-Von Neumann paradigm beyond Moore’s law, can offer superpolynomial speedups for certain problems. Yet its advantages in efficiency for tasks like machine learning remain under investigation, and quantum noise complicates resource estimations and classical comparisons. We provide a detailed estimation of space, time, and energy resources for fault-tolerant superconducting devices running the Harrow-Hassidim-Lloyd (HHL) algorithm, a quantum linear system solver relevant to linear algebra and machine learning. Excluding memory and data transfer, possible quantum advantages over the classical conjugate gradient method could emerge at N ≈ 233 ∼ 248 or even lower, requiring O(105) physical qubits, O(1012 ∼ 1013) Joules, and O(106) seconds under surface code fault-tolerance with three types of magic state distillation (15-1, 116-12, 225-1). Key parameters include condition number, sparsity, and precision κ,s ≈ O(10 ∼ 100), ϵ ∼ 0.01, and physical error 10−5. Our resource estimator adjusts N,κ,s,ϵ, providing a map of quantum-classical boundaries and revealing where a practical quantum advantage may arise. Our work quantitatively determine how advanced a fault-tolerant quantum computer should be to achieve possible, significant benefits on problems related to real-world. Copyright © 2025, The Authors. All rights reserved.

关键词： Quantum computers

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Real-World Wireless Network Modeling and Optimization:From Model/Data-Driven Perspective

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Chinese Journal of Electronics 2022年第6期31卷 991-1012页

作者： LI Yang ZHANG Shutao REN Xiaohui ZHU Jianhang HUANG Jiajie HE Pengcheng SHEN Kaiming YAO Zhiqiang GONG Jie CHANG Tsunghui SHI Qingjiang LUO Zhiquan Shenzhen Research Institute of Big Data Pengcheng Lab School of Science and Engineering The Chinese University of Hong Kong (Shenzhen) College of Mathematics and Computational Sciences Xiangtan University School of Computer Science and Engineering Sun Yat-Sen University School of Software Engineering Tongji University

With the rapid development of the fifth-generation wireless communication systems, a profound revolution in terms of transmission capacity, energy efficiency, reliability, latency, and connectivity is highly expected to support a new batch of industries and applications. To achieve this goal, wireless networks are becoming extremely dynamic, heterogeneous, and complex. The modeling and optimization for the performance of realworld wireless networks are extremely challenging due to the difficulty to predict the network performance as a function of network parameters, and the prohibitively huge number of parameters to optimize. The conventional network modeling and optimization approaches rely on drive test, trial-and-error, and engineering experience,which are labor intensive, error-prone, and far from optimal. On the other hand, while the research community has spent significant efforts in understanding the fundamental limits of radio channels and developing physical layer techniques to operate close to it, very little is known about the performance limits of wireless networks, where millions of radio channels interact with one another in complex manners. This paper reviews the very recent mathematical and learning based techniques for modeling and optimizing the performance of real-world wireless networks in five aspects, including channel modeling, user demand and traffic modeling, throughput modeling and prediction, network parameter optimization, and IRS empowered performance optimization, and also presents the corresponding notable performance gains.

关键词： network parameter optimization telecommunication computing optimisation mathematical techniques wireless channels performance optimization learning based techniques telecommunication traffic mathematical analysis physical layer techniques channel modeling wireless network modeling fifth generation wireless communication systems learning (artificial intelligence) model-data-driven perspective IRS empowered performance optimization radio channels traffic modeling radio networks

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