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27th International Conference on Pattern Recognition, ICPR 2024

作者： Balaha, Hossam Magdy Ali, Khadiga M. Gondim, Dibson Ghazal, Mohammed El-Baz, Ayman Bioengineering Department J.B. Speed School of Engineering University of Louisville LouisvilleKY United States Pathology Department Faculty of Medicine Mansoura University Mansoura Egypt Department of Pathology and Laboratory Medicine University of Louisville LouisvilleKY United States Electrical Computer and Biomedical Engineering Department Abu Dhabi University Abu Dhabi United Arab Emirates

ISBN: (纸本)9783031781940

Breast cancer (BC) remains a significant global health challenge, impacting millions of lives annually. Traditional histopathological analysis, while essential, can be subjective and time-consuming, potentially leading to diagnostic inaccuracies. This study proposes a novel computer-Aided Diagnosis (CAD) framework utilizing Vision Transformers (ViTs) for BC diagnosis from histopathology slides. ViTs excel in capturing global dependencies within images, offering enhanced diagnostic accuracy compared to conventional methods. The framework integrates ViTs with advanced decision-making techniques like 2-tier majority fusion and SHapley Additive exPlanations (SHAP) for improved interpretability. Experimental results on a dataset of post-neoadjuvant therapy breast cancer samples demonstrate the efficacy of the proposed approach, achieving high performance metrics and providing insights into model predictions. The proposed approach achieves state-of-the-art performance with an accuracy exceeding 97% surpassing existing methods both on the utilized dataset and an external benchmark, specifically the Breast Cancer Histopathological Database (BreakHis). Time complexity analysis suggests that the proposed framework offers computational efficiency, with the dominant factors influencing overall complexity being the number of patches, sequence length, and number of layers in the ViT model. This study contributes a robust methodology towards enhancing BC diagnostic precision and efficiency through cutting-edge AI technologies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Diseases

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A 2-Bit Beam-Steering Coding Array With High Beam Pointing Accuracy and Side Lobe Level For Wide-Angle Beam Scanning

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IEEE Internet of Things Journal 2025年

作者： Xu, Jifei Wang, Kai Lu, Yunlong Mei, Lin Wang, Ye Ding, Chaofeng Yu, Ming Pengcheng Laboratory Shenzhen518000 China Southern University of Science and Technology Shenzhen518000 China Ningbo University Faculty of Electrical Engineering and Computer Sciences Zhejiang Ningbo315211 China

We present a 2-bit coding array that features high beam-pointing accuracy and side-lobe-level (SLL) performance for wide-angle beam scanning. By symmetrically exciting a circular ring patch antenna and integrating a 90° phase shifter into the feeding line, a 2-bit characteristic is obtained. Four positive-intrinsic-negative (PIN) diodes are deployed in each antenna element and are controlled by a field-programmable gate array (FPGA) for switching the states of PINs. The 2-bit circular ring patch element is subsequently used for building a 1×12 array. Apart from introducing a set of initial phases, we add initial amplitudes to the array for the first time to further improve the array performance. Both initial phases and amplitudes are obtained from the invasive weed optimization (IWO) algorithm by setting the goals of desired beam pointing accuracy and SLL performance. In addition, the active element pattern (AEP) that accounts for the mutual coupling between elements is used in the optimization process to provide an accurate array response. The simulated and measured results agree well, and both show that the proposed array can achieve a scanning range of ± 50° with a beam pointing error (BPE) within ±1°, yet the simulated maximum SLL (MSLL), 9.2 dB, deteriorates to 8.3 dB in the measurement. The measured peak aperture efficiency is 43.2% with a peak gain as 13.2dBi. The proposed array, which features low cost, low profile, and low power consumption, is well suited for intelligent Internet of Things (IoT) applications where space and power are limited. © 2014 IEEE.

关键词： Slot antennas

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Responding to News Sensitively in Stock Attention Networks via Prompt-Adaptive Trimodal Model

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IEEE Transactions on Neural Networks and Learning Systems 2025年第6期36卷 10099-10113页

作者： Haotian Liu Bowen Hu Yadong Zhou Yuxun Zhou Ministry of Education Key Laboratory for Intelligent Networks and Network Security Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an Shaanxi China Department of Electrical Engineering and Computer Sciences University of California at Berkeley Berkeley CA USA

Modern quantitative finance and portfolio-based investment hinge on multimedia news and historical price trends for stock movement prediction. However, prior studies overlook the long tail effect in the feature distribution of stocks, inevitably leading to biased attention and thus degrading the efficiency of utilizing news. To this end, we propose a prompt-adaptive trimodal model (PA-TMM) to overcome the biased stock attention networks and tail feature scarcity problem. In this model, sentiments automatically extracted from trimodal information serve as prompts reflecting the market’s collective mood for other entities, and the interactions among stocks are dynamically inferred for integrating both news- and price-induced movements. By leveraging the movement prompt adaptation (MPA) strategy, our model proactively adapts to the feature-imbalanced phenomenon and converges toward being responsive to the news sensitively. Extensive experiments conducted on real-world datasets consistently demonstrate not only the superiority of the proposed framework over various state-of-the-art baselines, but also its effectiveness, profitability, and robustness in Fintech. The code is accessible at https://***/lauht/PA-TMM .

关键词： Adaptation models Peer-to-peer computing Investment Heavily-tailed distribution Tail Stock markets Feature extraction Correlation Attention mechanisms Time series analysis

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UCF-Crime-DVS: A Novel Event-Based Dataset for Video Anomaly Detection with Spiking Neural Networks

arXiv

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arXiv 2025年

作者： Qian, Yuanbin Ye, Shuhan Wang, Chong Cai, Xiaojie Qian, Jiangbo Wu, Jiafei Faculty of Electrical Engineering and Computer Science Ningbo University China Merchants’ Guild Economics and Cultural Intelligent Computing Laboratory Ningbo University China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong

Video anomaly detection plays a significant role in intelligent surveillance systems. To enhance model’s anomaly recognition ability, previous works have typically involved RGB, optical flow, and text features. Recently, dynamic vision sensors (DVS) have emerged as a promising technology, which capture visual information as discrete events with a very high dynamic range and temporal resolution. It reduces data redundancy and enhances the capture capacity of moving objects compared to conventional camera. To introduce this rich dynamic information into the surveillance field, we created the first DVS video anomaly detection benchmark, namely UCF-Crime-DVS. To fully utilize this new data modality, a multi-scale spiking fusion network (MSF) is designed based on spiking neural networks (SNNs). This work explores the potential application of dynamic information from event data in video anomaly detection. Our experiments demonstrate the effectiveness of our framework on UCF-Crime-DVS and its superior performance compared to other models, establishing a new baseline for SNN-based weakly supervised video anomaly detection. © 2025, CC BY-NC-SA.

关键词： Optical flows

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Probing the supersymmetry-mass scale with F-term hybrid inflation

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Physical Review D 2023年第9期108卷 095055-095055页

作者： G. Lazarides C. Pallis School of Electrical and Computer Engineering Faculty of Engineering Aristotle University of Thessaloniki GR-541 24 Thessaloniki Greece Faculty of Engineering Laboratory of Physics Aristotle University of Thessaloniki GR-541 24 Thessaloniki Greece

We consider F-term hybrid inflation and supersymmetry breaking in the context of a model that largely respects a global U(1) R symmetry. The Kähler potential parametrizes the Kähler manifold with an enhanced U(1)×(SU(1,1)/U(1)) symmetry, where the scalar curvature of the second factor is determined by the achievement of a supersymmetry-breaking de Sitter vacuum without ugly tuning. The magnitude of the emergent soft tadpole term for the inflaton can be adjusted in the range (1.2–460) TeV—increasing with the dimensionality of the representation of the waterfall fields—so that the inflationary observables are in agreement with the observational requirements. The mass scale of the supersymmetric partners turns out to lie in the region (0.09–253) PeV which is compatible with high-scale supersymmetry and the results of LHC on the Higgs boson mass. The μ parameter can be generated by conveniently applying the Giudice-Masiero mechanism and assures the out-of-equilibrium decay of the R saxion at a low reheat temperature Trh≤163 GeV.

关键词： Cosmology Inflation Supergravity

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SQAT-LD: SPeech Quality Assessment Transformer Utilizing Listener Dependent Modeling for Zero-Shot Out-of-Domain MOS Prediction

SQAT-LD: SPeech Quality Assessment Transformer Utilizing Lis...

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IEEE Workshop on Automatic Speech Recognition and Understanding

作者： Kailai Shen Diqun Yan Li Dong Ying Ren Xiaoxun Wu Jing Hu Faculty of Electrical Engineering and Computer Science Ningbo University Zhejiang Key Laboratory of Mobile Network Application Technology

In this paper, we propose the speech quality assessment transformer utilizing listener dependent modeling (SQAT-LD) mean opinion score (MOS) prediction system, which was submitted to the 2023 VoiceMOS Challenge. The system is based on a combination of self-supervised learning (SSL) models and listener-dependent modeling. Due to this challenge’s emphasis on real-world and challenging zero-shot out-of-domain MOS prediction in three different voice evaluation scenarios, we specifically designed a two-branch module to predict scores and weights for each frame, aiming to achieve better generalization. In the challenge, our system achieved fourth place in Track 1a, second place in Track 1b and first place in Track 2. Additionally, we conducted an ablation study to investigate the effectiveness of our proposed method.

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UCF-Crime-DVS: A Novel Event-Based Dataset for Video Anomaly Detection with Spiking Neural Networks 39

UCF-Crime-DVS: A Novel Event-Based Dataset for Video Anomaly...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

ISBN: (纸本)157735897X

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Two-Stage Robust Optimization Under Decision Dependent Uncertainty

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1295-1306页

作者： Yunfan Zhang Feng Liu Yifan Su Yue Chen Zhaojian Wang João P.S.Catalão State Key Laboratory of Power System and Generation Equipment the Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Mechanical and Automation Engineering the Chinese University of Hong KongHong Kong SARChina Ministry of Education Key Laboratory of System Control and Information Processing the Department of AutomationShanghai Jiao Tong Universityand also with Shanghai Engineering Research Center of Intelligent Control and ManagementShanghai 200240China Faculty of Engineering of the University of Porto and Institute for Systems and Computer Engineering Technology and Science(INESC TEC)Porto 4200-465Portugal IEEE

In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the current RO framework *** paper investigates a class of two-stage RO problems that involve decision-dependent *** introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision *** computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical *** motivating application examples that feature the decision-dependent uncertainties are ***,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.

关键词： Benders decomposition decision-dependent uncertainty endogenous uncertainty robust optimization(RO)

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Fiber Optic Devices for High-Resolution Refractive Index Sensing

Fiber Optic Devices for High-Resolution Refractive Index Sen...

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Progress in Electromagnetic Research Symposium (PIERS)

作者： S. Pevec M. Njegovec J. Javornik D. Donlagic Laboratory for Electro Optics and Sensor Systems Faculty of Electrical Engineering and Computer Science University of Maribor Maribor Slovenia

The paper describes a fiber optic Fabry-Perot (FP) refractive index (RI) sensors that provide performances required in gas sensing applications. Presented high-resolution FabryPerot Interferometer (FPI) sensors are based on open-path cells that can be integrated with the optical fiber in different ways. They can be fabricated as microcells on the fiber tip or along the optical fiber, or as external/extrinsic gas cells connected to the fiber. All presented configurations are based on low-finesse FPI as they allow for a simple and cost-efficient designs.

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A Multimode Null Reconfigurable Antenna for Grating Lobe Suppression

A Multimode Null Reconfigurable Antenna for Grating Lobe Sup...

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International Conference on Microwave and Millimeter Wave Technology Proceedings

作者： Shi-Tong Wang Lei Zhu Department of Electrical and Computer Engineering Faculty of Science and Technology State Key Laboratory of Internet of Things for Smart City University of Macau Macau China

ISBN: (数字)9798350389968

ISBN: (纸本)9798350389975

This paper introduces a novel multimode null reconfigurable antenna (NRA) and its attractive application in grating lobe suppression. The proposed NRA generates a pattern null by combining far fields of TM 01 and TM 02 modes of a rectangular patch antenna. The theoretical predictions of null angles versus feeding locations are presented based on electric field distributions under the patch. The proposed NRA is realized with a switchable feeding network. Simulated and measured results are presented demonstrating that the pattern null can be switched from -64° to 54° at 2.45 GHz. In final, the proposed NRA is utilized as antenna elements of a sparse array with the element spacing of one wavelength. Simulated array patterns strongly verify the grating lobe suppression capacity of the proposed NRA array compared to the conventional patch array.

关键词： Microwave measurement Microwave technology Wavelength measurement Patch antennas Millimeter wave technology Millimeter wave measurements Switches

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