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arXiv 2023年

作者： Li, Xinrui Dong, Zhenjun Zeng, Yong Jin, Shi Zhang, Rui National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China Shenzhen Research Institute of Big Data Shenzhen518172 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

In this paper, we investigate the near-field modelling and analyze the beam focusing pattern for modular extremely large-scale array (XL-array) communications. As modular XL-array is physically and electrically large in general, the accurate characterization of amplitude and phase variations across its array elements requires the non-uniform spherical wave (NUSW) model, which, however, is difficult for performance analysis and optimization. To address this issue, we first present two ways to simplify the NUSW model by exploiting the unique regular structure of modular XL-array, termed sub-array based uniform spherical wave (USW) models with different or common angles, respectively. Based on the developed models, the near-field beam focusing patterns of XL-array communications are derived. It is revealed that compared to the existing collocated XL-array with the same number of array elements, modular XL-array can significantly enhance the spatial resolution, but at the cost of generating undesired grating lobes. Fortunately, different from the conventional far-field uniform plane wave (UPW) model, the near-field USW model for modular XL-array exhibits a higher grating lobe suppression capability, thanks to the non-linear phase variations across the array elements. Finally, simulation results are provided to verify the near-field beam focusing pattern and grating lobe characteristics of modular XL-array. Copyright © 2023, The Authors. All rights reserved.

关键词： Focusing

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Bankruptcy forecasting in enterprises and its security using hybrid deep learning models

Cyber Security and Applications

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Cyber Security and Applications 2025年 3卷

作者： Gaurav, Akshat Gupta, Brij B. Bansal, Shavi Psannis, Konstantinos E. Ronin Institute Montclair NJ United States Department of Computer Science and Information Engineering Asia University Taiwan Taichung 413 Taiwan Symbiosis Centre for Information Technology (SCIT) Symbiosis International University Pune India Center for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun India Insights2Techinfo India UCRD Chandigarh University Chandigarh India University of Macedonia Greece

In current scenario when economic and risk management sectors need accurate predictions of enterprise bankruptcy, it is very importance issue to research in the field of security of enterprise bankruptcy. In this context, we propose an hybrid deep learning model through the use of convolutional neural network to enhance bankruptcy forecasting models. We address the high-dimensional data and imbalanced problems by introducing feature selection strategically and Synthetic Minority Over-sampling Technique (SMOTE). In a comparative evaluation, the performance of our model is over 81 %, which is better than that for Logistic Regression and Support Vector Machines. This leap in accuracy demonstrates the cutting edge unprecedented ability of our model to decrypt complex financial patterns and establishes a new precedent for deep learning applications in the nuanced field of financial analytics. © 2024 The Authors

关键词： Bankruptcy prediction Convolutional neural networks (CNN) Deep learning Financial risk management SMOTE

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Enhancing CBFM with Adaptive Frequency Sampling for Wide-Band Scattering from Objects Buried in Layered Media

TechRxiv

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TechRxiv 2022年

作者： Li, Chao Sharawi, Mohammad Mittra, Raj School of Information Science and Engineering Shandong Normal University Shandong Jinan China Department of Electrical Engineering and Poly-Grames Research Center École Polytechnique de Montréal MontréalQCH3T 1J4 Canada Department of Electrical and Computer Engineering University of Central Florida OrlandoFL32816 United States Department of Electrical and Computer Engineering King Abdulaziz University Jeddah21589 Saudi Arabia

An adaptive frequency sampling (AFS) strategy is proposed in conjunction with characteristic basis function method (CBFM) to investigate the problem of wide-band scattering from objects buried in layered media. Conventionally, the CBFM is implemented in the method of moments (MoM) formulation to reduce the solution time at a single frequency. However, wide-band analysis of the above problem is still time consuming when a large number of frequency samples are employed, together with uniform sampling. To mitigate this issue and speed up the process, we propose the AFS algorithm, which selects the frequency samples via an iterative process involving the error-estimates and in turn guarantees the convergence of the wide-band solution process. These samples are used as inputs to the vector fitting (VF) algorithm, which obtains a rational model and subsequently derives the scattered fields in the frequency range of interest efficiently. Numerical results are included to demonstrate that the number of required samples is significantly reduced without compromising the accuracy of the solution. © 2022, CC BY-NC-SA.

关键词： Method of moments

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Efficient product formulas for commutators and applications to quantum simulation

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Physical Review Research 2022年第1期4卷 013191-013191页

作者： Yu-An Chen Andrew M. Childs Mohammad Hafezi Zhang Jiang Hwanmun Kim Yijia Xu Google Quantum AI 340 Main Street Venice California 90291 USA Department of Physics and Joint Quantum Institute NIST/University of Maryland College Park Maryland 20742 USA Condensed Matter Theory Center University of Maryland College Park Maryland 20472 USA Joint Center for Quantum Information and Computer Science University of Maryland College Park Maryland 20742 USA Department of Computer Science University of Maryland College Park Maryland 20742 USA Institute for Advanced Computer Studies University of Maryland College Park Maryland 20742 USA Departments of Electrical and Computer Engineering and Institute for Research in Electronics and Applied Physics University of Maryland College Park Maryland 20742 USA Institute for Physical Science and Technology University of Maryland College Park Maryland 20742 USA

We construct product formulas for exponentials of commutators and explore their applications. First, we directly construct a third-order product formula with six exponentials by solving polynomial equations obtained using the operator differential method. We then derive higher-order product formulas recursively from the third-order formula. We improve over previous recursive constructions, reducing the number of gates required to achieve the same accuracy. In addition, we demonstrate that the constituent linear terms in the commutator can be included at no extra cost. As an application, we show how to use the product formulas in a digital protocol for counterdiabatic driving, which increases the fidelity for quantum state preparation. We also discuss applications to quantum simulation of one-dimensional fermion chains with nearest- and next-nearest-neighbor hopping terms, and two-dimensional fractional quantum Hall phases.

关键词： Fractional quantum Hall effect Quantum algorithms Quantum computation Quantum control Quantum simulation Abstract algebra

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All-Optical, Tunable Third Harmonic Generation in the Ultraviolet Range using Reconfigurable Chalcogenide Metasurfaces

All-Optical, Tunable Third Harmonic Generation in the Ultrav...

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CLEO: QELS_Fundamental science, QELS 2022

作者： Gao, Jiannan Vincenti, Maria Antonietta Frantz, Jesse Clabeau, Anthony Qiao, Xingdu Feng, Liang Scalora, Michael Litchinitser, Natalia M. Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States Department of Information Engineering University of Brescia Via Branze 38 Brescia25123 Italy US Naval Research Laboratory 4555 Overlook Ave. SW WashingtonDC20375 United States University Research Foundation 6411 Ivy Ln. 110 GreenbeltMD20770 United States Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA19104 United States Department of Materials Science and Engineering University of Pennsylvania PhiladelphiaPA19104 United States Aviation and Missile Center US Army CCDC Redstone ArsenalAL35898-5000 United States

We theoretically predict and experimentally demonstrate ultra-high-speed, tunable third harmonic generation (THG) in the opaque spectral range of Arsenic trisulfide (As2S3) glass metasurfaces. © Optica Publishing... 详细信息

ISBN: (纸本)9781557528209

关键词： Harmonic generation

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TransSounder: A hybrid TransUNet-TransFuse architectural framework for semantic segmentation of radar sounder data

TechRxiv

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TechRxiv 2021年

作者： Ghosh, Raktim Bovolo, Francesca The Center of Digital Society Fondazione Bruno kessler Trento38123 Italy The Department of Information Engineering and Computer Science University of Trento Trento38123 Italy

Radar Sounders (RSs) are nadir-looking sensors operating in high frequency (HF) or very high frequency (VHF) bands that profile subsurface targets to retrieve miscellaneous scientific information. Due to complex electromagnetic interaction between back-scattered returns, the interpretation of RS data is challenging. The investigations of ice-sheet subsurface structures require automatic techniques to account for both the sequential spatial distribution of subsurface targets and relevant statistical properties embedded in RS signals. Automatic techniques exists for characterizing these targets either related to probabilistic inference model or convolutional neural network (CNN) deep learning methods. Unfortunately, CNN-based methods capture local spatial context and merely model the long-range sequential context. In contrast to CNN, the Transformer-based models are reliable architectures for capturing long-range sequence-to-sequence global spatial contextual prior. Motivated by the aforementioned fact, we propose a novel Transformer-based semantic segmentation architecture named TransSounder to effectively encode the sequential structures of the RS signals. The TransSounder was constructed on a hybrid TransUNet-TransFuse architectural framework to systematically augment the modules from TransUNet and TransFuse architectures. Experimental results obtained on Multi-channel Coherent Radar Depth Sounder (MCoRDS) dataset confirm the robustness and capability of Transformers to accurately characterize the different subsurface targets. © 2021, CC BY.

关键词： Deep learning

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Counterfactual Inference-based Data Augmentation for Drug-side effect Associations Prediction

Counterfactual Inference-based Data Augmentation for Drug-si...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wenjie Yao Weizhong Zhao Xiaowei Xu Xingpeng Jiang Xianjun Shen Tingting He Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Central China Normal University Wuhan China School of Computer Central China Normal University Wuhan China National Language Resources Monitoring and Research Center for Network Media Central China Normal University Wuhan China Department of Information Science University of Arkansas at Little Rock Little Rock AR USA

Detecting drug side effects is crucial in development of drugs. As publicly available biomedical data expands, researchers have devised numerous computational methods for predicting drug-side effect associations (DSAs). Among these, network-based approaches have gained significant attention in the biomedical field. However, the challenge of data scarcity poses a significant hurdle for existing DSAs prediction models. While various data augmentation methods have been created to solve the proble, most rely on random alterations to the original networks, neglecting the causality of DSAs’ existence, thus impacting the predictive performance negatively. In this paper, we introduce a counterfactual inference-based data augmentation method to enhance performance. First,a heterogeneous information network (HIN) is construct by integrating multiple biomedical data sources. We employ community detection on the HIN to preform a counterfactual inference-based method, deriving augmented links and an augmented HIN. Subsequently, we apply a meta-path-based graph neural network to obtain high-quality representations of drugs and side effects, enabling the prediction of DSAs. Our comprehensive experiments confirm the effectiveness of this counterfactual inference-based data augmentation for DSAs prediction.

关键词：

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T-VAE: Transformer-Based Variational AutoEncoder for Perceiving Anomalies in Multivariate Time Series Data

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Expert Systems 2025年第7期42卷

作者： Li, Chang Kiat, Yeo Chai Jing, Jiwu Long, Chun Computer Network Information Center Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore School of Cryptology University of Chinese Academy of Sciences Beijing China

Anomaly perception in multivariate time series data has crucial applications in various domains such as industrial control and intrusion detection. In real-world scenarios, the sequence information in multivariate time series data, which encompasses the temporal order and dependencies among high-dimensional samples and features, can be complex and nonlinear. Additionally, the time series data often exhibit high volatility and are interspersed with noise data. These factors make anomaly perception in multivariate time series challenging. Despite the recent development of deep learning methods, only a few are able to address all of these challenges. In this paper, we propose a Transformer-based Variational AutoEncoder (T-VAE) for anomaly perception in multivariate time series data. The T-VAE consists of two sub-networks, the Representation network and the Memory network, and achieves end-to-end jointly optimisation. The Representation network leverages self-attention mechanisms and residual network structures to capture sequence information and metaphorical patterns from multivariate time series data. The Memory network employs a Variational AutoEncoder to learn the distribution of normal data. It employs Maximum Mean Discrepancy to approximate the distribution of high-volatility and noisy data to the distribution of the normal data. We evaluate T-VAE on five datasets, showing superior performance and validating its effectiveness and robustness through comprehensive ablation studies and sensitivity analyses. © 2025 John Wiley & Sons Ltd.

关键词： Variational techniques

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Two-dimensional optical spatial differentiation and high-contrast imaging

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National science Review 2021年第6期8卷 180-187页

作者： Junxiao Zhou Haoliang Qian Junxiang Zhao Min Tang Qianyi Wu Ming Lei Hailu Luo Shuangchun Wen Shaochen Chen Zhaowei Liu Key Laboratory for Micro-/NanoOptoelectronic Devices of Ministry of Education School of Physics and ElectronicsHunan University Department of Electrical and Computer Engineering University of California Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical InstrumentationZJU-Hangzhou Global Science and Technology Innovation CenterZhejiang University Department of Nano Engineering University of California

Optical analog signal processing technology has been widely studied and applied in a variety of science and engineering fields, with the advantages of overcoming the low-speed and high-power consumption associated with its digital counterparts. Much attention has been given to emerging metasurface technology in the field of optical imaging and processing systems. Here, we demonstrate, for the first time, broadband two-dimensional spatial differentiation and high-contrast edge imaging based on a dielectric metasurface across the whole visible spectrum. This edge detection method works for both intensity and phase objects simply by inserting the metasurface into a commercial optical microscope. This highly efficient metasurface performing a basic optical differentiation operation opens up new opportunities in applications of fast,compactible and power-efficient ultrathin devices for data processing and biological imaging.

关键词： metasurface edge detection spatial differentiation

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Positional Encoding-Guided Transformer-Based Multiple Instance Learning for Histopathology Whole Slide Images Classification

SSRN

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SSRN 2024年

作者： Shi, Jun Sun, Dongdong Wu, Kun Jiang, Zhiguo Kong, Xue Wang, Wei Wu, Haibo Zheng, Yushan School of Software Hefei University of Technology Anhui Province Hefei230601 China School of Computer Science and Information Engineering Hefei University of Technology Anhui Province Hefei230601 China Beijing102206 China Department of Pathology the First Affiliated Hospital of USTC Division of Life Sciences and Medicine University of Science and Technology of China Anhui Province Hefei230036 China Intelligent Pathology Institute Division of Life Sciences and Medicine University of Science and Technology of China Anhui Province Hefei230036 China School of Engineering Medicine Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Biomedical Engineering China

Whole slide image (WSI) classification is of great clinical significance in computer-aided pathological diagnosis. Due to the high cost of manual annotation, weakly supervised WSI classification methods have gained more attention. As the most representative, multiple instance learning (MIL) generally aggregates the predictions or features of the patches within a WSI to achieve the slidelevel classification under the weak supervision of WSI labels. However, most existing MIL methods ignore spatial position relationships of the patches which is likely to strengthen the discriminative ability of WSI-level features. In this paper, we propose a novel position encoding-guided transformerbased multiple instance learning (PEGTB-MIL) method for histopathology WSI classification. It aims to encode the spatial positional property of the patch into its corresponding semantic features and explore the potential correlation among the patches for improving the WSI classification performance. Concretely, the deep features of the patches in WSI are first extracted and simultaneously a position encoder is used to encode the spatial 2D positional information of the patches into the spatialaware features. After incorporating the semantic features and spatial embeddings, multi-head selfattention (MHSA) is applied to explore the contextual and spatial dependencies of the fused features. Particularly, we introduce an auxiliary reconstruction task to enhance the spatial-semantic consistency and generalization ability of features. The proposed method is evaluated on one public TCGA dataset and two in-house datasets. Experimental results validate it is effective in the tasks of lung cancer subtyping and gene mutation status prediction for lung cancer and gastrointestinal stromal tumor (GIST), and superior to the state-of-the-art MIL-based methods. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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