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Intelligent and Converged Networks 2022年第1期3卷 I0001-I0003页

作者： Linglong Dai Chan-Byoung Chae Shi Jin Marco Di Renzo Rui Zhang Beijing National Research Center for Information Science and Technology(BNRist)and the Department of Electronic Engineering Tsinghua UniversityBeijing 100084China School of Integrated Technology Yonsei Institute of Convergence TechnologyYonsei UniversityIncheon 406-840Republic of Korea. National Mobile Communications Research Laboratory Southeast UniversityNanjing 210096China. the UniversitéParis-Saclay CNRSCentraleSupélecLaboratoire des Signaux et Systèmes3 Rue Joliot-Curie91192 Gif-sur-YvetteFrance. Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore

Reconfigurable Intelligent Surface(RIS),also known as intelligent reflecting surface or large intelligent surface,is an emerging new physical-layer technology in the field of wireless *** basic idea of RIS is to deploy a reconfigurable passive array in the environment to manipulate the propagation of electron-magnetic *** promises a new design paradigm for wireless communications,where the wireless propagation environment can be dynamically controlled,which is substantially different from the conventional design that focuses only on the transmitter and *** RIS-aided wireless communications,some of the current methodologies in conventional communication systems need to be revised,and some novel solutions are required to realize the potential benefits of the *** the number of publications about RIS has recently sharply increased,there are still many challenging issues to be extensively investigated,such as the RIS channel modeling,fundamental performance limits,the system design,joint optimization of the RIS and the transceivers,channel state information acquisition,and interdisciplinary applications.

关键词： passive sharply revised

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Predicting Microsatellite Instability from Histology Images with Dilated Neighborhood Attention Transformer in Colorectal Cancer

Predicting Microsatellite Instability from Histology Images ...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Yun-Chi Chen Tung Chao Wimaya Nitya Phandita Tzu-Yu Sun Huai-Zhi Wang Yi-Hsien Hsieh Le-Yin Hsu Che Lin Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University (NTU) Taipei Taiwan College of Bioresources and Agriculture NTU Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Graduate Institute of Communication Engineering NTU Taipei Taiwan Data Science Degree Program NTU Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan

ISBN: (数字)9798331518622

ISBN: (纸本)9798331518639

Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, whereas histology images, widely available in colorectal cancer diagnosis, offer a valuable alternative for MSI prediction. Although Transformer-based models have demonstrated promising outcomes in predicting MSI from histology images, they are hampered by traditional local attention mechanisms that struggle to capture long-range interdependencies and establish a comprehensive global receptive field. In this study, we introduce DiNAT-MSI, a novel framework for histology-based MSI prediction that incorporates the Dilated Neighborhood Attention Transformer (DiNAT). This model enhances global context recognition and substantially expands receptive fields, all without additional computational burden. Our results demonstrate that DiNAT-MSI achieves a superior patientwise AUROC compared to ResNet18 and Swin Transformer, along with commendable explainability. Our work not only illustrates a more accessible diagnostic tool for leveraging histological data but also underscores the potential of Transformerbased models with sophisticated attention designs in advancing precision medicine for colorectal cancer patients.

关键词： Attention mechanisms Histopathology Computational modeling Precision medicine Small satellites Immunotherapy Colorectal cancer Predictive models Transformers Genetics

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BioADAPT-MRC: Adversarial Learning-based Domain Adaptation Improves Biomedical Machine Reading Comprehension Task

arXiv

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arXiv 2022年

作者： Mahbub, Maria Srinivasan, Sudarshan Begoli, Edmon Peterson, Gregory D. Department of Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN United States Cyber Resilience and Intelligence Division Oak Ridge National Laboratory Oak RidgeTN United States

Biomedical machine reading comprehension (biomedical-MRC) aims to comprehend complex biomedical narratives and assist healthcare professionals in retrieving information from them. The high performance of modern neural network-based MRC systems depends on high-quality, large-scale, human-annotated training datasets. In the biomedical domain, a crucial challenge in creating such datasets is the requirement for domain knowledge, inducing the scarcity of labeled data and the need for transfer learning from the labeled general-purpose (source) domain to the biomedical (target) domain. However, there is a discrepancy in marginal distributions between the general-purpose and biomedical domains due to the variances in topics. Therefore, direct-transferring of learned representations from a model trained on a general-purpose domain to the biomedical domain can hurt the model's performance. We present an adversarial learning-based domain adaptation framework for the biomedical machine reading comprehension task (BioADAPT-MRC), a neural network-based method to address the discrepancies in the marginal distributions between the general and biomedical domain datasets. BioADAPT-MRC relaxes the need for generating pseudo labels for training a well-performing biomedical-MRC model. We extensively evaluate the performance of BioADAPT-MRC by comparing it with the best existing methods on three widely used benchmark biomedical-MRC datasets - BioASQ-7b, BioASQ-8b, and BioASQ-9b. Our results suggest that without using any synthetic or human-annotated data from the biomedical domain, BioADAPT-MRC can achieve state-of-the-art performance on these datasets. BioADAPT-MRC is freely available as an open-source project at https://***/mmahbub/BioADAPT-MRC. © 2022, CC BY-NC-ND.

关键词： Benchmarking

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Smart Cloud Computing Approach for Electronic based Health Record System 5

Smart Cloud Computing Approach for Electronic based Health R...

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5th IEEE International Conference on Electro-Computing Technologies for Humanity, NIGERCON 2024

作者： Kanna, R. Kishore Anitha, R. Sundaramoorthy, K. Salau, Ayodeji Olalekan Danquah-Amoah, Archibald Banappagoudar, Sudharani B. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Biomedical Engineering Chennai India Jerusalem College of Engineering Department of Biomedical Engineering Chennai India Jerusalem College of Engineering Department of Information Technology Chennai India Afe Babalola University Department of Electrical/ Electronics and Computer Engineering Ado-Ekiti Nigeria Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamil Nadu Chennai India Accra Technical University Medical Laboratory Technology and Biomedical Engineering Ghana Itm University School of Nursing Science MP Gwalior India

ISBN: (纸本)9798331542559

This study provides a comprehensive examination of the potential benefits of speed and substantial change from integrating health measurement with cloud-based electronic health records (cloud EHRs) to expedite health care Many challenges a traditional health record systems meet include data fragmentation, limited accessibility, security. There were some concerns. The use of cloud computing in healthcare, which integrates patient data and provides data security and universal access, addresses these issues. The healthcare industry has rapidly embraced cloud computing, dramatically changing the way patient care is delivered and delivered. This study looks at the benefits of using a cloud-based electronic health record (Cloud EHR) in conjunction with the generation of health metrics, providing a detailed analysis of how this integration leads to improved health care delivery increase The article also discusses how cloud EHR systems use context -the-art technologies such as data analytics and machine learning and provide relevant data for prediction and analysis. They also review the security measures used in cloud systems to ensure that confidential health information is accessible, intact and private. © 2024 IEEE.

关键词： Patient treatment

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Cascaded Multi-path Shortcut Diffusion Model for Medical Image Translation

arXiv

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arXiv 2024年

作者： Zhou, Yinchi Chen, Tianqi Hou, Jun Xie, Huidong Dvornek, Nicha C. Zhou, S. Kevin Wilson, David L. Duncan, James S. Liu, Chi Zhou, Bo Department of Biomedical Engineering Yale University New HavenCT United States Department of Radiology and Biomedical Imaging Yale School of Medicine New HavenCT United States Department of Electrical Engineering Yale University New HavenCT United States Department of Computer Science University of California Irvine IrvineCA United States School of Biomedical Engineering Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Department of Biomedical Engineering Case Western Reserve University ClevelandOH United States Department of Radiology Northwestern University ChicagoIL United States

Image-to-image translation is a vital component in medical imaging processing, with many uses in a wide range of imaging modalities and clinical scenarios. Previous methods include Generative Adversarial Networks (GANs) and Diffusion Models (DMs), which offer realism but suffer from instability and lack uncertainty estimation. Even though both GAN and DM methods have individually exhibited their capability in medical image translation tasks, the potential of combining a GAN and DM to further improve translation performance and to enable uncertainty estimation remains largely unexplored. In this work, we address these challenges by proposing a Cascade Multi-path Shortcut Diffusion Model (CMDM) for high-quality medical image translation and uncertainty estimation. To reduce the required number of iterations and ensure robust performance, our method first obtains a conditional GAN-generated prior image that will be used for the efficient reverse translation with a DM in the subsequent step. Additionally, a multi-path shortcut diffusion strategy is employed to refine translation results and estimate uncertainty. A cascaded pipeline further enhances translation quality, incorporating residual averaging between cascades. We collected three different medical image datasets with two sub-tasks for each dataset to test the generalizability of our approach. Our experimental results found that CMDM can produce high-quality translations comparable to state-of-the-art methods while providing reasonable uncertainty estimations that correlate well with the translation error. © 2024, CC BY.

关键词： Medical imaging

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A novel machine learning hybrid model for heart disease risk assessment

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AIP Conference Proceedings 2025年第1期3224卷

作者： Saurabh Srivastava Tasneem Ahmed Amit Saxena Rajeev Kumar Department of Computer Science & Engineering Moradabad Institute of Technology Moradabad India Advanced Computing and Research Laboratory Dept. of Computer Application Integral University Lucknow India

Heart disease claims millions of lives annually in more than a hundred countries. Heart disease has become one of the world’s leading causes of mortality, according to a World Health Organization (WHO) report. Among the conditions that can cause heart attacks include angina pectoris, congestive heart failure, cardiomyopathy, congenital heart disease, arrhythmias, myocarditis, heart attacks, and heart cancer. Heart disease presents a significant global health challenge, prompting extensive research into early detection and preventive measures. This study introduces a predictive model leveraging advanced machine learning (ML) techniques to assess heart disease risk. The model development process encompasses several phases, including data preprocessing, feature selection, and model training employing state-of-the-art algorithms like logistic regression (LR), decision tree (DT), random forests (RF), artificial neural network (ANN), naïve Bayes (NB), K-Nearest Neighbor (KNN), and support vector machine (SVM). Additionally, feature importance analysis and model evaluation techniques are utilized to refine interpretability and performance. The results exhibit the model’s efficacy in accurately predicting heart disease risk, offering valuable insights for early detection and personalized healthcare strategies. Overall, this research emphasizes the potential of data-driven methodologies in constructing predictive models for heart disease, presenting a promising avenue for enhancing patient outcomes and alleviating the burden on global healthcare systems.

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Constructions of k-uniform states in heterogeneous systems

arXiv

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arXiv 2023年

作者： Feng, Keqin Jin, Lingfei Xing, Chaoping Yuan, Chen Department of Mathematical Sciences Tsinghua University Beijing China School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai200433 China The School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China

A pure quantum state of n parties associated with the Hilbert space Cd1☉ Cd2 ☉ · · · ☉ Cdn is called k-uniform if all the reductions to k-parties are maximally mixed. The n partite system is called homogenous if the local dimension d1 = d2 = · · · = dn, while it is called heterogeneous if the local dimension are not all equal. k-uniform sates play an important role in quantum information theory. There are many progress in characterizing and constructing k-uniform states in homogeneous systems. However, the study of entanglement for heterogeneous systems is much more challenging than that for the homogeneous case. There are very few results known for the k-uniform states in heterogeneous systems for k > 3. We present two general methods to construct k-uniform states in the heterogeneous systems for general k. The first construction is derived from the error correcting codes by establishing a connection between irredundant mixed orthogonal arrays and error correcting codes. We can produce many new k-uniform states such that the local dimension of each subsystem can be a prime power. The second construction is derived from a matrix H meeting the condition that HA×Ā + HĀT×A has full rank for any row index set A of size k. These matrix construction can provide more flexible choices for the local dimensions, i.e., the local dimensions can be any integer (not necessarily prime power) subject to some constraints. Our constructions imply that for any positive integer k, one can construct k-uniform states of a heterogeneous system in many different Hilbert spaces. Copyright © 2023, The Authors. All rights reserved.

关键词： Hilbert spaces

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Model Predictive Control with Fixed Switching Frequency for Thyristor-Controlled LC-Coupling Hybrid Active Power Filter

Model Predictive Control with Fixed Switching Frequency for ...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Wai-Kit Sou Rui P. Martins Chi-Seng Lam State Key Laboratory of Analog and Mixed-Signal VLSI University of Macau Macau China Institute of Microelectronics University of Macau Macau China Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macau China On leave from Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

The Thyristor-controlled LC-coupling hybrid active power filter (TCLC-HAPF) exhibits the characteristics of low DC-link voltage and a wide reactive power compensation range, making it a promising compensator for medium-voltage-level power systems power quality conditioning. Conventional finite-set model predictive control (FS-MPC) typically utilizes a single switching vector in each control period, resulting in a variable switching frequency and noticeable output ripple. This article addresses these concerns by presenting an enhanced MPC that incorporates a fixed switching frequency, thereby resolving these challenges. Preliminary simulation studies are provided to verify the feasibility and performance of the proposed method.

关键词： Industrial electronics Reactive power Switching frequency Power quality Switches Active filters Control systems

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Maximal Quantum Interaction between Free Electrons and Photons

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Physical Review Letters 2025年第4期134卷 043803-043803页

作者： Zetao Xie Zeling Chen Hao Li Qinghui Yan Hongsheng Chen Xiao Lin Ido Kaminer Owen D. Miller Yi Yang Department of Physics and HK Institute of Quantum Science and Technology The University of Hong Kong Pokfulam Hong Kong China Z. X. and Z. C. contributed equally to this Letter. Department of Applied Physics and Energy Sciences Institute Yale University New Haven Connecticut 06520 USA Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation College of Information Science and Electronic Engineering Zhejiang University Hangzhou 310027 China Solid State Institute and Faculty of Electrical and Computer Engineering Technion—Israel Institute of Technology 32000 Haifa Israel

The emerging field of free-electron quantum optics enables electron-photon entanglement and holds the potential for generating nontrivial photon states for quantum information processing. Although recent experimental studies have entered the quantum regime, rapid theoretical developments predict that qualitatively unique phenomena only emerge beyond a certain interaction strength. It is thus pertinent to identify the maximal electron-photon interaction strength and the materials, geometries, and particle energies that enable one to approach it. We derive an upper limit to the quantum vacuum interaction strength between free electrons and single-mode photons, which illuminates the conditions for the strongest interaction. Crucially, we obtain an explicit energy selection recipe for electrons and photons to achieve maximal interaction at arbitrary separations and identify two optimal regimes favoring either fast or slow electrons over those with intermediate velocities. We validate the limit by analytical and numerical calculations on canonical geometries and provide near-optimal designs indicating the feasibility of strong quantum interactions. Our findings offer fundamental intuition for maximizing the quantum interaction between free electrons and photons and provide practical design rules for future experiments on electron-photon and electron-mediated photon-photon entanglement. They should also enable the evaluation of key metrics for applications such as the maximum power of free-electron radiation sources and the maximum acceleration gradient of dielectric laser accelerators.

关键词： Quantum optics

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Convex Analytic Theory for Convex Q-Learning

Convex Analytic Theory for Convex Q-Learning

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IEEE Conference on Decision and Control

作者： Fan Lu Prashant G. Mehta Sean P. Meyn Gergely Neu Department of Electrical and Computer Engineering University of Florida Gainesville FL Coordinated Science Laboratory and the Department Mechanical Science and Engineering University of Illinois at Urbana-Champaign (UIUC) Inria International Chair Paris France Department of Information and Communication Technologies Universitat Pompeu Fabra (Barcelona Spain)

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

In recent years there has been a collective research effort to find new formulations of reinforcement learning that are simultaneously more efficient and more amenable to analysis. This paper concerns one approach that builds on the linear programming (LP) formulation of optimal control of Manne. A primal version is called logistic Q-learning, and a dual variant is convex Q-learning. This paper focuses on the latter, while building bridges with the former. The main contributions follow: (i) The dual of convex Q-learning is not precisely Manne’s LP or a version of logistic Q-learning, but has similar structure that reveals the need for regularization to avoid over-fitting. (ii) A sufficient condition is obtained for a bounded solution to the Q-learning LP. (iii) Simulation studies reveal numerical challenges when addressing sampled-data systems based on a continuous time model. The challenge is addressed using state-dependent sampling. The theory is illustrated with applications to examples from OpenAI gym. It is shown that convex Q-learning is successful in cases where standard Q-learning diverges, such as the LQR problem.

关键词： Sufficient conditions Q-learning Buildings Optimal control Sampled data systems Linear programming Continuous time systems

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