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arXiv 2023年

作者： Siegel, Joel Kim, Shinho Fortman, Margaret Wan, Chenghao Kats, Mikhail A. Hon, Phillip W.C. Sweatlock, Luke Jang, Min Seok Brar, Victor Watson Department of Physics University of Wisconsin-Madison MadisonWI53706 United States School of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of Department of Electrical and Computer Engineering University of Wisconsin-Madison MadisonWI53706 United States Northrop Grumman Corporation Redondo BeachCA90278 United States

We theoretically describe and experimentally demonstrate a graphene-integrated metasurface structure that enables electrically-tunable directional control of thermal emission. This device consists of a dielectric spacer that acts as a Fabry-Perot resonator supporting long-range delocalized modes bounded on one side by an electrostatically tunable metal-graphene metasurface. By varying the Fermi level of the graphene, the accumulated phase of the Fabry-Perot mode is shifted, which changes the direction of absorption and emission at a fixed frequency. We directly measure the frequency- and angle-dependent emissivity of the thermal emission from a fabricated device heated to 250 ◦C. Our results show that electrostatic control allows the thermal emission at 6.61 µm to be continuously steered over 16◦, with a peak emissivity maintained above 0.9. We analyze the dynamic behavior of the thermal emission steerer theoretically using a Fano interference model, and use the model to design optimized thermal steerer structures. © 2023, CC BY.

关键词： Graphene

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"Unlocking Electrocatalytic Dynamics with Anti-Mxene Borides Monolayers for Nitrate Reduction"

SSRN

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SSRN 2024年

作者： Ho, Thi H. Nguyen, Quynh Anh T. Le, Bich-Tram Truong Kim, Seong-Gon Bui, Viet Q. Laboratory for Computational Physics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Mechanical - Electrical and Computer Engineering School of Technology Van Lang University Ho Chi Minh City Viet Nam Advanced Institute of Science and Technology The University of Danang 41 Le Duan Danang Viet Nam Department of Science and International Cooperation The University of Danang Danang551000 Viet Nam Department of Physics & Astronomy Center for Computational Sciences Mississippi State University MS39762 United States

In this comprehensive study, we explore the electrocatalytic behavior of novel two-dimensional (2D) monolayers composed of anti-MXene borides (TMB) towards the nitrate reduction reaction (NO3RR) using density functional theory (DFT). The analysis reveals that these TMB monolayers emerge as promising candidates for electrochemical NO3RR, characterized by their exceptional stability, advantageous selectivity, and effective activation properties. Notably, our results highlight the preferential progression of the NO3-to-N2 pathway over the NO3-to-NH3 pathway in these TMB systems, presenting a significant insight into the mechanism of nitrate reduction. Among the monolayers, MnB distinguishes itself with a limiting potential of 0.79 eV, underscoring its substantial potential for application in the electroreduction of nitrate. Additionally, this study sheds light on the broader applicability of these innovative two-dimensional anti-MXene TMB monolayers, suggesting their versatility as electrocatalysts in a variety of potential applications, thereby opening new avenues for future research and development in electrocatalysis. © 2024, The Authors. All rights reserved.

关键词： Electrocatalysis

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Federated Edge Learning via Integrated Sensing, Computation, and Communication

Federated Edge Learning via Integrated Sensing, Computation,...

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IEEE International Conference on Communications (ICC)

作者： Peixi Liu Guangxu Zhu Shuai Wang Miaowen Wen Wu Luo H. Vincent Poor Shuguang Cui State Key Laboratory of Advanced Optical Communication Systems and Networks Peking University Shenzhen Research Institute of Big Data Chinese Academy of Sciences Shenzhen Institute of Advanced Technology School of Electronic and Information Engineering South China University of Technology Department of Electrical and Computer Engineering Princeton University the Guangdong Provincial Key Laboratory of Future Networks of Intelligence School of Science and Engineering (SSE) Future Network of Intelligence Institute (FNii) the Chinese University of Hong Kong Shenzhen

Sensing, computation, and communication (SC 2 ) are highly coupled processes in federated edge learning (FEEL) and need to be jointly designed in a task-oriented manner for pursuing the best FEEL performance under the stringent resource constraints at edge devices. However, this remains an open problem as there is a lack of theoretical understanding on how the SC 2 resources jointly affect the FEEL performance. In this paper, we address the problem of joint SC 2 resource allocation for FEEL via a concrete case study of human motion recognition based on wireless sensing. Specifically, the joint SC 2 resource allocation problem is cast to maximize the convergence speed of FEEL, under the constraints on training time and energy supply of each edge device. Solving this problem entails solving two subproblems in order: the first one reduces to determining a joint sensing and communication resource allocation that maximizes the total number of samples sensed during the entire training process; the second one concerns the partition of the total number of sensed samples over communication rounds to determine the batch size at each round for convergence speed maximization. Finally, extensive simulation results are provided to validate the superiority of the proposed scheme over several baseline schemes.

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Repeated behavioural evolution is associated with convergence of gene expression in cavity-nesting songbirds

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Nature Ecology and Evolution 2025年第5期9卷 845-856页

作者： Lipshutz, Sara E. Hibbins, Mark S. Bentz, Alexandra B. Buechlein, Aaron M. Empson, Tara A. George, Elizabeth M. Hauber, Mark E. Rusch, Douglas B. Schelsky, Wendy M. Thomas, Quinn K. Torneo, Samuel J. Turner, Abbigail M. Wolf, Sarah E. Woodruff, Mary J. Hahn, Matthew W. Rosvall, Kimberly A. Department of Biology Indiana University Bloomington IN United States Department of Biology Loyola University Chicago Chicago IL United States Department of Biology Duke University Durham NC United States Department of Ecology and Evolutionary Biology University of Toronto Toronto ON Canada School of Biological Sciences University of Oklahoma Norman OK United States Center for Genomics and Bioinformatics Indiana University Bloomington IN United States Department of Ecology and Evolutionary Biology University of Tennessee Knoxville TN United States Department of Evolution Ecology and Organismal Biology The Ohio State University Columbus OH United States Department of Evolution Ecology and Behavior University of Illinois Urbana-Champaign Urbana IL United States Illinois Natural History Survey Prairie Research Institute University of Illinois Urbana-Champaign Champaign IL United States Advanced Science Research Center and Programs in Biology and in Psychology Graduate Center of the City University of New York New York NY United States The Grainger College of Engineering University of Illinois Urbana-Champaign Urbana IL United States School of Biological Sciences University of Edinburgh Scotland Edinburgh United Kingdom United States Department of Agriculture Animal and Plant Health Inspection Services Veterinary Services Center for Epidemiology and Animal Health Fort Collins CO United States Department of Computer Science Indiana University Bloomington IN United States

Uncovering the genomic bases of phenotypic adaptation is a major goal in biology, but this has been hard to achieve for complex behavioural traits. Here we leverage the repeated, independent evolution of obligate cavity nesting in birds to test the hypothesis that pressure to compete for a limited breeding resource has facilitated convergent evolution in behaviour, hormones and gene expression. We used an integrative approach, combining aggression assays in the field, testosterone measures and transcriptome-wide analyses of the brain in wild-captured females and males. Our experimental design compared species pairs across five avian families, each including one obligate cavity-nesting species and a related species with a more flexible nest strategy. We find behavioural convergence, with higher levels of territorial aggression in obligate cavity nesters, particularly among females. Across species, levels of testosterone in circulation were not associated with nest strategy nor aggression. Phylogenetic analyses of individual genes and co-regulated gene networks revealed more shared patterns of brain gene expression than expected by drift, although the scope of convergent gene expression evolution was limited to a small percentage of the genome. When comparing our results to other studies that did not use phylogenetic methods, we suggest that accounting for shared evolutionary history may reduce the number of genes inferred as convergently evolving. Altogether, we find that behavioural convergence in response to shared ecological pressures is associated with largely independent evolution of gene expression across different avian families, punctuated by a narrow set of convergently evolving genes. © The Author(s), under exclusive licence to Springer Nature Limited 2025.

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Robust Secure Resource Optimization for RIS-Aided Backscatter Communication with HWIs and Channel Uncertainties

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IEEE Transactions on Vehicular Technology 2025年

作者： Gao, Jingjing Xu, Yongjun Huang, Chongwen Li, Xingwang Zhou, Jihua Yuan, Yiming Jia, Huibin Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China Chongqing University of Posts and Telecommunications Advanced Science Research Institute Chongqing400065 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454000 China Southwest University School of Software Southwest University College of Computer and Information Science Chongqing400715 China North China Electric Power University Department of Electronic and Communication Engineering Baoding071000 China

Reconfigurable intelligent surface (RIS)-aided backscatter communication (BackCom) has recently attracted a lot of attention due to its potential to enhance energy efficiency (EE) and transmission robustness by reconstructing radio channel environments in a low-power-consumption way. However, most of the existing resource optimization approaches focused on perfect channel state information and ideal hardware conditions, so that the received signals may be distorted by channel uncertainties (e.g., channel estimation errors) and hardware impairments (HWIs) (e.g., distortion noises). To this end, we proposed a more realistic robust secure structure for a RIS-aided BackCom system with the consideration of channel uncertainties, HWIs, and one eavesdropper. Then, we formulate an EE-based maximization resource allocation problem by optimizing the transmit power of base station, the artificial noise power, the RIS's phase shift matrix, and the time slots. To tackle this non-convex problem, an iteration-based robust resource allocation algorithm is proposed by employing the worst-case approach, the variable substitution approach, and the alternating optimization approach. Simulation results demonstrate that the proposed algorithm has 14.84% increase in the EE and 8.78% reduction in outage probability compared to the other algorithms. © 2025 IEEE.

关键词： Channel estimation

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Three-dimensional tracking and height verification of polystyrene particles with piezo actuator positioning by means of multi-wavelength evanescent fields 20

Three-dimensional tracking and height verification of polyst...

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20th International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2020

作者： Blattler, Aran Khajornrungruang, Panart Suzuki, Keisuke Permpatdechakul, Thitipat Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan Department of Intelligent and Control Systems Advanced Mechanical Division Kyushu Institute of Technology Fukuoka Japan

ISBN: (纸本)9780995775176

To understand the nanoparticle motion behaviours on a surface, we have studied to investigate the three-dimension tracking of an individual nanoparticle with nanometre localisation accuracy. In three-dimensional nanoparticle moving position, the X and Y coordinates are on the parallel plane to the surface, which their coordinates can be tracked in general microscopy. However, the Z coordinate is the nanoparticle height position perpendicularly to the surface plane, which could not be generally tracked without longitudinal scanning method. In this paper, the polystyrene 100 nm standard particles were used to investigate the three-dimensional tracking. The individual standard nanoparticles were fixed on the tip, which was vertically travelled with the piezo actuator for movement control on the third dimension to verify the actual height Z. Our developed optical microscopy apparatus has been applying multi-wavelength evanescent fields, and obtaining multi scattering light intensities from the observed nanoparticles near to surface. Commonly, the X and Y coordinates of nanoparticle were determined by the centre of mass of each scattering light intensity. The height Z was investigated from the integration of the detected scattering intensities of each wavelength. By using our method, the height Z uncertainty could be measured approximately less than ±20 nm, and the penetration height measurability was approximately 250 nm far from the reference surface. Consequently, we could three-dimensionally track the individual standard nanoparticle in recording speed higher than 50 frames per second. Copyright © Proceedings of the 20th International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2020. All rights reserved.

关键词： Nanoparticles

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Quantum Simulation of the First-Quantized Pauli-Fierz Hamiltonian

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PRX Quantum 2024年第1期5卷 010345-010345页

作者： Priyanka Mukhopadhyay Torin F. Stetina Nathan Wiebe Department of Physical & Environmental Sciences University of Toronto Ontario M1C 1A4 Canada Simons Institute for the Theory of Computing Berkeley California 94720 USA Berkeley Quantum Information and Computation Center University of California Berkeley California 94720-1460 USA Department of Computer Science University of Toronto Ontario M5S 2E4 Canada Pacific Northwest National Laboratory Richland Washington 99354 USA Canadian Institute for Advanced Research Toronto Ontario M5G 1M1 Canada

We provide an explicit recursive divide-and-conquer approach for simulating quantum dynamics and derive a discrete first-quantized nonrelativistic QED Hamiltonian based on the many-particle Pauli-Fierz Hamiltonian. We apply this recursive divide-and-conquer algorithm to this Hamiltonian and compare it to a concrete simulation algorithm that uses qubitization. Our divide-and-conquer algorithm, using lowest-order Trotterization, scales for fixed grid spacing as O~(ΛN2η2t2/ϵ) for grid size N, η particles, simulation time t, field cutoff Λ, and error ϵ. Our qubitization algorithm scales as O~(N(η+N)(η+Λ2)tlog (1/ϵ)). This shows that even a naive partitioning and low-order splitting formula can yield, through our divide-and-conquer formalism, superior scaling to qubitization for large Λ. We compare the relative costs of these two algorithms on systems that are relevant for applications such as the spontaneous emission of photons and the photoionization of electrons. We observe that for different parameter regimes, one method can be favored over the other. Finally, we give new algorithmic and circuit-level techniques for gate optimization, including a new way of implementing a group of multicontrolled-X gates that can be used for better analysis of circuit cost.

关键词： Quantum simulation

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Tailored Multi-Organ Segmentation with Model Adaptation and Ensemble

arXiv

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arXiv 2023年

作者： Dong, Jiahua Cheng, Guohua Zhang, Yue Peng, Chengtao Song, Yu Tong, Ruofeng Lin, Lanfen Chen, Yen-Wei College of Computer Science and Technology Zhejiang University Hangzhou310027 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215163 China Graduate School of Information Science and Engineering Ritsumeikan University Shiga525-8577 Japan Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230026 China School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei230026 China

Multi-organ segmentation, which identifies and separates different organs in medical images, is a fundamental task in medical image analysis. Recently, the immense success of deep learning motivated its wide adoption in multi-organ segmentation tasks. However, due to expensive labor costs and expertise, the availability of multi-organ annotations is usually limited and hence poses a challenge in obtaining sufficient training data for deep learning-based methods. In this paper, we aim to address this issue by combining off-the-shelf single-organ segmentation models to develop a multi-organ segmentation model on the target dataset, which helps get rid of the dependence on annotated data for multi-organ segmentation. To this end, we propose a novel dual-stage method that consists of a Model Adaptation stage and a Model Ensemble stage. The first stage enhances the generalization of each off-the-shelf segmentation model on the target domain, while the second stage distills and integrates knowledge from multiple adapted single-organ segmentation models. Extensive experiments on four abdomen datasets demonstrate that our proposed method can effectively leverage off-the-shelf single-organ segmentation models to obtain a tailored model for multi-organ segmentation with high accuracy. © 2023, CC0.

关键词： Image segmentation

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Predictive Analytics for Hospital Readmissions Using Logistic Regression and IoT Sensor Data

Predictive Analytics for Hospital Readmissions Using Logisti...

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International Conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE)

作者： Kiruthiga B Arshadh Ariff Mohamed Abuthahir Riazulhameed Rajapriya. M R. Sarasu N. Mohankumar A. Kasthuri Department of Management Studies SRM Valliammai Engineering College Chennai Tamil Nadu India Blackstraw Technologies Pvt Ltd Chennai Tamil Nadu India Department of Management Studies Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Spatial Informatics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Symbiosis Institute of Technology (SIT) Symbiosis International (Deemed University) Nagpur Maharashtra India Department of Computer Science and Engineering Vinayaka Mission's Kirupananda Variyar Engineering College Inayaka Mission's Research Foundation (DU) Salem Tamil Nadu India

ISBN: (数字)9798350379297

ISBN: (纸本)9798350379303

Hospital readmissions affect healthcare systems globally, increasing costs and morbidity. Readmissions must be predicted and prevented to improve patient outcomes and save healthcare costs. This research leverages the Internet of Things (IoT) sensor data and Logistic Regression (LR) to reduce hospital readmissions using predictive analytics. Patient health data from IoT devices during and after hospitalization is used in the paper. Heart rate, blood pressure, and oxygen saturation are constantly monitored. LR is used to examine this dataset and create a prediction model. The LR algorithm trains us to recognize IoT sensor data patterns and correlations that indicate readmission risks. Our methodology provides developed risk estimates incorporating patient-specific parameters, including age, multiple illnesses, and medical history. The temporal data permits dynamic predictions, considering patient health changes over time. This research detected hospital readmission risk with promising accuracy. These predictions allow healthcare practitioners to proactively intervene and deliver targeted treatments, including prescription modifications, lifestyle advice, and follow-up consultations to decrease readmission. This proactive strategy may improve patient outcomes and save healthcare expenses.

关键词： Logistic regression Costs Hospitals Transforms Predictive models Internet of Things Predictive analytics

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Patients’ and Health Care Professionals’ Expectations of Virtual Therapeutic Agents in Outpatient Aftercare: Qualitative Survey Study

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JMIR Formative Research 2025年 9卷 e59527页

作者： Immel, Diana Hilpert, Bernhard Schwarz, Patricia Hein, Andreas Gebhard, Patrick Barton, Simon Hurlemann, René Department of Psychiatry School of Medicine and Health Sciences Carl von Ossietzky Universität Oldenburg Oldenburg Germany Affective Computing Group Deutsches Forschungszentrum für Künstliche Intelligenz GmbH German Research Center for Artificial Intelligence Kaiserslautern Germany Leiden Institute of Advanced Computer Science Leiden University Snellius Gebouw Leiden Netherlands Department of Computer Science Vrije Universiteit Amsterdam Amsterdam Netherlands Assistance Systems and Medical Device Technology Department for Health Services Research School of Medicine & Health Sciences Carl von Ossietzky Universität Oldenburg Oldenburg Germany

Background: Depression is a serious mental health condition that can have a profound impact on the individual experiencing the disorder and those providing care. While psychotherapy and medication can be effective, there are gaps in current approaches, particularly in outpatient care. This phase is often associated with a high risk of relapse and readmission, and patients often report a lack of support. Socially interactive agents represent an innovative approach to the provision of assistance. Often powered by artificial intelligence, these virtual agents can interact socially and elicit humanlike emotions. In health care, they are used as virtual therapeutic assistants to fill gaps in outpatient aftercare. Objective: We aimed to explore the expectations of patients with depression and health care professionals by conducting a qualitative survey. Our analysis focused on research questions related to the appearance and role of the assistant, the assistant-patient interaction (time of interaction, skills and abilities of the assistant, and modes of interaction) and the therapist-assistant interaction. Methods: A 2-part qualitative study was conducted to explore the perspectives of the 2 groups (patients and care providers). In the first step, care providers (n=30) were recruited during a regional offline meeting. After a short presentation, they were given a link and were asked to complete a semistructured web-based questionnaire. Next, patients (n=20) were recruited from a clinic and were interviewed in a semistructured face-to-face interview. Results: The survey findings suggested that the assistant should be a multimodal communicator (voice, facial expressions, and gestures) and counteract negative self-evaluation. Most participants preferred a female assistant or wanted the option to choose the gender. In total, 24 (80%) health care professionals wanted a selectable option, while patients exhibited a marked preference for a female or diverse assistant. Regrading

关键词： artificial intelligence depression digital health care digital intervention digital technology e-mental health major depressive disorder mental disorder mental illness outpatient aftercare public health socially interactive agent suicidal ideation suicide prevention virtual therapeutic assistant

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