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Faithful Quantum Teleportation via a Nanophotonic Nonlinear Bell State Analyzer

Physical Review Letters 2025年第16期134卷 160802-160802页

作者： Joshua Akin Yunlei Zhao Paul G. Kwiat Elizabeth A. Goldschmidt Kejie Fang Holonyak Micro and Nanotechnology Laboratory and Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana Illinois 61801 USA Illinois Quantum Information Science and Technology Center University of Illinois at Urbana-Champaign Urbana Illinois 61801 USA Department of Physics University of Illinois at Urbana-Champaign Urbana Illinois 61801 USA

Quantum networking protocols, including quantum teleportation and entanglement swapping, use linear-optical Bell state measurements for heralding the distribution and transfer of quantum information. However, a linear-optical Bell state measurement requires identical photons and is susceptible to errors caused by multiphoton emission, fundamentally limiting the efficiency and fidelity of quantum networking protocols. Here we show a nonlinear Bell state analyzer for time-bin encoded photons based on a nanophotonic cavity with a sum-frequency generation efficiency of 4×10−5 to filter multiphoton emissions, and utilize it for faithful quantum teleportation involving spectrally distinct photons with fidelities ≥94% down to the single-photon level. Our result demonstrates that nonlinear-optical entangling operations, empowered by our efficient nanophotonics platform, can realize faithful quantum information protocols without requiring identical photons and without the fundamental limit on the fidelity of a Bell state measurement imposed by linear optics, which facilitates the realization of practical quantum networks.

关键词： Nanophotonics

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Learning on Forecasting HIV Epidemic Based on Individuals’ Contact Networks

Learning on Forecasting HIV Epidemic Based on Individuals’ ...

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17th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2024

作者： Sun, Chaoyue Liu, Yiyang Parisi, Christina Fisk-Hoffman, Rebecca Salemi, Marco Fang, Ruogu Danforth, Brandi Prosperi, Mattia Marini, Simone Department of Electrical and Computer Engineering Herbert Wertheim College of Engineering University of Florida GainesvilleFL United States Department of Epidemiology College of Public Health and Health Professions and College of Medicine University of Florida GainesvilleFL United States Department of Pathology Immunology and Laboratory Medicine College of Medicine University of Florida GainesvilleFL United States J. Crayton Pruitt Family Department of Biomedical Engineering Herbert Wertheim College of Engineering University of Florida GainesvilleFL United States Center for Cognitive Aging and Memory McKnight Brain Institute University of Florida GainesvilleFL United States Florida Department of Health 4025 Esplanade Way TallahasseeFL United States Emerging Pathogens Institute University of Florida GainesvilleFL United States

Improving the diagnosis of HIV is a fundamental objective of the Ending the HIV Epidemic initiative, as it represents the initial step toward treatment and achieving undetectable status, thereby reducing transmission. To attain these objectives effectively, it is crucial to identify the groups most susceptible to HIV, allowing interventions to be tailored to their specific needs. In this study, we developed a predictive model designed to assess individual HIV risk within a high-risk contact network – predicting treatment or at-risk – leveraging surveillance data collected through routine HIV case interviews in Florida. Unique to our analysis, we explored the incorporation of behavioral network information with Graph Neural Networks to enhance the predictive capacity for identifying individuals within the treatment or intervention categories, when compared to models that mainly consider conventional HIV risk factors. Our deployed Graph Isomorphism Network achieved 77.3% and 73.2% balanced accuracy in inductive and trans ductive learning scenarios respectively, outperforming the traditional prediction algorithms that do not leverage the network structure. We then usedour model to further investigate the importance of demographic and behavioral factors in the HIV risk prediction process. Our findings provide valuable insights for healthcare practitioners and policymakers in their efforts to combat HIV infection. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Contrastive Learning

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Synthetic Biology Construct of Ebola Virus in Bacteria Surrogate Is Stable and Safe for Rapid Detection Studies in a BSL-2 laboratory Setting

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Advances in Microbiology 2022年第1期12卷 25-41页

作者： Nwadiuto Esiobu Douglas Holmes Chad Coarsey Waseem Asghar Bodhi Stone Microbial Biotech Laboratory Biological Sciences Department Florida Atlantic University Boca Raton Florida USA Department of Electrical Engineering and Computer Science Florida Atlantic University Boca Raton Florida USA

Rapid detection of virulent pathogens during an outbreak is critical for public health advisories and control of the disease in a population. While many molecular techniques for point of care and clinical diagnosis abound, the US experience with the COVID-19 testing in the early stages of the pandemic underscores the critical importance of determining the appropriate target gene(s) with in-built controls that reliably detect pathogens with high sensitivity and specificity. Assays and research for diagnostics and therapy could be slowed during an epidemic because access to the required BSL-3 and BSL-4 laboratories are limited. So, during the 2014 West Africa Ebola outbreak, we tested the hypothesis that using synthetic cDNA of Ebolavirus in a bacteria surrogate (fit for all lab settings), would remain unmutated and safe after several generations, serving as an effective positive control in research settings, self test and point-of-care detection platforms. Primers were designed for the detection and quantification of the nucleoprotein (NP) gene of the 2014 Makona Ebola strain (KR781608.1, 733 - 1332 bp). To test the stability of artificially inserted translation arrest in the Orf of the model gene, it was edited to include three STOP codons in the RNA transcript using SNAP GENE. The segment was then spliced into a high copy number plasmid, cloned into One Shot^TM TOP10 Escherichia coli (Invitrogen), and tested for stability and safety by periodic subculture, extraction and sequencing. Unlike COVID-19, rapid detection of blood-borne etiologies like Ebola requires optimized protocols for blood matrix. Using real-time PCR and newly designed primer pairs, the EBOV surrogate was detected and enumerated in human blood and regular broth and buffers. Based on aligned sequence analysis, the EBOV synthetic NP gene was stable (>99.9999% similarity coefficient) for at least 3 months. Detection sensitivity in broth and blood was at least 100 cells/ml or about 5.8

关键词： Ebola Virus Synthetic Biology Biosafety Point-of-Care Rapid Detection Plasmid Vector

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Interpretable Data-Driven Probabilistic Power System Load Margin Assessment with Uncertain Renewable Energy and Loads

Interpretable Data-Driven Probabilistic Power System Load Ma...

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IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia)

作者： Bendong Tan Junbo Zhao Weijia Liu Nan Duan Department of Electrical and Computer Engineering University of Connecticut Storrs CT National Renewable Energy Laboratory Golden CO Lawrence Livermore National Laboratory Livermore CA

ISBN: (纸本)9798350399677

The increasing uncertainties caused by the high-penetration of stochastic renewable generation resources poses a significant threat to the power system voltage stability. To address this issue, this paper proposes a probabilistic deep kernel learning enabled surrogate model to extract the hidden relationship between uncertain sources, i.e., wind power and loads, and load margin for probabilistic load margin assessment (PLMA). Unlike other deep learning approaches, a kernel SHAP provides the sensitivity analysis as well as interpretability of the inputs to outputs influences. This allows identifying the critical factors that affect load margin so that corrective control can be initiated for stability enhancement. Numerical results carried out on the IEEE 118-bus power system demonstrate the accuracy and efficiency of the proposed data-driven PLMA scheme.

关键词： Deep learning Renewable energy sources Asia Power system stability Wind power generation Probabilistic logic Stability analysis

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Optical Integrated Sensing and Communication with Light-Emitting Diode

Optical Integrated Sensing and Communication with Light-Emit...

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IEEE International Conference on Communications Workshops, ICC

作者： Runxin Zhang Yulin Shao Menghan Li Lu Lu Yonina C. Eldar Key Laboratory of Space Utilization Technology and Engineering Center for Space Utilization Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Department of Electrical and Computer Engineering State Key Laboratory of Internet of Things for Smart City University of Macau Macau S.A.R Weizmann Institute of Science Rehovot Israel

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

This paper presents a new optical integrated sensing and communication (O-ISAC) framework tailored for cost-effective Light-Emitting Diode (LED). Unlike prior research on ISAC, which predominantly focused on radio frequency (RF) band, O-ISAC capitalizes on the inherent advantages of the optical spectrum, including the ultra-wide license-free bandwidth, immunity to RF interference, and energy efficiency. Moreover, the performance of communication and sensing in O-ISAC is positively correlated, circumventing the tradeoff that has plagued RF-ISAC systems. Our O-ISAC framework unfolds in two phases: directionless O-ISAC and directional O-ISAC. In the first phase, distributed optical access points emit non-directional light for communication and leverage small-aperture imaging principles for sensing. In the second phase, we put forth the concept of optical beamforming, using collimating lenses to concentrate light, resulting in substantial performance enhancements in both communication and sensing. Numerical and simulation results demonstrate the feasibility and impressive performance of O-ISAC benchmarked against separate communication and sensing systems.

关键词： Integrated optics Location awareness Performance evaluation Stimulated emission Simulation Optical imaging Light emitting diodes

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Microgrid Frequency Stability: A Proactive Scheme Based on Dynamic Predictions

Microgrid Frequency Stability: A Proactive Scheme Based on D...

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Innovative Smart Grid Technologies (ISGT)

作者： Gian Paramo Arturo Bretas Sean Meyn Department of Electrical and Computer Engineering University of Florida Gainesville FL G2Elab Grenoble INP CNRS Université Grenoble Alpes Grenoble France Distributed Systems Group Pacific Northwest National Laboratory Richland USA

The dynamic nature of microgrids introduces challenges in the context of frequency stability. This work presents a framework where the future state of microgrid frequency is predicted and corrective actions are optimized. Predictions are generated through Bayesian filters leveraging synchronized data acquired via PMUs. Taking a proactive approach makes it possible to optimize corrective actions considering dynamic system conditions. Testing is conducted via Matlab simulations. The performance of the solution presented in this work is compared to traditional load-shedding schemes and to predictive solutions found in literature. The results indicate that the proposed framework outperforms both. Some of the advantages of this framework include a reduction in amount of load dropped during compensation, the use of adaptive parameters which eliminates the need to simulate contingency conditions, and dynamic uncertainty quantification provided the particle filter.

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A Defense Method against Backdoor Attacks in Neural Networks Using an Image Repair Technique 12

A Defense Method against Backdoor Attacks in Neural Networks...

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12th International Conference on Information Technology in Medicine and Education, ITME 2022

作者： Chen, Jiangtao Lu, Huijuan Huo, Wanli Zhang, Shicong Chen, Yuefeng Yao, Yudong China Jiliang University Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province College of Information Engineering Hangzhou China Novel Hangzhou China Alibaba Group Artificial Intelligence Governance Research Center Hangzhou China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ United States

ISBN: (纸本)9798350310153

With the rapid development of deep learning research and applications, the problem of artificial intelligence security has become increasingly prominent, such as adversarial examples, universal adversarial patch, and data poisoning, especially for the backdoor attack, which is a new type of covert attack, leading to the vulnerability and non-robustness of deep learning models. In a backdoor attack, the attacker will conduct a malicious attack by inserting some poisoned samples into training dataset. Poisoned samples add triggers and modify the labels to the target labels to participate in the training. Infected model has the same accuracy as the clean model in the normal test set, but when confronted with poisoned samples, the triggers will be activated to make the infected model predict the target label. To solve this problem, model parameters adjustment and poisoned data removal methods are widely used. However, they lack real-time performance and accuracy is insufficient. In this paper, we propose a new backdoor attack defense method, in which trigger reverse engineering is used to obtain the right triggers and image repair techniques to make sure that the input model data can be real-time processed without any negative impacts on clean samples. © 2022 IEEE.

关键词： Repair

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Joint Beamforming Optimization and Mode Selection for RDARS-Aided MIMO Systems

arXiv

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arXiv 2024年

作者： Wang, Jintao Ma, Chengzhi Gong, Shiqi Yang, Xi Ma, Shaodan State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau China School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China Shanghai Key Laboratory of Multidimensional Information Processing School of Communication and Electronic Engineering East China Normal University Shanghai200241 China

Reconfigurable intelligent surface (RIS) has emerged as a cost-effective solution for green communications in 6G. However, its further extensive use has been greatly limited due to its fully passive characteristics. Considering the appealing distribution gains of distributed antenna systems (DAS), a flexible reconfigurable architecture called reconfigurable distributed antenna and reflecting surface (RDARS) is proposed. RDARS encompasses DAS and RIS as two special cases and maintains the advantages of distributed antennas while reducing the hardware cost by replacing some active antennas with low-cost passive reflecting surfaces. In this paper, we present a RDARS-aided uplink multi-user communication system and investigate the system transmission reliability with the newly proposed architecture. Specifically, in addition to the distribution gain and the reflection gain provided by the connection and reflection modes, respectively, we also consider the dynamic mode switching of each element which introduces an additional degree of freedom (DoF) and thus results in a selection gain. As such, we aim to minimize the total sum mean-square-error (MSE) of all data streams by jointly optimizing the receive beamforming matrix, the reflection phase shifts and the channel-aware placement of elements in the connection mode. To tackle this nonconvex problem with intractable binary and cardinality constraints, we propose an inexact block coordinate descent (BCD) based penalty dual decomposition (PDD) algorithm with the guaranteed convergence. Since the PDD algorithm usually suffers from high computational complexity, a low-complexity greedy-search-based alternating optimization (AO) algorithm is developed to yield a semi-closed-form solution with acceptable performance. Numerical results demonstrate the superiority of the proposed architecture compared to the conventional fully passive RIS or DAS. Furthermore, some insights about the practical implementation of RDARS are provided

关键词： Mean square error

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Optimal Information Provision for Strategic Hybrid Workers

arXiv

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arXiv 2022年

作者： Shah, Sohil Amin, Saurabh Jaillet, Patrick The Laboratory for Information and Decision Systems The Department of Electrical Engineering and Computer Science Laboratory for Information and Decision Systems Operations Research Center CambridgeMA United States

We study the problem of information provision by a strategic central planner who can publicly signal about an uncertain infectious risk parameter. Signalling leads to an updated public belief over the parameter, and agents then make equilibrium choices on whether to work remotely or in-person. The planner maintains a set of desirable outcomes for each realization of the uncertain parameter and seeks to maximize the probability that agents choose an acceptable outcome for the true parameter. We distinguish between stateless and stateful objectives. In the former, the set of desirable outcomes does not change as a function of the risk parameter, whereas in the latter it does. For stateless objectives, we reduce the problem to maximizing the probability of inducing mean beliefs that lie in intervals computable from the set of desirable outcomes. We derive the optimal signalling mechanism and show that it partitions the parameter domain into at most two intervals with the signals generated according to an interval-specific distribution. For the stateful case, we consider a practically relevant situation in which the planner can enforce in-person work capacity limits that progressively get more stringent as the risk parameter increases. We show that the optimal signalling mechanism for this case can be obtained by solving a linear program. We numerically verify the improvement in achieving desirable outcomes using our information design relative to no information and full information benchmarks. Copyright © 2022, The Authors. All rights reserved.

关键词： Uncertainty analysis

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Model-Driven Inverse Lithography Via Hypergraph Convolutional Neural Networks and its Experimental Demonstration

SSRN

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SSRN 2024年

作者： Zhang, Shengen Ma, Xu Huang, Chaojun Wang, Fuli Arce, Gonzalo R. Key Laboratory of Photoelectronic Imaging Technology and System Ministry of Education of China School of Optics and Photonics Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States

Inverse lithography technology (ILT) is a pivotal approach to correct the optical proximity effect in a lithography process by optimizing the mask pattern at the pixel level. However, traditional ILT algorithms are computationally intensive due to the huge data volume throughput. This paper proposes a novel ILT method based on model-driven hypergraph convolutional network (MHGCN) to improve the computational efficiency and lithography image fidelity simultaneously. In the MHGCN framework, a dense concentric circular sampling (DCCS) template is first used to extract the feature matrices of the target layout, which are then imported to a hyperedge graph convolutional network (encoder) to predict the ILT result. Then, a lithography-model-based decoder is used to calculate the wafer image of the ILT mask. A low-cost unsupervised training method of the network is developed through the collaboration between the encoder and decoder. To demonstrate the proposed method, a digital-micromirror-device (DMD) maskless lithography testbed is established, and the imaging model of the testbed is calibrated based on a set of training layout patterns. The superiority of the proposed method in both of optimization capacity and computational efficiency are proved by the simulation and experimental results. © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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