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Electron charge qubit with 0.1 millisecond coherence time

Nature Physics 2024年第1期20卷 116-122页

作者： Zhou, Xianjing Li, Xinhao Chen, Qianfan Koolstra, Gerwin Yang, Ge Dizdar, Brennan Huang, Yizhong Wang, Christopher S. Han, Xu Zhang, Xufeng Schuster, David I. Jin, Dafei Center for Nanoscale Materials Argonne National Laboratory LemontIL United States Pritzker School of Molecular Engineering University of Chicago ChicagoIL United States Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA United States The NSF AI Institute for Artificial Intelligence and Fundamental Interactions CambridgeMA United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA United States James Franck Institute and Department of Physics University of Chicago ChicagoIL United States Department of Electrical and Computer Engineering Northeastern University BostonMA United States Department of Applied Physics Stanford University StanfordCA United States Department of Physics and Astronomy University of Notre Dame Notre DameIN United States

Electron charge qubits are compelling candidates for solid-state quantum computing because of their inherent simplicity in qubit design, fabrication, control and readout. However, electron charge qubits built on conventional semiconductors and superconductors suffer from severe charge noise that limits their coherence time to the order of one microsecond. Here we report electron charge qubits that exceed this limit, based on isolated single electrons trapped on an ultraclean solid neon surface in a vacuum. Quantum information is encoded in the motional states of an electron that is strongly coupled with microwave photons in an on-chip superconducting resonator. The measured relaxation and coherence times are both on the order of 0.1 ms, surpassing all existing charge qubits and rivalling state-of-the-art superconducting transmon qubits. The simultaneous strong coupling of two qubits with a common resonator is also demonstrated, as the first step towards two-qubit entangling gates for universal quantum computing. © 2023, The Author(s), under exclusive licence to Springer Nature Limited.

关键词： Superconducting resonators

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Online demand peak shaving with machine-learned advice in digital twins

Digital Twins and Applications

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Digital Twins and Applications 2024年第1期1卷 38-50页

作者： Minxi Feng Wei Li Boyu Qin Albert Y.Zomaya Centre for Distributed and High Performance Computing School of Computer ScienceThe University of SydneySydneyNew South WalesAustralia State Key Laboratory of Electrical Insulation and Power Equipment Department of Electrical EngineeringXi'an Jiaotong UniversityXi'anChina

As the use of physical instruments grows,control algorithms are being increasingly deployed to enhance efficiency and reliability through digital twin *** load management is central to energy systems within digital twins,which significantly impacts operational *** demand loads can lead to substantial monthly utility expenses without proper ***,a randomised online algorithm incorporating machine-learned insights is introduced to optimise battery operations and mitigate peak demand *** leverages limited-bit information from ma-chine learning models to inform its online decision-making process for cost-effective load *** provide theoretical evidence demonstrating that AMPAMOD maintains minimal advice complexity,has a linear computational cost,and achieves a bounded competitive *** trace-driven experiments with real-world household data reveal that AMPAMOD successfully reduces peak loads by over 90%,outperforming other benchmarks by at least 50%.These experimental findings align with our theoretical assertions,showcasing the effectiveness of AMPAMOD.

关键词： operations maintain battery

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RF Challenge: The Data-Driven Radio Frequency Signal Separation Challenge

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2025年 6卷 4083-4100页

作者： Lancho, Alejandro Weiss, Amir Lee, Gary C. F. Jayashankar, Tejas Kurien, Binoy G. Polyanskiy, Yury Wornell, Gregory W. Universidad Carlos III de Madrid Signal Theory and Communications Leganés28911 Spain Gregorio Marañón Health Research Institute Madrid28007 Spain Bar-Ilan University Ramat Gan5290002 Israel Institute for Infocomm Research Communications and Networks Fusionopolis 138632 Singapore Massachusetts Institute of Technology Electrical Engineering and Computer Science Department CambridgeMA02139 United States MIT Lincoln Laboratory LexingtonMA02421 United States

We address the critical problem of interference rejection in radio-frequency (RF) signals using a data-driven approach that leverages deep-learning methods. A primary contribution of this paper is the introduction of the RF Challenge, which is a publicly available, diverse RF signal dataset for data-driven analyses of RF signal problems. Specifically, we adopt a simplified signal model for developing and analyzing interference rejection algorithms. For this signal model, we introduce a set of carefully chosen deep learning architectures, incorporating key domain-informed modifications alongside traditional benchmark solutions to establish baseline performance metrics for this intricate, ubiquitous problem. Through extensive simulations involving eight different signal mixture types, we demonstrate the superior performance (in some cases, by two orders of magnitude) of architectures such as UNet and WaveNet over traditional methods like matched filtering and linear minimum mean square error estimation. Our findings suggest that the data-driven approach can yield scalable solutions, in the sense that the same architectures may be similarly trained and deployed for different types of signals. Moreover, these findings further corroborate the promising potential of deep learning algorithms for enhancing communication systems, particularly via interference mitigation. This work also includes results from an open competition based on the RF Challenge, hosted at the 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'24). © 2020 IEEE.

关键词： Matched filters

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Open-Vocabulary Audio-Visual Semantic Segmentation 24

Open-Vocabulary Audio-Visual Semantic Segmentation

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32nd ACM International Conference on Multimedia, MM 2024

作者： Guo, Ruohao Qu, Liao Niu, Dantong Qi, Yanyu Yue, Wenzhen Shi, Ji Xing, Bowei Ying, Xianghua National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University Beijing China Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA United States Berkeley Ai Research University of California Berkeley BerkeleyCA United States College of Information and Electrical Engineering China Agricultural University Beijing China

ISBN: (纸本)9798400706868

Audio-visual semantic segmentation (AVSS) aims to segment and classify sounding objects in videos with acoustic cues. However, most approaches operate on the close-set assumption and only identify pre-defined categories from training data, lacking the generalization ability to detect novel categories in practical applications. In this paper, we introduce a new task: open-vocabulary audio-visual semantic segmentation, extending AVSS task to open-world scenarios beyond the annotated label space. This is a more challenging task that requires recognizing all categories, even those that have never been seen nor heard during training. Moreover, we propose the first open-vocabulary AVSS framework, OV-AVSS, which mainly consists of two parts: 1) a universal sound source localization module to perform audio-visual fusion and locate all potential sounding objects and 2) an open-vocabulary classification module to predict categories with the help of the prior knowledge from large-scale pre-trained vision-language models. To properly evaluate the open-vocabulary AVSS, we split zero-shot training and testing subsets based on the AVSBench-semantic benchmark, namely AVSBench-OV. Extensive experiments demonstrate the strong segmentation and zero-shot generalization ability of our model on all categories. On the AVSBench-OV dataset, OV-AVSS achieves 55.43% mIoU on base categories and 29.14% mIoU on novel categories, exceeding the state-of-the-art zero-shot method by 41.88%/20.61% and open-vocabulary method by 10.2%/11.6%. The code is available at https://***/ruohaoguo/ovavss. © 2024 ACM.

关键词： Semantic Segmentation

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Semantic Feature Division Multiple Access for Digital Semantic Broadcast Channels

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IEEE Internet of Things Journal 2025年第11期12卷 17123-17136页

作者： Ma, Shuai Sun, Zhiye Shen, Bin Wu, Youlong Li, Hang Shi, Guangming Li, Shiyin Al-Dhahir, Naofal Peng Cheng Laboratory Shenzhen518066 China China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China ShanghaiTech University School of Information Science and Technology Shanghai201210 China Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China Xidian University School of Artificial Intelligence Shaanxi Xi'an710071 China The University of Texas at Dallas Department of Electrical and Computer Engineering RichardsonTX75080 United States

In this paper, we propose a digital semantic feature division multiple access (SFDMA) paradigm in multi-user broadcast (BC) networks for the inference and the image reconstruction tasks. In this SFDMA scheme, the multi-user semantic information is encoded into discrete approximately orthogonal representations, and the encoded semantic features of multiple users can be simultaneously transmitted in the same time-frequency resource. Specifically, for inference tasks, we design a SFDMA digital BC network based on robust information bottleneck (RIB), which can achieve a tradeoff between inference performance, data compression and multi-user interference. Moreover, for image reconstruction tasks, we develop a SFDMA digital BC network by utilizing a Swin Transformer, which significantly reduces multi-user interference. More importantly, SFDMA can protect the privacy of users' semantic information, in which each receiver can only decode its own semantic information. Furthermore, we establish a relationship between performance and signal to interference plus noise ratio (SINR), which is fitted by an Alpha-Beta-Gamma (ABG) function. Furthermore, an optimal power allocation method is developed for the inference and reconstruction tasks. Extensive simulations verify the effectiveness and superiority of our proposed SFDMA scheme. © 2014 IEEE.

关键词： Image reconstruction

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Segmentation and Classification of Gastric Cancer from Endoscopic Image Dataset with the Aid of Artificial Intelligence 7

Segmentation and Classification of Gastric Cancer from Endos...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Anitha, Cuddapah Srinivas Rao, Vemu Ramyadevi, R. Maya, R. Debroy, Subhasish Mahaveerakannan, R. School of Computing Mohan Babu University Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati517102 India Shri Vishnu Engineering College for Women Andhra Pradesh Bhimavaram India Saveetha Engineering College Department of Computer Science & Engineering India Chennai Institute of Technology & Applied Research Department of Electrical & Electronics Engineering India Dr. B.C. Roy Engineering College West Bengal Durgapur India Saveetha School of Engineering Department of Computer Science and Engineering SIMETs Tamil Nadu Chennai India

ISBN: (纸本)9798350340600

In order to detect early stomach malignancies, upper GI endoscopy is commonly used. An object detection model, a form of deep learning, was projected as a means of automating the diagnosis of early stomach cancer using endoscopic pictures. Yet there were difficulties in reducing false positives in the results that were detected. Thus, this research proposes an automatic classification method for stomach cancer classification using a (CNN) that has been pre-trained. To classify cancer in endoscopic pictures automatically, our method is superior to those that rely on traditional, manual features. Thirteen convolutional layers with small 33 size kernels and three fully linked layers make up the suggested model. We use the learning approach, which involves pre-training the weight of fine-tuning the weight of layers with the Slime Mould Algorithm, to deal with the data scarcity (SMA). Experiments employing 1208 photos from healthy subjects and 533 photographs from patients with stomach cancer examined detection performance using the 5-fold cross validation approach. These findings suggest the projected strategy will be effective for automating the diagnosis of early stomach cancer in endoscopic pictures. © 2023 IEEE.

关键词： Endoscopy

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Active Grammatical Inference for Non-Markovian Planning 32

Active Grammatical Inference for Non-Markovian Planning

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32nd International Conference on Automated Planning and Scheduling, ICAPS 2022

作者： Topper, Noah Atia, George Trivedi, Ashutosh Velasquez, Alvaro Department of Computer Science University of Central Florida United States Department of Electrical and Computer Engineering University of Central Florida United States Department of Computer Science University of Colorado Boulder United States Information Directorate Air Force Research Laboratory United States

ISBN: (纸本)9781577358749

Planning in finite stochastic environments is canonically posed as a Markov decision process where the transition and reward structures are explicitly known. Reinforcement learning (RL) lifts the explicitness assumption by working with sampling models instead. Further, with the advent of reward machines, we can relax the Markovian assumption on the reward. Angluin's active grammatical inference algorithm L∗ has found novel application in explicating reward machines for non-Markovian RL. We propose maintaining the assumption of explicit transition dynamics, but with an implicit non-Markovian reward signal, which must be inferred from experiments. We call this setting non-Markovian planning, as opposed to non-Markovian RL. The proposed approach leverages L∗ to explicate an automaton structure for the underlying planning objective. We exploit the environment model to learn an automaton faster and integrate it with value iteration to accelerate the planning. We compare against recent non-Markovian RL solutions which leverage grammatical inference, and establish complexity results that illustrate the difference in runtime between grammatical inference in planning and RL settings. © 2022, Association for the Advancement of Artificial Intelligence.

关键词： Reinforcement learning

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PROVABLE SIM-TO-REAL TRANSFER IN CONTINUOUS DOMAIN WITH PARTIAL OBSERVATIONS 11

PROVABLE SIM-TO-REAL TRANSFER IN CONTINUOUS DOMAIN WITH PART...

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11th International Conference on Learning Representations, ICLR 2023

作者： Hu, Jiachen Zhong, Han Jin, Chi Wang, Liwei School of Computer Science Peking University China Center for Data Science Peking University China Department of Electrical and Computer Engineering Princeton University United States National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University Center for Data Science Peking University Beijing Institute of Big Data Research China

Sim-to-real transfer, which trains RL agents in the simulated environments and then deploys them in the real world, has been widely used to overcome the limitations of gathering samples in the real world. Despite the empirical success of the sim-to-real transfer, its theoretical foundation is much less understood. In this paper, we study the sim-to-real transfer in continuous domain with partial observations, where the simulated environments and real-world environments are modeled by linear quadratic Gaussian (LQG) systems. We show that a popular robust adversarial training algorithm is capable of learning a policy from the simulated environment that is competitive to the optimal policy in the real-world environment. To achieve our results, we design a new algorithm for infinite-horizon average-cost LQGs and establish a regret bound that depends on the intrinsic complexity of the model class. Our algorithm crucially relies on a novel history clipping scheme, which might be of independent interest. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

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Multi-agent Cooperative Games Using Belief Map Assisted Training

arXiv

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arXiv 2024年

作者： Luo, Chen Huang, Qinwei Wu, Alex B. Khan, Simon Li, Hai Qiu, Qinru Department of Electrical Engineering & Computer Science Syracuse University United States Fayetteville-Manlius High School United States Air Force Research Laboratory United States Department of Electrical Engineering & Computer Science Duke University United States

In a multi-agent system, agents share their local observations to gain global situational awareness for decision making and collaboration using a message passing system. When to send a message, how to encode a message, and how to leverage the received messages directly affect the effectiveness of the collaboration among agents. When training a multi-agent cooperative game using reinforcement learning (RL), the message passing system needs to be optimized together with the agent policies. This consequently increases the model’s complexity and poses significant challenges to the convergence and performance of learning. To address this issue, we propose the Belief-map Assisted Multi-agent System (BAMS), which leverages a neuro-symbolic belief map to enhance training. The belief map decodes the agent’s hidden state to provide a symbolic representation of the agent’s understanding of the environment and other agents’ status. The simplicity of symbolic representation allows the gathering and comparison of the ground truth information with the belief, which provides an additional channel of feedback for the learning. Compared to the sporadic and delayed feedback coming from the reward in RL, the feedback from the belief map is more consistent and reliable. Agents using BAMS can learn a more effective message passing network to better understand each other, resulting in better performance in the game. We evaluate BAMS’s performance in a cooperative predator and prey game with varying levels of map complexity and compare it to previous multi-agent message passing models. The simulation results showed that BAMS reduced training epochs by 66%, and agents who apply the BAMS model completed the game with 34.62% fewer steps on average. © 2024, CC BY.

关键词： Multi agent systems

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Surface-patterned chalcogenide glasses with high-aspect-ratio microstructures for long-wave infrared metalenses

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Opto-Electronic science 2024年第10期3卷 1-13页

作者： Zhaofeng Gu Yixiao Gao Kongsi Zhou Junyang Ge Chen Xu Lei Xu Mohsen Rahmani Ran Jiang Yimin Chen Zijun Liu Chenjie Gu Yaoguang Ma Jianrong Qiu Xiang Shen Laboratory of Infrared Materials and Devices Research Institute of Advanced TechnologiesNingbo UniversityNingbo 315211China Zhejiang Key Laboratory of Advanced Optical Functional Materials and Devices Ningbo 315211China Advanced Optics&Photonics Laboratory Department of EngineeringSchool of Science and TechnologyNottingham Trent UniversityNottingham NG118NSUnited Kingdom Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and EngineeringInternational ResearchCenter for Advanced PhotonicsZhejiang UniversityHangzhou 310027China Ningbo Institute of Oceanography Ningbo 315832China

Multidimensional-engineering chalcogenide glasses is widely explored to construct various infrared photonic devices,with their surface as a key dimension for wavefront ***,we demonstrate direct patterning high-aspect-ratio microstructures on the surface of chalcogenide glasses offers an efficient and robust method to manipulate longwave infrared *** chalcogenide glass being considered soft in terms of its mechanical properties,we successfully fabricate high-aspect-ratio micropillars with a height of 8μm using optimized deep etching process,and we demonstrate a 2-mm-diameter all-chalcogenide metalens with a numerical aperture of 0.45 on the surface of a 1.5-mm-thick As2Se3 *** the exceptional longwave infrared(LWIR)transparency and moderate refractive index of As2Se3 glass,the all-chalcogenide metalens produces a focal spot size of~1.39λ0 with a focusing efficiency of 47%at the wavelength of 9.78μm,while also exhibiting high-resolution imaging *** work provides a promising route to realize easy-to-fabricate,mass-producible planar infrared optics for compact,light-weight LWIR imaging systems.

关键词： chalcogenide glasses long wave infrared metalens

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