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Smoothed Least-Laxity-First Algorithm for EV Charging

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arXiv 2021年

作者： Chen, Niangjun Kurniawan, Christian Nakahira, Yorie Chen, Lijun Low, Steven H. the Department of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore the Institute for High Performance Computing Agency for Science Technology and Research Singapore138632 Singapore the Department of Electrical and Computer Engineering Carnegie Mellon University PA15213 United States the Department of Computer Science University of Colorado BoulderCO80309 United States the Department of Computing and Mathematical Sciences California Institute of Technology CA91125 United States

Adaptive charging can charge electric vehicles (EVs) at scale cost effectively, despite the uncertainty in EV arrivals. We formulate adaptive EV charging as a feasibility problem that meets all EVs’ energy demands before their deadlines while satisfying constraints in charging rate and total charging power. We propose an online algorithm, smoothed least-laxity-first (sLLF), that decides the current charging rates without the knowledge of future arrivals and demands. We characterize the performance of the sLLF algorithm analytically and numerically. Numerical experiments with real-world data show that it has a significantly higher rate of feasible EV charging than several other existing EV charging algorithms. Resource augmentation framework is employed to assess the feasibility condition of the algorithm. The assessment shows that the sLLF algorithm achieves perfect feasibility with only a 0.07 increase in resources. © 2021, CC BY.

关键词： Scheduling

Oxygen Octahedral Tilt Controlled Topological Hall Effect in Epitaxial and Freestanding SrRuO3/SrIrO3 Heterostructures

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arXiv 2022年

作者： Lim, Zhi Shiuh Khoo, Amy Khoong Hong Zhou, Zhou Omar, Ganesh Ji Yang, Ping Laskowski, Robert Ariando, Ariando Department of Physics National University of Singapore 2 Science Drive 3 117551 Singapore NUSNNI T-Lab National University of Singapore 5A Engineering Drive 1 117411 Singapore Institute of High-Performance Computing A*STAR 1 Fusionopolis Way #16-16 Connexis 138632 Singapore Singapore Synchrotron Light Source NUS 5 Research Link 117603 Singapore

The fabrication technique of oxide flakes by epitaxial lift-off has allowed the investigation of a freestanding version of multifunctional oxide thin films and heterostructures. Several recent reports have demonstrated the robustness of these freestanding oxides retaining their desirable properties after detachment from the substrate. Here, we first demonstrate various epitaxial SrRuO3/SrIrO3 heterostructures showing Hall-humps reminiscence of Topological Hall Effect. First-principle calculations reveal that octahedral tilts can modify the sign of the Dzyaloshinskii-Moriya interaction and avoid cancellation in a symmetric trilayer structure. Secondly, freestanding flakes of the trilayer can also retain the Hall-humps across a wide temperature range. The behavior of humps’ peak field does not vary with field direction rotation away from the surface normal, consistent with a micromagnetic simulation result of Néel-type magnetic Skyrmions. The layer-resolved octahedral tilts in the freestanding heterostructures also crucially control the occurrence of Hall-humps, consistent with the insight from the epitaxial ones. This work offers a new perspective to understanding Hall-humps in perovskite oxides, as well as demonstrates the fabrication of oxide heterostructure membranes with high interfacial quality. © 2022, CC BY.

关键词： Hall effect

Lecture Notes in Computer Science (including subseries Lectu...

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Preface

Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2022年 13214 LNCS卷 v页

作者： Panda, Dhabaleswar K. Sullivan, Michael Department of Computer Science and Engineering The Ohio State University ColumbusOH United States Material Science and Chemistry A*STAR Institute of High Performance Computing Singapore Singapore

Intensity correlations in decoy-state BB84 quantum key distribution systems

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arXiv 2024年

作者： Trefilov, Daniil Sixto, Xoel Zapatero, Víctor Huang, Anqi Curty, Marcos Makarov, Vadim Vigo Quantum Communication Center University of Vigo VigoE-36310 Spain School of Telecommunication Engineering Department of Signal Theory and Communications University of Vigo VigoE-36310 Spain atlanTTic Research Center University of Vigo VigoE-36310 Spain Russian Quantum Center Skolkovo Moscow121205 Russia National Research University Higher School of Economics Moscow101000 Russia Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow119049 Russia

The decoy-state method is a prominent approach to enhance the performance of quantum key distribution (QKD) systems that operate with weak coherent laser sources. Due to the limited transmissivity of single photons in optical fiber, current experimental decoy-state QKD setups increase their secret key rate by raising the repetition rate of the transmitter. However, this usually leads to correlations between subsequent optical pulses. This phenomenon leaks information about the encoding settings, including the intensities of the generated signals, which invalidates a basic premise of decoy-state QKD. Here we characterize intensity correlations between the emitted optical pulses in two industrial prototypes of decoy-state BB84 QKD systems and show that they significantly reduce the asymptotic key rate. In contrast to what has been conjectured, we experimentally confirm that the impact of higher-order correlations on the intensity of the generated signals can be much higher than that of nearest-neighbour correlations. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum cryptography

Diff-UNet: A diffusion embedded network for robust 3D medical image segmentation

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Medical Image Analysis 2025年

作者： Zhaohu Xing Liang Wan Huazhu Fu Guang Yang Yijun Yang Lequan Yu Baiying Lei Lei Zhu The Hong Kong University of Science and Technology (Guangzhou) PR China The College of Intelligence and Computing Medical College Tianjin University PR China The Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (A*STAR) Singapore The Imperial College London United Kingdom Department of Statistics and Actuarial Science The University of Hong Kong Hong Kong School of Biomedical Engineering Shenzhen University PR China The Department of Electronic and Computer Engineering Hong Kong Special Administrative Region of China

Benefiting from the powerful generative capabilities of diffusion models, recent studies have utilized these models to address 2D medical image segmentation problems. However, directly extending these methods to 3D medical image segmentation slice-by-slice does not yield satisfactory results. The reason is that these approaches often ignore the inter-slice relations of 3D medical data and require significant computational costs. To overcome these challenges, we devise the first diffusion-based model (i.e., Diff-UNet) with two branches for general 3D medical image segmentation. Specifically, we devise an additional boundary-prediction branch to predict the auxiliary boundary information of the target segmentation region, which assists the diffusion-denoising branch in predicting 3D segmentation results. Furthermore, we design a Multi-granularity Boundary Aggregation (MBA) module to embed both low-level and high-level boundary features into the diffusion denoising process. Then, we propose a Monte Carlo Diffusion (MC-Diff) module to generate an uncertainty map and define an uncertainty-guided segmentation loss to improve the segmentation results of uncertain pixels. Moreover, during our diffusion inference stage, we develop a Progressive Uncertainty-driven REfinement (PURE) strategy to fuse intermediate segmentation results at each diffusion inference step. Experimental results on the three latest large-scale datasets (i.e., BraTS2023, SegRap2023, and AIIB2023) with diverse organs and modalities show that our Diff-UNet quantitatively and qualitatively outperforms state-of-the-art 3D medical segmentation methods, especially on regions with small or complex structures. Our code is available at the following link: https://***/ge-xing/DiffUNet .

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SPEECH RECOVERY FOR REAL-WORLD SELF-POWERED INTERMITTENT DEVICES

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arXiv 2021年

作者： Lin, Yu-Chen Hsieh, Tsun-An Hung, Kuo-Hsuan Yu, Cheng Garudadri, Harinath Tsao, Yu Kuo, Tei-Wei Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan NTU High Performance and Scientific Computing Center National Taiwan University Taipei Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan Department of Computer Science City University of Hong Kong Hong Kong Qualcomm Institute University of California San Diego United States

The incompleteness of speech inputs severely degrades the performance of all the related speech signal processing applications. Although many researches have been proposed to address this issue, they controlled the data missing conditions by simulation with self-defined masking lengths or sizes. Besides, the masking definitions are different among all these experimental settings. This paper presents a novel intermittent speech recovery (ISR) system for real-world self-powered intermittent devices. Three contributive stages: interpolation, enhancement, and combination are applied to the ISR system for speech reconstruction. The experimental results show that our recovery system increases speech quality by up to 591.7%, while increasing speech intelligibility by up to 80.5%. Most importantly, the proposed ISR system improves the WER scores by up to 52.6%. The promising results not only confirm the effectiveness of the reconstruction but also encourage the utilization of these battery-free wearable/IoT devices. Copyright © 2021, The Authors. All rights reserved.

关键词： Energy harvesting

Quantum generative training using rényi divergences

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arXiv 2021年

作者： Kieferová, Mária Marrero, Carlos Ortiz Wiebe, Nathan Centre for Quantum Computation and Communication Technology Centre for Quantum Software and Information University of Technology Sydney Data Sciences & Analytics Group Department of Electrical & Computer Engineering Pacific Northwest National Laboratory North Carolina State University Department of Computer Science Department of Physics High Performance Computing Group University of Toronto University of Washington Pacific Northwest National Laboratory

Quantum neural networks (QNNs) are a framework for creating quantum algorithms that promises to combine the speedups of quantum computation with the widespread successes of machine learning. A major challenge in QNN development is a concentration of measure phenomenon known as a barren plateau that leads to exponentially small gradients for a range of QNNs models. In this work, we examine the assumptions that give rise to barren plateaus and show that an unbounded loss function can circumvent the existing no-go results. We propose a training algorithm that minimizes the maximal Rényi divergence of order two and present techniques for gradient computation. We compute the closed form of the gradients for Unitary QNNs and Quantum Boltzmann Machines and provide sufficient conditions for the absence of barren plateaus in these models. We demonstrate our approach in two use cases: thermal state learning and Hamiltonian learning. In our numerical experiments, we observed rapid convergence of our training loss function and frequently archived a 99% average fidelity in fewer than 100 epochs. © 2021, CC BY.

关键词： Quantum computers

Surgical video workflow analysis via visual-language learning

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npj Health Systems

npj Health Systems 2025年第1期2卷 1-12页

作者： Pengpeng Li Xiangbo Shu Jinhui Tang Chun-Mei Feng Yifei Feng Wangmeng Zuo School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (A*STAR) Singapore Republic of Singapore The First School of Clinical Medicine Nanjing Medical University Nanjing China Department of General Surgery The First Affiliated Hospital of Nanjing Medical University Nanjing China Faculty of Computing Harbin Institute of Technology Harbin China

Surgical video workflow analysis has made intensive development in computer-assisted surgery by combining deep learning models, aiming to enhance surgical scene analysis and decision-making. However, previous research has primarily focused on coarse-grained analysis of surgical videos, e.g., phase recognition, instrument recognition, and triplet recognition that only considers relationships within surgical triplets. In order to provide a more comprehensive fine-grained analysis of surgical videos, this work focuses on accurately identifying triplets from surgical videos. Specifically, we propose a vision-language deep learning framework that incorporates intra- and inter- triplet modeling, termed I2TM, to explore the relationships among triplets and leverage the model understanding of the entire surgical process, thereby enhancing the accuracy and robustness of recognition. Besides, we also develop a new surgical triplet semantic enhancer (TSE) to establish semantic relationships, both intra- and inter-triplets, across visual and textual modalities. Extensive experimental results on surgical video benchmark datasets demonstrate that our approach can capture finer semantics, achieve effective surgical video understanding and analysis, with potential for widespread medical applications.

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Security boundaries of an optical power limiter for protecting quantum key distribution systems

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arXiv 2023年

作者： Peng, Qingquan Gao, Binwu Zaitsev, Konstantin Wang, Dongyang Ding, Jiangfang Liu, Yingwen Liao, Qin Guo, Ying Huang, Anqi Wu, Junjie Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China School of Automation Central South University Changsha410083 China China Greatwall Research Institute China Greatwall Technology Group Co. Ltd. Shenzhen518057 China Russian Quantum Center Skolkovo Moscow121205 Russia NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow119049 Russia Vigo Quantum Communication Center University of Vigo Vigo Spain Escuela de Ingeniería de Telecomunicación Department of Signal Theory and Communications University of Vigo Vigo Spain atlanTTic Research Center University of Vigo Vigo Spain College of Computer Science and Electronic Engineering Hunan University Changsha410082 China School of Computer Science Beijing University of Posts and Telecommunications Beijing100876 China

Unauthorized light injection has always been a vital threat to the practical security of a quantum key distribution (QKD) system. An optical power limiter (OPL) based on the thermo-optical defocusing effect has been proposed and implemented, limiting the injected hacking light. As a hardware countermeasure, the performance of the OPL under various light-injection attacks shall be tested to clarify the security boundary before being widely deployed. To investigate the OPL’s security boundary in quantum cryptography, we comprehensively test and analyse the behavior of OPL under continuous-wave (c.w.) light-injection attacks and pulse illumination attacks with pulses’ repetition rate at 0.5-Hz, 40-MHz, and 1-GHz. The testing results illuminate the security boundary of the OPL, which allows one to properly employ the OPL in the use cases. The methodology of testing and analysis proposed here is applicable to other power-limitation components in a QKD system. Copyright © 2023, The Authors. All rights reserved.

关键词： Pulse repetition rate