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SSRN 2023年

作者： Nong, Qiong Zhang, Xuan Jin, Zhi Gao, Chen Shang, WeiYi Du, KunPeng Ding, ZiShuo Ma, YuBin School of Software Yunnan University Yunnan 650091 China Yunnan Key Laboratory of Software Engineering Yunnan 650091 China School of Computer Science Peking University Beijing100871 China Peking University Beijing100871 China School of Information Science and Engineering Yunnan University Yunnan 650091 China Department of Electrical and Computer Waterloo University OntarioN2L 3G1 Canada School of Electromechanical Information Yiwu Industrial & Commercial College 322000 China

Abstractive summarization models compress the content of the source text to generate smooth, concise versions, however, the models often produce factual errors that are inconsistent. To solve this problem, existing studies generally improved the factual consistency by sacrificing information. This kind of approach is considered to be contrary to the original purpose of the summarization task. In this paper, we investigate how to preserve the factual consistency of summaries while retaining the important information. We propose GCSum, a model that combines two types of external knowledge and graph contrastive learning to generate summaries. We emphasize the critical information and correct facts of positive samples through graph contrastive learning, and enhance contrastive learning by using reference summaries and negative samples constructed based on factual word lists. Our model can sufficiently learn critical and correct knowledge and enhance the judgment of incorrect knowledge, thus better solving the issues of insufficient information and factual inconsistency. Moreover, our model fuses semantic information with contextual information to ensure semantic consistency of summaries by strengthening the textual representation. We conducted experiments using two different semantic graphs on four benchmark datasets. Comprehensive experiments show that GCSum can significantly boost the informativeness of summaries while maintaining the factual consistency when comparing the existing baseline models. © 2023, The Authors. All rights reserved.

关键词： Semantics

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Modeling the performance of early fault-tolerant quantum algorithms

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Physical Review Research 2024年第2期6卷 023118-023118页

作者： Qiyao Liang Yiqing Zhou Archismita Dalal Peter Johnson Zapata AI Boston Massachusetts 02110 USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Laboratory of Atomic and Solid State Physics Cornell University Ithaca New York 14853 USA Zapata Computing Canada Inc. Toronto Ontario M5V 2Y1 Canada

Progress in fault-tolerant quantum computation (FTQC) has driven the pursuit of practical applications with early fault-tolerant quantum computers (EFTQC). These devices, limited in their qubit counts and fault-tolerance capabilities, require algorithms that can accommodate some degrees of error, which are known as EFTQC algorithms. To predict the onset of early quantum advantage, a comprehensive methodology is needed to develop and analyze EFTQC algorithms, drawing insights from both the methodologies of noisy intermediate-scale quantum and traditional FTQC. To address this need, we propose such a methodology for modeling algorithm performance on EFTQC devices under varying degrees of error. As a case study, we apply our methodology to analyze the performance of randomized Fourier estimation (RFE) [Kshirsagar, Katabarwa, and Johnson, arXiv:2209.11322], an EFTQC algorithm for phase estimation. We investigate the runtime performance and the fault-tolerant overhead of RFE in comparison to the traditional quantum phase estimation algorithm. Our analysis reveals that RFE achieves significant savings in physical qubit counts while having a much higher runtime upper bound. We anticipate even greater physical qubit savings when considering more realistic assumptions about the performance of EFTQC devices. By providing insights into the performance trade-offs and resource requirements of EFTQC algorithms, our work contributes to the development of practical and efficient quantum computing solutions on the path to quantum advantage.

关键词： Quantum algorithms & computation Quantum computation Quantum error correction Quantum information processing

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Simple and High-Precision Hamiltonian Simulation by Compensating Trotter Error with Linear Combination of Unitary Operations

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PRX Quantum 2025年第1期6卷 010359-010359页

作者： Pei Zeng Jinzhao Sun Liang Jiang Qi Zhao Pritzker School of Molecular Engineering The University of Chicago Illinois 60637 USA Clarendon Laboratory University of Oxford Parks Road Oxford OX1 3PU United Kingdom Blackett Laboratory Imperial College London London SW7 2AZ United Kingdom QICI Quantum Information and Computation Initiative Department of Computer Science School of Computing and Data Science The University of Hong Kong Pokfulam Road Hong Kong

Trotter and linear combination of unitary (LCU) operations are two popular Hamiltonian simulation methods. The Trotter method is easy to implement and enjoys good system-size dependence endowed by commutator scaling, while the LCU method admits high-accuracy simulation with a smaller gate cost. We propose Hamiltonian simulation algorithms using LCU to compensate Trotter error, which enjoy both of their advantages. By adding few gates after the Kth-order Trotter formula, we realize a better time scaling than 2Kth-order Trotter. Our first algorithm exponentially improves the accuracy scaling of the Kth-order Trotter formula. For a generic Hamiltonian, the estimated gate counts of the first algorithm can be 2 orders of magnitude smaller than the best analytical bound of fourth-order Trotter formula. In the second algorithm, we consider the detailed structure of Hamiltonians and construct LCU for Trotter errors with commutator scaling. Consequently, for lattice Hamiltonians, the algorithm enjoys almost linear system-size dependence and quadratically improves the accuracy of the Kth-order Trotter. For the lattice system, the second algorithm can achieve 3 to 4 orders of magnitude higher accuracy with the same gate costs as the optimal Trotter algorithm. These algorithms provide an easy-to-implement approach to achieve a low-cost and high-precision Hamiltonian simulation.

关键词： Size distribution

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Development of Localized Cutaneous Force Feedback System for Robotics Assisted Surgery Systems

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Procedia computer science 2024年 246卷 1160-1169页

作者： Aaron Raymond See Thad Jacob Tiong Lanz Benedict De Guzman Kenneth Contee Garfield Greg Lim Carl Sebastian Yebes Department of Electrical Engineering Southern Taiwan University of Science and Technology No. 1 Nantai St Yongkang District Tainan City 71005 Taiwan Department of Electronic Engineering National Chin-Yi University of Technology No. 57 Section 2 Zhongshan Rd Taiping District Taichung City 411030 Taiwan Department of Electrical Engineering Universitas Katolik Indonesia Atma Jaya Jl. Jend. Sudirman No.51 5 RT.004/RW.4 Karet Semanggi Kecamatan Setiabudi Kota Jakarta Selatan Daerah Khusus Jakarta 12930 Indonesia School of Computer Studies University of San Jose - Recoletos Basak Pardo Cebu City 6000 Philippines College of Computer Studies Cebu Institute of Technology - University Natalio B. Bacalso Ave Cebu City 6000 Cebu Philippines

This study explores integrating a novel cutaneous force feedback system for robotic-assisted surgery (RAS) systems aimed at improving tactile perception for surgical precision and safety. The system developed comprises two major components: a force-sensitive matrix sensor and a 4x4 tactile pin interface. Forces experienced by the sensors are mirrored through the tactile interface, conveying the sensation of knowing which part of the sensor is in contact and how much force is subjected to it. An algorithm called Dynamic Interval Localization (DIL) was developed to manipulate pin actuation by scheduling individualized dynamic oscillation. The system successfully detected operating forces from 0.1N to 5N while concurrently providing respective cutaneous feedback. The system’s performance in force localization suggests significant potential and promises in elevating the haptic features of RAS systems.

关键词： robotic-assisted Surgery (RAS) haptic feedback force localization force matrix sensor dynamic interval localiztion

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Data Tables and Best Practices for Post-2000 Nonlinear-Optical Materials and Measurements

Data Tables and Best Practices for Post-2000 Nonlinear-Optic...

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2023 Optica Nonlinear Optics Topical Meeting, NLO 2023

作者： Vermeulen, Nathalie Espinosa, Daniel Ball, Adam Ballato, John Boucaud, Philippe Boudebs, Georges Campos, Cecília L.A.V. Dragic, Peter Gomes, Anderson S.L. Huttunen, Mikko J. Kinsey, Nathaniel Mildren, Rich Neshev, Dragomir Padilha, Lázaro Pu, Minhao Secondo, Ray Tokunaga, Eiji Turchinovich, Dmitry Yan, Jingshi Yvind, Kresten Dolgaleva, Ksenia Van Stryland, Eric W. IR-TONA Vrije Universiteit Brussel Pleinlaan 2 Brussel1050 Belgium School of Electrical Engineering and Computer Science University of Ottawa OttawaONK1N 6N5 Canada Dept. Of Electrical & Computer Engineering Virginia Commonwealth University RichmondVA23284 United States Dept of Materials Science and Engineering Clemson University ClemsonSC29634 United States Université Côte d’Azur CNRS CRHEA Rue Bernard Grégory Sophia-Antipolis06905 France Univ Angers LPHIA SFR MATRIX AngersF-49000 France Departamento de Física Universidade Federal de Pernambuco Recife-PE50670-901 Brazil Dept. Of Electrical & Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Tampere University Photonics Laboratory Physics Unit TampereFI-33014 Finland MQ Photonics Research Centre Macquarie University NSW2109 Australia Department of Electronic Materials Engineering Research School of Physics Australian National University CanberraACT2601 Australia Instituto de Fisica "Gleb Wataghin" Universidade Estadual de Campinas Campinas Sao Paulo13083-970 Brazil Department of Electrical and Photonics engineering Technical University of Denmark Kgs. LyngbyDK-2800 Denmark Department of Physics Faculty of Science Tokyo University of Science 1-3 Kagurazaka Shinjuku-ku Tokyo162-8601 Japan Fakultät für Physik Universität Bielefeld Universitätsstr. 25 Bielefeld33615 Germany CREOL The College of Optics and Photonics University of Central Florida OrlandoFL United States

ISBN: (纸本)9781957171234

On the occasion of 60 years of nonlinear-optical research, we present new data tables listing nonlinear-optical properties for different material categories as reported in the literature since 2000, and provide best practices for performing experiments. Optica Nonlinear Optics Topical Meeting 2023 © Optica Publishing Group 2023 © 2023 The Author(s)

关键词： Nonlinear optics

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Continual Learning for Robust Gate Detection under Dynamic Lighting in Autonomous Drone Racing

Continual Learning for Robust Gate Detection under Dynamic L...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zhongzheng Qiao Xuan Huy Pham Savitha Ramasamy Xudong Jiang Erdal Kayacan Andriy Sarabakha ERI@N Interdisciplinary Graduate Programme Nanyang Technological University Singapore Department of Electrical and Computer Engineering Aarhus University Aarhus Denmark Institute of Infocomm Research (I2R) Agency for Science Technology and Research (A*STAR) Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore Automatic Control Group (RAT) Paderborn University Paderborn Germany

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In autonomous and mobile robotics, a principal challenge is resilient real-time environmental perception, particularly in situations characterized by unknown and dynamic elements, as exemplified in the context of autonomous drone racing. This study introduces a perception technique for detecting drone racing gates under illumination variations, which is common during high-speed drone flights. The proposed technique relies upon a lightweight neural network backbone augmented with capabilities for continual learning. The envisaged approach amalgamates predictions of the gates' positional coordinates, distance, and orientation, encapsulating them into a cohesive pose tuple. A comprehensive number of tests serve to underscore the efficacy of this approach in confronting diverse and challenging scenarios, specifically those involving variable lighting conditions. The proposed methodology exhibits notable robustness in the face of illumination variations, thereby substantiating its effectiveness.

关键词： Continuing education Robot kinematics Neural networks Lighting Logic gates Robustness Real-time systems

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Statistical Analysis of Intrinsic High-Frequency Characteristic Fluctuation of Emerging Silicon Gate-All-Around Nanosheet (NS) MOSFETs at Sub-3-nm Nodes

Statistical Analysis of Intrinsic High-Frequency Characteris...

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IEEE Electron Devices Technology and Manufacturing Conference (EDTM)

作者： Sekhar Reddy Kola Yiming Li Min-Hui Chuang Kazuhiko Endo Seiji Samukawa Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Institute of Communications Engineering Department of Electronics and Electrical Engineering Institute of Biomedical Engineering Center for mm Wave Smart Radar System and Technologies National Yang Ming Chiao Tung University Hsinchu Taiwan Institute of Fluid Science Tohoku University 2-1-1 Katahira Aoba-ku Sendai Japan National Institute of Advanced Industrial Science and Technology Tsukuba Japan

In recent years, the GAA NS Si MOSFET has been explored as a leading technology. However, the intrinsic parameters of GAA NS Si MOSFETs are affected to varying degrees by various fluctuation sources, Statistically independent and identically distributed $(iid)$ assumptions on the aforementioned random variables overestimate the variability of high-frequency characteristics, compared with considering all fluctuation factors simultaneously. Notably, the random nanosized metal grains dominates the variations of voltage gain, cut-off frequency, and 3dB frequency because the random work functions strongly alter the channel surface potential.

关键词： MOSFET Fluctuations Statistical analysis Gallium arsenide Cutoff frequency Metals Logic gates

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User Fairness in Energy Harvesting-Based LoRa Networks with Imperfect SF Orthogonality

User Fairness in Energy Harvesting-Based LoRa Networks with ...

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作者： Benkhelifa, Fatma Qin, Zhijin McCann, Julie A. Computing Department Imperial College London LondonSW7 2AZ United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom

Long range (LoRa) demonstrates high potential in supporting massive Internet-of-Things (IoT) applications. In this paper, we study the resource allocation in energy harvesting (EH)-enabled LoRa networks with imperfect spreading factor (SF) orthogonality. We maximize the user fairness in terms of the minimum time-averaged throughput while jointly optimizing the SF assignment, the EH time duration, and the transmit power of all LoRa users. First, we provide a general expression of the packet collision time between LoRa users which depends on the SFs and EH duration requirements of each user. Then, we develop two SF allocation schemes that either assure fairness or not for the LoRa users. Within this, we optimize the EH time and the power allocation for single and multiple uplink transmission attempts. For the single uplink transmission attempt, the optimal power allocation is obtained using bisection method. For the multiple uplink transmission attempts, the suboptimal power allocation is derived using concave-convex procedure (CCCP). Our results unearth new findings. Firstly, we demonstrate that the unfair SF allocation algorithm outperforms the others in terms of the minimum data rate. Additionally, we observe that co-SF interference is the main limitation in the throughput performance, and not really energy scarcity. © 1972-2012 IEEE.

关键词： Energy harvesting

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A discriminative channel diversification network for image classification

arXiv

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arXiv 2021年

作者： Patel, Krushi Wang, Guanghui Department of Electrical Engineering and Computer Science University of Kansas LawrenceKS66045 United States Department of Computer Science Ryerson University TorontoONM5B 2K3 Canada

Channel attention mechanisms in convolutional neural networks have been proven to be effective in various computer vision tasks. However, the performance improvement comes with additional model complexity and computation cost. In this paper, we propose a light-weight and effective attention module, called channel diversification block, to enhance the global context by establishing the channel relationship at the global level. Unlike other channel attention mechanisms, the proposed module focuses on the most discriminative features by giving more attention to the spatially distinguishable channels while taking account of the channel activation. Different from other attention models that plugin the module in between several intermediate layers, the proposed module is embedded at the end of the backbone networks, making it easy to implement. Extensive experiments on CIFAR-10, SVHN, and Tiny-ImageNet datasets demonstrate that the proposed module improves the performance of the baseline networks by a margin of 3% on average. Copyright © 2021, The Authors. All rights reserved.

关键词： Image classification

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Multiple Classifiers Based Adversarial Training for Unsupervised Domain Adaptation

arXiv

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arXiv 2021年

作者： Yang, Yiju Kim, Taejoon Wang, Guanghui Department of Electrical Engineering & Computer Science University of Kansas LawrenceKS66045 United States Department of Computer Science Ryerson University TorontoONM5B 2K3 Canada

Adversarial training based on the maximum classifier discrepancy between two classifier structures has achieved great success in unsupervised domain adaptation tasks for image classification. The approach adopts the structure of two classifiers, though simple and intuitive, the learned classification boundary may not well represent the data property in the new domain. In this paper, we propose to extend the structure to multiple classifiers to further boost its performance. To this end, we develop a very straightforward approach to adding more classifiers. We employ the principle that the classifiers are different from each other to construct a discrepancy loss function for multiple classifiers. The proposed construction method of loss function makes it possible to add any number of classifiers to the original framework. The proposed approach is validated through extensive experimental evaluations. We demonstrate that, on average, adopting the structure of three classifiers normally yields the best performance as a trade-off between accuracy and efficiency. With minimum extra computational costs, the proposed approach can significantly improve the performance of the original algorithm. The source code of the proposed approach can be downloaded from https://***/rucv/MMCD-DA. © 2021, CC BY.

关键词： Image classification

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