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arXiv 2024年

作者： An, Dong Childs, Andrew M. Lin, Lin Ying, Lexing Beijing International Center for Mathematical Research Peking University China Joint Center for Quantum Information and Computer Science University of Maryland College Park United States Department of Computer Science Institute for Advanced Computer Studies University of Maryland College Park United States Department of Mathematics University of California Berkeley United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory Berkeley United States Challenge Institute for Quantum Computation University of California Berkeley United States Department of Mathematics Stanford University United States Institute for Computational and Mathematical Engineering Stanford University United States

Eigenvalue transformations, which include solving time-dependent differential equations as a special case, have a wide range of applications in scientific and engineering computation. While quantum algorithms for singular value transformations are well studied, eigenvalue transformations are distinct, especially for non-normal matrices. We propose an efficient quantum algorithm for performing a class of eigenvalue transformations that can be expressed as a certain type of matrix Laplace transformation. This allows us to significantly extend the recently developed linear combination of Hamiltonian simulation (LCHS) method [An, Liu, Lin, Phys. Rev. Lett. 131, 150603, 2023;An, Childs, Lin, arXiv:2312.03916] to represent a wider class of eigenvalue transformations, such as powers of the matrix inverse, A-k, and the exponential of the matrix inverse, e-A-1. The latter can be interpreted as the solution of a mass-matrix differential equation of the form Au′(t) = -u(t). We demonstrate that our eigenvalue transformation approach can solve this problem without explicitly inverting A, reducing the computational complexity. Copyright © 2024, The Authors. All rights reserved.

关键词： Hamiltonians

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CT perfusion parameter estimation in stroke using neural network with transformer and physical model priors

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computers in Biology and Medicine 2024年 182卷 109134-109134页

作者： Luo, Luyao Liu, Pan Ye, Wanxing Chen, Fengwei Liu, Yu Liu, Ziyang Jing, Jing Xiong, Yunyun Zhu, Wanlin Jiang, Yong Cheng, Jian Wang, Yongjun Liu, Tao Beijing Advanced Innovation Center for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing China Medical Big Data Research Center Chinese PLA General Hospital Beijing China China National Clinical Research Center for Neurological Diseases Beijing China Department of Neurology Beijing Tiantan Hospital Capital Medical University Beijing China School of Computer Science and Engineering Beihang University Beijing China

Objectives: CT perfusion (CTP) imaging is vital in treating acute ischemic stroke by identifying salvageable tissue and the infarcted core. CTP images allow quantitative estimation of CT perfusion parameters, which can provide information on the degree of tissue hypoperfusion and its salvage potential. Traditional methods for estimating perfusion parameters, such as singular value decomposition (SVD) and its variations, are known to be sensitive to noise and inaccuracies in the arterial input function. To our knowledge, there has been no implementation of deep learning methods for CT perfusion parameter estimation. Materials & methods: In this work, we propose a deep learning method based on the Transformer model, named CTPerformer-Net, for CT perfusion parameter estimation. In addition, our method incorporates some physical priors. We integrate physical consistency prior, smoothness prior and the physical model prior through the design of the loss function. We also generate a simulation dataset based on physical model prior for training the network model. Results: In the simulation dataset, CTPerformer-Net exhibits a 23.4 % increase in correlation coefficients, a 95.2 % decrease in system error, and a 90.7 % reduction in random error when contrasted with block-circulant SVD. CTPerformer-Net successfully identifies hypoperfused and infarcted lesions in 103 real CTP images from the ISLES 2018 challenge dataset. It achieves a mean dice score of 0.36 for the infarct core segmentation, which is slightly higher than the commercially available software (dice coefficient: 0.34) used as a reference level by the challenge. Conclusion: Experimental results on the simulation dataset demonstrate that CTPerformer-Net achieves better performance compared to block-circulant SVD. The real-world patient dataset confirms the validity of CTPerformer-Net. © 2024 Elsevier Ltd

关键词： Image segmentation

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Efficient Visual Recognition:A Survey on Recent Advances and Brain-inspired Methodologies

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Machine Intelligence research 2022年第5期19卷 366-411页

作者： Yang Wu Ding-Heng Wang Xiao-Tong Lu Fan Yang Man Yao Wei-Sheng Dong Jian-Bo Shi Guo-Qi Li Applied Research Center Laboratory Tencent Platform and Content Group Shenzhen 518057China School of Automation Science and Engineering Faculty of Electronic and Information EngineeringXi′an Jiaotong UniversityXian710049China School of Artificial Intelligence Xidian UniversityXi'an 710071China Division of Information Science Nara Institute of Science and TechnologyNara 6300192Japan Peng Cheng Laboratory Shenzhen 518000China Department of Computer and Information Science University of PennsylvaniaPhiladelphia PA19104-6389USA Institute of Automation Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100190China

Visual recognition is currently one of the most important and active research areas in computer vision,pattern recognition,and even the general field of artificial *** has great fundamental importance and strong industrial needs,particularly the modern deep neural networks(DNNs)and some brain-inspired methodologies,have largely boosted the recognition performance on many concrete tasks,with the help of large amounts of training data and new powerful computation *** recognition accuracy is usually the first concern for new progresses,efficiency is actually rather important and sometimes critical for both academic research and industrial ***,insightful views on the opportunities and challenges of efficiency are also highly required for the entire *** general surveys on the efficiency issue have been done from various perspectives,as far as we are aware,scarcely any of them focused on visual recognition systematically,and thus it is unclear which progresses are applicable to it and what else should be *** this survey,we present the review of recent advances with our suggestions on the new possible directions towards improving the efficiency of DNN-related and brain-inspired visual recognition approaches,including efficient network compression and dynamic brain-inspired *** investigate not only from the model but also from the data point of view(which is not the case in existing surveys)and focus on four typical data types(images,video,points,and events).This survey attempts to provide a systematic summary via a comprehensive survey that can serve as a valuable reference and inspire both researchers and practitioners working on visual recognition problems.

关键词： Visual recognition deep neural networks(DNNS) brain-inspired methodologies network compression dynamic inference survey

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Comparison of Temporal Decorrelation Decay Functions Over Land Cover Types for L- and C-vand SAR

Comparison of Temporal Decorrelation Decay Functions Over La...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yukio Endo Yu Morishita Ryu Sugimoto Ryo Natsuaki Masanobu Shimada Chiaki Tsutsumi Toru Kouyama Ryosuke Nakamura Digital Architecture Research Center National Institute of Advanced Industrial Science and Technology Tokyo Japan Department of Electrical Engineering and Information Systems The University of Tokyo Tokyo Japan Division of Architectural Civil and Environmental Engineering School of Science and Engineering Tokyo Denki University Saitama Japan

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

In repeat-pass interferometric synthetic aperture radar (In-SAR), the coherence loss derived from temporal decorrelation can be modeled using an exponential decay function. Many researches have investigated the relationships between decay parameters over various land-cover types for C-band SAR satellites. Although ALOS PALSAR provides many L-band SAR images, its revisit time was insufficient for understanding the rapid exponential drop in temporal decorrelation. Due to the lack of an appropriate archived dataset, analyzing the decay parameters for L-band SAR was a challenging task. In this study, we processed four-year interferometric pairs derived from ALOS-2 PALSAR-2 full-aperture ScanSAR images. This dataset has a 14 day minimum temporal interval. We selected Nairobi, Kenya, in the middle of the East African rift, as the study area and investigated how decay functions differ over multiple land-cover types. In addition, we compared decay models for Sentinel-1 interferograms. This study demonstrates that the decay parameters of L-band coherence completely differs across forest types, whereas C-band coherence is low coherence regardless of forest type.

关键词： Grasslands C-band Vegetation mapping Sentinel-1 Coherence Forestry Radar polarimetry

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Implementation and Evaluation of 0.6 µm CMOS Complex RCPF for Accurate Frequency Response Measurement

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and Systems 2025年第1期145卷 25-37页

作者： Sato, Lina Fukagawa, Kanta Shouno, Kazuhiro Tanimoto, Hiroshi Muto, Cosy Moriyama, Seijiro Takahashi, Chikau Yoshino, Michitaka Graduate School of Science and Technology Degree Programs in Systems and Information Engineering Master’s Program in Computer Science University of Tsukuba Japan Division of Information Engineering University of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan Kitami Institute of Technology 165 Koen-cho Hokkaido Kitami090-8507 Japan Faculty of Education Nagasaki University 1-14 Bunkyo-machi Nagasaki852-8521 Japan Anagix Corporation 3-22-27 Numama Kanagawa Zushi249-0004 Japan Takamori Corporation Japan Research Center for Micro-Nano Technology Hosei University 3-11-15 Midori-cho Koganei Tokyo184-0003 Japan

This paper describes the implementation and evaluation of an RC polyphase filter (RCPF) and circuitry for measuring its frequency characteristics. The integrated circuit is fabricated on a 0.6 µm CMOS process and its supply voltage is 5 V. The integrated circuit includes a filter under test (FUT), a four-phase square wave generator, several analog switches and differential amplifiers to amplify the high-impedance signals of the RCPF. This FUT is a third-order complex RCPF whose passband is 8 − 40 MHz. This paper describes the design of these circuits in detail. This integrated circuit can select two measurement methods by means of external control signals. The first method uses a four-phase square wave. The frequency characteristics are measured by using an on-chip four-phase square wave generator driven by an external clock generator and by a spectrum analyzer tuned to the fundamental frequency. The second method uses an external network analyzer. Furthermore, this integrated circuit is equipped with a calibration circuit to measure the frequency characteristics of the circuitry except FUT by passing through the input / output signals of the FUT. As a result, the frequency characteristics of the FUT itself can be accurately measured. The effectiveness is confirmed through evaluation of the fabricated integrated circuit. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Differential amplifiers

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Auto-MatRegressor材料性能自动预测器:解放材料机器学习"调参师"

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science Bulletin 2023年第12期68卷 1259-1270,M0004页

作者：刘悦王双燕杨正伟 Maxim Avdeev 施思齐 School of Computer Engineering and Science Shanghai UniversityShanghai 200444China Shanghai Engineering Research Center of Intelligent Computing System Shanghai 200444China State Key Laboratory of Advanced Special Steel School of Materials Science and EngineeringShanghai UniversityShanghai 200444China Materials Genome Institute Shanghai UniversityShanghai 200444China Zhejiang Laboratory Hangzhou 311100China Australian Nuclear Science and Technology Organisation Sydney 2232Australia School of Chemistry The University of SydneySydney 2006Australia

机器学习因其能够快速、精准拟合数据的潜在模式而被广泛应用于材料构效关系研究。然而,材料科学家往往需要进行繁琐的模型选择及参数寻优才能构建出高精度预测模型,为了解放材料机器学习"调参师",本文研发了基于元学习的材... 详细信息

机器学习因其能够快速、精准拟合数据的潜在模式而被广泛应用于材料构效关系研究。然而,材料科学家往往需要进行繁琐的模型选择及参数寻优才能构建出高精度预测模型,为了解放材料机器学习"调参师",本文研发了基于元学习的材料性能自动预测器,采集了60份文献公开数据集与60份标准数据集,基于此训练18种常用回归算法并获得其预测性能,定义与计算了27个刻画数据集特点的元特征,以此构建了一份蕴含建模经验的元数据集;同时,创建了表征数据集所属材料类型的类别树,将其嵌入基于距离的元学习算法,进一步耦合贝叶斯优化算法,实现领域知识和元数据协同驱动下的自动算法推荐和模型参数确定,实验结果表明,材料科学家仅需为新材料性能预测任务提供数据集,便可利用该预测器高效地构建具有与文献报道相当或更高预测精度的机器学习模型.

关键词：机器学习贝叶斯优化算法预测器材料科学家元数据参数寻优预测性能材料性能

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Effects of Flexible Conjugation-Break Spacers of Non-Conjugated Polymer Acceptors on Photovoltaic and Mechanical Properties of All-Polymer Solar Cells

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Nano-Micro Letters 2022年第10期14卷 164-177页

作者： Qiaonan Chen Yung Hee Han Leandro R.Franco Cleber F.N.Marchiori Zewdneh Genene CMoyses Araujo Jin-Woo Lee Tan Ngoc-Lan Phan Jingnan Wu Donghong Yu Dong Jun Kim Taek-Soo Kim Lintao Hou Bumjoon J.Kim Ergang Wang Siyuan Laboratory Guangzhou Key Laboratory of Vacuum Coating Technologies and New Energy MaterialsDepartment of PhysicsJinan UniversityGuangzhou 510632People’s Republic of China Department of Chemistry and Chemical Engineering Chalmers University of TechnologySE-41296GöteborgSweden Department of Chemical and Biomolecular Engineering Korea Advanced Institute of Science and Technology(KAIST)Daejeon 34141Republic of Korea Department of Engineering and Physics Karlstad University65188 KarlstadSweden Department of Chemistry and Bioscience Aalborg University9220 AalborgDenmark Department of Mechanical Engineering Korea Advanced Institute of Science and Technology(KAIST)Daejeon 34141Republic of Korea Materials Theory Division Department of Physics and AstronomyUppsala University75120 UppsalaSweden Sino-Danish Center for Education and Research 8000 AarhusDenmark School of Materials Science and Engineering Zhengzhou UniversityZhengzhou 450001People’s Republic of China

All-polymer solar cells(all-PSCs)possess attractive merits including superior thermal stability and mechanical flexibility for large-area roll-to-roll *** flexible conjugation-break spacers(FCBSs)into backbones of polymer donor(P_(D))or polymer acceptor(P_(A))has been demonstrated as an efficient approach to enhance both the photovoltaic(PV)and mechanical properties of the ***,length dependency of FCBS on certain all-PSC related properties has not been systematically *** this regard,we report a series of new non-conjugated P_(A)s by incorporating FCBS with various lengths(2,4,and 8 carbon atoms in thioalkyl segments).Unlike com-mon studies on so-called side-chain engineering,where longer side chains would lead to better solubility of those resulting polymers,in this work,we observe that the solubilities and the resulting photovoltaic/mechanical properties are optimized by a proper FCBS length(i.e.,C2)in P_(A) named *** all-PSC achieves a high efficiency of 11.37%,and excellent mechanical robustness with a crack onset strain of 12.39%,significantly superior to those of the other P_(A)*** results firstly demonstrate the effects of FCBS lengths on the PV performance and mechanical properties of the all-PSCs,providing an effective strategy to fine-tune the structures of P_(A)s for highly efficient and mechanically robust PSCs.

关键词： All-polymer solar cells Flexible conjugation-break spacers Mechanical robustness Polymer acceptors Stretchability

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A 0.9–1.4-GHz Q-Enhanced CMOS Tunable Bandpass Filter With Constant Fractional Bandwidth and Two Transmission Zeros

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IEEE Microwave and Wireless Technology Letters 2023年第6期33卷 667-670页

作者： Kun Li Xiang Li Shuangyang Wu Pei-Ling Chi Tao Yang School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu China Research Center of Advanced RF Chips and Systems Nanhu Laboratory Jiaxing China Department of Electrical and Computer Engineering National Chiao Tung University Hsinchu Taiwan

A tunable differential $Q$ -enhanced bandpass filter covering 0.9–1.4 GHz with constant fractional bandwidth (FBW) is proposed using 40-nm CMOS technology in this letter. The proposed filter is constructed by two resonators which consist of ON-chip integrated inductors and varactors. The inductors of the two resonators are placed close to each other to form the mainline magnetic coupling of the filter. Besides the mainline coupling, the source port and load port are connected directly by capacitors, forming the source-to-load coupling. With the proposed structure, two transmission zeros (TZs) can be obtained, which can greatly increase the stopband rejection. Meanwhile, nMOS cross-coupled pairs are used as the $Q$ -enhanced cells to reduce the insertion loss (IL) and further improve the filter selectivity. A filter prototype based on 40-nm CMOS process is fabricated to validate the proposed structure. The center frequency (CF) of the proposed filter can be tuned from 0.9 to 1.4 GHz while keeping the FBW constant at 8%.

关键词： Resonator filters Band-pass filters Microwave filters Filtering theory Couplings Equivalent circuits Magnetic separation

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Decoupling semantic similarity from spatial alignment for neural networks 24

Decoupling semantic similarity from spatial alignment for ne...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Tassilo Wald Constantin Ulrich Gregor Köhler David Zimmerer Stefan Denner Michael Baumgartner Fabian Isensee Priyank Jaini Klaus H. Maier-Hein Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany and Helmholtz Imaging DKFZ Heidelberg Germany and Faculty of Mathematics and Computer Science University of Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany and Medical Faculty Heidelberg University of Heidelberg Germany and National Center for Tumor Diseases (NCT) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany and Faculty of Mathematics and Computer Science University of Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany and Helmholtz Imaging DKFZ Heidelberg Germany Google Deepmind Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany and Helmholtz Imaging DKFZ Heidelberg Germany and Faculty of Mathematics and Computer Science University of Heidelberg Germany and Medical Faculty Heidelberg University of Heidelberg Germany and Pattern Analysis and Learning Group Department of Radiation Oncology

ISBN: (纸本)9798331314385

What representation do deep neural networks learn? How similar are images to each other for neural networks? Despite the overwhelming success of deep learning methods key questions about their internal workings still remain largely unanswered, due to their internal high dimensionality and complexity. To address this, one approach is to measure the similarity of activation responses to various inputs. Representational Similarity Matrices (RSMs) distill this similarity into scalar values for each input pair. These matrices encapsulate the entire similarity structure of a system, indicating which input leads to similar responses. While the similarity between images is ambiguous, we argue that the spatial location of semantic objects does neither influence human perception nor deep learning classifiers. Thus this should be reflected in the definition of similarity between image responses for computer vision systems. Revisiting the established similarity calculations for RSMs we expose their sensitivity to spatial alignment. In this paper, we propose to solve this through semantic RSMs, which are invariant to spatial permutation. We measure semantic similarity between input responses by formulating it as a set-matching problem. Further, we quantify the superiority of semantic RSMs over spatio-semantic RSMs through image retrieval and by comparing the similarity between representations to the similarity between predicted class probabilities.

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An MCS Navigation System Based on Road Surface Quality for Bicycle Riders

An MCS Navigation System Based on Road Surface Quality for B...

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International Conference on Smart Computing (SMARTCOMP)

作者： Federico Montori Rocco Pastore Luca Sciullo Luciano Bononi Luca Bedogni Department of Computer Science and Engineering University of Bologna Italy Advanced Research Center on Electronic Systems “Ercole De Castro” University of Bologna Italy University of Modena and Reggio Emilia Italy

ISBN: (数字)9798350349948

ISBN: (纸本)9798350349955

Road surface quality is a major concern for bicycle riders and plays an important role in the mobility infrastructure. In the era where smarter cities aim to increase the well-being of citizens and the efficiency of infrastructures, navigation systems relying on Mobile Crowdsensing (MCS) are mostly designed for car drivers, and account for road traffic conditions. To cover the gap, in this paper, we propose a full architectural pipeline of an MCS-based navigation system for bicycle riders that accounts for the road surface quality. The MCS paradigm leverages the sensor data produced by the personal devices of participating citizens to describe phenomena of common interest. Our system classifies road segments using inertial sensor data gathered by users, using a combination of supervised and unsupervised methods, as human labeling in this context is impractical and too subjective. We prove the efficacy of our method in a controlled environment, and then we implement and deploy the full system in a real city, finally reporting on its results.

关键词： Navigation Roads Urban areas Pipelines Bicycles Road traffic Planning

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