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arXiv 2023年

作者： Konno, Shunya Asavanant, Warit Hanamura, Fumiya Nagayoshi, Hironari Fukui, Kosuke Sakaguchi, Atsushi Ide, Ryuhoh China, Fumihiro Yabuno, Masahiro Miki, Shigehito Terai, Hirotaka Takase, Kan Endo, Mamoru Marek, Petr Filip, Radim van Loock, Peter Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan Advanced ICR Research Institute National Institute of Information and Communications Technology 588-2 Iwaoka Nishi Kobe651-2492 Japan Graduate School of Engineering Kobe University 1-1 Rokko-dai Nada Kobe657-0013 Japan Department of Optics Palacky University 17. listopadu 1192/12 Olomouc77146 Czech Republic Institute of Physics Johannes-Gutenberg University of Mainz Staudingerweg 7 Mainz55128 Germany

A quantum computer with low-error, high-speed quantum operations and capability for interconnections is required for useful quantum computations. A logical qubit called Gottesman-Kitaev-Preskill (GKP) qubit1 in a single Bosonic harmonic oscillator is efficient for mitigating errors in a quantum computer. The particularly intriguing prospect of GKP qubits is that entangling gates as well as syndrome measurements for quantum error correction only require efficient, noise-robust linear operations. To date, however, GKP qubits have been only demonstrated at mechanical and microwave frequency in a highly nonlinear physical system2,3. The physical platform that naturally provides the scalable linear toolbox is optics, including near-ideal loss-free beam splitters and near-unit efficiency homodyne detectors that allow to obtain the complete analog syndrome for optimized quantum error correction4. Additional optical linear amplifiers5,6 and specifically designed GKP qubit states are then all that is needed for universal quantum computing7–9. In this work, we realize a GKP state in propagating light at the telecommunication wavelength and demonstrate homodyne meausurements on the GKP states for the first time without any loss corrections. Our GKP states do not only show non-classicality and non-Gaussianity at room temperature and atmospheric pressure, but unlike the existing schemes with stationary qubits, they are realizable in a propagating wave system. This property permits large-scale quantum computation10–14 and interconnections15,16, with strong compatibility to optical fibers and 5G telecommunication technology17 Copyright © 2023, The Authors. All rights reserved.

关键词： Atmospheric pressure

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Dual-View Pyramid Pooling in Deep Neural Networks for Improved Medical Image Classification and Confidence Calibration

arXiv

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arXiv 2024年

作者： Zhang, Xiaoqing Nie, Qiushi Xiao, Zunjie Zhao, Jilu Wu, Xiao Guo, Pengxin Li, Runzhi Liu, Jin Wei, Yanjie Pan, Yi Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen518055 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Statistics and Actuarial Science The University of Hong Kong 999077 Hong Kong Cooperative Innovation Center of Internet Healthcare Zhengzhou University Zhengzhou450001 China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi830046 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China

Spatial pooling (SP) and cross-channel pooling (CCP) operators have been applied to aggregate spatial features and pixel-wise features from feature maps in deep neural networks (DNNs), respectively. Their main goal is to reduce computation and memory overhead without visibly weakening the performance of DNNs. However, SP often faces the problem of losing the subtle feature representations, while CCP has a high possibility of ignoring salient feature representations, which may lead to both miscalibration of confidence issues and suboptimal medical classification results. To address these problems, we propose a novel dual-view framework, the first to systematically investigate the relative roles of SP and CCP by analyzing the difference between spatial features and pixel-wise features. Based on this framework, we propose a new pooling method, termed dual-view pyramid pooling (DVPP), to aggregate multi-scale dual-view features. DVPP aims to boost both medical image classification and confidence calibration performance by fully leveraging the merits of SP and CCP operators from a dual-axis perspective. Additionally, we discuss how to fulfill DVPP with five parameter-free implementations. Extensive experiments on six 2D/3D medical image classification tasks show that our DVPP surpasses state-of-the-art pooling methods in terms of medical image classification results and confidence calibration across different DNNs. © 2024, CC BY.

关键词： Deep neural networks

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Optically-Sampled Superconducting-Nanostrip Photon-Number Resolving Detector for Non-Classical Quantum State Generation

arXiv

引用

arXiv 2024年

作者： Endo, Mamoru Takahashi, Kazuma Nomura, Takefumi Sonoyama, Tatsuki Yabuno, Masahiro Miki, Shigehito Terai, Hirotaka Kashiwazaki, Takahiro Inoue, Asuka Umeki, Takeshi Nehra, Rajveer Takase, Kan Asavanant, Warit Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-8656 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan Advanced ICT Research Institute National Institute of Information and Communications Technology 588-2 Iwaoka Nishi Kobe651-2492 Japan NTT Device Technology Labs NTT Corporation 3-1 Morinosato Wakamiya Kanagawa Atsugi243-0198 Japan Department of Electrical and Computer Engineering University of Massachusetts Amherst AmherstMA01003 United States Department of Physics University of Massachusetts Amherst AmherstMA01003 United States College of Information and Computer Science University of Massachusetts Amherst AmherstMA01003 United States

Photon number-resolving detectors (PNRDs) are the ultimate optical sensors. Superconducting-nanostrip photon detectors (SNSPDs), traditionally known as ON-OFF detectors, have recently been found to have photon number resolving capability without multiplexing. This discovery positions them to become true PNRDs. However, their practical use is limited by the need to precisely detect tiny signal differences with low signal-to-noise ratios within sub-nanosecond time frames. We overcome this challenge using optical sampling with a dual-output Mach Zehnder modulator (DO-MZM) and ultra-short pulsed laser. By adjusting the DO-MZM’s bias voltage to nearly balance the outputs, this method enables sensitive detection of picosecond-order signal differences, achieving a temporal resolution of 1.9 ps and facilitating real-time photon number resolution. We applied this method to produce various non-classical quantum states, enhancing their non-classicality through photon number resolution. This advancement marks a significant shift from principle verification to practical application for SNSPD-type PNRDs in diverse quantum optics fields. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum optics

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High-rate Generation and State Tomography of Non-Gaussian Quantum States for Ultra-fast Clock Frequency Quantum Processors

arXiv

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arXiv 2024年

作者： Kawasaki, Akito Ide, Ryuhoh Brunel, Hector Suzuki, Takumi Nehra, Rajveer Nakashima, Katsuki Kashiwazaki, Takahiro Inoue, Asuka Umeki, Takeshi China, Fumihiro Yabuno, Masahiro Miki, Shigehito Terai, Hirotaka Yamashima, Taichi Sakaguchi, Atsushi Takase, Kan Endo, Mamoru Asavanant, Warit Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Department of Physics Ecole Normale Supérieure 24 rue Lhomond Paris75005 France Department of Electrical and Computer Engineering University of Massachusetts Amherst AmherstMA01003 United States Department of Physics University of Massachusetts Amherst AmherstMA01003 United States NTT Device Technology Labs NTT Corporation 3-1 Morinosato Wakamiya Kanagawa Atsugi243-0198 Japan Advanced ICR Research Institute National Institute of Information and Communications Technology 588-2 Iwaoka Nishi Kobe651-2492 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan

Quantum information processors greatly benefit from high clock frequency to fully harnessing the quantum advantages before they get washed out by the decoherence. In this pursuit, all-optical systems offer unique advantages due to their inherent 100 THz carrier frequency, permitting one to develop THz clock frequency processors. In practice, the bandwidth of the quantum light sources and the measurement devices has been limited to the MHz range and the generation rate of nonclassical states to kHz order—a tiny fraction of what can be achieved. In this work, we go beyond this limitation by utilizing optical parametric amplifier (OPA) as a squeezed-light source and optical phase-sensitive amplifiers (PSA) to realize high-rate generation of broadband non-Gaussian states and their quantum tomography. Our state generation and measurement system consists of a 6-THz squeezed-light source, a 6-THz PSA, and a 66-GHz homodyne detector. With this system, we have successfully demonstrated non-Gaussian state generation at a 0.9 MHz rate—almost three orders of magnitude higher than the current state-of-the-art experiments—with a sub-nanosecond wave packet using continuous-wave laser. The performance is constrained only by the superconducting detector’s jitter which currently limits the usable bandwidth of the squeezed light to 1 GHz, rather than the optical and electronic systems. Therefore, if we can overcome the limitation of the timing jitter of superconducting detector, non-Gaussian state generation and detection at GHz rate, or even THz rate, for optical quantum processors might be possible with OPAs. Copyright © 2024, The Authors. All rights reserved.

关键词： Optical parametric amplifiers

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Erratum - Modeling for Improved Performance of Ultra-Fast Nonvolatile Toggle Spin Torque MRAM Bit-Cell (SPIN (2022) 12(2):2250013 DOI: 10.1142/S2010324722500138)

引用

SPIN 2022年第3期12卷

作者： Jain, Divyansh Dahiya, Sunil Verma, Gaurav Department of VLSI Design Center for Development of Advanced Computing Punjab Mohali 160071 India Department of Physical Sciences Indian Institute of Science Education and Research Punjab Mohali 140306 India Department of Electronics and Communication Engineering Indian Institute of Technology Uttarakhand Roorkee 247667 India

Incorrect author list: Divyansh Jain, Sunil Dahiya and Gaurav Verma Correct author list: Divyansh Jain and Sunil Dahiya All the co-authors are aware of the updated list of authors, to which the Publisher is not liable to be responsible for any future dispute on the authorship of the paper. © 2022 World Scientific Publishing Co. Pte Ltd. All rights reserved.

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FedVQCS: Federated Learning via Vector Quantized Compressed Sensing

arXiv

引用

arXiv 2022年

作者： Oh, Yongjeong Jeon, Yo-Seb Chen, Mingzhe Saad, Walid The Department of Electrical Engineering POSTECH Gyeongbuk Pohang37673 Korea Republic of The Institute for Convergence Research and Education in Advanced Technology Yonsei University Incheon21983 Korea Republic of The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Wireless@VT Bradley Department of Electrical and Computer Engineering Virginia Tech ArlingtonVA22203 United States

In this paper, a new communication-efficient federated learning (FL) framework is proposed, inspired by vector quantized compressed sensing. The basic strategy of the proposed framework is to compress the local model update at each device by applying dimensionality reduction followed by vector quantization. Subsequently, the global model update is reconstructed at a parameter server by applying a sparse signal recovery algorithm to the aggregation of the compressed local model updates. By harnessing the benefits of both dimensionality reduction and vector quantization, the proposed framework effectively reduces the communication overhead of local update transmissions. Both the design of the vector quantizer and the key parameters for the compression are optimized so as to minimize the reconstruction error of the global model update under the constraint of wireless link capacity. By considering the reconstruction error, the convergence rate of the proposed framework is also analyzed for a non-convex loss function. Simulation results on the MNIST and FEMNIST datasets demonstrate that the proposed framework provides more than a 2.4% increase in classification accuracy compared to state-of-the-art FL frameworks when the communication overhead of the local model update transmission is 0.1 bit per local model entry. Copyright © 2022, The Authors. All rights reserved.

关键词： Vector quantization

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Incremental Value and Interpretability of Radiomics Features of Both Lung and Epicardial Adipose Tissue for Detecting the Severity of COVID-19 Infection

arXiv

引用

arXiv 2023年

作者： Yao, Ni Tian, Yanhui das Neves, Daniel Gama Zhao, Chen Mesquita, Claudio Tinoco de Andrade Martins, Wolney dos Santos, Alair Augusto Sarmet Moreira Damas Li, Yanting Han, Chuang Zhu, Fubao Dai, Neng Zhou, Weihua School of Computer and Communication Engineering Zhengzhou University of Light Industry Henan Zhengzhou China Department of Radiology Universidade Federal Fluminense Rio de Janeiro Brazil DASA Complexo Hospitalar de Niterói Rio de Janeiro Brazil Department of Applied Computing Michigan Technological University HoughtonMI United States Department of Cardiology Universidade Federal Fluminense Rio de Janeiro Brazil Department of Cardiology Zhongshan Hospital Fudan University Shanghai Institute of Cardiovascular Diseases Shanghai China National Clinical Research Center for Interventional Medicine Shanghai China Center for Biocomputing and Digital Health Institute of Computing and Cybersystems Health Research Institute Michigan Technological University HoughtonMI United States

Epicardial adipose tissue (EAT) is known for its pro-inflammatory properties and association with Coronavirus Disease 2019 (COVID-19) severity. However, current EAT segmentation methods do not consider positional information. Additionally, the detection of COVID-19 severity lacks consideration for EAT radiomics features, which limits interpretability. This study investigates the use of radiomics features from EAT and lungs to detect the severity of COVID-19 infections. A retrospective analysis of 515 patients with COVID-19 (Cohort1: 415, Cohort2: 100) was conducted using a proposed three-stage deep learning approach for EAT extraction. Lung segmentation was achieved using a published method. A hybrid model for detecting the severity of COVID-19 was built in a derivation cohort, and its performance and uncertainty were evaluated in internal (125, Cohort1) and external (100, Cohort2) validation cohorts. For EAT extraction, the Dice similarity coefficients (DSC) of the two centers were 0.972 (±0.011) and 0.968 (±0.005), respectively. For severity detection, the hybrid model with radiomics features of both lungs and EAT showed improvements in AUC, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) compared to the model with only lung radiomics features. The hybrid model exhibited an increase of 0.1 (p © 2023, CC BY.

关键词： COVID-19

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Automatic Classification of Strain in the Singing Voice Using Machine Learning

引用

Journal of Voice 2025年

作者： Liu, Yuanyuan Reddy, Mittapalle Kiran Yagnavajjula, Madhu Keerthana Räsänen, Okko Alku, Paavo Ikävalko, Tero Hakanpää, Tua Öyry, Aleksi Laukkanen, Anne-Maria Speech and Voice Research Laboratory Tampere University Tampere 33100 Finland Department of Computer Science and Engineering Indian Institute of Information Technology Raichur Karanataka Raichur 584135 India Advanced Technology Development Centre Indian Institute of Technology Kharagpur West Bengal Kharagpur 721302 India Unit of Computing Sciences Tampere University Tampere 33720 Finland Department of Information and Communications Engineering Aalto University Espoo 02150 Finland Faculty of Education University of Turku Turku 20014 Finland Aalto Acoustics Lab Aalto University Espoo 02150 Finland

Objectives: Classifying strain in the singing voice can help protect professional singers from vocal overuse and support singing training. This study investigates whether machine learning can automatically classify singing voices into two levels of perceived strain. The singing samples represent two genres: classical and contemporary commercial music (CCM). Methods: A total of 324 singing voice samples from 15 professional normophonic singers (nine female, six male) were analyzed. Nine singers were classical, and six were CCM singers. The samples consisted of syllable strings produced at three to six pitches and three loudness levels. Based on expert auditory-perceptual ratings, the samples were categorized into two strain levels: normal-mild and moderate-severe. Three acoustic feature sets (mel-frequency cepstral coefficients (MFCCs), the extended Geneva Minimalistic Acoustic Parameter Set (eGeMAPS), and wavelet scattering features) were compared using two classifier models [support vector machine (SVM) and multilayer perceptron (MLP)]. Feature selection was performed using recursive feature elimination, and the Mann-Whitney U test was used to assess the discriminative power of the selected features. Results: The highest classification accuracy of 86.1% was achieved using a subset of wavelet scattering features with the MLP classifier. A comparison between individual features showed that the first MFCC coefficient, representing spectral tilt, exhibited the greatest between-class separation. Conclusion: This study demonstrates that machine learning models utilizing selected acoustic features can classify perceptual strain of singing voices automatically with high accuracy. These preliminary findings highlight the potential for larger studies involving more diverse singer groups across different genres. © 2025 The Authors

关键词： Auditive-perceptual evaluation—Support vector machine—Multiple layer perceptron—Fisher vector—Wavelet scattering coefficients—Mel-frequency cepstral coefficients

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Privacy-Preserving Federated Foundation Model for Generalist Ultrasound Artificial Intelligence

arXiv

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arXiv 2024年

作者： Jiang, Yuncheng Feng, Chun-Mei Ren, Jinke Wei, Jun Zhang, Zixun Hu, Yiwen Liu, Yunbi Sun, Rui Tang, Xuemei Du, Juan Wan, Xiang Xu, Yong Du, Bo Gao, Xin Wang, Guangyu Zhou, Shaohua Cui, Shuguang Goh, Rick Siow Mong Liu, Yong Li, Zhen Shenzhen518172 China Shenzhen518172 China Institute of High Performance Computing Agency for Science Technology and Research Singapore138632 Singapore College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Research Institute of Big Data Shenzhen518172 China South China Hospital Health Science Center Shenzhen University Shenzhen518111 China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Affiliated Hospital of North Sichuan Medical College Sichuan637000 China North Sichuan Medical College Sichuan637000 China Shenzhen518055 China School of Computer Science Wuhan University Wuhan430072 China Thuwal23955-6900 Saudi Arabia Thuwal23955-6900 Saudi Arabia Beijing University of Posts and Telecommunications Beijing100876 China School of Biomedical Engineering Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

Ultrasound imaging is widely used in clinical diagnosis due to its non-invasive nature and real-time capabilities. However, conventional ultrasound diagnostics face several limitations, including high dependence on physician expertise and suboptimal image quality, which complicates interpretation and increases the likelihood of diagnostic errors. Artificial intelligence (AI) has emerged as a promising solution to enhance clinical diagnosis, particularly in detecting abnormalities across various biomedical imaging modalities. Nonetheless, current AI models for ultrasound imaging face critical challenges. First, these models often require large volumes of labeled medical data, raising concerns over patient privacy breaches. Second, most existing models are task-specific, which restricts their broader clinical utility. To overcome these challenges, we present UltraFedFM, an innovative privacy-preserving ultrasound foundation model. UltraFedFM is collaboratively pre-trained using federated learning across 16 distributed medical institutions in 9 countries, leveraging a dataset of over 1 million ultrasound images covering 19 organs and 10 ultrasound modalities. This extensive and diverse data, combined with a secure training framework, enables UltraFedFM to exhibit strong generalization and diagnostic capabilities. It achieves an average area under the receiver operating characteristic curve (AUROC) of 0.927 for disease diagnosis and a dice similarity coefficient (DSC) of 0.878 for lesion segmentation. Notably, UltraFedFM surpasses the diagnostic accuracy of mid-level ultrasonographers (4–8 years of experience) and matches the performance of expert-level sonographers (10+ years of experience) in the joint diagnosis of 8 common systemic diseases. These findings indicate that UltraFedFM can significantly enhance clinical diagnostics while safeguarding patient privacy, marking a significant advancement in AI-driven ultrasound imaging for future clinical applications. Copyri

关键词： Differential privacy

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NTIRE 2023 Quality Assessment of Video Enhancement Challenge

NTIRE 2023 Quality Assessment of Video Enhancement Challenge

引用

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Liu, Xiaohong Min, Xiongkuo Sun, Wei Zhang, Yulun Zhang, Kai Timofte, Radu Zhai, Guangtao Gao, Yixuan Cao, Yuqin Kou, Tengchuan Dong, Yunlong Jia, Ziheng Li, Yilin Wu, Wei Hu, Shuming Deng, Sibin Xiao, Pengxiang Chen, Ying Li, Kai Zhao, Kai Yuan, Kun Sun, Ming Cong, Heng Wang, Hao Fu, Lingzhi Zhang, Yusheng Zhang, Rongyu Shi, Hang Xu, Qihang Xiao, Longan Ma, Zhiliang Agarla, Mirko Celona, Luigi Rota, Claudio Schettini, Raimondo Huang, Zhiwei Li, Ya'nan Wang, Xiaotao Lei, Lei Liu, Hongye Hong, Wei Chuang, Ironhead Lin, Allen Guan, Drake Chen, Iris Lou, Kae Huang, Willy Tasi, Yachun Kao, Yvonne Fan, Haotian Kong, Fangyuan Zhou, Shiqi Liu, Hao Lai, Yu Chen, Shanshan Wang, Wenqi Wu, Haoning Chen, Chaofeng Zhu, Chunzheng Guo, Zekun Zhao, Shiling Yin, Haibing Wang, Hongkui Meftah, Hanene Brachemi Fezza, Sid Ahmed Hamidouche, Wassim Déforges, Olivier Shi, Tengfei Mansouri, Azadeh Motamednia, Hossein Bakhtiari, Amir Hossein Aznaveh, Ahmad Mahmoudi Shanghai Jiao Tong University China Eth Zürich Switzerland University of Würzburg Germany Department of Tao Technology Alibaba Group China Kuaishou Technology China Interactive Entertainment Group of Netease Inc Guangzhou China Transsion China Hefei University of Technology China Department of Informatics Systems and Communication University of Milano - Bicocca Italy Xiaomi Inc. China China Ji Liang University China FreeTech Advanced Research Center KKCompany Taiwan ByteDance China Mgtv China Fuzhou University China Shopee Information Technology Co. Ltd. S-Lab Nanyang Technological University Singapore Hunan University China Hangzhou Dianzi University China Insa Rennes Cnrs Ietr - Umr 6164 Rennes France National Higher School of Telecommunications and Ict Oran Algeria Buaa Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Cyberspace Research Institute Shahid Beheshti University Tehran Iran

ISBN: (纸本)9798350302493

This paper reports on the NTIRE 2023 Quality Assessment of Video Enhancement Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2023. This challenge is to address a major challenge in the field of video processing, namely, video quality assessment (VQA) for enhanced videos. The challenge uses the VQA Dataset for Perceptual Video Enhancement (VDPVE), which has a total of 1211 enhanced videos, including 600 videos with color, brightness, and contrast enhancements, 310 videos with deblurring, and 301 deshaked videos. The challenge has a total of 167 registered participants. 61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions. A total of 176 submissions were submitted by 37 participating teams during the final testing phase. Finally, 19 participating teams submitted their models and fact sheets, and detailed the methods they used. Some methods have achieved better results than baseline methods, and the winning methods have demonstrated superior prediction performance. © 2023 IEEE.

关键词： Image enhancement

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