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Soft computing 2023年第2期28卷 693-693页

作者： Addanke, Supriya Anandan, R. Krishna, Parimala Venkata Sathish, Kuppani Department of Computer Science & Engineering VELS Institute of Science Technology & Advanced Studies (VISTAS) Chennai India Department of Computing Science Sri Padmavati Mahila University Tirupati India Department of Computer Science and Engineering Tirumala Engineering College Narasaropet Guntur District India

Automatic Classification of Strain in the Singing Voice Using Machine Learning

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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

知识引导下生成式模型协同合作与联合进化——后大模型时代的发展蓝图（英文）

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Engineering 2025年第1期 87-100页

作者：吴飞沈弢 Thomas B?ck 陈静远黄刚金耀初况琨李孟择卢策吾苗嘉旭王永威魏颖吴帆严骏驰杨红霞杨易张圣宇赵洲庄越挺潘云鹤 College of Computer Science and Technology Zhejiang University Leiden Institute of Advanced Computer Science Leiden University College of Electrical Engineering Zhejiang University School of Engineering Westlake University Department of Computer Science and Engineering Hong Kong University of Science and Technology Department of Computer Science and Engineering Shanghai Jiao Tong University School of Cyber Science and Technology Sun Yat-Sen University Department of Computing The Hong Kong Polytechnic University

Large language models(LLMs) have significantly advanced artificial intelligence(AI) by excelling in tasks such as understanding, generation, and reasoning across multiple modalities. Despite these achievements, LLMs have inherent limitations including outdated information, hallucinations, inefficiency, lack of interpretability, and challenges in domain-specific accuracy. To address these issues, this survey explores three promising directions in the post-LLM era: knowledge empowerment, model collaboration, and model co-evolution. First, we examine methods of integrating external knowledge into LLMs to enhance factual accuracy, reasoning capabilities, and interpretability, including incorporating knowledge into training objectives, instruction tuning, retrieval-augmented inference, and knowledge prompting. Second, we discuss model collaboration strategies that leverage the complementary strengths of LLMs and smaller models to improve efficiency and domain-specific performance through techniques such as model merging, functional model collaboration, and knowledge injection. Third, we delve into model co-evolution, in which multiple models collaboratively evolve by sharing knowledge, parameters, and learning strategies to adapt to dynamic environments and tasks, thereby enhancing their adaptability and continual learning. We illustrate how the integration of these techniques advances AI capabilities in science, engineering, and society—particularly in hypothesis development, problem formulation, problem-solving, and interpretability across various domains. We conclude by outlining future pathways for further advancement and applications.

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Mutual Information Maximizing Quantum Generative Adversarial Network and Its Applications in Finance

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arXiv 2023年

作者： Lee, Mingyu Shin, Myeongjin Lee, Junseo Jeong, Kabgyun Department of Computer Science and Engineering Seoul National University Seoul08826 Korea Republic of QDQ Corp. Seoul06999 Korea Republic of School of Computing KAIST Daejeon34141 Korea Republic of School of Electrical and Electronic Engineering Yonsei University Seoul03722 Korea Republic of Quantum Computing R&D Norma Inc. Seoul04799 Korea Republic of Research Institute of Mathematics Seoul National University Seoul08826 Korea Republic of School of Computational Sciences Korea Institute for Advanced Study Seoul02455 Korea Republic of

One of the most promising applications in the era of NISQ (Noisy Intermediate-Scale Quantum) computing is quantum machine learning. Quantum machine learning offers significant quantum advantages over classical machine learning across various domains. Specifically, generative adversarial networks have been recognized for their potential utility in diverse fields such as image generation, finance, and probability distribution modeling. However, these networks necessitate solutions for inherent challenges like mode collapse. In this study, we capitalize on the concept that the estimation of mutual information between high-dimensional continuous random variables can be achieved through gradient descent using neural networks. We introduce a novel approach named InfoQGAN, which employs the Mutual Information Neural Estimator (MINE) within the framework of quantum generative adversarial networks to tackle the mode collapse issue. Furthermore, we elaborate on how this approach can be applied to a financial scenario, specifically addressing the problem of generating portfolio return distributions through dynamic asset allocation. This illustrates the potential practical applicability of InfoQGAN in real-world contexts. © 2023, CC BY.

关键词： Generative adversarial networks

Federated Learning for Violence Incident Prediction in a Simulated Cross-institutional Psychiatric Setting

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arXiv 2022年

作者： Borger, Thomas Mosteiro, Pablo Kaya, Heysem Rijcken, Emil Salah, Albert Ali Scheepers, Floortje Spruit, Marco Department of Information and Computing Sciences Utrecht University Utrecht Netherlands KPMG N.V. Amstelveen Netherlands Jheronimus Academy of Data Science Eindhoven University of Technology 's-Hertogenbosch Netherlands Department of Psychiatry University Medical Centre Utrecht Utrecht Netherlands Department of Public Health & Primary Care Leiden University Medical Centre Leiden Netherlands Department of Computer Engineering Boğaziçi University Istanbul Turkey Leiden Institute of Advanced Computer Science Leiden University Leiden Netherlands

Inpatient violence is a common and severe problem within psychiatry. Knowing who might become violent can influence staffing levels and mitigate severity. Predictive machine learning models can assess each patient's likelihood of becoming violent based on clinical notes. Yet, while machine learning models benefit from having more data, data availability is limited as hospitals typically do not share their data for privacy preservation. Federated Learning (FL) can overcome the problem of data limitation by training models in a decentralised manner, without disclosing data between collaborators. However, although several FL approaches exist, none of these train Natural Language Processing models on clinical notes. In this work, we investigate the application of Federated Learning to clinical Natural Language Processing, applied to the task of Violence Risk Assessment by simulating a cross-institutional psychiatric setting. We train and compare four models: two local models, a federated model and a data-centralised model. Our results indicate that the federated model outperforms the local models and has similar performance as the data-centralised model. These findings suggest that Federated Learning can be used successfully in a cross-institutional setting and is a step towards new applications of Federated Learning based on clinical notes. © 2022, CC BY.

关键词： Natural language processing systems

***: A differential evolution package for the analytic continuation of imaginary time correlation functions

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arXiv 2024年

作者： Neuhaus, James Nichols, Nathan S. Banerjee, Debshikha Cohen-Stead, Benjamin Maier, Thomas A. Del Maestro, Adrian Johnston, Steven Department of Physics and Astronomy The University of Tennessee KnoxvilleTN37996 United States Institute of Advanced Materials and Manufacturing The University of Tennessee KnoxvilleTN37996 United States Argonne Leadership Computing Facility Argonne National Laboratory ArgonneIL60439 United States Min H. Kao Department of Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN37996 United States Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831-6494 United States

We introduce the *** package, a Julia implementation of the Differential Evolution Analytic Continuation (DEAC) algorithm [N. S. Nichols et al., Phys. Rev. E 106, 025312 (2022)] for analytically continuing noisy imaginary time correlation functions to the real frequency axis. Our implementation supports fermionic and bosonic correlation functions on either the imaginary time or Matsubara frequency axes, and treatment of the covariance error in the input data. This paper presents an overview of the DEAC algorithm and the features implemented in the *** package. It also provides detailed benchmarks of the package's output against the popular maximum entropy and stochastic analytic continuation methods. The code for this package can be downloaded from our GitHub repository at https://***/SmoQySuite/*** or installed using the Julia package manager. The online documentation, including examples, can be accessed at https://***/***/stable/. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

Fast quantum algorithm for differential equations

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arXiv 2023年

作者： Bagherimehrab, Mohsen Nakaji, Kouhei Wiebe, Nathan Aspuru-Guzik, Alán Chemical Physics Theory Group Department of Chemistry University of Toronto TorontoON Canada Department of Computer Science University of Toronto TorontoON Canada 1-1-1 Umezono Ibaraki Tsukuba305-8568 Japan Quantum Computing Center Keio University 3-14-1 Hiyoshi Kohoku-ku Kanagawa Yokohama223-8522 Japan Pacific Northwest National Laboratory RichlandWA United States Canadian Institute for Advanced Research TorontoON Canada Vector Institute for Artificial Intelligence TorontoON Canada Department of Chemical Engineering & Applied Chemistry University of Toronto TorontoON Canada Department of Materials Science & Engineering University of Toronto TorontoON Canada

Partial differential equations (PDEs) are ubiquitous in science and engineering. Prior quantum algorithms for solving the system of linear algebraic equations obtained from discretizing a PDE have a computational complexity that scales at least linearly with the condition number κ of the matrices involved in the computation. For many practical applications, κ scales polynomially with the size N of the matrices, rendering a polynomial-in-N complexity for these algorithms. Here we present a quantum algorithm with a complexity that is polylogarithmic in N but is independent of κ for a large class of PDEs. Our algorithm generates a quantum state that enables extracting features of the solution. Central to our methodology is using a wavelet basis as an auxiliary system of coordinates in which the condition number of associated matrices is independent of N by a simple diagonal preconditioner. We present numerical simulations showing the effect of the wavelet preconditioner for several differential equations. Our work could provide a practical way to boost the performance of quantum-simulation algorithms where standard methods are used for discretization. © 2023, CC BY.

关键词： Matrix algebra

Surrogate approximation of the grad-shafranov free boundary problem via stochastic collocation on sparse grids

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arXiv 2021年

作者： Elman, Howard C. Liang, Jiaxing Sánchez-Vizuet, Tonatiuh Department of Computer Science Institute for Advanced Computer Studies University of Maryland College Park United States Applied Mathematics & Statistics and Scientific Computing Program University of Maryland College Park United States Courant Institute of Mathematical Sciences New York University Department of Mathematics The University of Arizona

In magnetic confinement fusion devices, the equilibrium configuration of a plasma is determined by the balance between the hydrostatic pressure in the fluid and the magnetic forces generated by an array of external coils and the plasma itself. The location of the plasma is not known a priori and must be obtained as the solution to a free boundary problem. The partial differential equation that determines the behavior of the combined magnetic field depends on a set of physical parameters (location of the coils, intensity of the electric currents going through them, magnetic permeability, etc.) that are subject to uncertainty and variability. The confinement region is in turn a function of these stochastic parameters as well. In this work, we consider variations on the current intensities running through the external coils as the dominant source of uncertainty. This leads to a parameter space of dimension equal to the number of coils in the reactor. With the aid of a surrogate function built on a sparse grid in parameter space, a Monte Carlo strategy is used to explore the effect that stochasticity in the parameters has on important features of the plasma boundary such as the location of the x-point, the strike points, and shaping attributes such as triangularity and elongation. The use of the surrogate function reduces the time required for the Monte Carlo simulations by factors that range between 7 and over *** Codes 65C30, 65C05, 65N35, 65N99, 65Z05 Copyright © 2021, The Authors. All rights reserved.

关键词： Monte Carlo methods

NTIRE 2023 Quality Assessment of Video Enhancement Challenge

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NTIRE 2023 Quality Assessment of Video Enhancement Challenge

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

High voltage-controlled magnetic anisotropy and interface magnetoelectric effect in sputtered multilayers annealed at high temperatures

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science China(Physics,Mechanics & Astronomy) 2020年第7期63卷 114-119页

作者： LeZhi Wang Xiang Li Taisuke Sasaki Kin Wong GuoQiang Yu ShouZhong Peng Chao Zhao Tadakatsu Ohkubo Kazuhiro Hono WeiSheng Zhao KangLong Wang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing 100191China School of Computer Science&Engineering Beihang UniversityBejing 100191China Department of Electrical Engineering University of CaliforniaLos AngelesLos Angeles 90095USA School of Microeletronics Beihang UniversityBeijing 100191China Inston IncLos Angeles 90095USA National Institute for Materials Science(NIMS) Tsukuba 305-0047Japan Institute of Microelectronics Chinese Academy of SciencesBejing 100029China

Voltage control of magnetism promises great energy efficiency in writing magnetic memory. Here, using Cr/Mo/CoFeB/MgO multilayers stable under high annealing temperatures up to 590°C, we significantly enhance the interfacial crystallinity, thereby the interface-originated perpendicular magnetic anisotropy(PMA), voltage-controlled magnetic anisotropy(VCMA), and interface magnetoelectric(ME) effect. High interfacial PMA of 1.35 mJ/m^2, VCMA coefficient of-138 fJ/(V m), and interface ME coefficient, which is 2-3 orders of magnitude larger than ab initio calculation results are simultaneously achieved after annealing at 500°C. These promising results enabled by the industry-applicable sputtering process will pave the way for highdensity voltage-controlled spintronic devices.

关键词： voltage-controlled magnetic anisotropy(VCMA) magnetoelectric(ME)effect perpendicular magnetic anisotropy