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A novel quaternion optimization model for imagined speech classification

Journal of the Franklin Institute 2025年第11期362卷

作者： Zheng, Xiao-Ben Ling, Bingo Wing-Kuen Xu, Nuo Chen, Jian-Rong Zheng, Song-Yi School of Computer Science and Engineering Guangdong Ocean University Yangjiang 529500 China Center for Integrated Circuits and Artificial Intelligence Tsientang Institute for Advanced Study Zhejiang 310024 China School of Information Engineering Guangdong University of Technology Guangzhou 510006 China School of Continuing Education Guangdong Jiangmen Chinese Medical College Jiangmen 529000 China Network and Educational Technology Centre Jinan University Guangzhou Guangdong 510632 China

Imagined speech, characterized by its extensive vocabulary and diverse applications, is a highly promising paradigm in BCI. Multi-channel EEGs are utilized for imagined speech classification. While low rank and sparse representation (LRSR) models have proven useful for EEG processing, they overlook the inter-channel correlations, leading to potential information loss. To address this issue, this paper proposes a quaternion valued LRSR (QLRSR) model for processing the EEGs. Quaternion algebra is a hypercomplex number system extending complex numbers with one real component and three orthogonal imaginary units(i,j,k). Since quaternion can excavate the potential connection of multi-dimensional signals, it is adopted in our QLRSR algorithm for processing muti-channel EEGs. The proposed QLRSR model for imagined speech classification involves two key steps. First, it models sampling points in four-channel EEGs as quaternion-valued numbers, exploiting cross-channel correlations during quaternion operations. Second, the model formulates the QLRSR problem as a quaternion-valued optimization problem. To validate the model's effectiveness, two public datasets are used: Track 3 of the BCI Competition 2020 and the Kara One dataset. By finding the solution of the quaternion valued optimization problem, it is found that the average classification accuracies of our proposed QLRSR model are 64.05 % and 75.46 %, which is higher than the existing models including low rank, sparse, LRSR-based model. Also, the required computational time for performing the classification of the imagined speech via our proposed QLRSR model is 5.12-17.33 % lower than of the aforementioned models. Since QLRSR integrates quaternion algebra into the LRSR-based method, which is more suitable for processing muti-channel signal than LRSR-based method. Hence, our proposed QLRSR model is efficient. Our work in the QLRSR is helpful for the researchers who work on finding a mathematical solution of the quaternion o

关键词： cross correlation Electroencephalogram Imagined speech classification Optimization Quaternion valued low rank and sparse representation

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Prediction of Alzheimer's disease using hybrid machine learning technique

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AIP Conference Proceedings 2023年 020091页

作者： M. Senthil Kumar H. Azath A. K. Velmurugan K. Padmanaban Murugan Subbiah Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science And Technology Chennai Tamil Nadu India School of Computing Science and Engineering VIT Bhopal University India Computer Science and Engineering Koneru Lakshmaiah Education Foundation Green Field Vaddeswaram Guntur Dist. Vijayawada Andhra Pradesh-522502 India Vee Eee Technologies Chennai Tamil Nadu India

Alzheimer's infection (AD) is a neurodegenerative illness and the most widely recognized reason for Dementia in more seasoned grown-ups. The piece of cerebrum that gets influenced by this illness is hippocampus degeneration. The Discovery of Alzheimer's sickness at the fundamental stage is vital as it can forestall genuine harm to the patient's mind. It becomes hazardous and here and there lethal in the event of individuals 65 years old or above. To fulfil the expectation, CNN calculations were used. The suggested technique has obtained a 20 percent improvement in characterisation accuracy, which is contrasted with conventional procedures, and suggests that neural organism is a tremendous advantage for neurological disease analysis. Due to our study, the analysis of different neurological diseases using intelligent medical services frameworks may be done by comparable or comparative *** principal objective of this paper is to utilize Hybrid AI calculations that coordinate SVM with CNN and component extraction and determination to anticipate Alzheimer's sickness and fabricate a helpful model. The dataset is taken as MRI pictures. The proposed approach identifies the different phases of Alzheimer's infection, for example, moderate-unbalanced and no sick utilizing CNN calculation.

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Regret and cumulative constraint violation analysis for online convex optimization with long term constraints

arXiv

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arXiv 2021年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Chai, Tianyou Johansson, Karl H. School of Electrical Engineering and Computer Science and Digital Futures KTH Royal Institute of Technology Stockholm Sweden Department of Control Science and Engineering College of Electronics and Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems Institute for Advanced Study Tongji University Shanghai China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore

This paper considers online convex optimization with long term constraints, where constraints can be violated in intermediate rounds, but need to be satisfied in the long run. The cumulative constraint violation is used as the metric to measure constraint violations, which excludes the situation that strictly feasible constraints can compensate the effects of violated constraints. A novel algorithm is first proposed and it achieves an O(Tmax{c,1-c}) bound for static regret and an O(T (1-c)/2) bound for cumulative constraint violation, where c ϵ (0, 1) is a user-defined trade-off parameter, and thus has improved performance compared with existing results. Both static regret and cumulative constraint violation bounds are reduced to O(log(T )) when the loss functions are strongly convex, which also improves existing results. In order to achieve the optimal regret with respect to any comparator sequence, another algorithm is then proposed and it achieves the optimal O( p T (1 + PT )) regret and an O(√T) cumulative constraint violation, where PT is the path-length of the comparator sequence. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results. Copyright © 2021, The Authors. All rights reserved.

关键词： Comparators (optical)

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Theory of Pressure-Induced Rejuvenation and Strain Hardening in Metallic Glasses

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Physical Review Letters 2021年第2期126卷 025502-025502页

作者： Anh D. Phan Alessio Zaccone Vu D. Lam Katsunori Wakabayashi Faculty of Materials Science and Engineering Computer Science Artificial Intelligence Laboratory Phenikaa Institute for Advanced Study Phenikaa University Hanoi 12116 Vietnam Department of Nanotechnology for Sustainable Energy School of Science and Technology Kwansei Gakuin University Sanda Hyogo 669-1337 Japan Department of Physics “A. Pontremoli” University of Milan via Celoria 16 20133 Milano Italy Cavendish Laboratory University of Cambridge JJ Thomson Avenue CB3 0HE Cambridge United Kingdom Department of Chemical Engineering and Biotechnology Statistical Physics Group University of Cambridge Philippa Fawcett Drive CB3 0AS Cambridge United Kingdom Institute of Materials Science Vietnam Academy of Science and Technology Hanoi 100000 Vietnam Graduate University of Science and Technology Vietnam Academy of Science and Technology Hanoi 100000 Vietnam

We theoretically investigate high-pressure effects on the atomic dynamics of metallic glasses. The theory predicts compression-induced rejuvenation and the resulting strain hardening that have been recently observed in metallic glasses. Structural relaxation under pressure is mainly governed by local cage dynamics. The external pressure restricts the dynamical constraints and slows down the atomic mobility. In addition, the compression induces a rejuvenated metastable state (local minimum) at a higher energy in the free-energy landscape. Thus, compressed metallic glasses can rejuvenate and the corresponding relaxation is reversible. This behavior leads to strain hardening in mechanical deformation experiments. Theoretical predictions agree well with experiments.

关键词： Plastic deformation Pressure effects Strain Glassy systems Metallic glasses Metals

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Laser Micro/Nano-Manufacturing

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engineering 2025年

作者： Minghui Hong Lianwei Chen Tun Cao Pen-Tung Sah Institute of Micro-Nano Science and Technology Xiamen University Xiamen 361005 China Department of Electrical and Computer Engineering National University of Singapore Singapore 117576 Singapore National Key Laboratory of Optical Field Manipulation Science and Technology Chinese Academy of Sciences Chengdu 610209 China State Key Laboratory of Optical Technologies on Nano-Fabrication and Micro-Engineering Institute of Optics and Electronics Chinese Academy of Sciences Chengdu 610209 China School of Optoelectronic Engineering and Instrumentation Science Dalian University of Technology Dalian 116024 China

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A pretrained transformer model for decoding individual glucose dynamics from continuous glucose monitoring data

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National science Review 2025年第5期 362-381页

作者： Yurun Lu Dan Liu Zhongming Liang Rui Liu Pei Chen Yitong Liu Jiachen Li Zhanying Feng Lei M.Li Bin Sheng Weiping Jia Luonan Chen Huating Li Yong Wang Center for Excellence in Mathematical Sciences National Center for Mathematics and Interdisciplinary Sciences Hua Loo-Keng Center for Mathematical Sciences Key Laboratory of Management Decision and Information System Academy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematics University of Chinese Academy of SciencesChinese Academy of Sciences Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of Sciences BGI-Research School of Mathematics South China University of Technology Department of Statistics Department of Biomedical Data Science Bio-X ProgramStanford University Department of Computer Science and Engineering Shanghai Jiao Tong University State Key Laboratory of Cell Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Guangdong Institute of Intelligence Science and Technology Pazhou Laboratory (Huangpu)

Continuous glucose monitoring(CGM) technology has grown rapidly to track real-time blood glucose levels and trends with improved sensor accuracy. The ease of use and wide availability of CGM will facilitate safe and effective decision making for diabetes management. Here, we developed an attention-based deep learning model, CGMformer, pretrained on a well-controlled and diverse corpus of CGM data to represent individual's intrinsic metabolic state and enable clinical applications. During pretraining, CGMformer encodes glucose dynamics including glucose level, fluctuation, hyperglycemia, and hypoglycemia into latent space with self-supervised learning. It shows generalizability in imputing glucose value across five external datasets with different populations and metabolic states(MAE = 3.7 mg/d L). We then fine-tuned CGMformer towards a diverse panel of downstream tasks in the screening of diabetes and its complications using task-specific data, which demonstrated a consistently boosted predictive accuracy over direct fine-tuning on a single task(AUROC = 0.914 for type 2 diabetes(T2D) screening and 0.741 for complication screening). By learning an intrinsic representation of an individual's glucose dynamics,CGMformer classifies non-diabetic individuals into six clusters with elevated T2D risks, and identifies a specific cluster with lean body-shape but high risk of glucose metabolism disorders, which is overlooked by traditional glucose measurements. Furthermore, CGMformer achieves high accuracy in predicting an individual's postprandial glucose response with dietary modelling(Pearson correlation coefficient = 0.763)and helps personalized dietary recommendations. Overall, CGMformer pretrains a transformer neural network architecture to learn an intrinsic representation by borrowing information from a large amount of daily glucose profiles, and demonstrates predictive capabilities fine-tuned towards a broad range of downstream applications, holding promise for the ear

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Adaptive optics two-photon microscopy for in vivo imaging of mouse retina 6

Adaptive optics two-photon microscopy for in vivo imaging of...

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Adaptive Optics and Wavefront Control for Biological Systems VI 2020

作者： Qin, Zhongya He, Sicong Chen, Congping Yang, Chao Yung, Jasmine Wu, Wanjie Leung, Christopher K. Liu, Kai Qu, Jianan Y. Biophotonics Research Laboratory Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Center of Systems Biology and Human Health School of Science and Institute for Advanced Study Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Division of Life Science Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Department of Ophthalmology and Visual Sciences Chinese University of Hong Kong Kowloon Hong Kong

ISBN: (纸本)9781510632592

Non-invasive retinal imaging has greatly facilitated the research of eye disease and neurodegenerative disorders in the central nervous system (CNS). Two-photon microscopy is a powerful tool for in vivo imaging of mouse retina because it provides intrinsic optical sectioning capability and the infrared laser is less likely to excite the photoreceptors. However, the dilated mouse eye has large optical aberrations, which must be corrected to achieve high-resolution or even diffraction-limited imaging. Here, we developed an adaptive optics (AO) two-photon microscope for in vivo imaging of retinal neurons through the eyeball of living mouse. We used the two-photon excited fluorescence signal of retina as the guide star to measure and correct the aberration of mouse eye. After AO correction, the fluorescence signal was increased by at least fivefold and the fine structures such as axons of retinal ganglion cells (RGC) were clearly resolved. To take advantage of the non-invasive high-resolution imaging, we demonstrated functional calcium imaging of RGC responding to the light stimulations. © 2020 SPIE.

关键词： Adaptive optics

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Coexistence of Interacting Charge Density Waves in a Layered Semiconductor

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Physical Review Letters 2024年第20期132卷 206401-206401页

作者： B. Q. Lv Alfred Zong Dong Wu Zhengwei Nie Yifan Su Dongsung Choi Batyr Ilyas Bryan T. Fichera Jiarui Li Edoardo Baldini Masataka Mogi Y.-B. Huang Hoi Chun Po Sheng Meng Yao Wang N. L. Wang Nuh Gedik Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai 200240 China Massachusetts Institute of Technology Department of Physics Cambridge Massachusetts 02139 USA School of Physics and Astronomy Shanghai Jiao Tong University Shanghai 200240 China Zhangjiang Institute for Advanced Study Shanghai Jiao Tong University Shanghai 200240 China University of California at Berkeley Department of Chemistry Berkeley California 94720 USA Beijing Academy of Quantum Information Sciences Beijing 100913 China Beijing National Laboratory for Condensed Matter Physics and Institute of Physics Chinese Academy of Sciences Beijing 100190 China Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science Cambridge Massachusetts 02139 USA SLAC National Accelerator Laboratory Menlo Park California 94025 USA Department of Applied Physics Stanford University Stanford California 94305 USA Shanghai Synchrotron Radiation Facility Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai 201204 China Department of Physics Hong Kong University of Science and Technology Clear Water Bay Hong Kong China Department of Chemistry Emory University Atlanta Georgia 30322 USA International Center for Quantum Materials School of Physics Peking University Beijing 100871 China

Coexisting orders are key features of strongly correlated materials and underlie many intriguing phenomena from unconventional superconductivity to topological orders. Here, we report the coexistence of two interacting charge-density-wave (CDW) orders in EuTe4, a layered crystal that has drawn considerable attention owing to its anomalous thermal hysteresis and a semiconducting CDW state despite the absence of perfect Fermi surface nesting. By accessing unoccupied conduction bands with time- and angle-resolved photoemission measurements, we find that monolayers and bilayers of Te in the unit cell host different CDWs that are associated with distinct energy gaps. The two gaps display dichotomous evolutions following photoexcitation, where the larger bilayer CDW gap exhibits less renormalization and faster recovery. Surprisingly, the CDW in the Te monolayer displays an additional momentum-dependent gap renormalization that cannot be captured by density-functional theory calculations. This phenomenon is attributed to interlayer interactions between the two CDW orders, which account for the semiconducting nature of the equilibrium state. Our findings not only offer microscopic insights into the correlated ground state of EuTe4 but also provide a general nonequilibrium approach to understand coexisting, layer-dependent orders in a complex system.

关键词： Charge density waves Electronic structure Layered crystals Layered semiconductors Angle-resolved photoemission spectroscopy Density functional theory Time & angle resolved photoemission spectroscopy

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Roles of scattered and ballistic photons in imaging through scattering media: a deep learning-based study

arXiv

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arXiv 2022年

作者： Zhang, Xuyu Gao, Jingjing Gan, Yu Song, Chunyuan Zhang, Dawei Zhuang, Songlin Han, Shensheng Lai, Puxiang Liu, Honglin School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Key Laboratory for Quantum Optics Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai201800 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Science Beijing100049 China Hangzhou Institute for Advanced study University of Chinese Academy of Sciences Hangzhou310024 China Department of Biomedical Engineering The Hong Kong Polytechnic University Hong Kong Hong Kong Polytechnic University Shenzhen Research Institute Shenzhen518000 China Photonics Research Institute The Hong Kong Polytechnic University Hong Kong

Scattering of light in complex media scrambles optical wavefronts and breaks the principles of conventional imaging methods. For decades, researchers have endeavored to conquer the problem by inventing approaches such as adaptive optics, iterative wavefront shaping, and transmission matrix measurement. That said, imaging through/into thick scattering media remains challenging to date. With the rapid development of computing power, deep learning has been introduced and shown potentials to reconstruct target information through complex media or from rough surfaces. But it also fails once coming to optically thick media where ballistic photons become negligible. Here, instead of treating deep learning only as an image extraction method, whose best-selling advantage is to avoid complicate physical models, we exploit it as a tool to explore the underlying physical principles. By adjusting the weights of ballistic and scattered photons through a random phasemask, it is found that although deep learning can extract images from both scattered and ballistic light, the mechanisms are different: scattering may function as an encryption key and decryption from scattered light is key sensitive, while extraction from ballistic light is stable. Based on this finding, it is hypothesized and experimentally confirmed that the foundation of the generalization capability of trained neural networks for different diffusers can trace back to the contribution of ballistic photons, even though their weights of photon counting in detection are not that significant. Moreover, the study may pave an avenue for using deep learning as a probe in exploring the unknown physical principles in various fields. Copyright © 2022, The Authors. All rights reserved.

关键词： Iterative methods

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Scientific discovery in the age of artificial intelligence

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NATURE 2023年第7978期621卷 E33-E33页

作者： Wang, Hanchen Fu, Tianfan Du, Yuanqi Gao, Wenhao Huang, Kexin Liu, Ziming Chandak, Payal Liu, Shengchao Van Katwyk, Peter Deac, Andreea Anandkumar, Anima Bergen, Karianne Gomes, Carla P. Ho, Shirley Kohli, Pushmeet Lasenby, Joan Leskovec, Jure Liu, Tie-Yan Manrai, Arjun Marks, Debora Ramsundar, Bharath Song, Le Sun, Jimeng Tang, Jian Velickovic, Petar Welling, Max Zhang, Linfeng Coley, Connor W. Bengio, Yoshua Zitnik, Marinka Department of Engineering University of Cambridge Cambridge UK Department of Computing and Mathematical Sciences California Institute of Technology Pasadena CA USA NVIDIA Santa Clara CA USA Department of Computational Science and Engineering Georgia Institute of Technology Atlanta GA USA Department of Computer Science Cornell University Ithaca NY USA Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Computer Science Stanford University Stanford CA USA Department of Physics Massachusetts Institute of Technology Cambridge MA USA Harvard-MIT Program in Health Sciences and Technology Cambridge MA USA Mila – Quebec AI Institute Montreal Quebec Canada Université de Montréal Montreal Quebec Canada HEC Montréal Montreal Quebec Canada CIFAR AI Chair Toronto Ontario Canada Department of Earth Environmental and Planetary Sciences Brown University Providence RI USA Data Science Institute Brown University Providence RI USA Center for Computational Astrophysics Flatiron Institute New York NY USA Department of Astrophysical Sciences Princeton University Princeton NJ USA Department of Physics Carnegie Mellon University Pittsburgh PA USA Department of Physics and Center for Data Science New York University New York NY USA Google DeepMind London UK Department of Computer Science and Technology University of Cambridge Cambridge UK Microsoft Research Beijing China Department of Biomedical Informatics Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Harvard Data Science Initiative Cambridge MA USA Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University Cambridge MA USA Department of Systems Biology Harvard Medical School Boston MA USA Deep Forest Sciences Palo Alto CA USA BioMap Beijing China Mo

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