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Time-Varying ℓ₀ Optimization for Spike Inference from Multi-Trial Calcium Recordings

Data science in science 2024年第1期3卷

作者： Shen, Tong Du, Mingyu Johnston, Kevin Grieco, Steven F. Crary, Rachel Guzowski, John F. Lur, Gyorgy Xu, Xiangmin Ombao, Hernando Guindani, Michele Yu, Zhaoxia Department of Statistics University of California Irvine 92697 CA United States Center for Complex Biological Systems University of California Irvine 92697 CA United States Department of Mathematics and The NSF-Simons Center for Multiscale Cell Fate Research University of California Irvine 92697 CA United States Department of Anatomy and Neurobiology School of Medicine University of California Irvine 92697 CA United States Department of Neurobiology and Behavior University of California Irvine 92697 CA United States The Center for the Neurobiology of Learning and Memory University of California Irvine 92697 CA United States Department of Biomedical Engineering University of California Irvine 92697 CA United States Department of Computer Science University of California Irvine 92697 CA United States The Center for Neural Circuit Mapping University of California Irvine 92697 CA United States Statistics Program KAUST Thuwal Saudi Arabia Department of Biostatistics University of California Los Angeles CA United States

Optical imaging of genetically encoded calcium indicators is a powerful tool to record the activity of a large number of neurons simultaneously over a long period of time from freely behaving animals. However, determining the exact time at which a neuron spikes and estimating the underlying firing rate from calcium fluorescence data remains challenging, especially for calcium imaging data obtained from a longitudinal study. We propose a multi-trial time-varying ℓ₀ penalized method to jointly detect spikes and estimate firing rates by robustly integrating evolving neural dynamics across trials. Our simulation study shows that the proposed method performs well in both spike detection and firing rate estimation. We demonstrate the usefulness of our method on calcium fluorescence trace data from two studies, with the first study showing differential firing rate functions between two behaviors and the second study showing evolving firing rate functions across trials due to learning. © 2024 The Author(s). Published with license by Taylor & Francis Group, LLC.

关键词： calcium imaging firing rate spike detection Time-varying penalty ℓ0 regularization

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The estrous cycle modulates hippocampal spine dynamics, dendritic processing, and spatial coding

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Neuron 2025年

作者： Wolcott, Nora S. Redman, William T. Karpinska, Marie Jacobs, Emily G. Goard, Michael J. Department of Molecular Cellular and Developmental Biology University of California Santa Barbara Santa Barbara 93106 CA United States Interdepartmental Graduate Program in Dynamical Neuroscience University of California Santa Barbara Santa Barbara 93106 CA United States Intelligent Systems Center Johns Hopkins University Applied Physics Laboratory Laurel 20723 MD United States Department of Computer Science University of California Santa Barbara Santa Barbara 93106 CA United States Department of Psychological & Brain Sciences University of California Santa Barbara Santa Barbara 93106 CA United States Ann S. Bowers Women's Brain Health Initiative University of California Santa Barbara Santa Barbara 93106 CA United States Neuroscience Research Institute University of California Santa Barbara Santa Barbara 93106 CA United States

Histological evidence suggests that the estrous cycle exerts a powerful influence on CA1 neurons in the mammalian hippocampus. Decades have passed since this landmark observation, yet how the estrous cycle shapes dendritic spine dynamics and hippocampal spatial coding in vivo remains a mystery. Here, we used a custom hippocampal microperiscope and two-photon calcium imaging to track CA1 pyramidal neurons in female mice across multiple cycles. Estrous cycle stage had a potent effect on spine dynamics, with spine density peaking during proestrus when estradiol levels are highest. These morphological changes coincided with greater somatodendritic coupling and increased infiltration of back-propagating action potentials into the apical dendrite. Finally, tracking CA1 response properties during navigation revealed greater place field stability during proestrus, evident at both the single-cell and population levels. These findings demonstrate that the estrous cycle drives large-scale structural and functional plasticity in hippocampal neurons essential for learning and memory. © 2025 The Author(s)

关键词： dendritic integration estradiol estrous cycle hippocampus multiphoton imaging neuroendocrine place cells spine morphology

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Preface

Lecture Notes in Networks and Systems

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Lecture Notes in Networks and Systems 2023年 547卷 v-vi页

作者： Thakur, Manoj Agnihotri, Samar Rajpurohit, Bharat Singh Pant, Millie Deep, Kusum Nagar, Atulya K. School of Mathematical and Statistical Sciences Indian Institute of Technology Mandi Himachal Pradesh Mandi India School of Computing and Electrical Engineering Indian Institute of Technology Mandi Himachal Pradesh Mandi India Department of Applied Science and Engineering Indian Institute of Technology Roorkee Uttarakhand Roorkee India Department of Mathematics Indian Institute of Technology Roorkee Uttarakhand Roorkee India School of Mathematics Computer Science and Engineering Liverpool Hope University Liverpool United Kingdom

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Perturbation Analysis of Gradient-based Adversarial Attacks

arXiv

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arXiv 2020年

作者： Ozbulak, Utku Gasparyan, Manvel de Neve, Wesley van Messem, Arnout Department of Electronics and Information Systems Ghent University Belgium Department of Applied Mathematics Computer Science and Statistics Ghent University Belgium Department of Data Analysis and Mathematical Modelling Ghent University Belgium Center for Biotech Data Science Ghent University Global Campus Korea Republic of

After the discovery of adversarial examples and their adverse effects on deep learning models, many studies focused on finding more diverse methods to generate these carefully crafted samples. Although empirical results on the effectiveness of adversarial example generation methods against defense mechanisms are discussed in detail in the literature, an in-depth study of the theoretical properties and the perturbation effectiveness of these adversarial attacks has largely been lacking. In this paper, we investigate the objective functions of three popular methods for adversarial example generation: the L-BFGS attack, the Iterative Fast Gradient Sign attack, and Carlini & Wagner’s attack. Specifically, we perform a comparative and formal analysis of the loss functions underlying the aforementioned attacks while laying out large-scale experimental results on ImageNet dataset. This analysis exposes (1) the faster optimization speed as well as the constrained optimization space of the cross-entropy loss, (2) the detrimental effects of using the signature of the cross-entropy loss on optimization precision as well as optimization space, and (3) the slow optimization speed of the logit loss in the context of adversariality. Our experiments reveal that the Iterative Fast Gradient Sign attack, which is thought to be fast for generating adversarial examples, is the worst attack in terms of the number of iterations required to create adversarial examples in the setting of equal perturbation. Moreover, our experiments show that the underlying loss function of Carlini & Wagner’s attack, which is criticized for being substantially slower than other adversarial attacks, is not that much slower than other loss functions. Finally, we analyze how well neural networks can identify adversarial perturbations generated by the attacks under consideration, hereby revisiting the idea of adversarial retraining on ImageNet. Copyright © 2020, The Authors. All rights reserved.

关键词： Constrained optimization

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Quantum linear algebra is all you need for Transformer architectures

arXiv

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arXiv 2024年

作者： Guo, Naixu Yu, Zhan Choi, Matthew Agrawal, Aman Nakaji, Kouhei Aspuru-Guzik, Alán Rebentrost, Patrick Centre for Quantum Technologies National University of Singapore 117543 Singapore Department of Computer Science University of Toronto TorontoONM5S 2E4 Canada Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Department of Mathematics National University of Singapore 119076 Singapore Department of Chemistry University of Toronto TorontoONM5G 1Z8 Canada 1-1-1 Umezono Ibaraki Tsukuba305-8568 Japan Quantum Computing Center Keio University 3-14-1 Hiyoshi Kohoku-ku Kanagawa Yokohama223-8522 Japan Department of Materials Science & Engineering University of Toronto TorontoONM5S 3E4 Canada Department of Chemical Engineering & Applied Chemistry University of Toronto TorontoONM5S 3E5 Canada Canadian Institute for Advanced Research TorontoONM5G 1Z8 Canada School of Computing National University of Singapore 117417 Singapore

Generative machine learning methods such as large-language models are revolutionizing the creation of text and images. While these models are powerful they also harness a large amount of computational resources. The transformer is a key component in large language models that aims to generate a suitable completion of a given partial sequence. In this work, we investigate transformer architectures under the lens of fault-tolerant quantum computing. The input model is one where trained weight matrices are given as block encodings and we construct the query, key, and value matrices for the transformer. We show how to prepare a block encoding of the self-attention matrix, with a new subroutine for the row-wise application of the softmax function. In addition, we combine quantum subroutines to construct important building blocks in the transformer, the residual connection and layer normalization, and the feed-forward neural network. Our subroutines prepare an amplitude encoding of the transformer output, which can be measured to obtain a prediction. Based on common open-source large-language models, we provide insights into the behavior of important parameters determining the run time of the quantum algorithm. We discuss the potential and challenges for obtaining a quantum advantage. © 2024, CC BY.

关键词： Network architecture

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Analysis of Students' Academic Achievements: A Personality Test Perspective 8

Analysis of Students' Academic Achievements: A Personality T...

引用

8th IIAI International Congress on Advanced applied Informatics, IIAI-AAI 2019

作者： Chen, Yen-Hung Chen, Huei-Yuan Chen, Miao-Ying Hsu, Yi-Jen Liao, Min-Fei Wu, Hsin-Ting Department of Computer Science University of Taipei Taipei Taiwan Department of Urban Development University of Taipei Taipei Taiwan Department of Mathematics Master Program of Mathematics Education University of Taipei Taipei Taiwan Office of General Affairs University of Taipei Taipei Taiwan Department of Special Education University of Taipei Taipei Taiwan Computer and Networking Center University of Taipei Taipei Taiwan

ISBN: (纸本)9781728126272

Using the personality test perspective, this study adopts Data Mining and Association Rule Learning in Big Data Analysis as the basis for finding correlations between the personality test and students' academic performance. The personality test results are divided into 6 types of personality characteristics: Type A, B, C, D, E and F. The most personality traits of the University are Type D, and the main purpose is to find out the most appropriate students for each department, through the relationship between personality traits and academic performance, helping to guide students to master their personality traits and develop their potentials for the future. © 2019 IEEE.

关键词： Students

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Learning interactions between Rydberg atoms

arXiv

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arXiv 2024年

作者： Simard, Olivier Dawid, Anna Tindall, Joseph Ferrero, Michel Sengupta, Anirvan M. Georges, Antoine Collège de France PSL University 11 place Marcelin Berthelot Paris75005 France CPHT CNRS École Polytechnique IP Paris PalaiseauF-91128 France Center for Computational Quantum Physics Flatiron Institute 162 Fifth Avenue New YorkNY10010 United States 〈aQaL〉 Applied Quantum Algorithms – Leiden Institute of Advanced Computer Science Lorentz Insitute for Theoretical Physics Universiteit Leiden Netherlands Center for Computational Mathematics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Department of Physics and Astronomy Rutgers University PiscatawayNJ08854 United States DQMP Université de Genève 24 quai Ernest Ansermet GenèveCH-1211 Switzerland

Quantum simulators have the potential to solve quantum many-body problems that are beyond the reach of classical computers, especially when they feature long-range entanglement. To fulfill their prospects, quantum simulators must be fully controllable, allowing for precise tuning of the microscopic physical parameters that define their implementation. We consider Rydberg-atom arrays, a promising platform for quantum simulations. Experimental control of such arrays is limited by the imprecision on the optical tweezers positions when assembling the array, hence introducing uncertainties in the simulated Hamiltonian. In this work, we introduce a scalable approach to Hamiltonian learning using graph neural networks (GNNs). We employ the Density Matrix Renormalization Group (DMRG) to generate ground-state snapshots of the transverse field Ising model realized by the array, for many realizations of the Hamiltonian parameters. Correlation functions reconstructed from these snapshots serve as input data to carry out the training. We demonstrate that our GNN model has a remarkable capacity to extrapolate beyond its training domain, both regarding the size and the shape of the system, yielding an accurate determination of the Hamiltonian parameters with a minimal set of measurements. We prove a theorem establishing a bijective correspondence between the correlation functions and the interaction parameters in the Hamiltonian, which provides a theoretical foundation for our learning algorithm. Our work could open the road to feedback control of the positions of the optical tweezers, hence providing a decisive improvement of analog quantum simulators. Copyright © 2024, The Authors. All rights reserved.

关键词： Optical tweezers

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Quantum copy-protection of compute-and-compare programs in the quantum random oracle model

arXiv

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arXiv 2020年

作者： Coladangelo, Andrea Majenz, Christian Poremba, Alexander University of California Berkeley United States QuSoft and Centrum Wiskunde & Informatica Netherlands Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Computing and Mathematical Sciences Caltech United States

Copy-protection allows a software distributor to encode a program in such a way that it can be evaluated on any input, yet it cannot be "pirated" - a notion that is impossible to achieve in a classical setting. Aaronson (CCC 2009) initiated the formal study of quantum copy-protection schemes, and speculated that quantum cryptography could offer a solution to the problem thanks to the quantum no-cloning theorem. In this work, we introduce a quantum copy-protection scheme for a large class of evasive functions known as "compute-and-compare programs" - a more expressive generalization of point functions. A compute-and-compare program CC[f, y] is specified by a function f and a string y within its range: on input x, CC[f, y] outputs 1, if f(x) = y, and 0 otherwise. We prove that our scheme achieves non-trivial security against fully malicious adversaries in the quantum random oracle model (QROM), which makes it the first copy-protection scheme to enjoy any level of provable security in a standard cryptographic model. As a complementary result, we show that the same scheme fulfills a weaker notion of software protection, called "secure software leasing", introduced very recently by Ananth and La Placa (eprint 2020), with a standard security bound in the QROM, i.e. guaranteeing negligible adversarial advantage. Finally, as a third contribution, we elucidate the relationship between unclonable encryption and copy-protection for multi-bit output point functions. © 2020, CC BY.

关键词： Quantum cryptography

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New approximate-analytical solutions to partial differential equations via auxiliary function method

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Partial Differential Equations in applied mathematics 2021年 4卷

作者： Zada, Laiq Nawaz, Rashid Nisar, Kottakkaran Sooppy Tahir, Muhammad Yavuz, Mehmet Kaabar, Mohammed K.A. Martínez, Francisco Department of Mathematics Abdul Wali Khan University Mardan Khyber Pakhtunkhwa Pakistan Department of Mathematics College of Arts and Sciences Wadi Aldawaser 11991 Prince Sattam bin Abdulaziz University Saudi Arabia Department of Mathematics and Computer Sciences Faculty of Science Necmettin Erbakan University Konya 42090 Turkey Jabalia Camp United Nations Relief and Works Agency (UNRWA) Palestinian Refugee Camp Gaza Strip Jabalya Palestine Institute of Mathematical Sciences University of Malaya Kuala Lumpur 50603 Malaysia Department of Mathematics and Statistics Washington State University Pullman 99163 WA United States Department of Applied Mathematics and Statistics Technological University of Cartagena Cartagena 30203 Spain

In this article, the optimal auxiliary function method (OAFM) is extended to general partial differential equations (PDEs). Our proposed method is highly efficient and provides the means of controlling the approximate solution's convergence. Illustrative examples are provided to prove the exceptional consistency of the PDEs’ analytical and numerical solutions. OAFM is practically very effective where with only one iteration, a very fast convergence is guaranteed. OAFM is considered a reliable and efficient technique with a high accuracy in finding solutions for such interesting problems while saving both volume and time in comparison to other related analytical techniques. © 2021 The Author(s)

关键词： Benjamin–Bona–Mahony (BBM) equation Exact solutions Heat equation Optimal axillary function method

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Stable Gaussian process tracking control of antagonistic variable stiffness actuators

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Scientific Reports 2025年第1期15卷 1-13页

作者： Zhigang Zou Yongping Pan Wei Li Chenguang Yang College of Mechatronics Engineering Guangdong Polytechnic Normal University Guangzhou 510665 China School of Computer Science and Engineering Sun Yat-sen University Guangzhou 510006 China Peng Cheng Laboratory Shenzhen 518057 China School of Mathematical Sciences Center for Applied Mathematics of Guangxi Guangxi Minzu University Nanning 530006 China School of Software Engineering Sun Yat-sen University Guangzhou 510006 China Department of Computer Science University of Liverpool L69 3BX Liverpool UK

The control of variable stiffness actuators (VSAs) is challenging because they have highly nonlinear characteristics and are difficult to model accurately. Classical control approaches using high control gains can make VSAs stiff, which alters the inherent compliance of VSAs. Iterative learning control can achieve high tracking accuracy for VSAs but generally lacks sufficient generalization. This study applies Gaussian process (GP) regression to design a stable tracking controller combining feedforward and low-gain feedback control actions for agonistic-antagonistic (AA) VSAs subjected to unknown dynamics. The GP model learns the inverse dynamics of AA-VSAs and provides model fidelity by the predicted variance. The stability analysis of the closed-loop system demonstrates that the tracking error is uniformly ultimately bounded and exponentially converges to a small ball under a given probability. Experiments on an AA-VSA named qbmove Advanced have validated the superiority of the proposed method with respect to tracking accuracy and generalization.

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