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arXiv 2022年

作者： Benisty, Hadas Song, Alexander Mishne, Gal Charles, Adam S. Yale Neuroscience New HavenCT06510 United States Max Planck Institute for Intelligent Systems Stuttgart Germany Halıcıoğlu Data Science Institute Department of Electrical and Computer Engineering Neurosciences Graduate Program UC San Diego 9500 Gilman Drive La Jolla CA92093 United States Department of Biomedical Engineering Kavli Neuroscience Discovery Institute Center for Imaging Science Department of Neuroscience Mathematical Institute for Data Science Johns Hopkins University BaltimoreMD21287 United States

Functional optical imaging in neuroscience is rapidly growing with the development of new optical systems and fluorescence indicators. To realize the potential of these massive spatiotemporal datasets for relating neuronal activity to behavior and stimuli and uncovering local circuits in the brain, accurate automated processing is increasingly essential. In this review, we cover recent computational developments in the full data processing pipeline of functional optical microscopy for neuroscience data and discuss ongoing and emerging challenges. © 2022, CC BY-NC-ND.

关键词： Fluorescence microscopy

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Magnetically tuned continuous transition from weak to strong coupling in terahertz magnon polaritons

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Physical Review Research 2023年第1期5卷 L012039-L012039页

作者： Andrey Baydin Kenji Hayashida Takuma Makihara Fuyang Tay Xiaoxuan Ma Wei Ren Guohong Ma G. Timothy Noe, II Ikufumi Katayama Jun Takeda Hiroyuki Nojiri Shixun Cao Motoaki Bamba Junichiro Kono Department of Electrical and Computer Engineering Rice University Houston Texas 77005 USA Smalley-Curl Institute Rice University Houston Texas 77005 USA Division of Applied Physics Graduate School of Engineering Hokkaido University Sapporo Hokkaido 060-8628 Japan Department of Physics and Astronomy Rice University Houston Texas 77005 USA Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston Texas 77005 USA Department of Physics International Center of Quantum and Molecular Structures and Materials Genome Institute Shanghai University 200444 Shanghai China Department of Physics Graduate School of Engineering Science Yokohama National University Yokohama 240-8501 Japan Institute for Materials Research Tohoku University Sendai 980-8577 Japan The Hakubi Center for Advanced Research Kyoto University Kyoto 606-8502 Japan Department of Physics I Kyoto University Kyoto 606-8502 Japan PRESTO Japan Science and Technology Agency Kawaguchi 332-0012 Japan Department of Materials Science and NanoEngineering Rice University Houston Texas 77005 USA

Depending on the relative rates of coupling and dissipation, a light-matter coupled system is either in the weak- or strong-coupling regime. Here, we present a unique system where the coupling rate continuously increases with an externally applied magnetic field while the dissipation rate remains constant, allowing us to monitor a weak-to-strong coupling transition as a function of magnetic field. We observed a Rabi splitting of a terahertz magnon mode in yttrium orthoferrite above a threshold magnetic field of ∼14 T. Based on a microscopic theoretical model, we show that with increasing magnetic field the magnons transition into magnon polaritons through an exceptional point, which will open up new opportunities for in situ control of non-Hermitian systems.

关键词： Antiferromagnetism High magnetic fields Magneto-optics Magnons Polaritons Non-Hermitean systems Terahertz time-domain spectroscopy Transfer matrix method

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Virtual quantum resource distillation: General framework and applications

arXiv

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arXiv 2024年

作者： Takagi, Ryuji Yuan, Xiao Regula, Bartosz Gu, Mile Department of Basic Science The University of Tokyo Tokyo153-8902 Japan Nanyang Quantum Hub School of Physical and Mathematical Sciences Nanyang Technological University Singapore637371 Singapore Center on Frontiers of Computing Studies Peking University Beijing100871 China School of Computer Science Peking University Beijing100871 China Stanford Institute for Theoretical Physics Stanford University StanfordCA94305 United States Wako Saitama351-0198 Japan Department of Physics Graduate School of Science The University of Tokyo Bunkyo-ku Tokyo113-0033 Japan Centre for Quantum Technologies National University of Singapore 3 Science Drive 2 Singapore117543 Singapore CNRS-UNS-NUS-NTU International Joint Research Unit UMI 3654 Singapore117543 Singapore

We develop the general framework of virtual resource distillation-an alternative distillation strategy proposed in Phys. Rev. Lett. 132, 050203 (2024), which extends conventional quantum resource distillation by integrating the power of classical postprocessing. The framework presented here is applicable not only to quantum states, but also dynamical quantum objects such as quantum channels and higher-order processes. We provide a general characterization and benchmarks for the performance of virtual resource distillation in the form of computable semidefinite programs as well as several operationally motivated quantities. We apply our general framework to various concrete settings of interest, including standard resource theories such as entanglement, coherence, and magic, as well as settings involving dynamical resources such as quantum memory, quantum communication, and non-Markovian dynamics. The framework of probabilistic distillation is also discussed. Copyright © 2024, The Authors. All rights reserved.

关键词： Distillation

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Self-supervised approach for facial movement based optical flow

arXiv

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arXiv 2021年

作者： Alkaddour, Muhannad Tariq, Usman Dhall, Abhinav Graduate Program in Mechatronics Engineering American University of Sharjah Sharjah United Arab Emirates Department of Electrical Engineering American University of Sharjah Sharjah United Arab Emirates Department of Human Centred Computing Monash University Melbourne Australia Department of Computer Science and Engineering Indian Institute of Technology Ropar Rupnagar India

computing optical flow is a fundamental problem in computer vision. However, deep learning-based optical flow techniques do not perform well for non-rigid movements such as those found in faces, primarily due to lack of the training data representing the fine facial motion. We hypothesize that learning optical flow on face motion data will improve the quality of predicted flow on faces. The aim of this work is threefold: (1) exploring self-supervised techniques to generate optical flow ground truth for face images;(2) computing baseline results on the effects of using face data to train Convolutional Neural Networks (CNN) for predicting optical flow;and (3) using the learned optical flow in micro-expression recognition to demonstrate its effectiveness. We generate optical flow ground truth using facial key-points in the BP4D-Spontaneous dataset. The generated optical flow is used to train the FlowNetS architecture to test its performance on the generated dataset. The performance of FlowNetS trained on face images surpassed that of other optical flow CNN architectures, demonstrating its usefulness. Our optical flow features are further compared with other methods using the STSTNet micro-expression classifier, and the results indicate that the optical flow obtained using this work has promising applications in facial expression analysis. © 2021, CC BY.

关键词： Optical flows

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Enhancing Diagnostic Accuracy for Knee Osteoarthritis Using a Deep Learning Approach

Enhancing Diagnostic Accuracy for Knee Osteoarthritis Using ...

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International Symposium on Applied science 2024, ISAS 2024

作者： Raza, Aquib Ching, Congo Tak Shing Liu, Peng-Ta Phan, Thien-Luan Tran, Ngoc-Luan Nguyen, Ha Anh T. Rameez, Mohammad Li, Hung-Chung Linh, Dao Nguyen Phuong Van Hieu, Nguyen Tran, Trung Nghia National Taiwan University Taipei City10673 Taiwan Research Center for Information Technology Academia Sinica Taipei City11529 Taiwan Graduate Institute of Biomedical Engineering National Chung Hsing University Taichung402 Taiwan Department of Electrical Engineering National Chi Nan University Nantou County Taiwan Advanced Plant and Food Crop Biotechnology Center National Chung Hsing University Taichung402 Taiwan International Doctoral Program in Agriculture National Chung Hsing University Taichung402 Taiwan Fall Prevention Center Department of Physical Medicine & Rehabilitation Changhua Christian Hospital Changhua500 Taiwan Department of Physics and Electronic Engineering University of Science Vietnam National University - Ho Chi Minh City Ho Chi Minh City70000 Viet Nam Department of Computer Science University of York York HeslingtonYO10 5DD United Kingdom Undergraduate Program of Intellectual Creativity Engineering National Chung Hsing University Taichung402 Taiwan Ho Chi Minh City72409 Viet Nam HCMUT Vietnam National University Ho Chi Minh City Thu Duc Ho Chi Minh City71308 Viet Nam

This research introduces a deep learning framework that combines convolutional neural networks with autoencoders to improve the diagnostic accuracy of knee osteoarthritis. The study utilized a publicly available dataset from the Mendeley Data repository, where knee joint images were preprocessed using Contrast Limited Adaptive Histogram Equalization to enhance contrast and ensure consistent image sizing. Initially, an autoencoder was employed in an unsupervised manner to learn compressed representations of the data. These representations were then used to train a CNN to classify knee images into 'healthy' and 'osteoarthritic' categories. The model was trained using an 80:20 split for training and testing data, incorporating early stopping and data augmentation techniques to prevent overfitting and enhance generalization. Performance metrics such as accuracy, precision, recall, and the area under the Receiver Operating Characteristic curve (ROCAUC) were used to evaluate the model, resulting in a high test accuracy of 98.95%. The high precision and recall rates further confirmed the model's robustness in diagnosing KOA. To enhance interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) was applied to identify critical regions in the images that influenced the model's decisions. Additionally, t-Distributed Stochastic Neighbor Embedding (t-SNE) was used to visualize the high-dimensional encoded features in a two-dimensional space, providing deeper insights into how the model differentiates between classes. The promising results of this deep learning approach suggest its potential for broader applications in medical imaging diagnostics. By providing healthcare professionals with a tool that enhances diagnostic accuracy and operational efficiency, this framework lays the foundation for developing scalable, AI-driven diagnostic systems for various complex medical conditions. © 2025 institute of Physics Publishing. All rights reserved.

关键词： Joints (anatomy)

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Upper Bounds on the Distillable Randomness of Bipartite Quantum States

arXiv

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arXiv 2022年

作者： Lami, Ludovico Regula, Bartosz Wang, Xin Wilde, Mark M. Institute for Theoretical Physics Korteweg-de Vries Institute for Mathematics QuSoft University of Amsterdam Netherlands Institute for Theoretical Physics IQST University of Ulm Albert-Einstein-Allee 11 UlmD-89069 Germany Department of Physics Graduate School of Science The University of Tokyo Bunkyo-ku Tokyo113-0033 Japan Institute for Quantum Computing Baidu Research Beijing100193 China School of Electrical and Computer Engineering Cornell University IthacaNY14850 United States

The distillable randomness of a bipartite quantum state is an information-theoretic quantity equal to the largest net rate at which shared randomness can be distilled from the state by means of local operations and classical communication. This quantity has been widely used as a measure of classical correlations, and one version of it is equal to the regularized Holevo information of the ensemble that results from measuring one share of the state. However, due to the regularization, the distillable randomness is difficult to compute in general. To address this problem, we define measures of classical correlations and prove a number of their properties, most importantly that they serve as upper bounds on the distillable randomness of an arbitrary bipartite state. We then further bound these measures from above by some that are efficiently computable by means of semi-definite programming, we evaluate one of them for the example of an isotropic state, and we remark on the relation to quantities previously proposed in the literature. Copyright © 2022, The Authors. All rights reserved.

关键词： Random processes

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Buzzer Detection to Maintain Information Neutrality in 2019 Indonesia Presidential Election 8

Buzzer Detection to Maintain Information Neutrality in 2019 ...

引用

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

作者： Panatra, Adrian Julianto Chandra, Felisitus Bryan Darmawan, Wiliam Warnars, Harco Leslie Hendric Spits Utomo, Wiranto Herry Matsuo, Tokuro Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Faculty of Computing President University Jawa Barat17550 Indonesia Graduate School of Industrial Technology Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (纸本)9781728126272

This paper proposed a method which detects a political buzzer in social media, specifically Instagram. With Indonesia undergoing 2019 presidential election, a detection of buzzers that causes much trouble in maintaining information neutrality is seen as a needed. One of the many reasons is because those buzzers spread false news making the information gained by the use of social media to be not neutral and deliberately offends or attack those that they are not in favor of. Those buzzers share a similar characteristic, tendency, or even possess the same pattern. Grouping, classification, and detection method are used to counter this problem. This research gives a slight overview of what is happening in social media and a theory of how to deal with those problems. The argument is expected to help to identify buzzer in real life, thus helps in maintaining information neutrality along with the social media in Indonesia. © 2019 IEEE.

关键词： Social networking (online)

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Transistor Placement for Automatic Cell Synthesis through Boolean Satisfiability

Transistor Placement for Automatic Cell Synthesis through Bo...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Maicon Cardoso Andrei Bubolz Jordi Cortadella Leomar Rosa Felipe Marques Graduate Program in Computing Federal University of Pelotas Pelotas Brazil Department of Computer Science Universitat Politècnica de Catalunya Barcelona Spain

ISBN: (数字)9781728133201

ISBN: (纸本)9781728133218

This paper presents a new transistor placement method applied to the ASTRAN EDA tool, an open-source solution for the automatic design of complex digital gates. Although it currently reaches an optimized solution through a Threshold Accepting approach, ASTRAN does not guarantee a minimum-width placement. In this paper, a method based on Boolean satisfiability is proposed, ensuring an optimal solution for the transistor placement task through modeling the problem into a set of Boolean variables and clauses aware of four design rule constraints. Experiments comparing the proposed method and the current ASTRAN placement technique have shown reductions in the layout area. Furthermore, our method achieved a significant improvement regarding runtime, an essential feature for designing digital circuits and systems on-demand.

关键词： Transistors Logic gates Layout Tools Open source software Optimization SPICE

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Optimizing the Montgomery Modular Multiplier for a Power- and Area-Efficient Hardware Architecture

Optimizing the Montgomery Modular Multiplier for a Power- an...

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Mateus Terribele Leme Guilherme Paim Leandro M. G Rocha Patricia Ücker Vitor G. Lima Rafael Soares Eduardo A. C. da Costa Sergio Bampi Graduate Program in Microelectronics (PGMICRO) Federal University of Rio Grande do Sul (UFRGS) Porto Alegre Graduate Program on Electronic Engineering and Computing Catholic University of Pelotas (UCPel) Pelotas Graduate Program on Computer Science (PPGC) Federal University of Pelotas (UFPel)

ISBN: (数字)9781728180588

ISBN: (纸本)9781728180595

Cryptography hardware design is a key challenge towards the confidentiality advance in the prominent field of the internet of things (IoT). The rise of IoT embedded devices boosts the demand for power- and area- efficient solutions for cryptography hardware. The higher the robustness of the cryptography algorithm is, the higher are the hardware complexity, the circuit area, and energy consumption. Asymmetric algorithms are a particular class widely employed in ultra-secure cryptosystems. The high time-hardness to break the private-key in asymmetric algorithms is a result of its high mathematical complexity. RSA is an asymmetric algorithm that performs successive modular multiplications to encrypt and de-encrypt the information. Therefore, arithmetic operators are the most significant part regarding circuit area and power dissipation. This work evaluates a design space exploration for power- and area-efficient hardware VLSI design in the modular Montgomery multiplier employed in the RSA algorithm.

关键词： Hardware computer architecture Adders Power dissipation Public key Very large scale integration

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Standalone Rate-Distortion FME Architecture

Standalone Rate-Distortion FME Architecture

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Symposium on Integrated Circuits and Systems Design (SBCCI)

作者： Vanio Rodrigues Filho Marcio Monteiro Ismael Seidel Mateus Grellert José Luís Güntzel Embedded Computing Lab. (ECL) Computer Science Graduate Program (PPGCC) Federal University of Santa Catarina (UFSC) Florianópolis Brazil

ISBN: (数字)9781728196251

ISBN: (纸本)9781728196268

The high computing complexity of video encoders hinders real-time throughput using a software implementation. This drawback can be amended by implementing hardware accelerators for the most computing-intensive encoder tools to achieve real-time processing while maintaining energy efficiency. In this work, we investigate how approximate and precise versions of the Fractional Motion Estimation (FME), one of the most demanding steps of modern encoders, compare in terms of hardware resources and coding efficiency. To that purpose, we designed a hardware architecture for FME targeting real-time throughput for High Definition (HD) sequences and beyond. This architecture searches over 48 candidates and, unlike most related work, uses 16-bit wide horizontal interpolated samples, which is in compliance with the High Efficiency Video Coding (HEVC) reference software. Also, it integrates all FME required computations, including the rate cost of each candidate block, what makes it a standalone FME accelerator. We analyze the impacts of our design choices in terms of coding efficiency, using software simulations, and in terms of power/energy consumption, using results obtained from standard cell synthesis.

关键词： computer architecture Interpolation Encoding Hardware Software Standards Distortion

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