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arXiv 2024年

作者： Nakamura, Daisuke Lee, Mu-Kun Karube, Kosuke Mochizuki, Masahito Nagaosa, Naoto Tokura, Yoshinori Taguchi, Yasujiro Wako351-0198 Japan Department of Applied Physics Waseda University Okubo Shinjuku-ku Tokyo169-8555 Japan Fundamental Quantum Science TRIP Headquarters RIKEN Wako351-0198 Japan Tokyo College Department of Applied Physics University of Tokyo Tokyo113-8656 Japan Baton Zone Program RIKEN Wako351-0198 Japan

Chiral magnets under broken time-reversal symmetry can give rise to rectification of moving electrons, called nonreciprocal transport. Several mechanisms, such as the spin-fluctuation-induced chiral scattering and asymmetry in the electronic band dispersion with and without the relativistic spin-orbit interaction, have been proposed, but clear identification as well as theoretical description of these different contributions are desired for full understanding of nonreciprocal transport phenomena. Here, we investigate a chiral magnet Co8Zn9Mn3 and find the nonreciprocal transport phenomena consisting of different contributions with distinct field- and temperature-dependence across the magnetic phase diagram over a wide temperature range including above room-temperature. We successfully separate the nonreciprocal resistivity into different components and identify their mechanisms as spin-fluctuation-induced chiral scattering and band asymmetry in a single material with the help of theoretical calculations. Copyright © 2024, The Authors. All rights reserved.

关键词： Spin fluctuations

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Bayesian Parameter Inference for Partially Observed SDEs driven by Fractional Brownian Motion

arXiv

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arXiv 2022年

作者： Maama, Mohamed Jasra, Ajay Ombao, Hernando Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia

In this paper we consider Bayesian parameter inference for partially observed fractional Brownian motion (fBM) models. The approach we follow is to time-discretize the hidden process and then to design Markov chain Monte Carlo (MCMC) algorithms to sample from the posterior density on the parameters given data. We rely on a novel representation of the time discretization, which seeks to sample from an approximation of the posterior and then corrects via importance sampling;the approximation reduces the time (in terms of total observation time T) by O(T). This method is extended by using a multilevel MCMC method which can reduce the computational cost to achieve a given mean square error (MSE) versus using a single time discretization. Our methods are illustrated on simulated and real data. Copyright © 2022, The Authors. All rights reserved.

关键词： Mean square error

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Strategies of Collaborative Filtering Recommendation Mechanism Using a Deep Learning Approach 3

Strategies of Collaborative Filtering Recommendation Mechani...

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3rd IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2021

作者： Chang, Chin-Chih Chang, Huan-Ming Chung Hua University Becholar Program in Applied Foreign Language and Smart Commerce Hsinchu30012 Taiwan Chung Hua University Department of Computer Science and Information Engineering Hsinchu30012 Taiwan

ISBN: (纸本)9781665445160

Nowadays, recommendation systems are widely used to help users locate the items they want. Collaborative filtering (CF) is a commonly used method for the recommendation. CF techniques use user-item ratings for prediction but suffer from the problems of data sparsity, cold start, and scalability. Though the Matrix Factorization (MF) techniques like Singular Value Decomposition (SVD) or Principal Component Analysis (PCA) overcome the above-mentioned problems, these methods are possible to deliver unmeaningful results in the condition of a low ranked approximation and denser singular vectors. In this paper, we review strategies of collaborative filtering recommendation mechanisms and propose an approach based on an autoencoder of convolutional neural network. Autoencoders are unsupervised learning methods in which neural networks are supported for the task of representation learning. We identify the user's features through learning, and then use these features to combine the collaborative filtering algorithm to recommend items. The experimental results show that the convolutional autoencoder can effectively reduce the computations when the amount of data is huge and benefited from the performance of its convolutional neural network. © 2021 IEEE.

关键词： Recommender systems

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Partial observation can induce mechanistic mismatches in data-constrained models of neural dynamics 38

Partial observation can induce mechanistic mismatches in dat...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Qian, William Zavatone-Veth, Jacob A. Ruben, Benjamin S. Pehlevan, Cengiz Biophysics Graduate Program Harvard University CambridgeMA02138 United States Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University CambridgeMA02138 United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Center for Brain Science Harvard University CambridgeMA02138 United States Department of Physics Harvard University CambridgeMA02138 United States

One of the central goals of neuroscience is to gain a mechanistic understanding of how the dynamics of neural circuits give rise to their observed function. A popular approach towards this end is to train recurrent neural networks (RNNs) to reproduce experimental recordings of neural activity. These trained RNNs are then treated as surrogate models of biological neural circuits, whose properties can be dissected via dynamical systems analysis. How reliable are the mechanistic insights derived from this procedure? While recent advances in population-level recording technologies have allowed simultaneous recording of up to tens of thousands of neurons, this represents only a tiny fraction of most cortical circuits. Here we show that observing only a subset of neurons in a circuit can create mechanistic mismatches between a simulated teacher network and a data-constrained student, even when the two networks have matching single-unit dynamics. In particular, partial observation of models of low-dimensional cortical dynamics based on functionally feedforward or low-rank connectivity can lead to surrogate models with spurious attractor structure. Our results illustrate the challenges inherent in accurately uncovering neural mechanisms from single-trial data, and suggest the need for new methods of validating data-constrained models for neural dynamics. © 2024 Neural information processing systems foundation. All rights reserved.

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Designing Classical Header Bits for Quantum Wrapper Networking with O-band Quantum Signal

Designing Classical Header Bits for Quantum Wrapper Networki...

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Frontiers in Optics + Laser science 2023, FiO, LS 203: Part of Frontiers in Optics + Laser science 2023

作者： Gül, G. Berkay On, M. Singh, S.K. Proietti, R. Kanter, G.S. Yoo, S.J.B. Kumar, P. Graduate Program in Applied Physics Northwestern University 2145 Sheridan Road EvanstonIL60208 United States Department of Electrical and Computer Engineering University of California Davis DavisCA95616 United States Department of Electronics and Communication Eng. Indian Institute of Technology Roorkee247667 India Department of Electronics and Telecommunications Engineering Politecnico di Torino Italy Center for Photonic Communication and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States

We investigate the impact of wavelength choice for the classical headers in quantum wrapper networking with O-band quantum payload. We show that a C-band header performs substantially better than an O-band header. ... 详细信息

ISBN: (纸本)9781957171296

关键词： Bandpass filters Beam splitters Bit error rate Entanglement distribution Quantum teleportation Raman scattering

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Surface Second Harmonic Generation from Topological Dirac Semimetal PdTe2

arXiv

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arXiv 2023年

作者： Faizanuddin, Syed Mohammed Chien, Ching-Hang Chan, Yao-Jui Kuo, Chia-Nung Lue, Chin Shuan Wen, Yu-Chieh Institute of Physics Academia Sinica Taipei11529 Taiwan Nano Science and Technology Program Taiwan International Graduate Program Academia Sinica Taipei11529 Taiwan Department of Engineering and System Science National Tsing Hua University Hsinchu300044 Taiwan Department of Physics National Cheng Kung University Tainan701401 Taiwan Research Center for Applied Sciences Academia Sinica Taipei11529 Taiwan

Three-dimensional topological semimetals were shown to host anomalously strong optical nonlinearity, but yet whether such enhancement can occur at their surfaces via topological boundary states remains an open question. Here we tackle this question by examining polarization-dependent rotational-anisotropy optical second harmonic generation (SHG) from transition metal dichalcogenide Dirac semimetal PdTe2. We find its reflected SHG to follow C3v surface symmetry with a time-varying intensity dictated by oxidation kinetics of the material after its surface cleavage, confirming the surface-derived origin of SHG. The complex surface nonlinear optical susceptibility tensor is fully characterized by heterodyne interferometry, unveiling a hidden giant out-of-plane response χzzz (2) (> 10.6 nm2/V or, equivalently, an effective bulk nonlinear susceptibility > 10.3 nm/V). Our results highlight the topological surfaces/interfaces as a new route toward nonlinear optical applications with released symmetry constraints, and demonstrate SHG as a viable means for in situ probing of surface chemistry of topological systems. Copyright © 2023, The Authors. All rights reserved.

关键词： Nonlinear optics

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Designing Classical Header Bits for Quantum Wrapper Networking with O-band Quantum Signal

Designing Classical Header Bits for Quantum Wrapper Networki...

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2023 Laser science, LS 2023

作者： Gül, G. On, M. Berkay Singh, S.K. Proietti, R. Kanter, G.S. Yoo, S.J.B. Kumar, P. Graduate Program in Applied Physics Northwestern University 2145 Sheridan Road EvanstonIL60208 United States Department of Electrical and Computer Engineering University of California Davis DavisCA95616 United States Department of Electronics and Communication Eng. Indian Institute of Technology Roorkee247667 India Department of Electronics and Telecommunications Engineering Politecnico di Torino Italy Center for Photonic Communication and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States

关键词： Quantum electronics

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A BCLC Staging System for Hepatocellular Carcinoma using Swin Transformer and CT Imaging 46

A BCLC Staging System for Hepatocellular Carcinoma using Swi...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Chang, Shun-Cheng Wang, Pochuang Wang, Weichung Su, Tung-Hung Kao, Jia-Horng Lin, Che Taipei10617 Taiwan Department of Computer Science and Information Engineering Ntu Taipei10617 Taiwan Institute of Applied Mathematical Sciences Ntu Taipei10617 Taiwan Division of Gastroenterology and Hepatology Department of Internal Medicine National Taiwan University Hospital Taipei10048 Taiwan Department of Electrical Engineering Ntu Taipei10617 Taiwan Center for Advanced Computing and Imaging in Biomedicine Ntu Taipei10617 Taiwan Smart Medicine and Health Informatics Program Ntu Taipei10617 Taiwan

ISBN: (纸本)9798350371499

The Barcelona Clinic Liver Cancer (BCLC) staging system plays a crucial role in clinical planning, offering valuable insights for effectively managing hepatocellular carcinoma. Accurate prediction of BCLC stages can significantly ease the workload on radiologists. However, few datasets are explicitly designed for discerning BCLC stages. Despite the common practice of appending BCLC labels to clinical data within datasets, the inherent imbalance in BCLC distribution is further amplified by the diverse purposes for which datasets are curated. In this study, we aim to develop a BCLC staging system using the advanced Swin Transformer model. Additionally, we explore the integration of two datasets, each originally intended for separate objectives, highlighting the critical challenge of preserving class distribution in practical study designs. This exploration is pivotal for ensuring the applicability of our developed staging system in the designed clinical settings. Our resulting BCLC staging system demonstrates an accuracy of 55.81% (±7.8%), contributing to advancing medical image-based research for predicting BCLC stages. © 2024 IEEE.

关键词： Distribution transformers

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Detailed dynamics of a moving magnetic skyrmion lattice in MnSi observed using small-angle neutron scattering under an alternating electric current flow

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Physical Review B 2024年第1期110卷 014431-014431页

作者： D. Okuyama M. Bleuel Q. Ye J. Krzywon N. Nagaosa A. Kikkawa Y. Taguchi Y. Tokura J. D. Reim Y. Nambu T. J. Sato Institute of Multidisciplinary Research for Advanced Materials (IMRAM) Institute of Materials Structure Science (IMSS) National Institute of Standards and Technology 100 Bureau Drive Gaithersburg Maryland 20899-8562 USA Department of Materials Science and Engineering (CEMS) Wako 351-0198 Japan Fundamental Quantum Science Program TRIP Headquarters RIKEN Wako 351-0198 Japan Tokyo College and Department of Applied Physics (IMR) Organization for Advanced Studies FOREST

Lattice formation of swirling textures is ubiquitous in solid-state materials, such as a magnetic skyrmion lattice in chiral magnets. In the magnetic skyrmion lattices, their moving states and dynamics under external perturbations are still unrevealed, although a detailed understanding of the dynamics is crucial to realizing spintronic applications, such as magnetic domain-wall racetrack memory [Parkin et al., science 320, 190 (2008); Fert et al., Nat. Nanotechnol. 8, 152 (2013)]. Here, we report in detail on the transient state of a moving magnetic skyrmion lattice in bulk single-crystalline MnSi under alternating current (AC) using small-angle neutron scattering. A rotation and concomitant broadening of the spot width in the azimuthal direction of the magnetic skyrmion reflections originating from the plastic deformation of the magnetic skyrmion lattice were found only at low AC frequencies [Okuyama et al., Commun. Phys. 2, 79 (2019)], whereas above the threshold AC frequency (ft∼0.12Hz) the rotation was not observed, and the spot width becomes sharper. The observed complex response of the magnetic skyrmion reflections can be explained by the change in dislocation density in the magnetic skyrmion lattice. At frequencies higher than ft, the magnetic skyrmions oscillate removing the dislocations, indicating that the dislocation density is controlled by the AC frequency.

关键词： Magnetic texture Topological defects Chiral magnets Neutron diffraction

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ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular Carcinoma Using 3D CT with Multiple Instance Learning

ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular ...

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2024 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2024

作者： Chang, Shun-Cheng Yu, Hsin-Pei Hsieh, Yi-Hsien Wang, Pochuang Wang, Weichung Su, Tung-Hung Kao, Jia-Horng Lin, Che Taipei10617 Taiwan Department of Computer Science and Information Engineering Ntu Taipei10617 Taiwan Institute of Applied Mathematical Sciences Ntu Taipei10617 Taiwan National Taiwan University Hospital Division of Gastroenterology and Hepatology Department of Internal Medicine Taipei10048 Taiwan Department of Electrical Engineering Ntu Taipei10617 Taiwan Center for Advanced Computing and Imaging in Biomedicine Ntu Taipei10617 Taiwan Smart Medicine and Health Informatics Program Ntu Taipei10617 Taiwan

ISBN: (纸本)9798350351552

Deep learning has revolutionized medical imaging, offering advanced methods for accurate diagnosis and treatment planning. The BCLC staging system is crucial for staging Hepatocellular Carcinoma (HCC), a high-mortality cancer. An automated BCLC staging system could significantly enhance diagnosis and treatment planning efficiency. However, we found that BCLC staging, which is directly related to the size and number of liver tumors, aligns well with the principles of the Multiple Instance Learning (MIL) framework. To effectively achieve this, we proposed a new preprocessing technique called Masked Cropping and Padding(MCP), which addresses the variability in liver volumes and ensures consistent input sizes. This technique preserves the structural integrity of the liver, facilitating more effective learning. Furthermore, we introduced Re ViT, a novel hybrid model that integrates the local feature extraction capabilities of Convolutional Neural Networks (CNNs) with the global context modeling of Vision Transformers (ViTs). Re ViT leverages the strengths of both architectures within the MIL framework, enabling a robust and accurate approach for BCLC staging. We will further explore the trade-off between performance and interpretability by employing TopK Pooling strategies, as our model focuses on the most informative instances within each bag. © 2024 IEEE.

关键词： Convolutional neural networks

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