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arXiv 2025年

作者： Han, Pei-Rong Wu, Fan Huang, Xin-Jie Wu, Huai-Zhi Zou, Chang-Ling Yi, Wei Zhang, Mengzhen Li, Hekang Xu, Kai Zheng, Dongning Fan, Heng Wen, Jianming Yang, Zhen-Biao Zheng, Shi-Biao School of Physics and Mechanical and Electrical Engineering Longyan University Longyan364012 China Fujian Key Laboratory of Quantum Information and Quantum Optics College of Physics and Information Engineering Fuzhou University Fuzhou350108 China CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei230026 China CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Hefei230026 China Pritzker School of Molecular Engineering University of Chicago ChicagoIL60637 United States Institute of Physics Chinese Academy of Sciences Beijing100190 China CAS Center for Excellence in Topological Quantum Computation University of Chinese Academy of Sciences Beijing100190 China Department of Electrical and Computer Engineering Binghamton University BinghamtonNY13902 United States Department of Physics Kennesaw State University MariettaGA30060 United States

Dissipation usually plays a negative role in quantum metrological technologies, which aim to improve measurement precision by leveraging quantum effects that are vulnerable to environment-induced decoherence. Recently, it has been demonstrated that dissipation can actually be used as a favorable resource for enhancing the susceptibility of signal detection. However, demonstrations of such enhancement for detecting physical quantities in open quantum systems are still lacking. Here we propose and demonstrate a protocol for realizing such non-Hermitian quantum sensors for probing the coupling between a qubit and a resonator subjecting to energy dissipations. The excitation-number conversion associated with the no-jump evolution trajectory enables removal of the noisy outcomes with quantum jumps, implementing the exceptional point (EP), where the Rabi splitting exhibits a divergent behavior in response to a tiny variation of the effective coupling. The sensitivity enhancement near the EP is confirmed by both theoretical calculation and experimental measurement. Copyright © 2025, The Authors. All rights reserved.

关键词： Qubits

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Switching between topological edge states in plasmonic systems using phase-change materials

arXiv

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arXiv 2022年

作者： Huang, Yin Shen, Yuecheng Veronis, Georgios Department of Optoelectrics Information Science and Engineering School of Physics and Electronics Central South University Hunan Changsha410012 China State Key Laboratory of Optoelectronic Materials and Technologies School of Electronics and Information Technology Sun Yat-Sen University Guangdong Guangzhou510275 China School of Electrical Engineering and Computer Science Louisiana State University Baton RougeLA70803 United States Center for Computation and Technology Louisiana State University Baton RougeLA70803 United States

We introduce non-Hermitian plasmonic waveguide-cavity structures based on the Aubry-Andre-Harper model to realize switching between right and left topological edge states (TESs) using the phase-change material Ge2Sb2Te5 (GST). We show that switching between the crystalline and amorphous phases of GST leads to a shift of the dispersion relation of the optimized structure so that a right TES for the crystalline phase, and a left TES for the amorphous phase occur at the same frequency. Thus, we realize switching between right and left TESs at that frequency by switching between the crystalline and amorphous phases of GST. Our results could be potentially important for developing compact reconfigurable topological photonic devices. Copyright © 2022, The Authors. All rights reserved.

关键词： Phase change materials

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ToHRE: A Top-Down Classification Strategy with Hierarchical Bag Representation for Distantly Supervised Relation Extraction 28

ToHRE: A Top-Down Classification Strategy with Hierarchical ...

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28th International Conference on computational Linguistics, COLING 2020

作者： Yu, Erxin Han, Wenjuan Tian, Yuan Chang, Yi School of Artificial Intelligence Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University China International Center of Future Science Jilin University China College of Biological and Agricultural Engineering Jilin University China Department of Computer Science National University of Singapore Singapore

ISBN: (纸本)9781952148279

Distantly Supervised Relation Extraction (DSRE) has proven to be effective to find relational facts from texts, but it still suffers from two main problems: the wrong labeling problem and the long-tail problem. Most of the existing approaches address these two problems through flat classification, which lacks hierarchical information of relations. To leverage the informative relation hierarchies, we formulate DSRE as a hierarchical classification task and propose a novel hierarchical classification framework, which extracts the relation in a top-down manner. Specifically, in our proposed framework, 1) we use a hierarchically-refined representation method to achieve hierarchy-specific representation;2) a top-down classification strategy is introduced instead of training a set of local classifiers. The experiments on NYT dataset demonstrate that our approach significantly outperforms other state-of-the-art approaches, especially for the long-tail problem. © 2020 COLING 2020 - 28th International Conference on computational Linguistics, Proceedings of the Conference. All rights reserved.

关键词： Classification (of information)

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Automated Detection of Epileptic Spikes and Seizures Incorporating a Novel Spatial Clustering Prior

Automated Detection of Epileptic Spikes and Seizures Incorpo...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Hanyang Dong Shurong Sheng Xiongfei Wang Jia-Hong Gao Yi Sun Wanli Yang Kuntao Xiao Pengfei Teng Guoming Luan Zhao Lv School of Artificial Intelligence Anhui University Hefei China Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Department of Neurosurgery Beijing Key Laboratory of Epilepsy Sanbo Brain Hospital Capital Medical University Beijing China McGovern Institute for Brain Research Peking University Beijing China Center for MRI Research Academy for Advanced Interdisciplinary Studies Peking University Beijing China Beijing City Key Lab for Medical Physics and Engineering Institute of Heavy Ion Physics School of Physics Peking University Beijing China Anhui Province Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

A Magnetoencephalography (MEG) time-series recording consists of multi-channel signals collected by superconducting sensors, with each signal’s intensity reflecting magnetic field changes over time at the sensor location. Automating epileptic MEG spike detection significantly reduces manual assessment time and effort, yielding substantial clinical benefits. Existing research addresses MEG spike detection by encoding neural network inputs with signals from all channel within a time segment, followed by classification. However, these methods overlook simultaneous spiking occurred from nearby sensors. We introduce a simple yet effective paradigm that first clusters MEG channels based on their sensor’s spatial position. Next, a novel convolutional input module is designed to integrate the spatial clustering and temporal changes of the signals. This module is fed into a custom MEEG-ResNet3D developed by the authors, which learns to extract relevant features and classify the input as a spike clip or not. Our method achieves an F1 score of 94.73% on a large real-world MEG dataset Sanbo-CMR collected from two centers, outperforming state-of-the-art approaches by 1.85%. Moreover, it demonstrates efficacy and stability in the Electroencephalographic (EEG) seizure detection task, yielding an improved weighted F1 score of 1.4% compared to current state-of-the-art techniques evaluated on TUSZ, whch is the largest EEG seizure dataset.

关键词： Convolution Magnetic sensors neural networks Magnetoencephalography Manuals Feature extraction Electroencephalography Encoding Recording Magnetic fields

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Method for Preoperative Prediction of Microvascular Invasion of Hepatocellular Carcinoma

Method for Preoperative Prediction of Microvascular Invasion...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Chen Zhao Tian Bai Tongjia Chu Feng Wei Fa Zhang College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China Hepatobiliary and Pancreas Department General Surgery Center First Hospital of Jilin University Changchun China School of Medical Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is of great guiding significance for the formulating treatment strategies and accessing the prognosis before the surgery. However, in traditional medicine, the gold standard for the diagnosis of MVI is obtained by examining pathological images which can only be obtained by sampling and sectioning tumors after surgery. At this time, MVI results have lost the timeliness of guiding tumor resection surgery. In order to solve this problem, existing studies began to use deep learning-based methods for preoperative prediction of MVI using non-invasive imaging. Most of these methods adopt the fusion methods of multi-sequence images to predict MVI, but fail to make full use of the characteristics of multiply sequences as prior knowledge to combine into the model, resulting in no further improvement of prediction performance. So we propose a multi-sequence image difference and correlation deep learning model. The model can extract the difference and correlation information between sequences from different scales and combine them into the model. To validate proposed model, we collected a data set consists of 120 HCC patients, including 50 MVI-positive patients. Compared with existing studies, our method has greatly improved in all evaluation metrics.

关键词： Deep learning Pathology Correlation Biological system modeling Surgery Predictive models Data mining

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Light Radiation Simulation of Micro-LED Array for the Optical neural Interface Targeted Cerebral Cortex in Rhesus Monkey

Light Radiation Simulation of Micro-LED Array for the Optica...

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International Winter Workshop on Brain-computer Interface (BCI)

作者： Lusheng Liu Wenlei Zhang Bo Wen Minjie Wang Aiping Wang Jing Zhang Yuan Zhang Song Le Haifeng Jiang Rongwei Zhai Lihua Zhang Xiaoyang Kang Shanghai Engineering Research Center of AI & Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of AI & Robotics Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI & Robotics Ministry of Education Institute of Meta-Medical Academy for Engineering & Technology Fudan University Shanghai China Ji Hua Laboratory Foshan Guangdong Province China Yiwu Research Institute of Fudan University Chengbei Road Yiwu City Zhejiang China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou China Shanghai Mental Health Center Shanghai China Jiao Tong University School of Medicine Shanghai China Lingang Laboratory Shanghai China

The advanced technology of optogenetics has greatly promoted the development of neuroscience. To further optimize the optical neural interface, understanding the propagation process of light in neural tissues is essential for neuroscience research on the application of LED in optogenetics. In this work, we use TracePro software to build geometric models and simulate LED devices. The Rhesus monkey brain model is established to simulate the neural tissue to analyze the irradiance distribution at a specific location, to obtain the effective lighting range. In addition, we analyzed the influencing factors of the illumination range of the implanted light source device. We believe that the distance between devices should be $1.1 \sim1.2$ times of the light range of the simulation result when the devices fit the brain.

关键词： Neuroscience Shape Simulation Lighting Implants Light emitting diodes Optogenetics

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A SURVEY ON BRAIN-INSPIRED DEEP LEARNING VIA PREDICTIVE CODING

arXiv

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arXiv 2023年

作者： Salvatori, Tommaso Mali, Ankur Buckley, Christopher L. Lukasiewicz, Thomas Rao, Rajesh P.N. Friston, Karl Ororbia, Alexander VERSES AI Research Lab Los AngelesCA United States Institute of Logic and Computation Vienna University of Technology Austria University of South Florida TampaFL33620 United States Sussex AI Group Department of Informatics University of Sussex Brighton United Kingdom Department of Computer Science University of Oxford United Kingdom Paul G. Allen School of Computer Science and Engineering University of Washington SeattleWA United States The Wellcome Centre for Human Neuroimaging Queen Square Institute of Neurology University College London United Kingdom The Neural Adaptive Computing Laboratory Rochester Institute of Technology RochesterNY14623 United States

Artificial intelligence (AI) is rapidly becoming one of the key technologies of this century. The majority of results in AI thus far have been achieved using deep neural networks trained with the error backpropagation learning algorithm. However, such an algorithm has always been considered biologically implausible. To this end, recent works have studied learning algorithms for deep neural networks inspired by the neurosciences. One such theory, called predictive coding (PC), has shown promising properties that make it potentially valuable for the machine learning community: it can model information processing in different areas of the brain, can be used in cognitive control and robotics, has a solid mathematical foundation in variational inference, and performs its computations asynchronously. Inspired by such properties, works that propose novel PC-like algorithms are starting to be present in multiple sub-fields of machine learning and artificial intelligence at large. Here, we survey such efforts by first providing a broad overview of the history of PC to provide a common ground for the understanding of the recent developments, then by surveying current efforts and results, and concluding with a large discussion of possible implications and ways forward. Copyright © 2023, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Distributed de novo assembler for large-scale long-read datasets

Distributed de novo assembler for large-scale long-read data...

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IEEE International Conference on Big Data

作者： Sayan Goswami Kisung Lee Seung-Jong Park Louisiana State University Shreveport School of Electrical Engineering & Computer Science Louisiana State University School of Electrical Engineering & Computer Science Center for Computation & Technology Louisiana State University

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

Third-generation DNA sequencing technologies such as single-molecule real-time sequencing (SMRT) and nanopore sequencing have the potential to fill the gaps in the existing genome databases since the raw sequences produced by these machines are much longer than those of previous generations and therefore result in more contiguous assemblies. However, these long reads have a high error rate, which makes the assembly process computationally challenging. Moreover, since existing long-read assemblers are designed to run on a single machine, they either take days to complete or run out of memory on even moderate-sized datasets. In this paper, we present a distributed long-read assembler that can assemble large-scale noisy sequence datasets on thousands of cores, resulting in orders of magnitude faster assembly times. By effectively using the map-reduce computation model with a distributed hash-map, both built using a high-performance active messaging middleware, we can assemble a PacBio human genome dataset with 139 billion base-pairs (about 130 GB) in about 33 minutes (using 2,560 cores) compared to more than 38 hours (using 28 cores) with the current state-of-the-art assembler.

关键词： Sequential analysis Genomics Big Data Real-time systems Noise measurement Bioinformatics Middleware

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Berry Curvature and Bulk-Boundary Correspondence from Transport Measurement for Photonic Chern Bands

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Physical Review Letters 2023年第13期131卷 133601-133601页

作者： Chao Chen Run-Ze Liu Jizhou Wu Zu-En Su Xing Ding Jian Qin Lin Wang Wei-Wei Zhang Yu He Xi-Lin Wang Chao-Yang Lu Li Li Barry C. Sanders Xiong-Jun Liu Jian-Wei Pan Hefei National Laboratory for Physical Sciences at Microscale and Department of Modern Physics University of Science and Technology of China Hefei Anhui 230026 China CAS Centre for Excellence and Synergetic Innovation Centre in Quantum Information and Quantum Physics University of Science and Technology of China Shanghai 201315 China National Laboratory of Solid State Microstructures School of Physics Nanjing University Nanjing 210093 China Department of Physics Southern University of Science and Technology Shenzhen 518055 China The Physics Department and the Solid State Institute Technion–Israel Institute of Technology Haifa 3200003 Israel Department of Physics University of Konstanz D-78457 Konstanz Germany School of Computer Science Northwestern Polytechnical University Xi’an 710129 China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen 518055 China Institute for Quantum Science and Technology University of Calgary Alberta T2N 1N4 Canada International Center for Quantum Materials School of Physics Peking University Beijing 100871 China CAS Center for Excellence in Topological Quantum Computation University of Chinese Academy of Sciences Beijing 100190 China International Quantum Academy Shenzhen 518048 China

Berry curvature is a fundamental element to characterize topological quantum physics, while a full measurement of Berry curvature in momentum space was not reported for topological states. Here we achieve two-dimensional Berry curvature reconstruction in a photonic quantum anomalous Hall system via Hall transport measurement of a momentum-resolved wave packet. Integrating measured Berry curvature over the two-dimensional Brillouin zone, we obtain Chern numbers corresponding to −1 and 0. Further, we identify bulk-boundary correspondence by measuring topology-linked chiral edge states at the boundary. The full topological characterization of photonic Chern bands from Berry curvature, Chern number, and edge transport measurements enables our photonic system to serve as a versatile platform for further in-depth study of novel topological physics.

关键词： Quantum simulation Quantum walks Topological effects in photonic systems Topological phase transition Topological phases of matter

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Differential-Critic GAN: Generating What You Want by a Cue of Preferences

arXiv

引用

arXiv 2021年

作者： Yao, Yinghua Pan, Yuangang Tsang, Ivor W. Yao, Xin Guangdong Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Australian Artificial Intelligence Institute University of Technology Sydney Australia A*STAR Center for Frontier AI Research Singapore. Australian Artificial Intelligence Institute University of Technology Sydney Australia Guangdong Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China School of Computer Science University of Birmingham United Kingdom

This paper proposes Differential-Critic Generative Adversarial Network (DiCGAN) to learn the distribution of user-desired data when only partial instead of the entire dataset possesses the desired property. DiCGAN generates desired data that meets the user’s expectations and can assist in designing biological products with desired properties. Existing approaches select the desired samples first and train regular GANs on the selected samples to derive the user-desired data distribution. However, the selection of the desired data relies on global knowledge and supervision over the entire dataset. DiCGAN introduces a differential critic that learns from pairwise preferences, which are local knowledge and can be defined on a part of training data. The critic is built by defining an additional ranking loss over the Wasserstein GAN’s critic. It endows the difference of critic values between each pair of samples with the user preference and guides the generation of the desired data instead of the whole data. For a more efficient solution to ensure data quality, we further reformulate DiCGAN as a constrained optimization problem, based on which we theoretically prove the convergence of our DiCGAN. Extensive experiments on a diverse set of datasets with various applications demonstrate that our DiCGAN achieves state-of-the-art performance in learning the user-desired data distributions, especially in the cases of insufficient desired data and limited supervision. The code is available in https://***/EvaFlower/Differential-Critic-GAN. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

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