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Loop Quantum Photonic Chip for Coherent Multi-Time-Step Evolution

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arXiv 2024年

作者： Zhan, Yuancheng Zhang, Hui Erbanni, Rebecca Burger, Andreas Wan, Lingxiao Jiang, Xudong Chae, Sanghoon Liu, Aiqun Poletti, Dario Kwek, Leong Chuan Nanyang Technological University Singapore Nanyang Technological University Singapore Institute of Precision Optical Engineering School of Physics Science and Engineering Tongji University Shanghai200092 China MOE Key Laboratory of Advanced Micro-Structured Materials Shanghai Institute of Intelligent Science and Technology Shanghai Frontiers Science Center of Digital Optics Tongji University Shanghai200092 China Science Mathematics and Technology Cluster Singapore University of Technology and Design 8 Somapah Road 487372 Singapore Department of Computer Science University of Toronto Canada The Hong Kong Polytechnic University Hong Kong Engineering Product Development Pillar Singapore University of Technology and Design 8 Somapah Road 487372 Singapore Centre for Quantum Technologies National University of Singapore 117543 Singapore MajuLab CNRS-UNS-NUS-NTU International Joint Research Unit UMI 3654 Singapore National Institute of Education Nanyang Technological University Singapore

Quantum evolution is crucial for the understanding of complex quantum systems. However, current implementations of time evolution on quantum photonic platforms face challenges of limited light source efficiency due to propagation loss and merely single-layer complexity. In this work, we present a loop quantum photonic chip (Loop-QPC) designed to efficiently simulate quantum dynamics over multiple time steps in a single chip. Our approach employs a recirculating loop structure to reuse computational resources and eliminate the need for multiple quantum tomography steps or chip reconfigurations. We experimentally demonstrate the dynamics of the spin-boson model on a low-loss Silicon Nitride (SiN) integrated photonic chip. The Loop-QPC achieves a three-step unitary evolution closely matching the theoretical predictions. These results establish the Loop-QPC as a promising method for efficient and scalable quantum simulation, advancing the development of quantum simulation on programmable photonic circuits. Copyright © 2024, The Authors. All rights reserved.

关键词： Silicon nitride

Surface state induced velocity transition for layered carbon nanotube films under transverse penetration loading

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Composite Structures 2025年 369卷

作者： Pengfei Wang Wenshuai Wang Jie Tian Zhenhua Song Songlin Xu CAS Key Laboratory of Mechanical Behavior and Design of Materials Department of Modern Mechanics University of Science and Technology of China Hefei 230027 China Engineering and Materials Science Experiment Center University of Science and Technology of China Hefei 230027 China Department of Aeronautics and Astronautics College of Intelligent Robotics and Advanced Manufacturing Fudan University Shanghai 200433 China

Carbon nanotube (CNT) films, consisting of entangled two-dimensional networks, exhibit prominent surface-dependent characteristics. Lubricants have been recognized as an effective strategy for reducing interfacial friction. However, experimental evidence indicates that lubricants unexpectedly increased the interfacial friction, demonstrating a counterintuitive velocity-dependent behavior. To elucidate the anomalous interfacial dynamics, experimental and computational methodologies were employed to systematically investigate the transverse penetrating behaviors of CNT films with different interface states and loading velocities. Experimental results indicate that the lubricant exhibits a decreasing trend at lower relative velocities, but transitions from decreasing to strengthening behavior as penetration velocity increases. The proposed equation reveals that the strengthening mechanism is not solely velocity-dependent but is jointly governed by the load magnitude and surface conditions. The high specific surface area of CNT films induces enhanced interface states and pronounced velocity-dependent characteristics, which collectively govern the fracture pattern observed in films subjected to distinct surface stress conditions. Deciphering the lubricant-induced velocity-transition mechanisms in CNT films yields new theoretical insights into interface-governed penetration behavior, while establishing material design principles for engineering impact-resistant membranes with tunable energy dissipation pathways.

关键词： Carbon films

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VarBen

The Journal of Molecular Diagnostics 2021年第3期23卷 285-299页

作者： Ziyang Li Shuangsang Fang Rui Zhang Lijia Yu Jiawei Zhang Dechao Bu Liang Sun Yi Zhao Jinming Li National Center for Clinical Laboratories Beijing Hospital National Center of Gerontology Beijing People's Republic of China Graduate School Peking Union Medical College Chinese Academy of Medical Sciences Beijing People's Republic of China Key Laboratory of Intelligent Information Processing Advanced Computer Research Center Institute of Computing Technology Chinese Academy of Medical Sciences Beijing People's Republic of China Beijing Engineering Research Center of Laboratory Medicine Beijing Hospital Beijing People's Republic of China

Next-generation sequencing is increasingly being adopted as a valuable method for the detection of somatic variants in clinical oncology. However, it is still challenging to reach a satisfactory level of robustness and standardization in clinical practice when using the currently available bioinformatics pipelines to detect variants from raw sequencing data. Moreover, appropriate reference data sets are lacking for clinical bioinformatics pipeline development, validation, and proficiency testing. Herein, we developed the Variant Benchmark tool (VarBen), an open-source software for variant simulation to generate customized reference data sets by directly editing the original sequencing reads. VarBen can introduce a variety of variants, including single-nucleotide variants, small insertions and deletions, and large structural variants, into targeted, exome, or whole-genome sequencing data, and can handle sequencing data from both the Illumina and Ion Torrent sequencing platforms. To demonstrate the feasibility and robustness of VarBen, we performed variant simulation on different sequencing data sets and compared the simulated variants with real-world data. The validation study showed that the simulated data are highly comparable to real-world data and that VarBen is a reliable tool for variant simulation. In addition, our collaborative study of somatic variant calling in 20 laboratories emphasizes the need for laboratories to evaluate their bioinformatics pipelines with customized reference data sets. VarBen may help users develop and validate their bioinformatics pipelines using locally generated sequencing data.

关键词：

Graph classification based on skeleton and component features

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arXiv 2021年

作者： Liu, Xue Wei, Wei Feng, Xiangnan Cao, Xiaobo Sun, Dan Beijing System Design Institute of Electro-Mechanic Engineering Beijing100854 China School of Mathematical Sciences Beihang University Beijing100191 China Key Laboratory of Mathematics Informatics and Behavioral Semantics Ministry of Education 100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Peng Cheng Laboratory Shenzhen Guangdong518066 China

Most existing popular methods for learning graph embedding only consider fixed-order global structural features and lack structures hierarchical representation. To address this weakness, we propose a novel graph embedding algorithm named GraphCSC that realizes classification based on skeleton information using fixed-order structures learned in anonymous random walks manner, and component information using different size subgraphs. Two graphs are similar if their skeletons and components are both similar, thus in our model, we integrate both of them together into embeddings as graph homogeneity characterization. We demonstrate our model on different datasets in comparison with a comprehensive list of up-to-date state-of-the-art baselines, and experiments show that our work is superior in real-world graph classification tasks. © 2021, CC BY.

关键词： Musculoskeletal system

Observation of Electric Hysteresis, Polarization Oscillation, and Pyroelectricity in Nonferroelectric p−n Heterojunctions

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Physical Review Letters 2023年第19期130卷 196801-196801页

作者： Yucheng Jiang Xinglong Ma Lin Wang Jinlei Zhang Zhichao Wang Run Zhao Guozhen Liu Yang Li Cheng Zhang Chunlan Ma Yaping Qi Lin Wu Ju Gao Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application School of Physical Science and Technology Suzhou University of Science and Technology Suzhou 215009 China School of Physics Nanjing University Nanjing 210093 China School of Materials Science and Engineering Shanghai University Shanghai 200444 China Macau Institute of Systems Engineering Macau University of Science and Technology Macau 999078 China Advanced Institute for Materials Research Tohoku University Sendai 980-8577 Japan Science Mathematics and Technology Singapore University of Technology and Design 8 Somapah Road Singapore 487372 Singapore Institute of High Performance Computing Agency for Science Technology and Research 1 Fusionopolis Way #16-16 Connexis Singapore 138632 Singapore School for Optoelectronic Engineering Zaozhuang University Shandong 277160 China

The switchable electric polarization is usually achieved in ferroelectric materials with noncentrosymmetric structures, which opens exciting opportunities for information storage and neuromorphic computing. In another polar system of p−n junction, there exists the electric polarization at the interface due to the Fermi level misalignment. However, the resultant built-in electric field is unavailable to manipulate, thus attracting less attention for memory devices. Here, we report the interfacial polarization hysteresis (IPH) in the vertical sidewall van der Waals heterojunctions of black phosphorus and quasi-two-dimensional electron gas on SrTiO3. A nonvolatile switching of electric polarization can be achieved by reconstructing the space charge region (SCR) with long-lifetime nonequilibrium carriers. The resulting electric-field controllable IPH is experimentally verified by electric hysteresis, polarization oscillation, and pyroelectric effect. Further studies confirm the transition temperature of 340 K, beyond which the IPH vanishes. The second transition is revealed with the temperature dropping below 230 K, corresponding to the sharp improvement of IPH and the freezing of SCR reconstruction. This work offers new possibilities for exploring the memory phenomena in nonferroelectric p−n heterojunctions.

关键词： Electric polarization Electroresistance Pyroelectricity

Exploring Multi-level Mutual Information for Drug-target Interaction Prediction

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Exploring Multi-level Mutual Information for Drug-target Int...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jiatao Chen Liang Zhang Ke Cheng Bo Jin Xinjiang Lu Chao Che Yiwei Liu Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian China International Business College Dongbei University of Finance and Economics Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China Business Intelligence Lab Baidu Research

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Recent advances in graph representation learning provide new opportunities for computational drug-target interaction (DTI) prediction. Inspired by the emerging graph mutual information-based algorithms, we propose MMIDTI, a multi-level mutual information-aware DTI prediction framework based on a heterogeneous network (i.e., drug-protein, drug-drug and protein-protein interaction network; drug-disease, drug-side-effect, and protein-disease association network; drug-structure and protein-sequence similarity network). More specifically, MMIDTI leverages an encoder-decoder framework that can learn the type-aware and meta-path augmented node representations by following a contrastive learning paradigm. The encoder part is a Graph Convolutional Network (GCN) and the decoder is an inner product of the learned representations to recover the original heterogeneous network. Meanwhile, MMIDTI exploits two levels of mutual information: (1) maximizing local mutual information, to obtain node representations that capture the global information content of the entire heterogeneous graph. (2) maximizing the global mutual information, to constrain the node representation to have desired statistical characteristics. Experimental results show that our model can achieve better prediction performance than state-of-the-art methods.

关键词： Mutual information Proteins Drugs Predictive models Diffusion tensor imaging Heterogeneous networks Task analysis

Magnesium-based metasurfaces for dual-function switching between dynamic holography and dynamic color display

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arXiv 2021年

作者： Li, Jianxiong Chen, Yiqin Hu, Yueqiang Duan, Huigao Liu, Na Max Planck Institute for Intelligent Systems Heisenbergstrasse 3 Stuttgart70569 Germany State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body College of Mechanical and Vehicle Engineering Hunan University Changsha410082 China Advanced Manufacturing Laboratory of Micro-nano Optical Devices Shenzhen Research Institute Hunan University Shenzhen518000 China Kirchhoff Institute for Physics University of Heidelberg Im Neuenheimer Feld 227 Heidelberg69120 Germany Centre for Advanced Materials University of Heidelberg Im Neuenheimer Feld 225 Heidelberg69120 Germany

Metasurface-based color display and holography have greatly advanced the state of the art display technologies. To further enrich the metasurface functionalities, recently a lot of research endeavors have been made to combine these two display functions within a single device. However, so far such metasurfaces have remained static and lack tunability once the devices are fabricated. In this work, we demonstrate a dynamic dual-function metasurface device at visible frequencies. It allows for switching between dynamic holography and dynamic color display, taking advantage of the reversible phase-transition of magnesium through hydrogenation and dehydrogenation. Spatially-arranged stepwise nanocavity pixels are employed to accurately control the amplitude and phase of light, enabling the generation of high-quality color prints and holograms. Our work represents a paradigm towards compact and multifunctional optical elements for future display technologies. © 2021, CC BY.

关键词： Magnesium

Does Thermal Really Always Matter for RGB-T Salient Object Detection?

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arXiv 2022年

作者： Cong, Runmin Zhang, Kepu Zhang, Chen Zheng, Feng Zhao, Yao Huang, Qingming Kwong, Sam The Institute of Information Science Beijing Jiaotong University Beijing100044 China The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China The Department of Computer Science City University of Hong Kong China The Department of Computer Science and Technology Southern University of Science and Technology Shenzhen518055 China The Research Institute of Trustworthy Autonomous Systems Shenzhen518055 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518055 China The City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

In recent years, RGB-T salient object detection (SOD) has attracted continuous attention, which makes it possible to identify salient objects in environments such as low light by introducing thermal image. However, most of the existing RGBT SOD models focus on how to perform cross-modality feature fusion, ignoring whether thermal image is really always matter in SOD task. Starting from the definition and nature of this task, this paper rethinks the connotation of thermal modality, and proposes a network named TNet to solve the RGB-T SOD task. In this paper, we introduce a global illumination estimation module to predict the global illuminance score of the image, so as to regulate the role played by the two modalities. In addition, considering the role of thermal modality, we set up different cross-modality interaction mechanisms in the encoding phase and the decoding phase. On the one hand, we introduce a semantic constraint provider to enrich the semantics of thermal images in the encoding phase, which makes thermal modality more suitable for the SOD task. On the other hand, we introduce a two-stage localization and complementation module in the decoding phase to transfer object localization cue and internal integrity cue in thermal features to the RGB modality. Extensive experiments on three datasets show that the proposed TNet achieves competitive performance compared with 20 state-of-the-art methods. The code and results can be found from the link of https://***/proj_***. © 2022, CC BY-NC-SA.

关键词： Semantics

Pulmonary nodule detection based on multi-branch 3D squeeze-and-excitation network 17

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Pulmonary nodule detection based on multi-branch 3D squeeze-...

17th IEEE International Conference on Dependable, Autonomic and Secure computing, IEEE 17th International Conference on Pervasive Intelligence and computing, IEEE 5th International Conference on Cloud and Big Data computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019

作者： Zhang, Yuechao Zhang, Jianxin Che, Chao Lin, Kai Zhou, Dongsheng Zhang, Qiang Wei, Xiaopeng Key Lab of Advanced Design and Intelligent Computing (Ministry of Education) Dalian University Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (纸本)9781728130255;9781728130248

Convolutional neural networks based regional proposal networks (RPN) have recently achieved breakthrough results in a variety of medical image detection tasks. Among them, 3D RPN network in conjunction with advanced ResNet gains the state-of-the-art performance. However, current 3D RPN network cannot fully consider different hierarchically spatial information and relationship between channels contained in networks themselves. To overcome this problem, this work proposes a novel multi-branch 3D Squeeze-and-Excitation (SE) network, i.e., Mb3DSENet, which embeds SE-Inception and SE-Residual-Inception blocks into existing 3D RPN network by appropriately assembling SE unit, Inception module and residual connection. Mb3DSENet not only inherits the merits of the basic 3D RPN network, but also enhances its spatial representation power and selectively highlights channel-wise feature responses. Experiment results on the public LUNA16 dataset demonstrate the effectiveness of the proposed network. © 2019 IEEE.

关键词： 3D RPN network Pulmonary nodule detection SE-inception block SE-residual-inception block