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2024 6th International Conference on Artificial Intelligence Technologies and Applications, ICAITA 2024

作者： Zhao, Zhuoyue Wang, Haotian Xu, Liyang Wang, Qian Tang, Yuhua Department of Intelligent Data Science National University of Defense Technology Changsha410073 China Intelligent Game and Decision Lab. Academy of Military Science Beijing100071 China Institute for Quantum Information State Key Laboratory of High-Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China

Existing methods estimate treatment effects from observational data and assume that covariates are all confounders. However, observed covariates may not directly represent confounding variables that influence both treatment and outcome. They always include variables that only affect the treatment or the outcome. In addition, for multi-dimensional binary treatments, disentangled methods are mainly designed for binary variables and ignore the impact of multi-cause treatment variables on the inference of latent factors. To address these two issues, based on variable decomposition and proxy inference, we propose the Disentangled Latent Factors for Multi-cause Treatment Estimation (DEMTE) algorithm. It utilizes an identifiable autoencoder to infer and disentangle latent factors based on the joint distribution of variables in observational data. DEMTE evaluates the treatment effect on the disentangled factors. Synthetic experiments and semi-synthetic experiments demonstrate the effectiveness of the inference and disentanglement techniques and our method achieves more accurate treatment effect estimation. © 2024 Institute of Physics Publishing. All rights reserved.

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Interacting plexcitons for designed ultrafast optical nonlinearity in a monolayer semiconductor

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Light(science & Applications) 2022年第5期11卷 877-887页

作者： Yuxiang Tang Yanbin Zhang Qirui Liu Ke Wei Xiang’ai Cheng Lei Shi Tian Jiang College of Advanced Interdisciplinary Studies National University of Defense Technology410073 ChangshaChina Key Laboratory of Micro-and NanoPhotonic Structures(Ministry of Education) and State Key Laboratory of Surface PhysicsDepartment of PhysicsFudan University200433 ShanghaiChina State Key Laboratory of High Performance Computing College of ComputerNational University of Defense Technology410073 ChangshaChina Beijing Institute for Advanced Study National University of Defense Technology100000 BeijingChina

Searching for ideal materials with strong effective optical nonlinear responses is a long-term task enabling remarkable breakthroughs in contemporary quantum and nonlinear ***,hybridized light-matter quasiparticles,are an appealing candidate to realize such ***,we explore a class of peculiar polaritons,named plasmon–exciton polaritons(plexcitons),in a hybrid system composed of silver nanodisk arrays and monolayer tungsten-disulfide(WS2),which shows giant room-temperature nonlinearity due to their deep-subwavelength localized ***,comprehensive ultrafast pump–probe measurements reveal that plexciton nonlinearity is dominated by the saturation and higher-order excitation-induced dephasing interactions,rather than the well-known exchange interaction in traditional microcavity ***,we demonstrate this giant nonlinearity can be exploited to manipulate the ultrafast nonlinear absorption properties of the solid-state *** findings suggest that plexcitons are intrinsically strongly interacting,thereby pioneering new horizons for practical implementations such as energy-efficient ultrafast all-optical switching and information processing.

关键词： nonlinearity monolayer nonlinear

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LightTraffic: On Optimizing CPU-GPU Data Traffic for Efficient Large-scale Random Walks 39

LightTraffic: On Optimizing CPU-GPU Data Traffic for Efficie...

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39th IEEE International Conference on Data Engineering, ICDE 2023

作者： Xing, Yipeng Li, Yongkun Wang, Zhiqiang Xu, Yinlong Lui, John C. S. University of Science and Technology of China China Anhui Province Key Laboratory of High Performance Computing China The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350322279

As a fundamental tool for graph analysis, random walk receives extensive attention in both industry and academia. For computing massive random walks, recent works show that GPUs provide a good option to accelerate the performance. However, due to the limited memory space of modern GPUs, it is infeasible to have both the graph data and walk index fully reside in GPU memory when running large-scale random walks. Thus, it necessitates an out-of-GPU-memory design, but this inevitably induces large amounts of CPU-GPU data transmission traffic and thus hinders the overall performance. In this paper, we develop LightTraffic, which optimizes the data transmission between CPU and GPU memory under the constraint of GPU memory capacity with various system designs, including a memory-efficient scheme for partition-based management and multiple scheduling techniques. LightTraffic is a fully out-of-GPU-memory design, so it supports running large-scale random walks on GPUs. Experiments on our prototype show that LightTraffic outperforms various state-of-the-art CPU-based in-memory systems which also support large-scale random walks. For example, compared to the CPU-based systems FlashMob and ThunderRW, which are highly optimized for random walks, LightTraffic achieves 1.7-5.0× and 1.4 - 12.8× performance speedup, respectively. It also achieves up to an order of magnitude speedup when compared to the GPU-based system Subway which also supports large-scale random walks with an out-of-GPU-memory design for graph data. © 2023 IEEE.

关键词： Graphics processing unit

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Experimental demonstration of quantum transport enhancement using time-reversal symmetry breaking on a silicon photonic chip

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science China(Physics,Mechanics & Astronomy) 2022年第10期65卷 2-9页

作者： Yang Wang Xinyao Yu Shichuan Xue Yizhi Wang Junwei Zhan Chao Wu Pingyu Zhu Qilin Zheng Miaomiao Yu Yingwen Liu Xiaogang Qiang Junjie Wu Xuejun Yang Ping Xu Institute for Quantum Information&State Key Laboratory of High Performance Computing College of Computer Science and TechnologyNational University of Defense TechnologyChangsha 410073China National Innovation Institute of Defense Technology Academy of Military SciencesBeijing 100071China National Laboratory of Solid State Microstructures and School of Physics Nanjing UniversityNanjing 210093China

The continuous-time quantum walk is a basic model for studying quantum transport and developing quantum-enhanced algorithms. Recent studies show that by introducing a phase into the standard continuous-time quantum walk model, the time-reversal symmetry can be broken without changing the Hermitian property of the Hamiltonian. The time-reversal symmetry breaking quantum walk shows advantages in quantum transport, such as perfect state transfer, directional control, transport speedup, and quantum transport efficiency enhancement. In this work, we implement the time-reversal symmetry breaking quantum walks on a reconfigurable silicon photonic chip and demonstrate the enhancement introduced by breaking time-reversal symmetry. Perfect state transfer on a three-site ring, a quantum switch implemented on a six-site graph, and transport speedup using a linear chain of triangles are demonstrated with high fidelity. Time-reversal asymmetry has also been used in a simplified light-harvesting model,implying the potential of time-reversal symmetry breaking in photosynthesis investigations.

关键词： quantum walk time-reversal asymmetry quantum transport

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Quantum algorithm and experimental demonstration for the subset sum problem

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science China(Information sciences) 2022年第8期65卷 261-274页

作者： Qilin ZHENG Pingyu ZHU Shichuan XUE Yang WANG Chao WU Xinyao YU Miaomiao YU Yingwen LIU Mingtang DENG Junjie WU Ping XU Institute for Quantum Information and State Key Laboratory of High Performance Computing College of Computer Science and TechnologyNational University of Defense Technology

To solve the subset sum problem, a well-known nondeterministic polynomial-time complete problem that is widely used in encryption and resource scheduling, we propose a feasible quantum algorithm that utilizes fewer qubits to encode and achieves quadratic speedup. Specifically, this algorithm combines an amplitude amplification algorithm with quantum phase estimation, and requires n + t + 1 qubits and O(2(0.5+o(1))n) operations to obtain the solution, where n is the number of elements, and t is the number of qubits used to store the eigenvalues. To verify the performance of the algorithm, we simulate the algorithm with the online quantum simulator of IBM named ibmq simulator using Qiskit and then run it on two IBM quantum computers called ibmq santiago and ibmq bogota. The experimental results indicate that compared with the brute force algorithm, the proposed algorithm results in quadratic acceleration for the problem of a set S with four elements and two subsets whose sum equals target w. Using the iterator twice, we obtain success probabilities of 0.940 ± 0.004, 0.751 ± 0.040, and 0.665 ± 0.060 on the simulator, ibmq santiago, and ibmq bogota, respectively, and the fidelity between the theoretical and experimental quantum states is calculated to be 0.944 ± 0.002, 0.753 ± 0.017, and 0.657 ± 0.028, respectively. If the error rates of the experimental quantum logic gates can be reduced, the success probabilities of the proposed algorithm on real quantum devices can be further improved.

关键词： quantum algorithm subset sum quadratic speedup encryption algorithm complexity

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DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for highly-Effective Drug-Protein Interaction Representation

DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Qiu, Yanlong Wang, Siqi Yang, Xi Qiu, Xinyuan Wu, Chengkun Cui, Yingbo Yang, Canqun National University of Defense Technology Institute for Quantum Information State Key Laboratory of High-Performance Computing College of Computer Science Hunan Changsha410073 China National Supercomputer Center in Tianjin Tianjin300457 China National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing College of Computer Science Hunan Changsha410073 China National University of Defense Technology Department of Biology and Chemistry College of Science Hunan Changsha410073 China

ISBN: (纸本)9798350337488

With the exponential growth of biomedical knowledge in unstructured text repositories such as PubMed, it is imminent to establish a knowledge graph-style, efficient searchable and targeted database that can support the need of information retrieval from researchers and clinicians. To mine knowledge from graph databases, most previous methods view a triple in a graph (see Fig. 1) as the basic processing unit and embed the triplet element (i.e. drugs/chemicals, proteins/genes and their interaction) as separated embedding matrices, which cannot capture the semantic correlation among triple elements. To remedy the loss of semantic correlation caused by disjoint embeddings, we propose a novel approach to learn triple embeddings by combining entities and interactions into a unified representation. Furthermore, traditional methods usually learn triple embeddings from scratch, which cannot take advantage of the rich domain knowledge embedded in pre-trained models, and is also another significant reason for the fact that they cannot distinguish the differences implied by the same entity in the multi-interaction triples. In this paper, we propose a novel fine-tuning based approach to learn better triple embeddings by creating weakly supervised signals from pre-trained knowledge graph embeddings. The method automatically samples triples from knowledge graphs and estimates their pairwise similarity from pre-trained embedding models. The triples are then fed pairwise into a Siamese-like neural architecture, where the triple representation is fine-tuned in the manner bootstrapped by triple similarity scores. Finally, we demonstrate that triple embeddings learned with our method can be readily applied to several downstream applications (e.g. triple classification and triple clustering). We evaluated the proposed method on two open-source drug-protein knowledge graphs constructed from PubMed abstracts, as provided by BioCreative. Our method achieves consistent improvement in both t

关键词： Drug-Protein Interaction Knowledge Graph Embedding Triple Embedding Weakly Supervised Learning

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M-DFCPP: A runtime library for multi-machine dataflow computing

M-DFCPP: A runtime library for multi-machine dataflow comput...

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作者： Luo, Qiuming Liu, Senhong Huang, Jinke Li, Jinrong College of Computer Science and Software Engineering Shenzhen University Shenzhen China SKT Group Guangdong Province Key Laboratory of Popular High-Performance Computers Shenzhen China SKT Group Guangdong Province Engineering Center of China-made High Performance Data Computing System Shenzhen China

This article designs and implements a runtime library for general dataflow programming, DFCPP (Luo Q, Huang J, Li J, Du Z. Proceedings of the 52nd International Conference on Parallel Processing Workshops. ACM;2023:145-152.), and builds upon it to design and implement a multi-machine C++ dataflow library, M-DFCPP. In comparison to existing dataflow programming environments, DFCPP features a user-friendly interface and richer expressive capabilities (Luo Q, Huang J, Li J, Du Z. Proceedings of the 52nd International Conference on Parallel Processing Workshops. ACM;2023:145-152.), enabling the representation of various types of dataflow actor tasks (static, dynamic and conditional task). Besides that, DFCPP addresses the memory management and task scheduling for non-uniform memory access architectures, while other dataflow libraries lack attention to these issues. M-DFCPP extends the capability of current dataflow runtime libraries (DFCPP, taskflow, openstream, etc.) and capable of multi-machine computing, while maintains the API compatible with DFCPP. M-DFCPP adopts the concepts of master and follower (Dean J, Ghemawat S. Commun ACM. 2008;51(1):107-113;Ghemawat S, Gobioff H, Leung ST. ACM SIGOPS Operating Systems Review. ACM;2003:29-43.), which form a worksharing framework as many multi-machine system. To shift to the M-DFCPP framework, a filtering layer is inserted to the original DFCPP, transforming it into followers that can cooperate with each other. The master is made of modules for scheduling, data processing, graph partition, state management and so forth. In benchmark tests with workload with directed acyclic graph topology of binary trees and random graphs, DFCPP demonstrated performance improvements of 20% and 8%, respectively, compared to the second fastest library. M-DFCPP consistently exhibits outstanding performance across varying levels of concurrency and task workloads, achieving a maximum speedup of more than 20 over DFCPP, when the task parallelism e

关键词： Libraries

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Pronounced advance on typhoon track forecast with global convection-permitting model

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science Bulletin 2025年第9期70卷 1424-1428页

作者： Gu, Jun Zhao, Chun Li, Gudongze Feng, Jiawang Xu, Mingyue Du, Qiuyan Xia, Zihan Li, Yubin Chen, Guanghua Hao, Xiaoyu Chen, Junshi An, Hong Deep Space Exploration Laboratory/School of Earth and Space Sciences/CMA-USTC Laboratory of Fengyun Remote Sensing/State Key Laboratory of Fire Science/Institute of Advanced Interdisciplinary Research on High-Performance Computing Systems and Software University of Science and Technology of China Hefei230026 China Laoshan Laboratory Qingdao266237 China CAS Center for Excellence in Comparative Planetology University of Science and Technology of China Hefei230026 China School of Atmospheric Physics Nanjing University of Information Science and Technology Nanjing210044 China Key Laboratory of Cloud-Precipitation Physics and Severe Storms Institute of Atmospheric Physics Chinese Academy of Sciences Beijing100029 China School of Computer Science and Technology University of Science and Technology of China Hefei230026 China

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Generation of Adversarial Malware Based on Genetic Algorithm and Instruction Replacement 23

Generation of Adversarial Malware Based on Genetic Algorithm...

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4th International Conference on computing, Networks and Internet of Things, CNIOT 2023

作者： Pan, Siyi Sun, Aoxiang Xu, Yingmei Liang, Zhuoqian Sun, Yuxia College of Information Science and Technology Jinan University Guangzhou China College of Information and Computational Science Jilin University China Guangdong Science and Technology Infrastructure Center China Guangdong Provincial Key Laboratory of High-Performance Computing China

ISBN: (纸本)9798400700705

With the development of cyberspace security attack and defense, the malware detection model based on machine learning is also facing the threat of adversarial examples. An important way to defend against such threats is to generate effective adversarial examples and then use them to conduct adversarial training on the model. For the end-To-end PE malware detection model, most of the existing generation technologies for adversarial examples adopt the method of inserting dead codes, but the dead codes are easy to be filtered out by preprocessing. A novel adversarial-example generation approach for PE malware, called AGA, is proposed based on a genetic algorithm and equivalent-instruction replacement. The experimental studies show that the AGA approach outperforms the existing generation approach, which is based on a particle-swarm-optimization algorithm, in terms of attack effectiveness and attack-generation efficiency. © 2023 ACM.

关键词： Genetic algorithms

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Anomalous negative magnetoresistance in quantum dot Josephson junctions with Kondo correlations

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science China(Physics,Mechanics & Astronomy) 2024年第8期67卷 3-13页

作者： Mingtang Deng Chunlin Yu Guangyao Huang Rosa López Philippe Caroff Sepideh Ghalamestani Gloria Platero Hongqi Xu Institute for Quantum Information&State Key Laboratory of High Performance Computing College of Computer Science and TechnologyNational University of Defense TechnologyChangsha 410073China NanoLund and Division of Solid State Physics Lund UniversityLund S-22100Sweden China Greatwall Quantum Laboratory Changsha 410006China Institut de Física Interdisciplinària i de Sistemes Complexos IFISC Palma de Mallorca E-07122Spain Instituto de Ciencia de Materiales de Madrid Madrid 28049Spain Beijing Key Laboratory of Quantum Devices Key Laboratory for the Physics and Chemistry of Nanodevicesand Department of ElectronicsPeking UniversityBeijing 100871China Beijing Academy of Quantum Information Sciences Beijing 100193China Hefei National Laboratory Hefei 230088China

The interplay between superconductivity and the Kondo effect has stimulated significant interest in condensed matter *** compete when their critical temperatures are close and can give rise to a quantum phase transition that can mimic Majorana zero ***,we have fabricated and measured Al-InSb nanowire quantum dot-Al *** the Kondo regime,a supercurrent-induced zero-bias conductance peak *** zero-bias peak shows an anomalous negative magnetoresistance(NMR)at weak magnetic *** attribute this anomalous NMR to quasiparticle trapping at vortices in the superconductor leads as a weak magnetic field is *** trapping effect lowers the quasiparticle-caused dissipation and thus enhances the Josephson *** work connects the vortex physics and the supercurrent tunneling in Kondo regimes and can help further understand the physics of Josephson quantum dot system.

关键词： quantum dot Kondo correlation InSb nanowire quasiparticle trapping

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