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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Li, Shuo Li, Hongjia Zhang, Chi Zhang, Fa Wan, Xiaohua Institute of Computing Technology Chinese Academy of Sciences High Performance Computer Research Center Beijing China University of Chinese Academy of Sciences Beijing China Zhejiang University State Key Laboratory of Cad&cg Hangzhou China Beijing Institute of Technology Beijing China

ISBN: (纸本)9781665468190

Cryo-electron microscopy (cryo-EM) single particle analysis (SPA) has been an indispensable technology to reconstruct three-dimensional (3D) structures of biomolecules at near-atomic resolution. Tens of thousands of particles are required to obtain high-resolution 3D reconstructions, nevertheless, it is rather challenging due to the extremely noisy microscopy images and the diversity of particles. Recently, while deep learning-based methods have been devoted into the improvement of particle feature extraction and location estimation, most of them are plagued with vulnerable feature representation, inexact supervised ground truth. Furthermore, these DL-methods usually adopt denoising and particle picking as two-stage operations in the existing pipeline, which is inadequate to achieve accurate estimation for location. In this paper, we propose a segmentation-aware synergy framework to automatically select particles in which two tightly-coupled networks are designed including a multiple output convolution subnet for denoise to jointly learn strong object representation and pixel representation simultaneously and a deep convolution subnet for particle location. Furthermore, joint learning of the two networks can effectively enhance the synergy relationship between denoising and downstream recognition, thus leading to accurate and reliable location estimations for SPA. When applied with various EMPAIR real-world datasets, our model improves the performance of particle detection and exaction, especially intersection over union metric, and this strength has important implications for the next 2D alignment, 2D classification averaging, and high-resolution 3D refinement steps in SPA. © 2022 IEEE.

关键词： Convolution

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Strain controlled thermal regulator realized in two-dimensional black and blue phosphorene in-plane heterostructure

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Physical Review B 2024年第10期109卷 104106-104106页

作者： Mingyue Xia Luneng Zhao Yuan Chang Hongsheng Liu Gang Zhang Wuxing Zhou Jijun Zhao Junfeng Gao State Key Laboratory of Structural Analysis for Industrial Equipment Dalian University of Technology Dalian 116024 China Laboratory of Materials Modification by Laser Ion and Electron Beams Ministry of Education Dalian University of Technology Dalian 116024 China Institute of High Performance Computing A*STAR Singapore 138632 Singapore School of Materials Science and Engineering Hunan University of Science and Technology Xiangtan 411201 China

The reversible phase transition of two-dimensional material allotropes induced by strain engineering has promising applications in nanoelectromechanical systems and memories. Here, we report a series of black and blue phosphorene superlattice (BBPS) can be formed by reversible phase transition between them. The phase transition is with low-energy barrier and can be well regulated by strain engineering, until black phosphorene phase is fully transformed into blue phosphorene phase under strain of ε = 23%. At room temperature, BBPS exhibits a high phase transition rate of about 106 Hz, which enables reversible phase transition rapidly. Beyond, the unique in-plane heterostructure of BBPS suppresses phonon transmission, lowering thermal conductivity to only 25.6% of black phosphorene. Beyond, nonequilibrium Green's function calculations indicate the thermal conductivity crossing the black-blue phosphorene heterojunction suddenly drops to only 1/3 that of black phosphorene. Therefore, strain engineering can not only achieve reversible phase transition but also modulate the thermal conductivity in very large range, which is excellent in the field of heat flow manipulation and thermal management.

关键词： Electrical properties Phase transitions Thermal conductivity 2-dimensional systems Phosphorene Strain engineering

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Enhanced dispersion of graphene sheets in various liquid solvents supported by poly(ionic liquid) via non-covalent interaction

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Journal of Molecular Liquids 2024年 415卷

作者： Liu, Wei Wu, Routeng Luo, Ting Liang, Yuan Wan, Hai Chen, Yuhan Chen, Yuwei Wei, Peng Huang, Shuohan He, Yong Wang, Yanping Xia, Yumin State Key Laboratory for Modification of Chemical Fibers and Polymer Materials Key Laboratory of High Performance Fibers & Products Ministry of Education College of Materials Science and Engineering College of Science Innovation Center for Textile Science and Technology Donghua University Shanghai201600 China Key Laboratory of Rubber-Plastics Ministry of Education/Shandong Provincial Key Laboratory of Rubber-Plastics Qingdao University of Science & Technology Qingdao City266042 China College of Textiles Henan Key Laboratory of Functional Textile Materials Zhongyuan University of Technology Zhengzhou450007 China

In this study, a new method for the preparation of graphene using poly(ionic liquid) [PEP-MIM]Cl was found and the complex [PEP-MIM]Cl-GO, which combines with graphene oxide (GO) via electrostatic and π-π interactions, was investigated to be dissolved in the organic or aqueous phase, depending on the concentration of the poly(ionic liquid), and to be stably dispersed and liquid crystalline. After reduction to graphene (rGO) by L(+)-ascorbic acid, this stable dispersion can still exist without any agglomerates, and thus the poly(ionic liquid)- functionalised graphene [PEP-MIM]Cl-rGO with good dispersibility in water was prepared, and some explorations of phase transfer and anion exchange of [PEP-MIM]Cl-rGO based on the ion exchangeability of the poly(ionic liquid) were made. exchange, and the success of the preparation of poly(ionic liquid)-functionalized graphene was confirmed by TEM, AFM, FTIR, Raman and other characterizations. © 2024 Elsevier B.V.

关键词： Ascorbic acid

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Practical security of twin-field quantum key distribution with optical phase-locked loop under wavelength-switching attack

arXiv

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arXiv 2024年

作者： Peng, Qingquan Chen, Jiu-Peng Xing, Tianyi Wang, Dongyang Wang, Yizhi Liu, Yang Huang, Anqi Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Hunan Changsha410073 China Jinan Institute of Quantum Technology Hefei National Laboratory Jinan Branch Shandong Jinan250101 China

The twin-field class quantum key distribution (TF-class QKD) has experimentally demonstrated the ability to surpass the fundamental rate-distance limit without requiring a quantum repeater, as a revolutional milestone. In TF-class QKD implementation, an optical phase-locked loop (OPLL) structure is commonly employed to generate a reference light with correlated phase, ensuring coherence of optical fields between Alice and Bob. In this configuration, the reference light, typically located in the untrusted station Charlie, solely provides wavelength reference for OPLL and does not participate in quantum-state encoding. However, the reference light may open a door for Eve to enter the source stations that are supposed to be well protected. Here, by identifying vulnerabilities of an acousto-optic modulator (AOM) in the OPLL scheme, we propose and demonstrate a wavelength-switching attack on a TF-class QKD system. This attack involves Eve deliberately manipulating the wavelength of the reference light to increase mean photon number of prepared quantum states, while maintaining stable interference between Alice and Bob as required by TF-class QKD protocols. The maximum observed increase in mean photon number is 8.7%, which has been theoretically proven to compromise the security of a TF-class QKD system. Moreover, we have shown that with well calibration of the modulators, the attack can be eliminated. Through this study, we highlight the importance of system calibration in the practical security in TF-class QKD *** Codes 81P94 Copyright © 2024, The Authors. All rights reserved.

关键词： Modulators

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Personalized Federated Learning on Heterogeneous and Long-Tailed Data via Expert Collaborative Learning

arXiv

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arXiv 2024年

作者： Lv, Fengling Shang, Xinyi Zhou, Yang Zhang, Yiqun Li, Mengke Lu, Yang Key Laboratory of Multimedia Trusted Perception and Efficient Computing Xiamen University China University College London United Kingdom A*STAR Institute of High Performance Computing Singapore School of Computer Science and Technology Guangdong University of Technology Guangzhou China Guangdong Laboratory of Artificial Intelligence and Digital Economy Shenzhen China Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University China

Personalized Federated Learning (PFL) aims to acquire customized models for each client without disclosing raw data by leveraging the collective knowledge of distributed clients. However, the data collected in real-world scenarios is likely to follow a long-tailed distribution. For example, in the medical domain, it is more common for the number of general health notes to be much larger than those specifically related to certain diseases. The presence of long-tailed data can significantly degrade the performance of PFL models. Additionally, due to the diverse environments in which each client operates, data heterogeneity is also a classic challenge in federated learning. In this paper, we explore the joint problem of global long-tailed distribution and data heterogeneity in PFL and propose a method called Expert Collaborative Learning (ECL) to tackle this problem. Specifically, each client has multiple experts, and each expert has a different training subset, which ensures that each class, especially the minority classes, receives sufficient training. Multiple experts collaborate synergistically to produce the final prediction output. Without special bells and whistles, the vanilla ECL outperforms other state-of-the-art PFL methods on several benchmark datasets under different degrees of data heterogeneity and long-tailed distribution. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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Making rigorous linear programming practical for program analysis 27

Making rigorous linear programming practical for program ana...

引用

27th International Conference on Principles and Practice of Constraint Programming, CP 2021

作者： Wang, Tengbin Chen, Liqian Chen, Taoqing Fan, Guangsheng Wang, Ji College of Computer National University of Defense Technology Changsha China State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9783959772112

Linear programming is a key technique for analysis and verification of numerical properties in programs, neural networks, etc. In particular, in program analysis based on abstract interpretation, many numerical abstract domains (such as Template Constraint Matrix, constraint-only polyhedra, etc.) are designed on top of linear programming. However, most state-of-the-art linear programming solvers use floating-point arithmetic in their implementations, leading to an approximate result that may be unsound. On the other hand, the solvers implemented using exact arithmetic are too costly. To this end, this paper focuses on advancing rigorous linear programming techniques based on floating-point arithmetic for building sound and efficient program analysis. Particularly, as a supplement to existing techniques, we present a novel rigorous linear programming technique based on Fourier-Mozkin elimination. On this basis, we implement a tool, namely, RlpSolver, combining our technique with existing techniques to lift effectiveness of rigorous linear programming in the scene of analysis and verification. Experimental results show that our technique is complementary to existing techniques, and their combination (RlpSolver) can achieve a better trade-off between cost and precision via heuristic rules. © Tengbin Wang, Liqian Chen, Taoqing Chen, Guangsheng Fan, and Ji Wang.

关键词： Linear programming

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LLM Knows Geometry Better than Algebra: Numerical Understanding of LLM-Based Agents in A Trading Arena

arXiv

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arXiv 2025年

作者： Ma, Tianmi Du, Jiawei Huang, Wenxin Wang, Wenjie Xie, Liang Zhong, Xian Zhou, Joey Tianyi Hubei Key Laboratory of Transportation Internet of Things Wuhan University of Technology China Hubei Key Laboratory of Big Data Intelligent Analysis and Application Hubei University China Centre for Frontier AI Research Agency for Science Technology and Research Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore School of Computing National University of Singapore Singapore School of Science Wuhan University of Technology China

Recent advancements in large language models (LLMs) have significantly improved performance in natural language processing tasks. However, their ability to generalize to dynamic, unseen tasks, particularly in numerical reasoning, remains a challenge. Existing benchmarks mainly evaluate LLMs on problems with predefined optimal solutions, which may not align with real-world scenarios where clear answers are absent. To bridge this gap, we design the Agent Trading Arena, a virtual numerical game simulating complex economic systems through zero-sum games, where agents invest in stock portfolios. Our experiments reveal that LLMs, including GPT-4o, struggle with algebraic reasoning when dealing with plain-text stock data, often focusing on local details rather than global trends. In contrast, LLMs perform significantly better with geometric reasoning when presented with visual data, such as scatter plots or K-line charts, suggesting that visual representations enhance numerical reasoning. This capability is further improved by incorporating the reflection module, which aids in the analysis and interpretation of complex data. We validate our findings on NASDAQ STOCK dataset, where LLMs demonstrate stronger reasoning with visual data compared to text. Our code and data are publicly available at https://***/wekjsdvnm/***. © 2025, CC BY.

关键词： Natural language processing systems

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Z16b,a natural compound from Ganoderma cochlear is a novel RyR2 stabilizer preventing catecholaminergic polymorphic ventricular tachycardia

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中国药理学报（英文版） 2022年第9期43卷 2340-2350页

作者： Jiang-fan Wan Gang Wang Fu-ying Qin Dan-ling Huang Yan Wang Ai-ling Su Hai-ping Zhang Yang Liu Shao-yin Zeng Chao-liang Wei Yong-xian Cheng Jie Liu Guangdong Key Laboratory of Genome Stability and Human Disease Prevention Department of PathophysiologySchool of MedicineShenzhen UniversityShenzhen 518000China College of Physics and Optoelectronic Engineering Shenzhen UniversityShenzhen 518060China Guangdong Key Laboratory of Genome Stability and Human Disease Prevention Department of PathophysiologySchool of MedicineShenzhen UniversityShenzhen 518000China School of Pharmaceutical Sciences Shenzhen University Health Science CenterShenzhen 518000China Center for Translation Medicine Research and Development Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518000China Center for High Performance Computing Joint Engineering Research Center for Health Big Data Intelligent Analysis TechnologyShenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen 518000China Guangdong Cardiovascular Institute Guangdong Provincial People's HospitalGuangdong Academy of Medical SciencesGuangzhou 510000China Guangdong Provincial key laboratory of South China Structure Heart Disease Department of Pediatric CardiologyGuangdong Cardiovascular InstituteGuangdong Provincial People's HospitalGuangdong Academy of Medical SciencesGuangzhou 510000China

Catecholaminergic polymorphic ventricular tachycardia(CPVT)is an inherited,lethal ventricular arrhythmia triggered by *** in genes that encode cardiac ryanodine receptor(RyR2)and proteins that regulate RyR2 activity cause enhanced diastolic Ca2+release(leak)through the RyR2 channels,resulting in *** therapies for CPVT are *** found that Z16b,a meroterpenoid isolated from Ganoderma cochlear,inhibited Ca2+spark frequency(CaSF)in R2474S/+cardiomyocytes in a dose-dependent manner,with an IC50 of 3.2 μM.Z16b also dose-dependently suppressed abnormal post-pacing Ca2+release *** injection(i.p.)of epinephrine and caffeine stimulated sustained ventricular tachycardia in all R2474S/+mice,while pretreatment with Z16b(0.5 mg/kg,i.p.)prevented ventricular arrhythmia in 9 of 10 mice,and Z16b administration immediately after the onset of VT abolished sVT in 9 of 12 *** translational significance,Z16b significantly inhibited CaSF and abnormal Ca2+release events in human CPVT ***,Z16b interacts with RyR2,enhancing the"zipping"state of the N-terminal and central domains of RyR2.A molecular docking simulation and point mutation and pulldown assays identified Z16b forms hydrogen bonds with Arg626,His1670,and Gln2126 in RyR2 as a triangle shape that anchors the NTD and CD interaction and thus stabilizes RyR2 in a tight"zipping"*** findings support that Z16b is a novel RyR2 stabilizer that can prevent *** may also serve as a lead compound with a new scaffold for the design of safer and more efficient drugs for treating CPVT.

关键词： arrhythmia catecholaminergic polymorphic ventricular tachycardia natural product ryanodine receptor induced pluripotent stem cell-derived cardiomyocytes

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Is Dataset Quality Still a Concern in Diagnosis Using Large Foundation Model?

arXiv

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arXiv 2024年

作者： Lin, Ziqin Li, Heng Li, Zinan Fu, Huazhu Liu, Jiang Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Institute of High Performance Computing Agency for Science Technology and Research Singapore Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

Recent advancements in pre-trained large foundation models (LFM) have yielded significant breakthroughs across various domains, including natural language processing and computer vision. These models have been particularly impactful in the domain of medical diagnostic tasks. With abundant unlabeled data, an LFM has been developed for fundus images using the Vision Transformer (VIT) and a self-supervised learning framework. This LFM has shown promising performance in fundus disease diagnosis across multiple datasets. On the other hand, deep learning models have long been challenged by dataset quality issues, such as image quality and dataset bias. To investigate the influence of data quality on LFM, we conducted explorations in two fundus diagnosis tasks using datasets of varying quality. Specifically, we explored the following questions: Is LFM more robust to image quality? Is LFM affected by dataset bias? Can fine-tuning techniques alleviate these effects? Our investigation found that LFM exhibits greater resilience to dataset quality issues, including image quality and dataset bias, compared to typical convolutional networks. Furthermore, we discovered that overall fine-tuning is an effective adapter for LFM to mitigate the impact of dataset quality issues. Copyright © 2024, The Authors. All rights reserved.

关键词： Image quality

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Coherent optical link phase noise control based on SPGD algorithm with optimal estimation 6

Coherent optical link phase noise control based on SPGD algo...

引用

6th Optics Young Scientist Summit, OYSS 2023

作者： Luo, Yihao Zhang, Jun Du, Shiyin Yan, Qiuquan Zhou, Tong Jiang, Tian College of Advanced Interdisciplinary Studies National University of Defense Technology Changsha410073 China National Innovation Institute of Defense Technology Academy of Military Sciences Pla China 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 Institute of Quantum Information Science and Technology College of Science National University of Defense Technology Changsha410073 China

ISBN: (纸本)9781510672642

The Stochastic Parallel Gradient Descent (SPGD) algorithm is a widely used model-free optimization algorithm in adaptive optical systems. However, when affected by noise, the SPGD algorithm may incorrectly estimate the gradient, resulting in a significant decrease in its performance and limitations in its application in complex environments. To address this issue, this paper introduces the optimal estimation algorithm and adds a self-checking process to the SPGD algorithm, proposing a Kal-SPGD algorithm. The feasibility of the Kal-SPGD algorithm is verified through simulation and experimentation. The experimental results show that compared with the standard SPGD, the use of the Kal-SPGD algorithm can reduce the signal amplitude variance by up to 92.15%. The Kal-SPGD algorithm provides a more favorable control technique for adaptive optical systems. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Stochastic systems

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