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SSRN 2023年

作者： Han, Dan Chen, Yi Li, Donghui Shi, Jiexu Wang, Hongtao He, Xiuli Zhao, Li Wang, Weidong Sang, Shengbo Ji, Jianlong Shanxi Key Laboratory of Micro Nano Sensors and Artificial Intelligence Perception College of Electronic Information and Optical Engineering Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System The Ministry of Education Taiyuan University of Technology Taiyuan030024 China State Key Laboratory of Transducer Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China Bioengineering Research Center Medical Innovation Research Division Chinese PLA General Hospital Beijing China Shanxi Research Institute of 6D Artificial Intelligence Biomedical Science China

Surface modification with noble metal is considered as an effective strategy to enhance sensing performance of semiconductor gas sensors. In this work, gold nanoparticles (Au NPs) decorated Gallium Nitride nanoflowers (Au-GaN NFs) have been synthesized successfully by a feasible hydrothermal method combined with high temperature nitridation and followed by in-situ reduction process. The characterizations of the structure and morphology of as-obtained Au-GaN NFs indicated that the Au NPs were successfully loaded on the surface of GaN. The excellent gas sensing performance was observed for GaN of 2 mol% Au loaded, including the high response (86.8%) to 100 ppm NH3 with fast response/recovery time (41 s/169 s) at room temperature, low theoretical limit of detection (72 ppb), good stability and selectivity. Besides, the influence of humidity on the performance of sensors was explored, where the response can be reached 56.5% towards 100 ppm NH3 under 70% RH. The improvement of the sensing performance of Au-GaN NFs sensor can be attributed to the combined action of chemical and electronic sensitization of Au NPs. This work provides a candidate of Au-GaN NFs for the development of wireless sensing system in NH3 monitoring. © 2023, The Authors. All rights reserved.

关键词： Nanoflowers

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SASA-Net: A spatial-aware self-attention mechanism for building protein 3D structure directly from inter-residue distances

SASA-Net: A spatial-aware self-attention mechanism for build...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Tiansu Gong Fusong Ju Shiwei Sun Dongbo Bu Chinese Academy of Sciences Key Lab of Intelligent Information Processing Institute of Computing Technology Beijing China University of Chinese Academy of Sciences Beijing China Zhongke Big Data Academy Zhengzhou Henan China

ISBN: (纸本)9781665429825

Protein structure prediction has achieved considerable progresses mainly due to the increased accuracy of inter-residue distance estimation and the application of deep learning *** of the distance-based ab initio prediction approaches adopt a two-step diagram: constructing a potential function based on the estimated inter-residue distances, and then build a 3D structure that minimizes the potential function. These approaches have proven very promising; however, they still suffer from several limitations, especially the inaccuracies incurred by the handcrafted potential ***, we present SASA-Net, a deep learning-based approach that directly learns protein 3D structure from the estimated inter-residue distances. Unlike the existing approach simply representing protein structures as coordinates of atoms, SASA-Net represents protein structures using pose of residues, i.e., the coordinate system of each individual residue in which all backbone atoms of this residue are fixed. The key element of SASA-Net is a spatial-aware self-attention mechanism, which is able to adjust a residue’s pose according to all other residues’ features and the estimated distances between residues. By iteratively applying the spatial-aware self-attention mechanism, SASA-Net continuously improves the structure and finally acquires a structure with high accuracy. Using the CATH35 proteins as representatives, we demonstrate that SASA-Net is able to accurately and efficiently build structures from the estimated inter-residue distances. The high accuracy and efficiency of SASA-Net enables an end-to-end neural network model for protein structure prediction through combining SASA-Net and an neural network for inter-residue distance prediction. Source code of SASA-Net is available at https://*** gongtiansu/SASA-Net/

关键词： Proteins Deep learning Protein engineering Solid modeling Three-dimensional displays Conferences Neural networks

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Dfier: A Directed Vulnerability Verifier for Ethereum Smart Contracts

SSRN

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SSRN 2023年

作者： Wang, Zeli Dai, Weiqi Li, Ming Choo, Kim-Kwang Raymond Zou, Deqing Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing40065 China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio United States

Existing smart contract vulnerability identification approaches mainly focus on complete program detection. Consequently, lots of known potentially vulnerable locations need manual verification, which is energy-exhaustive and error-prone. Fuzzing is an effective way to detect vulnerabilities. However, current fuzzers cannot effectively probe these suspicious targets because they overlook two key factors: exploring all paths toward the targets and providing high-quality directed seed inputs. This paper presents a Directed vulnerability veriFier (DFier), elaborating effective transaction sequences with directed inputs for the fuzzer, to focus on analyzing target paths and automatically detect whether the given locations are vulnerable. Specifically, DFier fuses the effectiveness of static analysis and the efficiency of fuzzing. The static analysis helps locate target paths and generate directed inputs for the fuzzer under three heuristic strategies. The first prefers inputs with the larger possibility of triggering potential vulnerabilities based on target features. The second guides the value mutation to satisfy the path constraints. The third adequately leverages execution information to help find exploits faster. Extensive experiments demonstrate that DFier is effective in verifying contract security, compared with three existing contract fuzzers (i.e., contractFuzzer, sFuzz, and conFuzzius). And the performance losses are in an acceptable range. © 2023, The Authors. All rights reserved.

关键词： Smart contract

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Application of the High-Q Bouncing Ball Model Based on Fabry-Pérot Cavity for Alcohol Gas Concentration Detection

SSRN

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SSRN 2023年

作者： Sang, Luxiao Wang, Teng Zhao, Biao Ding, Jinyu Jian, Aoqun Sang, Shengbo Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Electronic Information and Optical Engineering Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan University of Technology Taiyuan030024 China Shanxi Research Institute of 6D Artificial Intelligence Biomedical Science Taiyuan030031 China

This paper presents a method to enhance the Q factor of Fabry-Pérot cavities using a vertical dielectric interface (DI) made of silicon and air, which generates a bouncing ball model. The method is applied to detect alcohol gas concentrations with high resolution. Specifically, we introduce a 450 µm-thick silicon wafer into the Fabry-Pérot (FP) cavity, where it is positioned at an angle of inclination. This addition considerably augments the light-matter interaction, effectively confining the light within the cavity. The Q factor of the FP cavity with DI is always higher than that of the open FP cavity in the inclination angle range of 0 to 1°. Moreover, the effective mode area of the FP cavity with DI is smaller. Furthermore, the FP cavity with DI reduces the effective mode area and diminishes the lateral walk-off loss experienced in the open FP cavity. Finally, we experimentally verified the effectiveness of this method by detecting alcohol gas concentrations ranging from 28.5 ppm to 142.5 ppm and achieving a Q factor improvement from 5.9×104 to 1.2×105. The system sensitivity was calculated to be 0.5294 pm/ppm, and the resolution obtained was 28.5 ppm. © 2023, The Authors. All rights reserved.

关键词： Silicon wafers

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VANER: Leveraging Large Language Model for Versatile and Adaptive Biomedical Named Entity Recognition

arXiv

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arXiv 2024年

作者： Bian, Junyi Zhai, Weiqi Huang, Xiaodi Zheng, Jiaxuan Zhu, Shanfeng School of Computer Science Fudan University Shanghai200433 China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University China Ministry of Education Shanghai200433 China MOE Frontiers Center for Brain Science Fudan University Shanghai200433 China Zhangjiang Fudan International Innovation Center Shanghai200433 China Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai200433 China School of Computing and Mathematics Charles Sturt University AlburyNSW2640 Australia

Prevalent solution for BioNER involves using representation learning techniques coupled with sequence labeling. However, such methods are inherently task-specific, demonstrate poor generalizability, and often require dedicated model for each dataset. To leverage the versatile capabilities of recently remarkable large language models (LLMs), several endeavors have explored generative approaches to entity extraction. Yet, these approaches often fall short of the effectiveness of previouly sequence labeling approaches. In this paper, we utilize the open-sourced LLM LLaMA2 as the backbone model, and design specific instructions to distinguish between different types of entities and datasets. By combining the LLM’s understanding of instructions with sequence labeling techniques, we use mix of datasets to train a model capable of extracting various types of entities. Given that the backbone LLMs lacks specialized medical knowledge, we also integrate external entity knowledge bases and employ instruction tuning to compel the model to densely recognize carefully curated entities. Our model VANER, trained with a small partition of parameters, significantly outperforms previous LLMs-based models and, for the first time, as a model based on LLM, surpasses the majority of conventional state-of-the-art BioNER systems, achieving the highest F1 scores across three datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Computational linguistics

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Pan-omics-based characterization and prediction of highly multidrugadapted strains from an outbreak fungal species complex

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The Innovation 2024年第5期5卷 101-109,100页

作者： Xin Fan Lei Chen Min Chen Na Zhang Hong Chang Mingjie He Zhenghao Shen Lanyue Zhang Hao Ding Yuyan Xie Yemei Huang Weixin Ke Meng Xiao Xuelei Zang Heping Xu Wenxia Fang Shaojie Li Cunwei Cao Yingchun Xu Shiguang Shan Wenjuan Wu Changbin Chen Xinying Xue Linqi Wang Department of Infectious Diseases and Clinical Microbiology Beijing Institute of Respiratory Medicine and Beijing Chao-Yang HospitalCapital Medical UniversityBeijing 100020China Beijing Research Center for Respiratory Infectious Diseases Beijing 100020China State Key Laboratory of Mycology Institute of MicrobiologyChinese Academy of SciencesBeijing 100101China Department of Dermatology Shanghai Key Laboratory of Molecular Medical MycologyChangzheng HospitalShanghai 200003China University of Chinese Academy of Sciences Beijing 100049China Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China Department of Respiratory and Critical Care Emergency and Critical Care Medical CenterBeijing Shijitan HospitalCapital Medical UniversityBeijing 100038China Department of Laboratory Medicine State Key Laboratory of Complex Severe and Rare DiseasesPeking Union Medical College HospitalChinese Academy of Medical SciencesBeijing 100730China Beijing Key Laboratory for Mechanisms Research and Precision Diagnosis of Invasive Fungal Diseases Beijing 100730China Department of Clinical Laboratory First Affiliated Hospital of Xiamen UniversityXiamen 361003China Institute of Biological Science and Technology Guangxi Academy of SciencesNanning 530007China Department of Dermatology The First Affiliated Hospital of Guangxi Medical UniversityNanning 530021China Guangxi Key Laboratory of Mycosis Prevention and Treatment Nanning 530021China Department of Laboratory Medicine Shanghai East HospitalTongji University School of MedicineShanghai 200120China The Unit of Pathogenic Fungal Infection&Host Immunity Shanghai Institute of Immunity and InfectionChinese Academy of SciencesShanghai 200031China Nanjing Advanced Academy of Life and Health Nanjing 211135China Department of Respiratory and Critical Care Shandong Second Medical UniversityWeifang 261035China

Strains from the Cryptococcus gattii species complex(CGSC)have caused the Pacific Northwest cryptococcosis outbreak,the largest cluster of lifethreatening fungal infections in otherwise healthy human hosts known to *** this study,we utilized a pan-phenome-based method to assess the fitness outcomes of CGSC strains under 31 stress conditions,providing a comprehensive overview of 2,821 phenotype-strain associations within this pathogenic *** clustering analysis revealed a strong correlation between distinct types of stress phenotypes in a subset of CGSC strains,suggesting that shared determinants coordinate their adaptations to various ***,a specific group of strains,including the outbreak isolates,exhibited a remarkable ability to adapt to all three of the most commonly used antifungal drugs for treating cryptococcosis(amphotericin B,5-fluorocytosine,and fluconazole).By integrating pan-genomic and pan-transcriptomic analyses,we identified previously unrecognized genes that play crucial roles in conferring multidrug resistance in an outbreak strain with high multidrug *** these genes,we identified biomarkers that enable the accurate prediction of highly multidrug-adapted CGSC strains,achieving maximum accuracy and area under the curve(AUC)of 0.79 and 0.86,respectively,using machine learning ***,we developed a pan-omic approach to identify cryptococcal multidrug resistance determinants and predict highly multidrug-adapted CGSC strains that may pose significant clinical concern.

关键词： multidrug adapted prediction

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Visual Alignment Constraint for Continuous Sign Language Recognition

Visual Alignment Constraint for Continuous Sign Language Rec...

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International Conference on Computer Vision (ICCV)

作者： Yuecong Min Aiming Hao Xiujuan Chai Xilin Chen Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China Agricultural Information Institute Chinese Academy of Agricultural Sciences Beijing China

ISBN: (纸本)9781665428132

Vision-based Continuous Sign Language Recognition (CSLR) aims to recognize unsegmented signs from image streams. Overfitting is one of the most critical problems in CSLR training, and previous works show that the iterative training scheme can partially solve this problem while also costing more training time. In this study, we revisit the iterative training scheme in recent CSLR works and realize that sufficient training of the feature extractor is critical to solving the overfitting problem. Therefore, we propose a Visual Alignment Constraint (VAC) to enhance the feature extractor with alignment supervision. Specifically, the proposed VAC comprises two auxiliary losses: one focuses on visual features only, and the other enforces prediction alignment between the feature extractor and the alignment module. Moreover, we propose two metrics to reflect overfitting by measuring the prediction inconsistency between the feature extractor and the alignment module. Experimental results on two challenging CSLR datasets show that the proposed VAC makes CSLR networks end-to-end trainable and achieves competitive performance.

关键词： Training Measurement Visualization Image recognition Costing Gesture recognition Assistive technologies

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Ultra-High Dynamic Extinction Ratio Electro-Optical Modulator with Coupled Microring on SOI Platform

Ultra-High Dynamic Extinction Ratio Electro-Optical Modulato...

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Zhuo Cheng Bigeng Chen Chunlei Sun Maoliang Wei Shaoliang Yu Lan Li Hongtao Lin Yunjiang Rao Research Center for Optical Fiber Sensing Zhejiang Lab Hangzhou China Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province School of Engineering Westlake University Hangzhou China State Key Laboratory of Modern Optical Instrumentation College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Research Center for Intelligent Optoelectronic Computing Zhejiang Lab Hangzhou China Key Lab of Optical Fiber Sensing & Communications University of Electronic Science &Technology of China Chengdu China

A 5 th -order coupled microring electro-optical modulator with PIN structures is proposed and experimentally demonstrated on SOI integrated photonics platform. Ultra-high dynamic extinction ratio (ER) up to 60 dB is achieved benefiting from the high-order optical band-pass filter design. The device size on chip is only 400×220 μm 2 including electrodes and the driving voltage is 2.1 V corresponding to 0.23 W power consumption. Such integrated modulators would be promising in building compact and low-power distributed optical fiber sensing systems using low-background-noise optical pulses.

关键词： Optical filters Optical fibers Power demand Voltage Electro-optic modulators Extinction ratio Pins

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Estimation of Speech Features Using a Wearable Inertial Sensor

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Journal of Voice 2024年

作者： Du, Zuyu Xu, Yaodan Yu, Xinsheng Wang, Sen Xu, Lin School of Information Science and Technology ShanghaiTech University Shanghai China Shanghai Advanced Research Institute Chinese Academy of Science Shanghai China Shanghai Ruiwei Digital Technology Shanghai China Shanghai Frontiers Science Center of Human-centered Artificial Intelligence Shanghai China MoE Key Lab of Intelligent Perception and Human-Machine Collaboration (ShanghaiTech University) Shanghai China

Speech features have been investigated as novel digital biomarkers for many psychiatric and neurocognitive diseases. Microphones are the most used devices for speech recording but inevitably suffering from several disadvantages such as privacy leakage and environmental noises, limiting their clinical applications particularly for long-term ambulatory monitoring. The aim of the present study is therefore to explore the feasibility of extracting speech features from the acceleration recorded on the sternum. Ten healthy subjects volunteered in our study. Two speech tasks, that is, repeating one sentence 20 times and reading 20 different sentences, were performed by each subject, with each task repeated eight times under different speech rate and loudness. Voice signals and speech-caused chest vibrations were simultaneously recorded by a microphone and an accelerometer placed on the sternum. Forty-two acoustic features and six time-related prosodic features were extracted from both signals using a standard toolbox, and then compared by a linear fit and correlation analysis. Good agreement between the acceleration features and microphone features is observed in all six time-related prosodic features for both tasks, but only in 19 and 17 acoustic features for task 1 and 2, respectively, with most of them loudness- or pitch-related. Our results suggest the sternum acceleration to track time-related speech prosody, loudness, and pitch very well, demonstrating the feasibility of deriving digital biomarkers from the acceleration signal for diseases strongly related to time-related prosodic and loudness features. © 2024 The Voice Foundation

关键词： Correlation coefficient Digital biomarker Inertial sensor Microphone Speech features

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Gate-tuned antiferromagnetic MnBi4Te7: A candidate for high-temperature high-Chern-number insulator at zero magnetic field

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Physical Review B 2025年第11期111卷 115420-115420页

作者： Hengning Wang Ding Li Yan Feng Tao Qin Yingze Jia Hongwei Zhang Chuandi Pan Yan Wang Wei Ning Anhui Key Laboratory of Low-Energy Quantum Materials and Devices High Magnetic Field Laboratory HFIPS Chinese Academy of Sciences Hefei Anhui 230031 China University of Science and Technology of China Hefei 230026 China Department of Materials Science & Engineering Hefei National Research Center for Physical Sciences at the Microscale CAS Key Lab of Materials for Energy Conversion Anhui Laboratory of Advanced Photon Science and Technology University of Science and Technology of China Hefei China School of Physics and Optoelectronics Engineering Anhui University Hefei Anhui Province 230601 People's Republic of China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Key Laboratory of Magnetic Functional Materials and Devices Institutes of Physical Science and Information Technology Anhui University Hefei 230601 China Department of Physics Shaanxi University of Science and Technology Xi'an 710021 China

The quantum anomalous Hall insulator is characterized by a quantized Hall resistance plateau of h/(Ce2) with C being the Chern number. Previously, high-Chern-number insulators with C≥2 that intrinsically suppress the energy dissipation in quantized electrical transport have been realized in few-layer MnBi2Te4 nanoflakes under moderate magnetic fields or in Cr-doped topological insulators at extremely low temperatures. It has nevertheless proven elusive to achieve a high-temperature high-Chern-number insulator at zero magnetic field. Here, we demonstrate that the magnetic state in MnBi4Te7 nanoflakes can be consecutively tuned from an antiferromagnetic (AFM) state to an AFM-ferromagnetic (FM) coexistence state, and finally to a robust ferromagnetic (FM) state with large coercivities at temperatures up to 3 K via a protonic gate. This AFM-FM phase transition is well captured by density functional theory simulations that the FM state develops in MnBi4Te7 under hole doping. Notably, we find that the Chern number of FM MnBi4Te7 can be largely tuned not merely by the Zeeman field but also by the sample thickness. Our work demonstrates that gate-tuned MnBi4Te7 nanoflakes hold high potentials for realizing a high-temperature high-Chern-number insulator at zero magnetic field and promising applications in the field of low-energy electronics.

关键词： Ferromagnetism

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