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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Liao, Zhirui Huang, Xiaodi Mamitsuka, Hiroshi Zhu, Shanfeng Fudan University School of Computer Science Shanghai200433 China Institute of Artificial Intelligence Biomedicine Nanjing University Nanjing China School of Computing Mathematics and Engineering Charles Sturt University AlburyNSW2640 Australia Bioinformatics Center Institute for Chemical Research Kyoto University Kyoto Uji611-0011 Japan Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai200433 China Ministry of Education 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

ISBN: (纸本)9781665401265

A number of machine learning (ML) approaches for drug discovery have been available that rely only on sequential (1D) and planar (2D) information without effectively using the 3D information for generating features of drugs. However, 3D information of small molecules can reflect relative position of atoms more directly, which affects molecular properties. In this work, we present a new deep learning model called Drug3D-DTI for drug-target interaction prediction. Drug3D-DTI takes advantage of molecular spatial information, i.e., atom proximity in three-dimensional (3D) structures. We comprehensively evaluated the performance of Drug3D-DTI on two datasets with two tasks of regression and classification. In particular, we compared Drug3D-DTI with several existing methods including the two cutting-edge methods for compound-protein interaction prediction. From the experimental results, Drug3D-DTI clearly outperformed other methods under all settings. Further, this performance improvement was validated by ablation experiments and a case study. The implementation of Drug3D-DTI is available at (https://***/zhiruiliao/Drug3D-DTI). © 2021 IEEE.

关键词： Molecules

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CRSOT: Cross-Resolution Object Tracking using Unaligned Frame and Event Cameras

arXiv

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arXiv 2024年

作者： Zhu, Yabin Wang, Xiao Li, Chenglong Jiang, Bo Zhu, Lin Huang, Zhixiang Tian, Yonghong Tang, Jin Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University China School of Computer Science and Technology Anhui University China School of Artificial Intelligence Anhui University China Beijing Institute of Technology Beijing China School of Electronic and Information Engineering Anhui University China Peng Cheng Laboratory Shenzhen China School of Computer Science Peking University Beijing China School of Electronic and Computer Engineering Peking University Shenzhen China

Existing datasets for RGB-DVS tracking are collected with DVS346 camera and their resolution (346 × 260) is low for practical applications. Actually, only visible cameras are deployed in many practical systems, and the newly designed neuromorphic cameras may have different resolutions. The latest neuromorphic sensors can output high-definition event streams, but it is very difficult to achieve strict alignment between events and frames on both spatial and temporal views. Therefore, how to achieve accurate tracking with unaligned neuromorphic and visible sensors is a valuable but unresearched problem. In this work, we formally propose the task of object tracking using unaligned neuromorphic and visible cameras. We build the first unaligned frame-event dataset CRSOT collected with a specially built data acquisition system, which contains 1,030 high-definition RGB-Event video pairs, 304,974 video frames. In addition, we propose a novel unaligned object tracking framework that can realize robust tracking even using the loosely aligned RGB-Event data. Specifically, we extract the template and search regions of RGB and Event data and feed them into a unified ViT backbone for feature embedding. Then, we propose uncertainty perception modules to encode the RGB and Event features, respectively, then, we propose a modality uncertainty fusion module to aggregate the two modalities. These three branches are jointly optimized in the training phase. Extensive experiments demonstrate that our tracker can collaborate the dual modalities for high-performance track- Copyright © 2024, The Authors. All rights reserved.

关键词： Cameras

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Double circulant self-dual and LCD codes over Galois rings

arXiv

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arXiv 2018年

作者： Shi, Minjia Huang, Daitao Sok, Lin Solé, Patrick School of Mathematical Sciences Anhui University Hefei230601 China Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University No. 3 Feixi Road Hefei Anhui Province230039 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Anhui University Hefei230601 China CNRS/LAGA University of Paris 8 2 rue de la Liberté Saint-Denis93 526 France

This paper investigates the existence, enumeration and asymptotic performance of self-dual and LCD double circulant codes over Galois rings of characteristic p2 and order p4 with p and odd prime. When p ≡ 3 (mod 4), we give an algorithm to construct a duality preserving bijective Gray map from such a Galois ring to Z2p2. Using random coding, we obtain families of asymptotically good self-dual and LCD codes over Zp2, for the metric induced by the standard Fp-valued Gray maps. Copyright © 2018, The Authors. All rights reserved.

关键词： Codes (symbols)

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A research of eyes location algorithm based on expanded Haar features and fast Hough Transformation

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ICIC Express Letters 2014年第2期8卷 333-339页

作者： Huang, Xuhui Feng, Zuren Dong, Xiucheng Zhang, Fan Shi, Peng Institute of System Engineering Xi'an Jiaotong University No. 28 Xianning West Road Xi'an 710049 China Provincial Key Lab on Signal and Information Processing Xihua University No. 999 Jinzhou Rd. Jinniu Dist. Chengdu 610039 China Department of Computing and Mathematical Sciences University of South Wales Pontypridd CF37 1DL United Kingdom College of Engineering and Science Victoria University Melbourne VIC 8001 Australia

Eyes location is an essential part of human computer interaction. The precise of eyes location decides the feasibility of interaction. In this paper, we expand the haar features by introducing a new type of characteristic rectangle which can distinguish the eyes and brow more efficiently. The edge of iris is detected and recovered during preprocessing procedure. By using the distance information, the radius of iris can be predicted and the calculation of Hough Transformation can be optimized. By comparing the two improved algorithms, the one which constrained the range of radius can balance the detection accuracy and calculation efficiency better. This improved algorithm has a good performance on eyes location by enhancing the calculation efficiency one order of magnitude. © 2014 ISSN 1881-803X.

关键词： Human computer interaction

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Dual-State Personalized Knowledge Tracing with Emotional Incorporation

arXiv

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arXiv 2024年

作者： Wang, Shanshan Yuan, Fangzheng Wang, keyang Yang, Xun Zhang, Xingyi Wang, Meng Information Materials and Intelligent Sensing Laboratory of Anhui Province Institute of Physical Science and Information Technology Anhui University Hefei230601 China Zhejiang Dahua Technology Co. Ltd China Department of Electronic Engineering and Information Science School of information Science and Technology University of Science and Technology of China Hefei230026 China Key Laboratory of intelligent computing and Signal Processing Ministry of Education school of Computer Science and Technology Anhui University Hefei230601 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China

Knowledge tracing has been widely used in online learning systems to guide the students' future learning. However, most existing KT models primarily focus on extracting abundant information from the question sets and explore the relationships between them, but ignore the personalized student behavioral information in the learning process. This will limit the model's ability to accurately capture the personalized knowledge states of students and reasonably predict their performances. To alleviate this limitation, we explicitly models the personalized learning process by incorporating the emotions, a representative personalized behavior in the learning process, into KT framework. Specifically, we present a novel Dual-State Personalized Knowledge Tracing with Emotional Incorporation model to achieve this goal: Firstly, we incorporate emotional information into the modeling process of knowledge state, resulting in the Knowledge State Boosting Module. Secondly, we design an Emotional State Tracing Module to monitor students' personalized emotional states, and propose an emotion prediction method based on personalized emotional states. Finally, we apply the predicted emotions to enhance students' response prediction. Furthermore, to extend the generalization capability of our model across different datasets, we design a transferred version of DEKT, named Transfer Learningbased Self-loop model (T-DEKT). Extensive experiments show our method achieves the state-of-the-art performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

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Nematode Sperm Secrete Serpin to Coordinate both Sperm Motility and Sperm Competition

Nematode Sperm Secrete Serpin to Coordinate both Sperm Motil...

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中国细胞生物学学会全体会员代表大会暨第十二次学术大会

作者： Yanmei Zhao Youqiao Hu Zhiqi Hao Andreas Hühmer Fanxia Meng Steven L'Hernault Si-Min He Meng-Qiu Dong Long Miao Wei Sun Pan Zhang Hao Chi Meijun Zhang Chun-Qing Song Xuan Ma Yunlong Shang Bin Wang National Laboratory of Biomacromolecules Institute of Biophysics Chinese Academy of Sciences Beijing 100101 China Thermo Fisher Scientific San Jose CA 94539 USA Department of Biology Emory University Atlanta GA 30322 USA Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190China National Institute of Biological Sciences Beijing 102206 China

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An Audio-Visual Whisper Database in Chinese

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Journal of Physics: Conference Series 2019年第2期1237卷

作者： Jian Zhou Yuting Hu Hailun Lian Cong Pang Huabin Wang Liang Tao Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei AnHui 230039 China School of Computer Science and Technology Anhui University Hefei AnHui 230601 China

Converting whisper to normal vocalized speech has been a hot research topic in speech signal processing area. A complete and large scale whisper database is a major basis for this task. In this paper, we propose a multimodal whisper database in Chinese mandarin. A total of 103 syllables and 100 sentences were carefully selected. 5 male and 5 female participants pronounced the syllables and sentences in whisper and normal styles respectively, result in 4096 parallel speech utterances and 263, 849 frames of voicing face and lip image sequences. The beginning and ending sample point of each syllable were labeled both for speech signal and voicing face video. The lip region of interest were also extracted and provided in the proposed database. Experiments in various speech conversion tasks in different speech database show the effectiveness of the proposed multimodal whisper speech database.

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Corrigendum to “Discovering and learning sensational episodes of news events” [Inf. Syst. 78 (2018) 68–80]

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Information Systems 2020年 93卷

作者： Xiang Ao Ping Luo Chengkai Li Fuzhen Zhuang Qing He Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China University of Texas at Arlington USA

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Corrigendum to “Discovering and learning sensational episodes of news events” [Information Systems 78 (2018) 68–80]

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Information Systems 2019年 81卷 178-178页

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Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features

arXiv

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arXiv 2024年

作者： Meng, Benyuan Xu, Qianqian Wang, Zitai Cao, Xiaochun Huang, Qingming Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China Peng Cheng Laboratory China School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University China School of Computer Science and Tech. University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management CAS China

Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations, can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations, selecting a small yet effective subset poses a fundamental problem. To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations. However, we find that many potential activations have not been evaluated, such as the queries and keys used to compute attention scores. Moreover, recent advancements in diffusion architectures bring many new activations, such as those within embedded ViT modules. Both combined, activation selection remains unresolved but overlooked. To tackle this issue, this paper takes a further step with a much broader range of activations evaluated. Considering the significant increase in activations, a full-scale quantitative comparison is no longer operational. Instead, we seek to understand the properties of these activations, such that the activations that are clearly inferior can be filtered out in advance via simple qualitative evaluation. After careful analysis, we discover three properties universal among diffusion models, enabling this study to go beyond specific models. On top of this, we present effective feature selection solutions for several popular diffusion models. Finally, the experiments across multiple discriminative tasks validate the superiority of our method over the SOTA competitors. Our code is available at this url. © 2024, CC BY.

关键词： Semantics

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