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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Yang, Xinquan Xie, Jinheng Li, Xuechen Li, Xuguang Shen, Linlin Deng, Yongqiang Shenzhen University College of Computer Science and Software Engineering Shenzhen China National University of Singapore Show Lab Singapore Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China Shenzhen University General Hospital Department of Stomatology Shenzhen China

ISBN: (纸本)9798350337488

In implant prosthesis treatment, the surgical guide of implant is used to ensure accurate implantation. However, such design heavily relies on the manual location of the implant position. When deep neural network has been proposed to assist the dentist in locating the implant position, most of them take a single slice as input, which do not fully explore 3D contextual information and ignores the influence of implant slope. In this paper, we design a Text Guided 3D Context and Slope Aware Triple Network (TCSloT) to integrate the perception of contextual information from multiple adjacent slices and awareness of variation of implant slopes. A Texture Variation Perception (TVP) module is correspondingly design to process the multiple slices and capture the texture variation among slices and a Slope-Aware Loss (SAL) is proposed to dynamically assign adaptive weights for the regression head. Additionally, we design a conditional text guidance (CTG) module to integrate the text condition (i.e., left, middle and right) from the CLIP to assist the implant position prediction. Extensive experiments on a dental implant dataset through five-fold cross-validation, demonstrated that the proposed TCSloT achieves superior performance than existing methods. © 2023 IEEE.

关键词： Dental Implant Multi-module Detection Multi-slice Learning Text Guided Detection Transformer

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QGEval: Benchmarking Multi-dimensional Evaluation for Question Generation

QGEval: Benchmarking Multi-dimensional Evaluation for Questi...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Fu, Weiping Wei, Bifan Hu, Jianxiang Cai, Zhongmin Liu, Jun School of Computer Science and Technology Xi'an Jiaotong University Xi'an China School of Continuing Education Xi'an Jiaotong University Xi'an China MOE KLINNS Lab School of Automation Science and Engineering Xi'an Jiaotong University Xi'an China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9798891761643

Automatically generated questions often suffer from problems such as unclear expression or factual inaccuracies, requiring a reliable and comprehensive evaluation of their quality. Human evaluation is widely used in the field of question generation (QG) and serves as the gold standard for automatic metrics. However, there is a lack of unified human evaluation criteria, which hampers consistent and reliable evaluations of both QG models and automatic metrics. To address this, we propose QGEval, a multi-dimensional Evaluation benchmark for Question Generation, which evaluates both generated questions and existing automatic metrics across 7 dimensions: fluency, clarity, conciseness, relevance, consistency, answerability, and answer consistency. We demonstrate the appropriateness of these dimensions by examining their correlations and distinctions. Through consistent evaluations of QG models and automatic metrics with QGEval, we find that 1) most QG models perform unsatisfactorily in terms of answerability and answer consistency, and 2) existing metrics fail to align well with human judgments when evaluating generated questions across the 7 dimensions. We expect this work to foster the development of both QG technologies and their evaluation. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Unlearnable 3D Point Clouds: Class-wise Transformation Is All You Need

arXiv

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arXiv 2024年

作者： Wang, Xianlong Li, Minghui Liu, Wei Zhang, Hangtao Hu, Shengshan Zhang, Yechao Zhou, Ziqi Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China

Traditional unlearnable strategies have been proposed to prevent unauthorized users from training on the 2D image data. With more 3D point cloud data containing sensitivity information, unauthorized usage of this new type data has also become a serious concern. To address this, we propose the first integral unlearnable framework for 3D point clouds including two processes: (i) we propose an unlearnable data protection scheme, involving a class-wise setting established by a category-adaptive allocation strategy and multi-transformations assigned to samples;(ii) we propose a data restoration scheme that utilizes class-wise inverse matrix transformation, thus enabling authorized-only training for unlearnable data. This restoration process is a practical issue overlooked in most existing unlearnable literature, i.e., even authorized users struggle to gain knowledge from 3D unlearnable data. Both theoretical and empirical results (including 6 datasets, 16 models, and 2 tasks) demonstrate the effectiveness of our proposed unlearnable framework. Our code is available at https://***/CGCL-codes/UnlearnablePC Copyright © 2024, The Authors. All rights reserved.

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Totally asymmetric simple exclusion process on a dynamic lattice with local inhomogeneity

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Results in Physics 2024年 64卷

作者： Lin, Congping Huang, Yuesong Xu, Zihui Zhang, Yiwei School of Mathematics and Statistics Huazhong University of Science and Technology China Hubei Key Lab of Engineering Modelling and Scientific Huazhong University of Science and Technology China School of Mathematics and Big Data Anhui University of Science and Technology China

In this manuscript, we introduce a totally asymmetric simple exclusion process on a dynamic lattice with inhomogeneity to model motor-based long-range transport along microtubules (MTs) which exhibit cycles of growth, shortening and regrowth at their growing tips. By mean-field approximation and numerical simulations, we explore phase diagrams for the particle density near the dynamic end of the lattice as well as over the entire lattice. In particular, we find seven different phases for the density over the entire lattice and explore the corresponding phase diagram by analytical analysis. Interestingly, we find that the density may have a linear profile in part of the lattice. When the lattice has a defect, we separate the lattice into two subsystems: a right subsystem with fixed length and a defect in the middle and a left subsystem with dynamic length. When the right subsystem is in maximum-current phase, we calculate an analytical approximation of the density away from the boundaries and the defect site and the density is in association with the reduced hopping rate at the defect site;the density at the left side of the defect site serves as the effective entry rate of the left subsystem. We find that with defects, the linear profiles no longer appear at the left end of the left subsystem and only five possible phases could occur. This is a step-forward to a more comprehensive mathematical description of intracellular long-range transport of organelles along MTs with instability. © 2024

关键词： Dynamic lattice Inhomogeneity Phase diagram TASEP

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Global and Adaptive Local label Correlation for Multi-label Learning with Missing labels

Global and Adaptive Local Label Correlation for Multi-label ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Qingxia Jiang Peipei Li Yuhong Zhang Xuegang Hu Key Laboratory of Knowledge Engineering with Big Data (the ministry of Education) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui Province China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei Anhui Province China

label missing is a major challenge in multi-label learning. Many existing methods try to use label correlation to recover ground-truth labels, but they only focus on the label correlation within the original label space, however, the label correlation learned in this way is incomplete. Thus, inspired $b$ y the matrix adaptive column correlation, we propose a method to continuously adjust the label correlation matrix while the labels are filled in by adaptive column correlation learning method. Specifically, to reduce the impact of the missing label s on label correlation, the label space is firstly completed through manifold regularization while learning the local label information by adaptive column correlation learning in the complemented label space. Secondly, the global label correlation is utilized by adding a low-rank constraint to the entire label space. Finally, by jointly taking advantage of the global and adaptive local label correlation, our proposed approach achieves superior performance on both synthetic and real-world data sets from diverse domains compared to state-of-the art baselines.

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Robust Visual Odometry Using Semantic Information in Complex Dynamic Scenes 5th

Robust Visual Odometry Using Semantic Information in Complex...

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5th International Conference on Cognitive Systems and Signal Processing, ICCSIP 2020

作者： Wang, Hao Wang, Le Fang, Baofu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei Anhui230009 China Department of Computer Science and Technology Hefei University of Technology Hefei Anhui230009 China

ISBN: (纸本)9789811623356

Traditional vision-based simultaneous localization and mapping (SLAM) technology cannot obtain the semantic information of the surrounding environment, which will cause the robot to fail to complete intelligent grasping, human-computer interaction, and other advanced decision tasks. There are many challenges to solve this problem, such as obtaining less semantic information of the environment with low precision and being unable to deal with dynamic objects in a real environment quickly and effectively. In this paper, a robust visual odometry using semantic information in complex dynamic scenes was designed. Specifically, we present a refined instance segmentation method based on the contextual information of the frame to improve the accuracy of segmentation. On this basis, a feature detection and elimination algorithm for dynamic objects based on instance-level semantic information is proposed to improve the localization accuracy of camera pose. We extensively evaluate our system on the public TUM data set and compare it with ORB-SLAM2 and other methods. Experiments show that our methods greatly improve the localization accuracy of the camera pose and the robustness of the system, which verifies that our system is effective in complex dynamic scenes. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Cameras

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Learning Subpocket Prototypes for Generalizable Structure-based Drug Design

arXiv

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arXiv 2023年

作者： Zhang, Zaixi Liu, Qi Anhui Province Key Lab of Big Data Analysis and Application University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence China

Generating molecules with high binding affinities to target proteins (a.k.a. structure-based drug design) is a fundamental and challenging task in drug discovery. Recently, deep generative models have achieved remarkable success in generating 3D molecules conditioned on the protein pocket. However, most existing methods consider molecular generation for protein pockets independently while neglecting the underlying connections such as subpocket-level similarities. Subpockets are the local protein environments of lig- and fragments and pockets with similar subpockets may bind the same molecular fragment (motif) even though their overall structures are different. Therefore, the trained models can hardly generalize to unseen protein pockets in real-world applications. In this paper, we propose a novel method DrugGPS for generalizable structure-based drug design. With the biochemical priors, we propose to learn subpocket prototypes and construct a global interaction graph to model the interactions between subpocket prototypes and molecular motifs. Moreover, a hierarchical graph transformer encoder and motif-based 3D molecule generation scheme are used to improve the model’s performance. The experimental results show that our model consistently outperforms baselines in generating realistic drug candidates with high affinities in challenging out-of-distribution settings. © 2023, CC BY-NC-SA.

关键词： Proteins

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SumPA: Efficient Pattern-Centric Graph Mining with Pattern Abstraction 21

SumPA: Efficient Pattern-Centric Graph Mining with Pattern A...

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Proceedings of the 30th International Conference on Parallel Architectures and Compilation Techniques

作者： Chuangyi Gui Xiaofei Liao Long Zheng Pengcheng Yao Qinggang Wang Hai Jin National Engineering Research Center for Big Data Technology and System/Service Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781665442787

Graph mining aims to explore interesting structural information of a graph. Pattern-centric systems typically transform a generic-purpose graph mining problem into a series of subgraph matching problems for high performance. Existing pattern-centric mining systems reduce the substantial search space towards a single pattern by exploring a highly-optimized matching order, but inherent computational redundancies of such a matching order itself still suffer severely, leading to significant performance degradation. The key innovation of this work lies in a general redundancy criterion that characterizes computational redundancies arising in not only handing a single pattern but also matching multiple patterns simultaneously. In this paper, we present SumPA, a high-performance pattern-centric graph mining system that can sufficiently remove redundant computations for any complex graph mining problems. SumPA features three key designs: (1) a pattern abstraction technique that can simplify numerous complex patterns into a few simple abstract patterns based on pattern similarity, (2) abstraction-guided pattern matching that completely eliminates (totally and partially) redundant computations during subgraph enumeration, and (3) a suite of system optimizations to maximize storage and computation efficiency. Our evaluation on a wide variety of real-world graphs shows that SumPA outperforms the two state-of-the-art systems Peregrine and GraphPi by up to 61.89× and 8.94×, respectively. For many mining problems on large graphs, Peregrine takes hours or even days while SumPA finishes in only a few minutes.

关键词： data reuse

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CoCGAN: Contrastive Learning for Adversarial Category Text Generation 29

CoCGAN: Contrastive Learning for Adversarial Category Text G...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Sheng, Xin Xu, Linli Xu, Yinlong Bao, Changcun Chen, Huang Ren, Bo Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China China School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence China Tencent Youtu Lab China

The task of generating texts of different categories has attracted more and more attention in the area of natural language generation recently. Meanwhile, generative adversarial net (GAN) has demonstrated its effectiveness on text generation, and is further applied to category text generation in later works. Different from existing methods, which mainly consider the pairwise relations between the text embedding and the corresponding fixed one-hot class label (data-to-class relations), this paper proposes a novel Contrastive Category Generative Adversarial Net (CoCGAN) to incorporate contrastive learning into adversarial category text generation, considering more flexible data-to-class relations as well as relations between the multiple text embeddings in the same batch (data-to-data relations). The discriminator of CoCGAN discriminates the authenticity of given samples and optimizes a contrastive learning objective to capture both more flexible data-to-class relations and data-to-data relations among training samples. Accordingly, the generator tries to produce more realistic samples which can confuse the discriminator. Experimental results on both synthetic and real category text generation datasets demonstrate that CoCGAN can achieve significant improvements over the baseline category text generation models. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Embeddings

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Development and Implementation of Automatic Trolley System for Disabled, Aged and Nursing using Arduino

Development and Implementation of Automatic Trolley System f...

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2022 IEEE Delhi Section Conference, DELCON 2022

作者： Tetteh, Akiti Kenneth Agordzo, George K. Bright, Amenya Tochukwu, Nwodom Eric-Theophilus Al-Omari, Abdullah Mma Yeboah, Derrick School of Automation Zhejiang University of Science and Technology Hangzhou China School of Big Data and Mathematics Anhui University of Science and Technology Huainan China School of Information Engineering Huzhou University Huzhou China College of Overseas Education Chengdu University Chengdu China

ISBN: (纸本)9781665458832

Every day in different countries, shopping is carried out by every category of persons. Currently, people going shopping have to push around trolleys loaded with a lot of merchandise and this is so stressful and difficult to push around. We aim to solve the current problem stated above and also ease mobility during shopping as customers such as nursing mothers with more than one child to take care of find it stressful to shop. To overcome this, we plan on automating the current trolley system by controlling the robotic trolley with hand gestures. The transmission device interfaces with an Arduino ATMEGA microcontroller, which receives signals and propels the robot based on its user's hand gesture. It reduces the stress of pushing around a shopping cart and can be controlled by hand gestures. © 2022 IEEE.

关键词： Nursing

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