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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Guo, Kuo Li, Yifan Chen, Hao Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Institute of Image Processing and Pattern Recognition Shanghai200240 China Carnegie Mellon University School of Computer Science Computational Biology Department PittsburghPA15213 United States

ISBN: (纸本)9798350337488

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function labels are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific labels, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function labels at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function. © 2023 IEEE.

关键词： alternative splicing gene ontology heterogeneous graph attention network isoform function prediction protein language model

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Lightweight Vision Transformer for Small Data Sets 38

Lightweight Vision Transformer for Small Data Sets

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38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023

作者： Fang, Wenxing Su, Yanxu Institutes of Physical Science and Information Technology Anhui University Hefei China School of Artificial Intelligence Anhui University Hefei230601 China Southeast University Key Laboratory of Measurement and Control of Complex System of Engineering Ministry of Education Nanjing210096 China

ISBN: (纸本)9798350303636

With the rapid development of Transformers in the field of computer vision, models based on Transformers have become highly competitive architectures in the area. Although variants of Transformer models have achieved increasing accuracy on image classification tasks, the size of the training set and the number of parameters required by the models have increased dramatically. When dealing with small datasets, such models face problems such as overfitting and undergeneralization, leading to poor accuracy on the test set. We propose a new lightweight vision transformer (LVT) to address these issues. We reconstructed the backbone network, which learns the relationship between pixels through local window self-attention and global self-attention computation. We also use the attention pooling approach to fuse the token sequences generated by the backbone network more meticulously. We trained on the CIFAR-10 and CIFAR-100 datasets from scratch and compared them with a modern convolutional neural network. The experimental results show that LVT outperforms the modern convolutional neural network in terms of accuracy and efficiency. On the test set of CIFAR-10, we have obtained an accuracy of 96.83%, which indicates that our model can effectively solve the problems facing the training of small datasets and has a wide range of application prospects. © 2023 IEEE.

关键词： lightweight pooling self-attention windows

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Oxidation Mechanism and Toxicity Evolution of Linalool,a Typical Indoor Volatile Chemical Product

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Environment & Health 2024年第7期2卷 486-498页

作者： Zihao Fu Song Guo Ying Yu Hong-Bin Xie Shiyu Li Daqi Lv Putian Zhou Kai Song Zheng Chen Rui Tan Kun Hu Ruizhe Shen Maosheng Yao Min Hu State Key Joint Laboratory of Environmental Simulation and Pollution Control International Joint Laboratory for Regional Pollution ControlMinistry of Education(IJRC)College of Environmental Sciences and EngineeringPeking UniversityBeijing 100871China Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science&TechnologyNanjing 210044China Key Laboratory of Industrial Ecology and Environmental Engineering(Ministry of Education) School of Environmental Science and TechnologyDalian University of TechnologyDalian 116024China Institute for Atmospheric and Earth System Research/Physics Faculty of ScienceUniversity of HelsinkiP.O.Box 64FIN-00014 HelsinkiFinland

Linalool,a high-reactivity volatile chemical product(VCP)commonly found in cleaning products and disinfectants,is increasingly recognized as an emerging contaminant,especially in indoor *** the gas-phase oxidation mechanism of linalool is crucial for assessing its impact on atmospheric chemistry and human *** quantum chemical calculations and computational toxicology simulations,we investigated the atmospheric transformation and toxicity evolution of linalool under low and high NO/HO_(2)·levels,representing indoor and outdoor *** findings reveal that linalool can undergo the novel mechanisms involving concerted peroxy(RO_(2)·)and alkoxy radical(RO·)modulated autoxidation,particularly emphasizing the importance of cyclization reactions *** expands the widely known RO_(2)·-dominated H-shift-driven autoxidation and proposes a generalized autoxidation mechanism that leads to the formation of low-volatility secondary organic aerosol(SOA)*** analysis shows that over half of transformation products(TPs)exhibited higher carcinogenicity and respiratory toxicity compared to *** also propose time-dependent toxic effects of TPs to assess their long-term *** results indicate that the strong indoor emission coupled with slow consumption rates lead to significant health risks under an indoor *** results highlight complex indoor air chemistry and health concerns regarding persistent toxic products during indoor cleaning,which involves the use of linalool or other VCPs.

关键词： volatile chemical products(VCPs) peroxy radicals(RO_(2)·) atmospheric autoxidation secondary organic aerosol(SOA) computational toxicology

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Edge-assisted Prediction and Predictive control for Flexible Platooning under Mixed Traffic Flow

Edge-assisted Prediction and Predictive Control for Flexible...

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IEEE Conference on Vehicular Technology (VTC)

作者： Fengkun Gao Bo Yang Jiahang Li Cailian Chen Xinping Guan Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai P. R. China

The uncertainty of human-driven vehicles (HDVs) has a significant impact on the movement of connected and automated vehicles (CAVs), especially on the CAV platoon. In this regard, the future trajectory information of the preceding HDV is essential for CAV platoon to develop a safe and smooth speed control strategy. However, constrained by computation and energy resources, on-board unit can not provide accurate predictions, which results in inefficient control strategy and even safety issues. Hence, we propose an edge-assisted prediction and a flexible cooperative adaptive cruise control (CACC) strategy in this paper. The challenges mainly lie in the design of coordination mechanism and control strategy. Considering the inaccuracy of prediction, an error-aware communication trigger scheme is first designed to decide whether to request the roadside unit (RSU) for performing accurate prediction with the aid of mobile edge computing (MEC). Further, a novel flexible tracking policy is proposed to deal with prediction error, which is integrated with model predictive control method to determine control inputs. Simulations based on real data, collected in Shanghai, China, demonstrate that our proposed method can significantly improve driving safety and speed smoothness.

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NNgTL: Neural Network Guided Optimal Temporal Logic Task Planning for Mobile Robots

arXiv

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arXiv 2023年

作者： Liu, Ruijia Li, Shaoyuan Yin, Xiang The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Key Laboratory of System Control and Information Processing The Ministry of Education of China Shanghai200240 China

In this work, we investigate task planning for mobile robots under linear temporal logic (LTL) specifications. This problem is particularly challenging when robots navigate in continuous workspaces due to the high computational complexity involved. Sampling-based methods have emerged as a promising avenue for addressing this challenge by incrementally constructing random trees, thereby sidestepping the need to explicitly explore the entire state-space. However, the performance of this sampling-based approach hinges crucially on the chosen sampling strategy, and a well-informed heuristic can notably enhance sample efficiency. In this work, we propose a novel neural-network guided (NN-guided) sampling strategy tailored for LTL planning. Specifically, we employ a multi-modal neural network capable of extracting features concurrently from both the workspace and the Büchi automaton. This neural network generates predictions that serve as guidance for random tree construction, directing the sampling process toward more optimal directions. Through numerical experiments, we compare our approach with existing methods and demonstrate its superior efficiency, requiring less than 15% of the time of the existing methods to find a feasible solution. © 2023, CC BY-NC-SA.

关键词： Mobile robots

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De-Redundant Biomedical Feature Extraction Based On Improved MultiSURF

De-Redundant Biomedical Feature Extraction Based On Improved...

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Chinese Automation Congress (CAC)

作者： Ruixiang Ma Bin Zhang Cailian Chen Department of Automation Shanghai Jiao Tong University Shanghai PR China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai PR China

Biomedical data contain valuable information which contributes to the prediction of clinical disease risk. However, the presence of redundant and irrelevant features will diminish prediction accuracy and augment model complexity. According to our research, MultiSURF is a famous feature selection method but falls short in eliminating redundant features. To address this constraint, this paper introduced an improved MultiSURF method known as EN-MultiSURF. The proposed method integrated the strengths of Elastic Net and MultiSURF, which is capable of detecting similarity between features as well as eliminating redundant and irrelevant features. The proposed method was compared with Relief, MultiSURF, Elastic Net, Random Forest and Decision Tree on one binary along with four multiclass numerical datasets. The experimental results substantiated that our method surpassed other methods and exhibited a higher degree of consistency with medical prior knowledge, which established itself as a valuable tool for biomedical data mining.

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Bev-Cfkt: A Lidar-Camera Cross-Modality-Interaction Fusion and Knowledge Transfer Framework with Transformer for Bev 3d Object Detection

SSRN

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SSRN 2023年

作者： Wei, Ming Lu, Jun-Guo Li, Jiachen Kang, Hongyi Huang, Yijie Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

The BEV-CFKT in this paper is proposed to LiDAR-Camera fusion using knowledge transfer with transformer in the Bird’s-Eye-View (BEV) space for accurate and robust 3D object detection. BEV-CFKT consists of three main parts, including generation of BEV features from images and point clouds, cross-modality interaction, and hybrid object queries based on a monocular detection head. By unifying the features from point clouds and images into the BEV space, we simplify the modal interaction and facilitate knowledge transfer, and obtain richer structural and semantic information from multimodal data, which effectively improves the network performance. BEVCFKT also adds a temporal fusion module to enhance the effect of detection. Additionally, a hybrid object queries module based on a monocular detection head is added to accelerate the convergence of our model. The effectiveness of our approach is demonstrated through a large number of experiments. © 2023, The Authors. All rights reserved.

关键词： Cameras

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V2X Based Cooperative Motion control and Energy Management for Electronic Vehicles

V2X Based Cooperative Motion Control and Energy Management f...

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IEEE Conference on Vehicular Technology (VTC)

作者： Jiahang Li Cailian Chen Fengkun Gao Bo Yang Xinping Guan Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai P. R. China

V2X communication is a key technology for intelligent transportation system to improve traffic safety and efficiency. Electric vehicles can reduce greenhouse gas emissions and fossil fuel dependence, but they face challenges such as limited driving range, high battery cost, and long charging time. This paper proposes a V2X communication assisted hierarchical cooperative control strategy for EVs that aims at improving driving performance and energy efficiency. The upper layer uses improved model predictive control (MPC) method for cooperative motion control. A mechanism is designed for V2X communication loss in the algorithm. The lower layer employs a hybrid energy storage system for powertrain management. The algorithm in the lower layer uses the predictive information from the upper layer to enhance the powertrain efficiency and prolong the battery life. The proposed strategy is simulated using a Prescan-Carsim simulation platform. The results demonstrate that the proposed method can improve the battery lifetime over 13% compared with baseline method.

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V2X Communication Assisted Hierarchical Path Planning for Connected and Automated Vehicles

V2X Communication Assisted Hierarchical Path Planning for Co...

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Chinese Automation Congress (CAC)

作者： Jiahang Li Cailian Chen Fengkun Gao Bo Yang Xinping Guan Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai P. R. China

Connected and Automated Vehicles (CAVs) have attracted increasing attention in intelligent transportation systems. An effective path planning enables the efficient operation of CAVs so that they can coordinate traffic flow and avoid accidents. This paper presents a framework of V2X communication assisted hierarchical path planning system for CAVs that aims to improve computational efficiency and safety performance. The proposed framework consists of two stages: pre-planning and online-planning. The pre-planning stage leverages the computing and storage capabilities of Roadside Units (RSUs) to generate and filter candidate trajectories for different driving scenarios. The online-planning stage adjusts the pre-planned trajectory based on the vehicle's driving risk index, which considers the distance and relative speed to the surrounding vehicles. The performance of effectiveness, safety, and timeliness of the proposed framework is evaluated and demonstrated by a simulation study in different scenarios. Compared with conventional methods, the proposed method improves the computational efficiency by 23% and reduces the collision rate by 13 %.

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Grain size control in quasi-two-dimensional perovskite thin film via intermediate phase engineering for efficient bound exciton generation

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Science China Materials 2024年第12期67卷 3925-3931页

作者： Guohui Li Wenhui Zhao Kai Lin Kefan Zhao Yujing Wang Aohua Niu Rong Weng Kaibo Zheng Yanxia Cui College of Electronic Information and Optical Engineering Key Laboratory of Interface Science and Engineering in Advanced MaterialsKey Lab of Advanced Transducers and Intelligent Control System of Ministry of EducationTaiyuan University of TechnologyTaiyuan 030024China Chemical Physics Division and NanoLund Lund UniversityBox 12422100LundSweden

Quasi-two dimensional(2D)perovskites have emerged as a promising class of materials due to their remarkable photoluminescence efficiency,which stems from their exceptionally high exciton binding *** spatial confinement of excitons within smaller grain sizes could enhance the formation of biexcitons leading to higher radiative recombination ***,the synthesis of high-quality quasi-2D perovskite thin films with controllable grain sizes remains a challenging *** this study,we present a facile method for achieving quasi-2D perovskite thin films with controllable grain sizes ranging from 500 to 900 *** is accomplished by intermediate phase engineering during the film fabrication *** results demonstrate that quasi-2D perovskite films with smaller grain sizes exhibit more efficient bound exciton generation and a reduced stimulated emission threshold down to 15.89µJ cm^(−2).Furthermore,femtosecond transient absorption measurements reveal that the decay time of bound excitons is shorter in quasi-2D perovskites with smaller grain sizes compared to that of those with larger grains at the same pump density,which is 230.5 *** observation suggests a more efficient exciton recombination process in the smaller grain size *** findings would offer a promising approach for the development of efficient bound exciton lasers.

关键词： quasi-2D perovskite intermediate phase engineering bound exciton generation grain size control

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