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4th International Conference on Neural Networks, Information and Communication Engineering, NNICE 2024

作者： Zhang, Fucheng Tian, Pinfang Wuhan University of Science and Technology School of Computer Science and Technology Wuhan China Hubei Key Laboratory of Intelligent Information Processing and Real-TimeIndustrial Systems Wuhan China

ISBN: (纸本)9798350394375

In the current field of object detection, the YOLOv7 model demonstrates significant advantages in terms of both detection speed and accuracy. However, the model shows deficiencies in focussing on key information, and its loss function's sensitivity to bounding boxes results in suboptimal performance in densely populated environments and small target detection scenarios. To address these issues, this study proposes a mask target detection method based on YOLOv7. By integrating a channel attention mechanism into the YOLOv7 model, this method enhances the model's focus on important information in images, effectively reducing interference from background factors. Additionally, the method incorporates Normalized Wasserstein Distance (NWD) into the loss function to optimize the model's evaluation of small target detection. Experimental verification shows that the improved model maintains its original detection speed while increasing the overall accuracy to 92.1%, a 2.9% improvement over the original YOLOv7 model. It achieves a 3.3% increase in the mAP.5 metric. This indicates that the optimised YOLOv7 model has superior detection capabilities, allowing it to perform mask-wearing detection tasks more rapidly and accurately in natural environments. © 2024 IEEE.

关键词： Object detection

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CFinDEE: A Chinese Fine-Grained Financial Dataset for Document-Level Event Extraction 24

CFinDEE: A Chinese Fine-Grained Financial Dataset for Docume...

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33rd Companion of the ACM World Wide Web Conference, WWW 2023

作者： Zhang, Tian Liu, Maofu Zhou, Bingying School of Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9798400701726

Document-level event extraction faces numerous challenges in accurately modeling real-world financial scenarios, particularly due to the inadequacies in existing datasets regarding data scale and fine-grained annotations. The development of datasets is a crucial factor in driving research progress;therefore, we present a high-quality Chinese document-level event extraction dataset, CFinDEE. This dataset, grounded in real-world financial news, defines 22 event types and 116 argument roles, annotating 26,483 events and 107,096 event arguments. CFinDEE aims to address these shortcomings by providing more comprehensive annotations and data augmentation, offering richer resources for document-level event extraction in the financial domain. CFinDEE extends data both horizontally and vertically, where horizontal expansion enriches the types of financial events, enhancing the diversity of the dataset;vertical expansion, by increasing the scale of the data, effectively boosts the practical value of the dataset. Experiments conducted on multiple advanced models have validated the high applicability and effectiveness of the CFinDEE dataset for document-level event extraction tasks in the financial field. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Expansion

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MATL: A deep neural network using multi-scale convolutions and transformer for transcription factor binding site prediction

MATL: A deep neural network using multi-scale convolutions a...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Fan, Jinli Shen, Ruyi Huang, Xiaodi Zhang, Yanju Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China College of Computer Science and Technology Huaqiao University Xiamen China

ISBN: (纸本)9798350386226

Transcription factors bind to specific sequences of DNA known as transcription factor binding sites (TFBSs) in order to regulate gene expression. Identifying TFBSs is crucial for deciphering gene expression mechanisms, understanding in vitro life characteristics, and drug design. In recent years, many deep learning methods that utilize only the sequence features or a combination of sequence and shape features have been developed to predict TFBSs. However, these methods have not fully considered the characteristics of TFBSs, and their encoding strategies are relatively simplistic, thereby failing to capture the complex dependencies hidden within TFBSs. To address these issues, we propose a novel deep neural network model called MATL, which integrates sequence and shape features for TFBSs prediction. The model employs techniques such as multi-scale convolution and Transformer, and combines different modules for feature extraction based on the distribution patterns of different feature matrices. Additionally, we introduce transfer learning to address the issue of unsatisfactory prediction results due to insufficient training data in some datasets. As the results indicate, MATL exhibits excellent predictive performance on 165 validated ChIP-seq datasets, surpassing state-of-the-art methods. We also conducted ablation experiments on sequence encoding, feature extraction, and shape modules used in this study, and demonstrated that these modules play a positive role in predicting TFBSs. Moreover, the results also indicated that transfer learning can improve prediction accuracy for some TFBSs datasets with small amounts of data. The source code of MATL is available at https://***/LittleWhiteKing/MATL. © 2024 IEEE.

关键词： Transcription

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From Static to Dynamic: Adapting Landmark-Aware Image Models for Facial Expression Recognition in Videos

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IEEE Transactions on Affective Computing 2024年第2期16卷 624-638页

作者： Chen, Yin Li, Jia Shan, Shiguang Wang, Meng Hong, Richang Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China Institute of Computing Technology Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

Dynamic facial expression recognition (DFER) in the wild is still hindered by data limitations, e.g., insufficient quantity and diversity of pose, occlusion and illumination, as well as the inherent ambiguity of facial expressions. In contrast, static facial expression recognition (SFER) currently shows much higher performance and can benefit from more abundant high-quality training data. Moreover, the appearance features and dynamic dependencies of DFER remain largely unexplored. Recognizing the potential in leveraging SFER knowledge for DFER, we introduce a novel Static-to-Dynamic model (S2D) that leverages existing SFER knowledge and dynamic information implicitly encoded in extracted facial landmark-aware features, thereby significantly improving DFER performance. Firstly, we build and train an image model for SFER, which incorporates a standard Vision Transformer (ViT) and Multi-View Complementary Prompters (MCPs) only. Then, we obtain our video model (i.e., S2D), for DFER, by inserting Temporal-Modeling Adapters (TMAs) into the image model. MCPs enhance facial expression features with landmark-aware features inferred by an off-the-shelf facial landmark detector. And the TMAs capture and model the relationships of dynamic changes in facial expressions, effectively extending the pre-trained image model for videos. Notably, MCPs and TMAs only increase a fraction of trainable parameters (less than +10%) to the original image model. Moreover, we present a novel Emotion-Anchors (i.e., reference samples for each emotion category) based Self-Distillation Loss to reduce the detrimental influence of ambiguous emotion labels, further enhancing our S2D. Experiments conducted on popular SFER and DFER datasets show that we have achieved a new state of the art. © 2010-2012 IEEE.

关键词： Face recognition

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An intrinsically self-healing and anti-freezing molecular chains induced polyacrylamide-based hydrogel electrolytes for zinc manganese dioxide batteries

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Journal of Energy Chemistry 2024年第2期89卷 565-578,I0013页

作者： Haiyang Liao Wenzhao Zhong Chen Li Jieling Han Xiao Sun Xinhui Xia Ting Li Abolhassan Noori Mir F.Mousavi Xin Liu Yongqi Zhang Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 611731SichuanChina School of Mechanical Engineering Hunan University of TechnologyZhuzhou 412007HunanChina School of Materials Science and Engineering Zhejiang University of TechnologyHangzhou 310014ZhejiangChina Department of Chemistry Faculty of Basic SciencesTarbiat Modares UniversityTehran 14117-13116Iran State Key Laboratory of New Textile Materials and Advanced Processing Technologies Wuhan Textile UniversityWuhan 430200HubeiChina

The anti-freezing strategy of hydrogels and their self-healing structure are often contradictory,it is vital to break through the molecular structure to design and construct hydrogels with intrinsic anti-freezing/self-healing for meeting the rapid development of flexible and wearable devices in diverse service ***,we design a new hydrogel electrolyte(AF/SH-Hydrogel)with intrinsic anti-freezing/self-healing capabilities by introducing ethylene glycol molecules,dynamic chemical bonding(disulfide bond),and supramolecular interaction(multi-hydrogen bond)into the polyacrylamide molecular *** to the exceptional freeze resistance(84%capacity retention at-20℃)and intrinsic self-healing capabilities(95%capacity retention after 5 cutting/self-healing cycles),the obtained AF/SH-Hydrogel makes the zinc||manganese dioxide cell an economically feasible battery for the state-of-the-art *** Zn||AF/SH-Hydrogel||MnO_(2)device offers a near-theoretical specific capacity of 285 m A h g^(-1)at 0.1 A g^(-1)(Coulombic efficiency≈100%),as well as good self-healing capability and mechanical flexibility in an ice *** work provides insight that can be utilized to develop multifunctional hydrogel electrolytes for application in next generation of self-healable and freeze-resistance smart aqueous energy storage devices.

关键词： Flexible aqueous battery Hydrogel electrolyte Anti-freezing Self-healing Dual-dynamic reversible bond

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Analytical and numerical study of chirped optical solitons in a spatially inhomogeneous polynomial law fiber with parity-time symmetry potential

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Communications in Theoretical Physics 2023年第2期75卷 28-38页

作者： Yu Zhong Houria Triki Qin Zhou Research Group of Nonlinear Optical Science and Technology School of Mathematical and Physical SciencesWuhan Textile UniversityWuhan 430200China Radiation Physics Laboratory Department of PhysicsFaculty of SciencesBadji Mokhtar UniversityP.O.Box 1223000 AnnabaAlgeria State Key Laboratory of New Textile Materials and Advanced Processing Technologies Wuhan Textile UniversityWuhan 430200China

This work studies the dynamical transmission of chirped optical solitons in a spatially inhomogeneous nonlinear fiber with cubic-quintic-septic nonlinearity,weak nonlocal nonlinearity,self-frequency shift and parity-time(PT)symmetry potential.A generalized variable-coefficient nonlinear Schr?dinger equation that models the dynamical evolution of solitons has been investigated by the analytical method of similarity transformation and the numerical mixed method of split-step Fourier method and Runge–Kutta *** analytical self-similar bright and kink solitons,as well as their associated frequency chirps,are derived for the first *** found that the amplitude of the bright and kink solitons can be controlled by adjusting the imaginary part of the PT-symmetric ***,the influence of the initial chirp parameter on the soliton pulse widths is quantitatively *** is worth emphasizing that we could control the chirp whether it is linear or nonlinear by adjusting optical fiber *** simulation results of bright and kink solitons fit perfectly with the analytical ones,and the stabilities of these soliton solutions against noises are checked by numerical simulation.

关键词： solitons symmetry inhomogeneous

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Heterogeneous Cu_(x)O Nano‑Skeletons from Waste Electronics for Enhanced Glucose Detection

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Nano-Micro Letters 2024年第11期16卷 554-568页

作者： Yexin Pan Ruohan Yu Yalong Jiang Haosong Zhong Qiaoyaxiao Yuan Connie Kong Wai Lee Rongliang Yang Siyu Chen Yi Chen Wing Yan Poon Mitch Guijun Li Center on Smart Manufacturing Division of Integrative Systems and DesignThe Hong Kong University of Science and TechnologyClear Water BayKowloonHong Kong SAR 999077People’s Republic of China The Sanya Science and Education Innovation Park of Wuhan University of Technology Sanya 572000People’s Republic of China State Key Laboratory of New Textile Materials and Advanced Processing Technologies Wuhan Textile UniversityWuhan 430200People’s Republic of China

Electronic waste(e-waste)and diabetes are global challenges to modern ***,solving these two challenges together has been challenging until ***,we propose a laser-induced transfer method to fabricate portable glucose sensors by recycling copper from *** bring up a laser-induced full-automatic fabrication method for synthesizing continuous heterogeneous Cu_(x)O(h-Cu_(x)O)nano-skeletons electrode for glucose sensing,offering rapid(<1 min),clean,air-compatible,and continuous fabrication,applicable to a wide range of Cu-containing *** this approach,h-Cu_(x)O nanoskeletons,with an inner core predominantly composed of Cu_(2)O with lower oxygen content,juxtaposed with an outer layer rich in amorphous Cu_(x)O(a-Cu_(x)O)with higher oxygen content,are derived from discarded printed circuit *** employed in glucose detection,the h-Cu_(x)O nano-skeletons undergo a structural evolution process,transitioning into rigid Cu_(2)O@CuO nano-skeletons prompted by electrochemical *** transformation yields exceptional glucose-sensing performance(sensitivity:9.893 mA mM^(-1) cm^(-2);detection limit:0.34μM),outperforming most previously reported glucose *** functional theory analysis elucidates that the heterogeneous structure facilitates gluconolactone *** glucose detection device has also been downsized to optimize its scalability and portability for convenient integration into people’s everyday lives.

关键词： Copper oxide Electron 3D tomography E-waste Glucose detection Electrochemical activation

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Lightweight Monocular 3D Object Detection Based on Attention Mechanism 5

Lightweight Monocular 3D Object Detection Based on Attention...

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5th Asia-Pacific Conference on Image processing, Electronics and Computers, IPEC 2024

作者： Zhan, Feng Zhang, Zhi Wuhan University of Science and Technology College of Computer Science and Technology Hubei Wuhan430065 China Information Processing and Real-time Industrial System Hubei Province Key Laboratory of Intelligent Hubei Wuhan430065 China Information Processing and Real-time Industrial System Hubei Province Key Laboratory of Intelligent Hubei Wuhan30065 China

ISBN: (纸本)9798350374407

In the field of autonomous driving, 3D target detection is an important technology. In view of the shortcomings of existing monocular 3D detection algorithms in terms of accuracy and real-time performance, we propose a lightweight 3D target detection algorithm based on the attention mechanism. In order to ensure the real-time performance of the model, we use depthwise separable convolutions in the backbone network. The feature maps of each scale are then connected to an EMA module, which can fuse spatial attention and cross-dimensional Coordinate attention is used to enhance the feature extraction capability of the network, and BiFPN is subsequently used for multi-scale feature fusion. Finally, experiments were conducted on the KITTI dataset, and the results showed that our algorithm was superior to other methods in both accuracy and speed. © 2024 IEEE.

关键词： Object recognition

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Metabolism and metabolomics in senescence, aging, and age-related diseases: a multiscale perspective

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Frontiers of Medicine 2025年第2期19卷 200-225页

作者： Ziyi Wang Hongying Zhu Wei Xiong Center for Advanced Interdisciplinary Science and Biomedicine of IHM Division of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefei 230027China Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial IntelligenceHefei Comprehensive National Science CenterHefei 230088China CAS Key Laboratory of Brain Function and Disease Hefei 230026China Anhui Province Key Laboratory of Biomedical Aging Research Hefei 230026China

The pursuit of healthy aging has long rendered aging and senescence ***-related ailments,such as cardiovascular diseases,diabetes,and neurodegenerative disorders,pose significant threats to *** studies have shed light on the intricate mechanisms encompassing genetics,epigenetics,transcriptomics,and metabolomics in the processes of senescence and aging,as well as the establishment of age-related *** these underlying mechanisms governing aging and related pathology metabolism assumes a pivotal role that holds promise for intervention and *** advancements in metabolomics techniques and analysis methods have significantly propelled the study of senescence and aging,particularly with the aid of multiscale metabolomics which has facilitated the discovery of metabolic markers and therapeutic *** review provides an overview of senescence and aging,emphasizing the crucial role metabolism plays in the aging process as well as age-related diseases.

关键词： metabolism aging senescence age-related diseases metabolomics

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intelligent Test Paper Generation Algorithm Integrating Normal Distribution and Parallel Genetic Algorithm 5

Intelligent Test Paper Generation Algorithm Integrating Norm...

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5th International Conference on intelligent Computing and Human-Computer Interaction, ICHCI 2024

作者： Liu, Chuangye Huang, Guimin School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9798350368284

Genetic algorithms have been widely used in intelligent test paper generation systems. However, traditional genetic algorithms cannot ensure that the difficulty of test questions is normally distributed, and are prone to falling into local optimal solutions. To address the above problems, we proposed an intelligent test paper generation algorithm that combines normal distribution and parallel genetic algorithms. In the population initialization stage, we use the cumulative distribution function (CDF) of the normal distribution to initialize the population. In the subsequent genetic operations, we designed a directed mutation mechanism and added KL divergence as a penalty number in the design of the fitness function to ensure that the difficulty of the test questions is normally distributed. At the same time, in order to speed up the efficiency of the algorithm and escape from the local optimal solution, an adaptive migration strategy was introduced. Experimental results show that the difficulty of the test questions generated by this algorithm is normally distributed, and the algorithm performance is better than other algorithms. © 2024 IEEE.

关键词： Consensus algorithm

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