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SSRN 2024年

作者： Peng, Jingyi Hu, Chengjun Zhou, Xiaohan Wu, Dingyu Sun, Xun Tang, Anqi Long, Xing-Yu Li, Xiaoqiang Peng, Jie Tian, Junlong Hunan Key Laboratory of Micro-Nano Energy Materials and Devices Laboratory for Quantum Engineering and Micro-Nano Energy Technology School of Physics and Optoelectronic Xiangtan University Hunan Xiangtan411105 China Department of Plastic Surgery National Clinical Research Center for Geriatric Disorders Xiangya Hospital Central South University Changsha410008 China Institute of Guizhou Aerospace Measuring and Testing Technology Guiyang550009 China Department of Electronic Science College of Big Data and Information Engineering Guizhou University Guiyang550025 China School of Chemistry and Materials Science Guizhou Normal University Guiyang550025 China

The pH value of a wound during the healing process significantly impacts the speed and quality of healing. Real-time monitoring of the wound's healing status is crucial for doctors to assess infection and healing conditions and provide appropriate treatment plans. The carbon nanoparticles prepared in this study exhibit good fluorescence properties and pH-responsive characteristics. They also possess strong antioxidant and antibacterial properties, making them a promising nanomaterial for real-time pH monitoring and the promotion of wound healing. The work provides a new approach for the development of wound care materials capable of real-time pH monitoring. © 2024, The Authors. All rights reserved.

关键词： Carbon Quantum Dots

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Joint Statistical and Causal Feature Modulated Face Anti-Spoofing

Joint Statistical and Causal Feature Modulated Face Anti-Spo...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xin Dong Tao Wang Zhendong Li Hao Liu School of Information Engineering Ningxia University Yinchuan China Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence Co-Founded by Ningxia Municipality and Ministry of Education Yinchuan China Key Laboratory of the Internet of Water and Digital Water Governance of the Yellow River in Ningxia Yinchuan China

In this paper, we propose a hierarchical feature modulation (HFM) approach for stable face anti-spoofing in unseen domains and unseen attacks. The conventional multi-domain based generalizable approaches likely lead to local optima due to the complicated or heuristic learning paradigm. Inspired by the fact that high-level semantic disturbances and low-level miscellaneous bias jointly cause the distribution shift, HFM aims to modulate the fine-grained feature in a hierarchical manner. Specifically, we complement the structural feature with patch-wise learnable statistical information, i.e. local difference histogram, to relieve the overfitting on high-level semantics. We further introduce the structural causal model (SCM) with imaging color model to reveal that presenting mediums and capturing devices destroy the liveness-relevant information from the low level. Thus we model this hidden entanglement as a distribution mixture problem and propose the expectation-maximization (EM) based causal intervention to remove these miscellanies. Experimental results on public datasets demonstrate the effectiveness of HFM, especially in out-of-distribution settings.

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Group Multi-View Transformer for 3D Shape Analysis with Spatial Encoding

arXiv

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arXiv 2023年

作者： Xu, Lixiang Cui, Qingzhe Hong, Richang Xu, Wei Chen, Enhong Yuan, Xin Li, Chenglong Tang, Yuanyan The College of Artificial Intelligence and Big Data Hefei University Hefei230027 China The School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China The Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Anhui Hefei230000 China The School of Electrical and Mechanical Engineering The University of Adelaide AdelaideSA5005 Australia The School of Artificial Intelligence Anhui University Hefei230601 China The Zhuhai UM Science and Technology Research Institute FST University of Macau China

In recent years, the results of view-based 3D shape recognition methods have saturated, and models with excellent performance cannot be deployed on memory-limited devices due to their huge size of parameters. To address this problem, we introduce a compression method based on knowledge distillation for this field, which largely reduces the number of parameters while preserving model performance as much as possible. Specifically, to enhance the capabilities of smaller models, we design a high-performing large model called Group Multi-view Vision Transformer (GMViT). In GMViT, the view-level ViT first establishes relationships between view-level features. Additionally, to capture deeper features, we employ the grouping module to enhance view-level features into group-level features. Finally, the group-level ViT aggregates group-level features into complete, well-formed 3D shape descriptors. Notably, in both ViTs, we introduce spatial encoding of camera coordinates as innovative position embeddings. Furthermore, we propose two compressed versions based on GMViT, namely GMViT-simple and GMViT-mini. To enhance the training effectiveness of the small models, we introduce a knowledge distillation method throughout the GMViT process, where the key outputs of each GMViT component serve as distillation targets. Extensive experiments demonstrate the efficacy of the proposed method. The large model GMViT achieves excellent 3D classification and retrieval results on the benchmark datasets ModelNet, ShapeNetCore55, and MCB. The smaller models, GMViT-simple and GMViT-mini, reduce the parameter size by 8 and 17.6 times, respectively, and improve shape recognition speed by 1.5 times on average, while preserving at least 90% of the classification and retrieval performance. The code is available at https://***/bigdatagraph/*** Codes 68 Copyright © 2023, The Authors. All rights reserved.

关键词： Encoding (symbols)

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Hierarchical Interest Modeling of Long-tailed Users for Click-Through Rate Prediction

Hierarchical Interest Modeling of Long-tailed Users for Clic...

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International Conference on data engineering

作者： Xu Xie Jin Niu Lifang Deng Dan Wang Jiandong Zhang Zhihua Wu Kaigui Bian Gang Cao Bin Cui School of CS & Key Laboratory of High Confidence Software Technologies (MOE) Peking University Alibaba Group China Lazada National Engineering Lab for Big Data Analysis and Applications Beijing Academy of Artificial Intelligence (BAAI) Institute of Computational Social Science Peking University Qingdao China

Click-through rate (CTR) prediction, whose purpose is to predict the probability of a user clicking on an item, plays a pivotal role in recommender systems. Capturing users’ accurate preferences from their historical interactions (e.g., clicks) is an essential step for handling this task and has aroused wide concern in both academia and industry. However, most of the previous methods focus on the users with abundant clicks and ill-serve the users who rarely click or purchase items. Though the ratio of these long-tailed users may be small on popular platforms, such as Amazon and Taobao, they are the majority on the newborn e-commerce company like Lazada. To extract the interests of long-tailed users, several works attempt to integrate the side information, such as demographic features. Nevertheless, these features are usually not available and may even lead to privacy concerns. Therefore, how to utilize the noisy and limited clicks becomes the key *** this paper, we propose a novel model called Hierarchical Interest Modeling (HIM). It hierarchically utilizes long-tailed users’ limited behaviors and captures their preferences from both personalized and group-wise perspectives. HIM consists of two main components, including User Behavior Pyramid (UBP) and User Behavior Clustering (UBC). The UBP module utilizes additional negative feedback to reduce the noises in positive feedback, thus obtaining reliable user personalized representations. Then, the UBC module automatically discovers latent user groups with self-supervised reconstruction loss and learns another interest representation for each user in a group-wise aspect. Extensive experiments on both public and industrial datasets verify the superiority of HIM compared with the state-of-the-art baselines. Moreover, HIM has already been deployed on Lazada recommendation scenario and gains 3.38% on CTR prediction on average on the online A/B test. Our codes are available in https://***/xiaojin-nj/HIM.

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Polycyclic Phenazine-Derived Rigid Donors Construct Thermally Activated Delayed Fluorescence Emitters for Highly Efficient Orange Oleds with Extremely Low Roll-Off

SSRN

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SSRN 2022年

作者： Yang, Wei Ning, Weimin Jungchi, Hsin Liu, Tengxiao Yin, Xiaojun Ye, Changqing Gong, Shaolong Yang, Chuluo Department of Chemistry Renmin Hospital of Wuhan University Hubei Key Lab on Organic and Polymeric Optoelectronic Materials Wuhan University Wuhan430072 China College of Materials Science and Engineering Shenzhen University Shenzhen518060 China Suzhou Key Laboratory of Flexible & Printing Optoelectronic Materials School of Materials Science and Engineering Suzhou University of Science and Technology Suzhou215009 China

Two new thermally activated delayed fluorescence (TADF) molecules, tBuInPz-BO and InPz-BO , are constructed via connecting novel rigid polycyclic phenazine-derived donors with oxygen-bridged boron acceptor. Both emitters exhibit fast reverse intersystem crossing rates and high photoluminescence quantum yields, owing to the high molecular structural rigidity. As a result, the orange organic light-emitting diode (OLED) fabricated with tBuInPz-BO shows a maximum external quantum efficiency (EQE max ) of 22.0% with the electroluminescence peak at 585 nm. Moreover, the device displays extremely low efficiency roll-off. At high luminance of 1000 and 10000 cd/m 2 , the EQE values still maintain a high level of 20.2% and 14.8%, respectively, achieving state-of-the-art electroluminescence performance among TADF-based orange OLEDs. This work reveals the great potential of the new phenazine-derived donors for constructing highly efficient TADF emitters. © 2022, The Authors. All rights reserved.

关键词： Efficiency

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AdAUC: End-to-end Adversarial AUC Optimization Against Long-tail Problems

arXiv

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arXiv 2022年

作者： Hou, Wenzheng Xu, Qianqian Yang, Zhiyong Bao, Shilong He, Yuan Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China State Key Laboratory of Information Security Institute of Information Engineering CAS Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Alibaba Group Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen China

It is well-known that deep learning models are vulnerable to adversarial examples. Existing studies of adversarial training have made great progress against this challenge. As a typical trait, they often assume that the class distribution is overall balanced. However, long-tail datasets are ubiquitous in a wide spectrum of applications, where the amount of head class instances is larger than the tail classes. Under such a scenario, AUC is a much more reasonable metric than accuracy since it is insensitive toward class distribution. Motivated by this, we present an early trial to explore adversarial training methods to optimize AUC. The main challenge lies in that the positive and negative examples are tightly coupled in the objective function. As a direct result, one cannot generate adversarial examples without a full scan of the dataset. To address this issue, based on a concavity regularization scheme, we reformulate the AUC optimization problem as a saddle point problem, where the objective becomes an instance-wise function. This leads to an end-to-end training protocol. Furthermore, we provide a convergence guarantee of the proposed algorithm. Our analysis differs from the existing studies since the algorithm is asked to generate adversarial examples by calculating the gradient of a min-max problem. Finally, the extensive experimental results show the performance and robustness of our algorithm in three long-tail datasets. © 2022, CC BY.

关键词： Deep learning

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High Sensitivity Surrounding Refractive Index Sensor Based on Helical Long-Period Fiber Gratings Inscribed in Tapered Double-Cladding Fiber

High Sensitivity Surrounding Refractive Index Sensor Based o...

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Yanping He Yuehui Ma Chen Jiang Peng Wei Yunqi Liu Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China Advanced Photonic Technology Lab College of Electronics and Optical Engineering Nanjing University of Posts and Telecommunications Nanjing China

We demonstrate a surrounding refractive index (SRI) sensor based on helical long-period fiber grating inscribed in the tapered double-cladding fiber. The maximum SRI sensitivity is 5483.33 nm/RIU.

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GK: A Graphormer Architecture Based knowledge Graph Reasoning Model for Link Prediction

GK: A Graphormer Architecture Based Knowledge Graph Reasonin...

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IEEE International Conference on Big data and Cloud Computing (BdCloud)

作者： Yueiia Wu Jian-tao Zhou Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing College of Computer Science Inner Mongolia University National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Hohhot China

knowledge Graph has become a dominant research field in graph theory, but its incompleteness and sparsity hinder its application in various fields. knowledge Graph Reasoning aims to alleviate these problems by deducing new knowledge or identifying false knowledge from existing knowledge. Recently, the Graph Convolution Network (GCN) based method is one of the most advanced methods to realize knowledge graph reasoning. However, it still suffers from some problems such as incomplete neighbor information aggregation and slow training speed. This paper proposes a knowledge graph reasoning model named GK for the link prediction task, which obtains better performance than existing GCN-based methods by introducing Graphormer into knowledge reasoning. The GK first proposes that nodes and their surroundings can be regarded as a hierarchical architecture that enables the model to capture more practical reasoning information to improve prediction accuracy. In addition, to accelerate the training speed of the model on the large-scale knowledge Graph, we present a faster shortest path-finding method F-SPF in the edge coding process. Extensive experimental results show that the GK model can obtain the state-of-the-art prediction results of current GCN-based methods and can improve the training speed.

关键词： Training knowledge engineering knowledge graphs Predictive models Prediction algorithms Feature extraction Cognition

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Shared Autonomous Vehicle Integrated with Public Transit: Service Characteristics and System Design Model

SSRN

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SSRN 2023年

作者： Shi, Ziyi Luo, Sida Niu, Luyao Shao, Chunfu Institute of Intelligent Transportation Systems College of Civil Engineering and Architecture Zhejiang University Hangzhou310058 China Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Ministry of Transport Beijing Jiaotong University #3 Shangyuancun Haidian District Beijing100044 China

Promoting shared autonomous vehicles (SAVs) in tourist cities is promising, but probably induces so-called "round trips" — Travelers start and back to the same location after a tour for entertainment in SAV (e.g. sightseeing and photography) due to lack of drivers and better privacy. To serve different types of demand including round trips, this study proposes a mobility system called the Shared Autonomous Vehicle and Transit (SAV-T) integrated system, which strikes a balance between accessibility and efficiency through feeding fixed-route transit (FRT) by SAV. Service characteristics of SAVs in the proposed system are devised: Some SAVs connect passengers to FRT stops who travel across different zones;another portion of SAVs serves passengers whose trips are within the zone;the rest serves round trips. SAVs serving round trips prohibit carpool mainly to enhance privacy while allowing carpool as they serve normal trips. To overcome the difficulty to determine SAV fleet size required to accommodate different types of demand, we derive flow equilibrium equations using network queuing theory. The fleet size contributes to the system cost function, which is the objective of SAV-T system design program that is formulated as a mixed integer program by parsimonious continuum modeling and solved numerically. We find that despite serving round trips that allow no carpooling, agency costs and system costs of SAV-T are still greatly saved compared to the FRT-only system (up to 25% saving in low-demand cities). Also, cities with higher trip density can allow a larger proportion of round trips when a target system cost is given. © 2023, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Event Argument Extraction with Two-Dimensional Enhanced Fusion Mechanism

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Computer engineering and Applications 2025年第10期61卷 111-119页

作者： Wang, Luxiang Chen, Yanping Huang, Hui Huang, Ruizhang Qin, Yongbin Engineering Research Center of Ministry of Education for Text Computing and Cognitive Intelligence School of Computer Science and Technology Guizhou University Guiyang550025 China State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China

Addressing the lack of interaction between trigger and arguments, and the lack of interaction within channels in existing studies of event argument extraction, this paper proposes a two-dimensional enhanced fusion mechanism for event argument extraction (W2-ARG) that incorporates a 2D enhanced fusion method. It inserts identifiers on both sides of a trigger word in a sentence, which encodes information about the event type. In the learning process, it is effective to enhance the interaction between trigger and argument, and encode the trigger separately to highlight its information in the sentence. The event argument extraction is implemented as a two-dimensional representation of label prediction. It has the advantage to capture semantic interactions of words at different distances through dilated convolution. Subsequently, a channel attention module is applied to enhance the interactions within the channel to strengthen the information transfer within the channel. Finally, the Laplace operator is utilized to highlight the positional features of event arguments in the semantic space to improve the performance. The proposed model is experimented on the ACE05-EN and ERE-EN datasets. Experimental results show clear performance improvement of the proposed model compared with other event argument extraction methods. © 2025 Journal of Computer engineering and Applications Beijing Co., Ltd.;Science Press. All rights reserved.

关键词： BERT channel attention event argument extraction Laplace operator sentence planarization representation

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