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SSRN 2024年

作者： Wu, Yilin Guo, Wenzhong Lin, Yaojin College of Computer and Data Science Fuzhou University Fuzhou350116 China Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China School of Computer Science Minnan Normal University Zhangzhou363000 China

In label distribution learning, an instance is involved with many labels in different importance degrees, and the feature space of instances is accompanied with thousands of redundant and/or irrelevant features. Therefore, the main characteristic of feature selection in label distribution learning is to evaluate the discriminable ability of each feature. Motivated by neighborhood rough set, which can be used to measure the dependency degree of feature via constructing neighborhood relations on feature space and label space, respectively, this paper proposes a novel lable distribution feature selection method. In this paper, the neighborhood class of instance in label distribution space is defined, which is beneficial to recognize the logical class of target instance. Then, a new neighborhood rough set model for label distribution learning is proposed. Specially, the dependency degree of feature combining label weight is defined. Finally, a label distribution feature selection based on neighborhood rough set is presented. Extensive experiments on 12 data sets show the effectiveness of the proposed algorithm. © 2024, The Authors. All rights reserved.

关键词： Feature Selection

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ResFormer: Scaling ViTs with Multi-Resolution Training

ResFormer: Scaling ViTs with Multi-Resolution Training

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Rui Tian Zuxuan Wu Qi Dai Han Hu Yu Qiao Yu-Gang Jiang Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University Shanghai Collaborative Innovation Center of Intelligent Visual Computing Microsoft Research Asia Shanghai AI Laboratory

Vision Transformers (ViTs) have achieved overwhelming success, yet they suffer from vulnerable resolution scalability, i.e., the performance drops drastically when presented with input resolutions that are unseen during training. We introduce, ResFormer, a framework that is built upon the seminal idea of multi-resolution training for improved performance on a wide spectrum of, mostly unseen, testing resolutions. In particular, ResFormer operates on replicated images of different resolutions and enforces a scale consistency loss to engage interactive information across different scales. More importantly, to alternate among varying resolutions effectively, especially novel ones in testing, we propose a global-local positional embedding strategy that changes smoothly conditioned on input sizes. We conduct extensive experiments for image classification on ImageNet. The results provide strong quantitative evidence that ResFormer has promising scaling abilities towards a wide range of resolutions. For instance, ResFormer-B-MR achieves a Top-1 accuracy of 75.86% and 81.72% when evaluated on relatively low and high resolutions respectively (i.e., 96 and 640), which are 48% and 7.49% better than DeiT-B. We also demonstrate, moreover, ResFormer is flexible and can be easily extended to semantic segmentation, object detection and video action recognition.

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Prediction of exosomal piRNAs based on deep learning for sequence embedding with attention mechanism

Prediction of exosomal piRNAs based on deep learning for seq...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Liu, Yajun Ding, Yulian Li, Aimin Fei, Rong Guo, Xie Wu, Fangxiang Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China University of Saskatchewan Division of Biomedical Engineering Saskatoon Canada Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China University of Saskatchewan Division of Biomedical Engineering Department of Computer Science Department of Mechanical Engineering Saskatoon Canada

ISBN: (纸本)9781665468190

PIWI-interacting RNAs (piRNAs) are a type of small non-coding RNAs which bind with the PIWI proteins to exert biological effects in various regulatory mechanisms. A growing amount of evidence reveals that exosomal piRNAs are potential biomarkers for diagnosis and treatment of complex diseases. Effective methods for the prediction of exosomal piRNAs are the foundation of piRNA functional research. In this study, we propose an end-to-end deep network for identifying exosomal piRNAs based on features learned from natural language processing (NLP) models for sequence embedding with attention mechanism. First, a benchmark dataset is constructed by processing piRNA subcellular localization annotated data and sequence data. Moreover, bagging positive unlabeled learning is applied to get the reliable negative set. Finally, we treat a piRNA sequence as a sentence and its k-mer subsequence as a token. Sequence embedding models with self-attention mechanism is designed to extract features from exosome piRNA sequences, which are used for the prediction task. Compared with three competing methods, our model achieves the best performance and reveals the key factors of exosomal piRNA sequences by the attention mechanism. Our model characterizes exosomal piRNAs and could be beneficial for researchers to investigate exosomal piRNAs' functions. © 2022 IEEE.

关键词： Embeddings

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Ascl: Accelerating Semi-Supervised Learning Via Contrastive Learning

SSRN

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SSRN 2024年

作者： Liu, Haixiong Li, Zuoyong Wu, Jiawei Zeng, Kun Hu, Rong Zeng, Wei Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Data Science Minjiang University Fuzhou350121 China School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University Guangdong Shenzhen518107 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan354300 China School of Physics and Mechanical and Electrical Engineering Longyan University Longyan364012 China

SSL(Semi-supervised learning) is widely used in machine learning, which leverages labeled and unlabeled data to improve model performance. SSL aims to optimize class mutual information, but noisy pseudo-labels introduce false class information due to the scarcity of labels. Therefore, these algorithms often need significant training time to refine pseudo-labels for performance improvement iteratively. To tackle this challenge, we propose a novel plug-and-play method named ASCL(Accelerating Semi-Supervised Learning via Contrastive Learning). This method combines contrastive learning with uncertainty-based selection for performance improvement and accelerates the convergence of SSL algorithms. Contrastive learning initially emphasizes the mutual information between samples as a means to decrease dependence on pseudo-labels. Subsequently, it gradually turns to maximizing the mutual information between classes, aligning with the objective of semi-supervised learning. Uncertainty-based selection provides a robust mechanism for acquiring pseudo-labels. The combination of the contrastive learning module and the uncertainty-based selection module forms a virtuous cycle to improve the performance of the proposed model. Extensive experiments demonstrate that ASCL outperforms state-of-the-art methods in terms of both convergence efficiency and performance. In the experimental scenario where only one label is assigned per class in the CIFAR-10 dataset, the application of ASCL to Pseudo-label, UDA (Unsupervised Data Augmentation for Consistency Training), and Fixmatch benefits substantial improvements in classification accuracy. Specifically, the results demonstrate notable improvements in respect of 16.32%, 6.9%, and 24.43% when compared to the original outcomes. Moreover, the required training time is reduced by almost 50%. © 2024, The Authors. All rights reserved.

关键词： Image classification

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Self-decoupling 5G MIMO Antenna via Grounding for Mobile Phones

Self-decoupling 5G MIMO Antenna via Grounding for Mobile Pho...

引用

IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Wei Zhou Junwei Qi Yingsong Li College of Information and Communication Engineering Harbin Engineering University (HEU) China College of Computer Science and Technology HEU China Key Laboratory of Intelligent Computing Signal Processing Ministry of Education Anhui University Hefei China

This paper presents a self-decoupling high isolation terminal antenna for mobile phones which can applied to 5G multiple-input-multiple-output (MIMO) communication systems. The significant decoupling effect is achieved by introducing only one ground point in each asymmetric antenna pair with an interval of very close (2mm). Through the simulations and optimizations, it can be obtained that the bandwidth of the two antennas is different owing-to the asymmetric structures of the constructed MIMO antenna. The -10 dB resonant frequency ranges of the two antennas are 4.65 - 5.17 GHz and 4.45 - 5.58 GHz, respectively, but the coincidence bandwidth of the two antennas covers the 5G band of 4.8 - 5 GHz, and the coupling of the proposed MIMO antenna in the same bandwidth is lower than -18dB as well.

关键词： Couplings Wireless communication 5G mobile communication Grounding Resonant frequency Bandwidth Mobile antennas

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Article reranking by memory-enhanced key sentence matching for detecting previously fact-checked claims 59

Article reranking by memory-enhanced key sentence matching f...

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Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language processing, ACL-IJCNLP 2021

作者： Sheng, Qiang Cao, Juan Zhang, Xueyao Li, Xirong Zhong, Lei Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Key Lab of Data Engineering and Knowledge Engineering Renmin University of China China

ISBN: (纸本)9781954085527

False claims that have been previously fact-checked can still spread on social media. To mitigate their continual spread, detecting previously fact-checked claims is indispensable. Given a claim, existing works retrieve fact-checking articles (FC-articles) for detection and focus on reranking candidate articles in the typical two-stage retrieval framework. However, their performance may be limited as they ignore the following characteristics of FC-articles: (1) claims are often quoted to describe the checked events, providing lexical information besides semantics;and (2) sentence templates to introduce or debunk claims are common across articles, providing pattern information. In this paper, we propose a novel reranker, MTM (Memoryenhanced Transformers for Matching), to rank FC-articles using key sentences selected using event (lexical and semantic) and pattern information. For event information, we propose to finetune the Transformer with regression of ROUGE. For pattern information, we generate pattern vectors as a memory bank to match with the parts containing patterns. By fusing event and pattern information, we select key sentences to represent an article and then predict if the article fact-checks the given claim using the claim, key sentences, and patterns. Experiments on two real-world datasets show that MTM outperforms existing methods. Human evaluation proves that MTM can capture key sentences for explanations. The code and the dataset are at https://***/ICTMCG/MTM. © 2021 Association for Computational Linguistics

关键词： Semantics

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Interpretable Solutions for Multi-Physics PDEs Using T-NNGP 39

Interpretable Solutions for Multi-Physics PDEs Using T-NNGP

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Cao, Lulu Lin, Zexin Tan, Kay Chen Jiang, Min Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China School of Informatics Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen University China Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)157735897X

Multiphysics simulation aims to predict and understand interactions between multiple physical phenomena, aiding in comprehending natural processes and guiding engineering design. The system of Partial Differential Equations (PDEs) is crucial for representing these physical fields, and solving these PDEs is fundamental to such simulations. However, current methods primarily yield numerical outputs, limiting interpretability and generalizability. We introduce T-NNGP, a hybrid genetic programming algorithm that integrates traditional numerical methods with deep learning to derive approximate symbolic expressions for multiple unknown functions within a system of PDEs. T-NNGP initially obtains numerical solutions using traditional methods, then generates candidate symbolic expressions via deep reinforcement learning, and finally optimizes these expressions using genetic programming. Furthermore, a universal decoupling strategy guides the search direction and addresses coupling problems, thereby accelerating the search process. Experimental results on three types of PDEs demonstrate that our method can reliably obtain human-understandable symbolic expressions that fit both the PDEs and the numerical solutions from traditional methods. This work advances multiphysics simulation by enhancing our ability to derive approximate symbolic solutions for PDEs, thereby improving our understanding of complex physical phenomena. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Numerical methods

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Edge-fault-tolerance about the SM-λ property of hypercube-like networks

arXiv

引用

arXiv 2022年

作者： Liu, Dong Li, Pingshan Zhang, Bicheng Key Laboratory of Intelligent Computing & Information Processing Ministry of Education School of Mathematical and Computational Sicence Xiangtan University Hunan Xiangtan41105 China

The edge-fault-tolerance of networks is of great significance to the design and maintenance of networks. For any pair of vertices u and v of the connected graph G, if they are connected by min{degG(u), degG(v)} edge-disjoint paths, then G is strong Menger edge connected (SM-λ for short). The conditional edge-fault-tolerance about the SM-λ property of G, written smrλ(G), is the maximum value of m such that G − F is still SM-λ for any edge subset F with |F| ≤ m and δ(G−F) ≥ r, where δ(G−F) is the minimum degree of G−F. Previously, most of the exact value for smrλ(G) is aimed at some well-known networks when r ≤ 2, and a few of the lower bounds on some well-known networks for r ≥ 3. In this paper, we firstly determine the exact value of smrλ(G) on class of hypercube-like networks (HL-networks for short, including hypercubes, twisted cubes, crossed cubes etc.) for a general r, that is, smrλ(Gn) = 2r(n − r) − n for every Gn ∈ HLn, where n ≥ 3 and 1 ≤ r ≤ n − 2. Copyright © 2022, The Authors. All rights reserved.

关键词： Fault tolerance

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Fingerprint Presentation Attack Detector Using Global-Local Model

arXiv

引用

arXiv 2024年

作者： Liu, Haozhe Zhang, Wentian Liu, Feng Wu, Haoqian Shen, Linlin The Computer Vision Institute College of Computer Science and Software Engineering SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society The National Engineering Laboratory for Big Data System Computing Technology The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

The vulnerability of automated fingerprint recognition systems (AFRSs) to presentation attacks (PAs) promotes the vigorous development of PA detection (PAD) technology. However, PAD methods have been limited by information loss and poor generalization ability, resulting in new PA materials and fingerprint sensors. This paper thus proposes a global-local model-based PAD (RTK-PAD) method to overcome those limitations to some extent. The proposed method consists of three modules, called: 1) the global module;2) the local module;and 3) the rethinking module. By adopting the cut-out-based global module, a global spoofness score predicted from nonlocal features of the entire fingerprint images can be achieved. While by using the texture in-painting-based local module, a local spoofness score predicted from fingerprint patches is obtained. The two modules are not independent but connected through our proposed rethinking module by localizing two discriminative patches for the local module based on the global spoofness score. Finally, the fusion spoofness score by averaging the global and local spoofness scores is used for PAD. Our experimental results evaluated on LivDet 2017 show that the proposed RTK-PAD can achieve an average classification error (ACE) of 2.28% and a true detection rate (TDR) of 91.19% when the false detection rate (FDR) equals 1.0%, which significantly outperformed the state-of-the-art methods by ∼10% in terms of TDR (91.19% versus 80.74%). Copyright © 2024, The Authors. All rights reserved.

关键词： Supervised learning

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Efficient numerical methods for computing stationary states of spherical Landau-Brazovskii model

arXiv

引用

arXiv 2025年

作者： Qiu, Qun Si, Wei Ji, Guanghua Jiang, Kai School of Mathematical Sciences Beijing Normal University Beijing100875 China School of Mathematics Sciences Peking University Beijing100871 China School of Mathematics and Computational Science Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Hunan Xiangtan411105 China

In this paper, we develop a set of efficient methods to compute stationary states of the spherical Landau-Brazovskii (LB) model in a discretization-then-optimization way. First, we discretize the spherical LB energy functional into a finite-dimensional energy function by the spherical harmonic expansion. Then five optimization methods are developed to compute stationary states of the discretized energy function, including the accelerated adaptive Bregman proximal gradient, Nesterov, adaptive Nesterov, adaptive nonlinear conjugate gradient and adaptive gradient descent methods. To speed up the convergence, we propose a principal mode analysis (PMA) method to estimate good initial configurations and sphere radius. The PMA method also reveals the relationship between the optimal sphere radius and the dominant degree of spherical harmonics. Numerical experiments show that our approaches significantly reduce the number of iterations and the computational time. Copyright © 2025, The Authors. All rights reserved.

关键词： Conjugate gradient method

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