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CSCL-DTI: Predicting drug-target interaction through cross-view and self-supervised contrastive learning

CSCL-DTI: Predicting drug-target interaction through cross-v...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lin, Xuan Zhang, Xi Yu, Zu-Guo Long, Yahui Zeng, Xiangxiang Yu, Philip S. Xiangtan University College of Computer Science China Xiangtan University Key Laboratory of Intelligent Computing and Information Processing Ministry of Education China Singapore Hunan University College of Information Science and Engineering China University of Illinois Chicago Department of Computer Science United States

ISBN: (纸本)9798350386226

Accurately predicting drug-target interactions (DTI) is a critical step in drug discovery. Existing methods of DTI prediction primarily employ Simplified Molecular-Input Line-Entry System (SMILES) sequences or molecular graphs to learn drug representations. However, the features learned by such single-view approach is prone to incomplete. While some multiview methods that consider the views of both SMILES sequences and molecular graphs have been developed, these methods often fall in short in capturing potential interactions between views. In this work, we propose a novel dual contrastive learning framework CSCL-DTI for DTI prediction. First, we design a contrastive-enhanced cross-view representation learning (CVRL) to learn representations for drugs. In this module, Transformer-based and graph convolutional network (GCN)-based encoders are separately adopted to learn view-specific representations, followed by contrastive learning to enrich the representations by accounting for the potential interplay between local chemical context and topological structure. Second, we combine Transformer with self-supervised contrastive learning (SSCL) to learn representations for targets by modelling protein amino acids sequences. The scheme allows to effectively preserve the intrinsic characteristics of the sequences. Finally, we introduce a bilinear attention network to obtain an integrated representation by adaptively incorporating drug and target representations. Benchmarking experiments on two datasets demonstrated that CSCL-DTI1 outperforms six state-of-the-art methods. © 2024 IEEE.

关键词： Contrastive Learning

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Emotional Knowledge Self-Distillation in Dialogue

Emotional Knowledge Self-Distillation in Dialogue

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2025 IEEE International Conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Jian, Zhongquan Wang, Yancheng Wu, Weichao Yao, Junfeng Wang, Meihong Wu, Qingqiang School of Informatics Xiamen University Xiamen China School of Film Xiamen University Xiamen China School of Computing and Data Science Xiamen University Malaysia Selangor Malaysia School of Mechanics and Civil Engineering China University of Mining & Technology Beijing China Xiamen Key Laboratory of Intelligent Storage and Computing School of Informatics Xiamen University Xiamen China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen China

ISBN: (纸本)9798350368741

Recognizing emotions in dialogues is vital for effective human-computer interaction, yet remains a challenging task in Natural Language processing (NLP). Previous studies in Emotion Recognition in Conversation (ERC) have primarily focused on contextual features, while overlooking the importance of emotional features in emotion recognition. To address this gap, we focus on the role of emotional features in ERC and propose a novel method, Emotional Knowledge Self-Distillation (EmoKSD1), to enhance the model's emotional sensitivity. In EmoKSD, utterances are enriched with implicit 〈mask〉 tokens to represent conveyed emotions, allowing the distillation of emotional knowledge from explicit emotional tokens to implicit 〈mask〉 tokens, thereby enhancing the model's ability to perceive subtle emotions within the dialogue. Through thorough evaluations on two public ERC datasets (i.e., IEMOCAP and MELD) using proposed coarse-grained utterance distillation and fine-grained token distillation techniques, EmoKSD demonstrates superior performance compared to existing methods, highlighting the significance of emotional features in ERC. © 2025 IEEE.

关键词： Dialogue System Emotion Recognition in Conversation Knowledge Distillation Natural Language processing

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Affine-Projection Maximum Asymmetric Correntropy Criterion Algorithm Under System Identifications

Affine-Projection Maximum Asymmetric Correntropy Criterion A...

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International Conference on Computer Science and Network Technology (ICCSNT)

作者： Yan Peng Xinqi Huang Canping Yu Qi Mao Yingsong Li College of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

The Affine-Projection Maximum Asymmetric Correntropy Criterion (APMACC) is constructed, drawing upon the fundamental principles of the maximum asymmetric correntropy criterion and an affine-projection scheme. The APMACC algorithm incorporates the asymmetric Gaussian model as a kernel function within the Affine-Projection (AP) algorithm framework, thereby endowing it with robustness against asymmetrically distributed noise. Furthermore, the bound for step-size is established in the literature. Simulation results demonstrate that the APMACC has fast convergence and low steady-state.

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Complex-valued cross-domain few-shot learning network for PolSAR image classification

Complex-valued cross-domain few-shot learning network for Po...

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IET International Radar Conference (IRC 2023)

作者： Yice Cao Dayi Zhu Zhenhua Wu Jie Chen Zhixiang Huang Lixia Yang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Electronics and Information Engineering Anhui University Hefei 230601 People's Republic of China

ISBN: (数字)9781837240982

Gathering reliable labeled samples for polarimetric synthetic aperture (PolSAR) image classification is laborious. Moreover, applying a trained classifier to new domains often leads to noticeable performance degradation due to domain disparities. Therefore, this paper proposes the novel complex-valued cross-domain (CD) few-shot learning classification (CCFSLC) method for PolSAR images to address these issues. Firstly, the transferrable knowledge learning module (TKLM) with a complex-valued feature encoder (CVFE) is trained using source data with sufficient labeled samples. Then, the deep few-shot learning module (DFSLM), constructed using the pre-trained CVFE, is trained by episodes in both source and target domains, with only minimal target labeled samples. Meanwhile, the adversarial domain adaptation module (ADAM) is employed to eliminate domain shift. The proposed CCFSLC mainly focuses on exploring discriminative information from raw PolSAR data, while reducing the domain gap to recognize novel categories in unseen domains with only a few annotated samples. Experiments on typical PolSAR datasets validate the effectiveness of the proposed method.

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A Dual-band Low-profile Quadrifilar-Helix Antenna for Satellite Navigation System

A Dual-band Low-profile Quadrifilar-Helix Antenna for Satell...

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IEEE International Workshop on Electromagnetics, Applications and Student Innovation (iWEM)

作者： Jiahong Wang Junwei Qi Yingsong Li College of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

A new dual-band low-profile quadrifilar-helical antenna (QHA) was presented. The spiral radiation arm of QHA is printed to three circular dielectric plates to reduce the antenna height. In this case, the radiation arm of the lower layer is connected to the feed, and the L-shaped branch is connected to the ground through a 50 ohm resistance. The radiation arm in the middle layer adds branches to realize dual frequency. The upper radiation arm rotates inward and folds, further realizing miniaturization. As a result, the height of QHA is only 20mm. Through CST simulation and optimization, The operating frequency bands of QHA are 1GHz - 1.45GHz and 1.54GHz - 1.74GHz. The beam width of QHA is more than 120°, which is to provide service for satellite navigating systems. The AR for the operation bands are less than 3dB.

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A RIS-Aided MIMO Secure Communication Scheme Based on Subarray Partition

A RIS-Aided MIMO Secure Communication Scheme Based on Subarr...

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International Conference on Computer and Communications (ICCC)

作者： Suzhen Wang Hui Zhi Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei China School of Electronic and Information Engineering Anhui University Hefei China

Subarray partition of reconfigurable intelligent surface (RIS) can significantly reduce the computational complexity of solving optimal reflection coefficients. However, there is no research about the RIS subarray partition of RIS-aided multiple-input multiple-output (MIMO) communication for the eavesdropper scenario. In this paper, we intend to solve the optimization subarray partition problem for the eavesdropper scenario, design a RIS-aided MIMO secure communication scheme based on subarray partition (RSC-SP). We consider minimizing the number of subarrays while satisfying secrecy rate requirements. First, this problem is described as a nonconvex combinatorial optimization problem, then we solve it by combining alternating optimization and bisection. In this scheme, we derive the closed expressions of the optimal transmit covariance matrix and the optimal reflection coefficients of RIS. The alternating optimization algorithm is used to jointly optimize the transmit covariance matrix and the reflection coefficients of RIS, the bisection method is used to calculate the minimum number of subarrays. Simulation results show that compared with the traditional scheme without subarray partition, RSC-SP can significantly reduce the computational complexity while meeting secrecy rate requirements.

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Adaptive Step-Size Sparse Millimeter Wave Imaging Algorithm Based on Approximate Observations

Adaptive Step-Size Sparse Millimeter Wave Imaging Algorithm ...

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International Conference on Computer Science and Network Technology (ICCSNT)

作者： Dianhao Wang Xinqi Huang Canping Yu Qi Mao Yingsong Li College of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

The currently constructed millimeter wave imaging system has the problems of long sampling time and more sampling points of antenna units, and the use of compressed perception algorithm can improve the imaging quality when the number of sampling points are much smaller than the Nyquist distance sampling. The traditional compressed perception algorithm can achieve better sparse recovery than the matched filter imaging algorithm, but there is a large dimension of the measurement matrix and high computational complexity. For the problems of difficult data processing and large dimensions of measurement matrix, a sparse imaging regularization model is constructed based on approximate observation, and an improved soft threshold iterative algorithm is used with adaptive step size, which improves the convergence performance, reduces the computational complexity of the sparse recovery dramatically and achieves a better quality of sparse imaging than the traditional compressed perception algorithm.

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CMA: A Chromaticity Map Adapter for Robust Detection of Screen-Recapture Document Images

CMA: A Chromaticity Map Adapter for Robust Detection of Scre...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Changsheng Chen Liangwei Lin Yongqi Chen Bin Li Jishen Zeng Jiwu Huang Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security Shenzhen University Alibaba Group Guangdong Laboratory of Machine Perception and Intelligent Computing Faculty of Engineering Shenzhen MSU-BIT University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

The rebroadcasting of screen-recaptured document images introduces a significant risk to the confidential docu-ments processed in government departments and commer-cial companies. However, detecting recaptured document images subjected to distortions from online social networks (OSNs) is challenging since the common forensics cues, such as moiré pattern, are weakened during transmission. In this work, we first devise a pixel-level distortion model of the screen-recaptured document image to identify the robust features of color artifacts. Then, we extract a chromaticity map from the recaptured image to highlight the presence of color artifacts even under low-quality samples. Based on the prior understanding, we design a chromaticity map adapter (CMA) to efficiently extract the chromaticity map, and feed it into the transformer backbone as multi-modal prompt tokens. To evaluate the performance of the pro-posed method, we collect a recaptured office document im-age dataset with over 10K diverse samples. Experimental results demonstrate that the proposed CMA method outper-forms a SOTA approach (with RGB modality only), reducing the average EER from 26.82% to 16.78%. Robustness eval-uation shows that our method achieves 0.8688 and 0.7554 AUCs under samples with JPEG compression $(QF=70)$ and resolution as low as $534\times 503$ pixels.

关键词： Image color analysis Social networking (online) Forensics Splicing Transform coding Feature extraction Distortion

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An Energy-Efficient Virtual Machine Placement Algorithm Based on Improved Grouping GA

An Energy-Efficient Virtual Machine Placement Algorithm Base...

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IEEE International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Taisheng Zeng Xiaodong Wu School of Mathematics and Computer Science Quanzhou Normal University Fujian Provincial Key Laboratory of Data-Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian Province University Quanzhou China

ISBN: (数字)9798350377033

ISBN: (纸本)9798350377040

Virtual machine placement (VMP) plays a crucial role in optimizing the efficiency and sustainability of data centers. By consolidating virtual machines (VMs) onto fewer physical machines (PMs), VMP can significantly reduce power consumption and enhance resource utilization. This not only leads to cost savings but also contributes to environmental sustainability by minimizing energy waste. This paper focuses on addressing the energy-efficient VMP problem, which is critical for modern data centers aiming to balance performance and energy consumption. To tackle this challenge, we propose an improved group-based genetic algorithm (GGA). The GGA employs a novel grouping approach to encode chromosomes, allowing for more effective representation of VM-to-PM assignments. Additionally, optimized crossover and mutation operators are designed to improve the algorithm's performance, ensuring faster convergence and better solutions. Experimental results demonstrate that the proposed algorithm outperforms traditional algorithms in terms of energy-saving efficiency and convergence speed.

关键词： Data centers Power demand Virtual machines Energy efficiency Resource management Sustainable development Biological cells Optimization Genetic algorithms Convergence

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M3GPT: an advanced multimodal, multitask framework for motion comprehension and generation 24

M3GPT: an advanced multimodal, multitask framework for motio...

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Proceedings of the 38th International Conference on Neural Information processing Systems

作者： Mingshuang Luo Ruibing Hou Zhuo Li Hong Chang Zimo Liu Yaowei Wang Shiguang Shan Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS China and Peng Cheng Laboratory China and University of Chinese Academy of Sciences China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS China WeChat Tencent Inc Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS China and University of Chinese Academy of Sciences China Peng Cheng Laboratory China Peng Cheng Laboratory China and Harbin Institute of Technology Shenzhen

ISBN: (纸本)9798331314385

This paper presents M3 GPT, an advanced Multimodal, Multitask framework for Motion comprehension and generation. M3 GPT operates on three fundamental principles. The first focuses on creating a unified representation space for various motion-relevant modalities. We employ discrete vector quantization for multimodal conditional signals, such as text, music and motion/dance, enabling seamless integration into a large language model (LLM) with a single vocabulary. The second involves modeling motion generation directly in the raw motion space. This strategy circumvents the information loss associated with a discrete tokenizer, resulting in more detailed and comprehensive motion generation. Third, M3 GPT learns to model the connections and synergies among various motion-relevant tasks. Text, the most familiar and well-understood modality for LLMs, is utilized as a bridge to establish connections between different motion tasks, facilitating mutual reinforcement. To our knowledge, M3 GPT is the first model capable of comprehending and generating motions based on multiple signals. Extensive experiments highlight M3GPT's superior performance across various motion-relevant tasks and its powerful zero-shot generalization capabilities for extremely challenging tasks. Project page: https://***/M3GPT/.

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