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Learning Generalized Residual Exchange-Correlation-Uncertain Functional for Density Functional Theory 39

Learning Generalized Residual Exchange-Correlation-Uncertain...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Jin, Sizhuo Chen, Shuo Qian, Jianjun Tai, Ying Li, Jun School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Intelligence Science and Technology Nanjing University Suzhou China

ISBN: (纸本)157735897X

Density Functional Theory (DFT) stands as a widely used and efficient approach for addressing the many-electron Schrödinger equation across various domains such as physics, chemistry, and biology. However, a core challenge that persists over the long term pertains to refining the exchange-correlation (XC) approximation. This approximation significantly influences the triumphs and shortcomings observed in DFT applications. Nonetheless, a prevalent issue among XC approximations is the presence of systematic errors, stemming from deviations from the mathematical properties of the exact XC functional. For example, although both B3LYP and DM21 (DeepMind 21) exhibit improvements over previous benchmarks, there is still potential for further refinement. In this paper, we propose a strategy for enhancing XC approximations by estimating the neural uncertainty of the XC functional, named Residual XC-Uncertain Functional. Specifically, our approach involves training a neural network to predict both the mean and variance of the XC functional, treating it as a Gaussian distribution. To ensure stability in each sampling point, we construct the mean by combining traditional XC approximations with our neural predictions, mitigating the risk of divergence or vanishing values. It is crucial to highlight that our methodology excels particularly in cases where systematic errors are pronounced. Empirical outcomes from three benchmark tests substantiate the superiority of our approach over existing state-of-the-art methods. Our approach not only surpasses related techniques but also significantly outperforms both the popular B3LYP and the recent DM21 methods, achieving average RMSE improvements of 62% and 37%, respectively, across the three benchmarks: W4-17, G21EA, and G21IP. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Density functional theory

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A Few-Shot and Anti-Forgetting Network Intrusion Detection System based on Online Meta Learning

A Few-Shot and Anti-Forgetting Network Intrusion Detection S...

引用

2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Wang, Zhen Lu, Yifei Wu, Wenxin Lu, Yun Wang, Hongxiang Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350351255

In the actual Internet of Things (IoT) environment, the proportion of abnormal behavior is much lower than that of normal behavior, and abnormal samples are often scarce, so it is a significant challenge to train efficient network intrusion detection systems using limited labeled samples. Meanwhile, intrusion detection systems based on online learning are prone to catastrophic forgetting, which greatly reduces the performance of online models. Previous research has not comprehensively addressed these two issues. Therefore, this paper proposes a few-shot and anti-forgetting network intrusion detection system based on online meta-learning. The system uses meta-learning as the basic algorithm to efficiently utilize data to train well-performing models with very few samples, thereby addressing the problem of insufficient samples;meanwhile, by saving previous models and invoking them when necessary to combat catastrophic forgetting. The experiments on the CIC-IDS2017 and CIC-IDS2018 datasets show that our proposed detection system can maintain good performance over the long term without forgetting. © 2024 IEEE.

关键词： Network intrusion

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A Multimodal Named Entity Recognition Approach Based on Multi-Perspective Contrastive Learning 7

A Multimodal Named Entity Recognition Approach Based on Mult...

引用

7th International Conference on Machine Learning and Natural Language Processing, MLNLP 2024

作者： Liu, Huafu Wang, Yongli Liu, Dongmei School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798350354973

Multimodal Named Entity Recognition (MNER) is a vision-language task aimed at detecting the scope of entities and determining their types in given text-image pairs. Although existing MNER models have made some progress, they often encounter issues like mismatched feature dimensions and inconsistent feature representations when interacting with text sequences. This makes it difficult to bridge the semantic gap between images and text. Therefore, this paper proposes a multimodal named entity recognition method based on multiperspective contrastive learning (MPCL-NER). Initially, the ALBERT pretrained language model and the ViT visual model are used to extract features from text and images, respectively. Based on this, image feature representations are enhanced through multi-perspective contrastive learning, which also promotes the integration of image and text information. Finally, the two types of features are merged through a multi-head adaptive attention mechanism and a gated fusion mechanism. This is followed by entity tagging with a CRF (Conditional Random Field) decoder. Experimental results show that this method outperforms other baseline methods on the public dataset Twitter-2017, achieving an F1 score of 86.11%, demonstrating the effectiveness of this approach. © 2024 IEEE.

关键词： Contrastive Learning

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FibGrid: Characterizing Anatomical Pathways in Hypergrid for Direct Tract Segmentation

FibGrid: Characterizing Anatomical Pathways in Hypergrid for...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Xie, Yu Zhai, Yazhe He, Yifei Yu, Jiahao Qin, Jiaolong Wu, Ye Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350386226

Diffusion MRI tractography is employed to delineate the white matter tracts in the brain, which plays a pivotal role in investigating brain development and psychiatric disorders. The segmentation of fibers into coherent bundles holds paramount importance for brain function investigation and preoperative strategizing in medicine. We propose a novel registration-free method for segmenting whole-brain fibers, called as FibGrid. This approach encodes fibers into a combination of four independent features, resulting in a concise representation of the fiber structures. Subsequently, segmentation is performed within low-dimensional feature spaces defined by these encoded features, followed by classification using a fiber classifier. Compared to the two traditional methods, FibGrid exhibits stable and promising identification of anatomical pathways. Generalization experiments across the dataset and tract atlas, our method is significantly more efficient than others, consistently delivering good segmentation outcomes. The code is available at https://***/x-y-u/FibGrid. © 2024 IEEE.

关键词： Signal encoding

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Infrared Image Human Detection Based on Improved RT-DETR Algorithm 4

Infrared Image Human Detection Based on Improved RT-DETR Alg...

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4th International Conference on Electronic Information engineering and computer technology, EIECT 2024

作者： Xie, Long Ren, Mingwu School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798331528850

This study presents a novel approach for human detection in infrared images by enhancing the RT-DETR algorithm. The aim is to overcome challenges related to low detection accuracy and real-time performance issues in infrared target detection. The proposed method involves replacing the original RT-DETR algorithm's backbone network with ConvNeXt to improve feature extraction capabilities for low-resolution infrared images. Additionally, a Feature Pyramid Network (FPN) is incorporated to facilitate multi-scale feature fusion, thereby enhancing small target detection accuracy. Furthermore, optimization of the detection head and the replacement of GIoU loss with CIoU loss are carried out to enhance bounding box localization precision. Experimental results demonstrate that the proposed method achieves higher mean Average Precision (mAP) and precision on the Teledyne FLIR ADAS infrared dataset. This improvement effectively balances inference time and real-time performance, offering an efficient solution for human detection in infrared images suitable for practical applications. © 2024 IEEE.

关键词： Infrared imaging

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Self-Supervised Learning Recommendation with Enhanced Long-Tail Nodes 5

Self-Supervised Learning Recommendation with Enhanced Long-T...

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5th International Conference on Artificial Intelligence and computer engineering, ICAICE 2024

作者： Zhao, Zhiyu Wang, Yongli Liu, Dongmei School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798331528911

Achieving a balance between recommending popular items and long-tail items has consistently posed a challenge in the field of recommendation systems. Traditional recommendation algorithms often exhibit a bias towards recommending popular items, resulting in limited exposure for long-tail items. To address this issue, our study introduces an innovative recommendation algorithm based on graph contrastive learning. This algorithm incorporates a dual-view learning mechanism with the objective of increasing the exposure of long-tail items while ensuring the accuracy of recommendations. Two distinct views are generated: one captures in-depth information of long-tail nodes through random walk strategies, while the other utilizes Graph Attention Networks (GAT) to acquire node importance and contextual information. By means of contrastive learning with these two views, our model attains comprehensive node representations. The study strives to achieve a balanced learning process between long-tail and high-frequency nodes. Experimental results conducted on multiple recommendation system datasets substantiate that our approach significantly enhances the exposure of long-tail items compared to existing recommendation algorithms, while maintaining high accuracy and recall in recommendations. This research attests that the contrastive learning framework provides an effective strategy for addressing the long-tail recommendation problem. © 2024 IEEE.

关键词： Contrastive Learning

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AS-FIBA: Adaptive Selective Frequency-Injection for Backdoor Attack on Deep Face Restoration 23

AS-FIBA: Adaptive Selective Frequency-Injection for Backdoor...

引用

23rd IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2024

作者： Song, Zhenbo Gao, Wenhao Zhang, Zhenyuan Lu, Jianfeng Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

Deep learning-based face restoration models, increasingly prevalent in smart devices, have become targets for sophisticated backdoor attacks. Through subtle trigger injection into input face images, these attacks can lead to unexpected restoration outcomes. Unlike conventional methods focused on classification tasks, our approach introduces a unique degradation objective tailored for attacking restoration models. Moreover, we propose the Adaptive Selective Frequency Injection Backdoor Attack (AS-FIBA) framework, employing a neural network for input-specific trigger generation in the frequency domain, seamlessly blending triggers with benign images. This results in imperceptible yet effective attacks, guiding restoration predictions towards subtly degraded outputs rather than conspicuous targets. Extensive experiments demonstrate the efficacy of the degradation objective on state-of-the-art face restoration models. Additionally, it is notable that AS-FIBA can insert effective backdoors that are more imperceptible than existing backdoor attack methods, including WaNet, ISSBA, and FIBA. © 2024 IEEE.

关键词： Frequency domain analysis

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Extrinsic Calibration Method for LiDAR and Millimeter-Wave Radar Systems

Extrinsic Calibration Method for LiDAR and Millimeter-Wave R...

引用

2024 International Conference on Advanced Control Systems and Automation Technologies, ACSAT 2024

作者： Yang, Yanping Ren, Mingwu Nanjing University of Science & Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798331541668

Millimeter-wave radar, with its high frequency and short wavelength, is increasingly becoming a critical sensor in autonomous driving systems, where precise external calibration with lidar is essential for efficient sensor fusion. However, existing calibration algorithms for these two sensors are still limited. To address this, we propose an offline external calibration method based on a novel 3D calibration target that combines absorbing materials and corner reflectors. Using a point cloud segmentation network, we extract lidar target point clouds and utilize RCS information with clustering to accurately locate radar point clouds, achieving precise data extraction for different sensors. Additionally, we introduce a model coordinate system as a bridge to resolve the spatial relationship between lidar and radar, and enhance the ICP alignment algorithm by calculating an initial alignment matrix with minimal error straight-line alignment, allowing for coarse-to-fine optimization. This approach significantly improves ICP precision and efficiency, with experimental results in various scenarios validating the calibration accuracy and reliability of this method. © 2024 IEEE.

关键词： Calibration

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A Unimodal Valence-Arousal Driven Contrastive Learning Framework for Multimodal Multi-Label Emotion Recognition 24

A Unimodal Valence-Arousal Driven Contrastive Learning Frame...

引用

32nd ACM International Conference on Multimedia, MM 2024

作者： Zheng, Wenjie Yu, Jianfei Xia, Rui School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798400706868

Multimodal Multi-Label Emotion Recognition (MMER) aims to identify one or more emotion categories expressed by an utterance of a speaker. Despite obtaining promising results, previous studies on MMER represent each emotion category using a one-hot vector and ignore the intrinsic relations between emotions. Moreover, existing works mainly learn the unimodal representation based on the multimodal supervision signal of a single sample, failing to explicitly capture the unique emotional state of each modality as well as its emotional correlation between samples. To overcome these issues, we propose a Unimodal Valence-Arousal driven contrastive learning framework (univ.) for the MMER task. Specifically, we adopt the valence-arousal (VA) space to represent each emotion category and regard the emotion correlation in the VA space as priors to learn the emotion category representation. Moreover, we employ pre-trained unimodal VA models to obtain the VA scores for each modality of the training samples, and then leverage the VA scores to construct positive and negative samples, followed by applying supervised contrastive learning to learn the VA-aware unimodal representations for multi-label emotion prediction. Experimental results on two benchmark datasets MOSEI and M3ED show that the proposed univ. framework consistently outperforms a number of existing methods for the MMER task. The source code is publicly released at https://***/NUSTM/univ.. © 2024 ACM.

关键词： Contrastive Learning

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Dual-Agent Multi-Hop Reasoning Based on Reward Shaping and Action Dropout 5

Dual-Agent Multi-Hop Reasoning Based on Reward Shaping and A...

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5th International Conference on Machine Learning and computer Application, ICMLCA 2024

作者： Sun, Qiyue Wang, Yongli Liu, Dongmei School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798331530334

The dual-agent multi-hop reasoning method addresses the issue of target entity omission in single-agent systems caused by increased reasoning path length. However, this method still has the following challenges: First, even if the agent finds the correct answer within the knowledge graph, it may misclassify it as incorrect due to training set limitations, known as the false negative target problem. Second, agents may be misled by erroneous paths, leading to the creation of false positive paths. To tackle these issues, this paper proposes a dual-agent multi-hop reasoning method based on Reward Shaping and Action Dropout (RADAM). This approach improves the model in two ways: (1) by introducing an embedding-based single-hop reasoning model, TransE, to optimize the reward function and reduce false negatives;and (2) by incorporating a random masking mechanism to diminish the agent's sensitivity to spurious paths, thereby reducing false positive paths. Experimental results demonstrate that RADAM achieves more accurate and efficient answer retrieval across most benchmark datasets compared to baseline algorithms, with ablation experiments further confirming the effectiveness and synergy of reward shaping and action dropout. © 2024 IEEE.

关键词： Reinforcement learning

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