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S2KAN-SLAM: Elastic Neural LiDAR SLAM with SDF Submaps and Kolmogorov-Arnold Networks

IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Wang, Zhong Zhang, Lin Wang, Hesheng Shanghai Jiao Tong University State Key Laboratory of Avionics Integration and Aviation System-of-Systems Synthesis Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai200240 China Tongji University School of Computer Science and Technology National Pilot Software Engineering School with Chinese Characteristics Shanghai201804 China

Traditional LiDAR SLAM approaches prioritize localization over mapping, yet high-precision dense maps are essential for numerous applications involving intelligent agents. Recent advancements have introduced methods leveraging neural fields to enhance mapping capabilities;however, these approaches still face several limitations. Firstly, concerning scene representation, they typically employ neural fields with high-dimensional features and multi-layer perceptron decoders utilizing non-continuous activation functions. This results in low learning efficiency and challenges in capturing high-frequency signals. Secondly, in terms of scene organization, these methods often treat the entire scene as a singular neural field, leading to inefficiencies, inflexibility, and difficulties in rectifying accumulated errors when mapping large-scale environments over extended periods. To tackle the first issue, we propose a lightweight continuous SDF regression approach by encoding the scene in single-valued embeddings and decoding SDF values from a Kolmogorov-Arnold Network. By minimizing discrepancies in measuring range, sampling distance, and decoded SDF values, we facilitate iterative frame-to-model tracking and bundle adjustment neural mapping. To mitigate the second challenge, we propose structuring the whole scene into multiple neural SDF submaps. By establishing node-node, node-submap, and loop closure constraints into a global pose graph, the system can create dense neural maps with global consistency across large-scale scenes. Experimental evaluations in both real-world and simulated settings indicate that our system achieves superior mapping completeness and accuracy, enhanced learning efficiency, reduced memory consumption, and greater flexibility compared to its counterparts. © 2025 IEEE.

关键词： Mapping

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PC-BNA: Parallel Convolution Binary Neural Network Accelerator

PC-BNA: Parallel Convolution Binary Neural Network Accelerat...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Haihui Liu Yuang Zhang Xiangwei Zheng Mengxin Yu Baoxu An Xiaojie Liu Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology School of Information Science and Engineering Shandong Normal University Jinan China

Binary neural network (BNN) is widely used in speech recognition, image processing and other fields to save memory and speed up computing. However, the accuracy of the existing binarization scheme in the realistic dataset is obviously low, and the input layer uses 32-bit floating point to avoid excessive precision loss, which requires additional computing units and increases the computational burden. Therefore, it is very important to improve the input layer, save computing resources and reduce precision loss. In this paper, we propose a parallel convolution binary neural network accelerator architecture (PC-BNA). Based on our proposed BNN model, we design an efficient field programmable gate array (FPGA) based accelerator. The input of the first layer is binarized, the traditional binary convolution layer is replaced by a parallel binary convolution, and the network building blocks are improved. The experimental results show that the proposed PC-BNA has higher accuracy and better performance on CIFAR-10. The image recognition accuracy is as high as 91.4%, which is superior to the state-of-art of the BNN accelerator. Compared to the state-of-the-art BNN model, when using the same model size, the use of look-up-table (LUT) saves 9.08%, and the digital signal processor (DSP) saves 27.7%. The results suggest that it is promising for PC-BNA in future high performance mobile artificial intelligence applications.

关键词： Performance evaluation Convolution Neural networks Digital signal processors Speech recognition Logic gates Mobile handsets

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Wavelet-based Global-Local Interaction Network with Cross-Attention for Multi-View Diabetic Retinopathy Detection

arXiv

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arXiv 2025年

作者： Hu, Yongting Lin, Yuxin Liu, Chengliang Luo, Xiaoling Dou, Xiaoyan Xu, Qihao Xu, Yong School of Computer Science and Technology Harbin Institute of Technology Shenzhen Shenzhen China Shenzhen Key Laboratory of Visual Object Detection and Recognition Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Ophthalmology Department Shenzhen Second People’s Hospital Shenzhen China

Multi-view diabetic retinopathy (DR) detection has recently emerged as a promising method to address the issue of incomplete lesions faced by single-view DR. However, it is still challenging due to the variable sizes and scattered locations of lesions. Furthermore, existing multi-view DR methods typically merge multiple views without considering the correlations and redundancies of lesion information across them. Therefore, we propose a novel method to overcome the challenges of difficult lesion information learning and inadequate multi-view fusion. Specifically, we introduce a two-branch network to obtain both local lesion features and their global dependencies. The high-frequency component of the wavelet transform is used to exploit lesion edge information, which is then enhanced by global semantic to facilitate difficult lesion learning. Additionally, we present a cross-view fusion module to improve multi-view fusion and reduce redundancy. Experimental results on large public datasets demonstrate the effectiveness of our method. The code is open sourced on https://***/HuYongting/WGLIN. Copyright © 2025, The Authors. All rights reserved.

关键词： Wavelet transforms

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Vulnerability Detection with Graph Simplification and Enhanced Graph Representation Learning

Vulnerability Detection with Graph Simplification and Enhanc...

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International Conference on software Engineering (ICSE)

作者： Xin-Cheng Wen Yupan Chen Cuiyun Gao Hongyu Zhang Jie M. Zhang Qing Liao School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Peng Cheng Laboratory and Guangdong Provincial Key Laboratory Novel Security Intelligence Technologies School of Big Data and Software Engineering Chongqing University China Department of Informatics King's College London UK Peng Cheng Laboratory

Prior studies have demonstrated the effectiveness of Deep Learning (DL) in automated software vulnerability detection. Graph Neural Networks (GNNs) have proven effective in learning the graph representations of source code and are commonly adopted by existing DL-based vulnerability detection methods. However, the existing methods are still limited by the fact that GNNs are essentially difficult to handle the connections between long-distance nodes in a code structure graph. Besides, they do not well exploit the multiple types of edges in a code structure graph (such as edges representing data flow and control flow). Consequently, despite achieving state-of-the-art performance, the existing GNN-based methods tend to fail to capture global information (i.e., long-range dependencies among nodes) of code graphs. To mitigate these issues, in this paper, we propose a novel vulnerability detection framework with grAph siMplification and enhanced graph rePresentation LEarning, named AMPLE. AMPLE mainly contains two parts: 1) graph simplification, which aims at reducing the distances between nodes by shrinking the node sizes of code structure graphs; 2) enhanced graph representation learning, which involves one edge-aware graph convolutional network module for fusing heterogeneous edge information into node representations and one kernel-scaled representation module for well capturing the relations between distant graph nodes. Experiments on three public benchmark datasets show that AMPLE outperforms the state-of-the-art methods by 0.39%-35.32% and 7.64%-199.81% with respect to the accuracy and F1 score metrics, respectively. The results demonstrate the effectiveness of AMPLE in learning global information of code graphs for vulnerability detection.

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Learning to Substitute Spans towards Improving Compositional Generalization

arXiv

引用

arXiv 2023年

作者： Li, Zhaoyi Wei, Ying Lian, Defu School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence Anhui Hefei China Department of Computer Science City University of Hong Kong Hong Kong

Despite the rising prevalence of neural sequence models, recent empirical evidences suggest their deficiency in compositional generalization. One of the current de-facto solutions to this problem is compositional data augmentation, aiming to incur additional compositional inductive bias. Nonetheless, the improvement offered by existing handcrafted augmentation strategies is limited when successful systematic generalization of neural sequence models requires multi-grained compositional bias (i.e., not limited to either lexical or structural biases only) or differentiation of training sequences in an imbalanced difficulty distribution. To address the two challenges, we first propose a novel compositional augmentation strategy dubbed Span Substitution (SpanSub) that enables multi-grained composition of substantial substructures in the whole training set. Over and above that, we introduce the Learning to Substitute Span (L2S2) framework which empowers the learning of span substitution probabilities in SpanSub in an end-to-end manner by maximizing the loss of neural sequence models, so as to outweigh those challenging compositions with elusive concepts and novel surroundings. Our empirical results on three standard compositional generalization benchmarks, including SCAN, COGS and GeoQuery (with an improvement of at most 66.5%, 10.3%, 1.2%, respectively), demonstrate the superiority of SpanSub, L2S2 and their combination. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Not All Semantics are Created Equal: Contrastive Self-supervised Learning with Automatic Temperature Individualization

arXiv

引用

arXiv 2023年

作者： Qiu, Zi-Hao Hu, Quanqi Yuan, Zhuoning Zhou, Denny Zhang, Lijun Yang, Tianbao National Key Laboratory for Novel Software Technology Nanjing University Nanjing China Computer Science and Engineering Texas A&M University College Station United States Department of Computer Science The University of Iowa Iowa City United States Google Research United States

In this paper, we aim to optimize a contrastive loss with individualized temperatures in a principled and systematic manner for self-supervised learning. The common practice of using a global temperature parameter τ ignores the fact that "not all semantics are created equal", meaning that different anchor data may have different numbers of samples with similar semantics, especially when data exhibits long-tails. First, we propose a new robust contrastive loss inspired by distributionally robust optimization (DRO), providing us an intuition about the effect of τ and a mechanism for automatic temperature individualization. Then, we propose an efficient stochastic algorithm for optimizing the robust contrastive loss with a provable convergence guarantee without using large mini-batch sizes. Theoretical and experimental results show that our algorithm automatically learns a suitable τ for each sample. Specifically, samples with frequent semantics use large temperatures to keep local semantic structures, while samples with rare semantics use small temperatures to induce more separable features. Our method not only outperforms prior strong baselines (e.g., SimCLR, CLIP) on unimodal and bimodal datasets with larger improvements on imbalanced data but also is less sensitive to hyper-parameters. To our best knowledge, this is the first methodical approach to optimizing a contrastive loss with individualized temperatures. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Fault Diagnosis for Limited Similar Batch Process using Two-dimensional Adversarial Domain Adaptation-based Graph Convolutional Network

Fault Diagnosis for Limited Similar Batch Process using Two-...

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第43届中国控制会议

作者： Xingke Gao Zheng Zhang Jinlin Zhu School of Artificial Intelligence and Computer Science Jiangnan University Department of Chemical and Biological Engineering Hong Kong University of Science and Technology State Key Laboratory of Food Science and Resources Jiangnan University School of Food Science and Technology Jiangnan University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Batch process fault diagnosis is critical for ensuring the quality and safety of batch processes ***,recognizing fault types in low-volume and similar batch processes poses a significant *** paper proposes a novel domain-adaptive transfer learning model named two-dimensional adversarial domain adaptation-based graph convolutional network(2D-ADGCN) for limited similar batch process fault ***,the method creates enhanced samples by integrating statistical information within a two-dimensional sliding window,significantly expanding the scale of the analyzed data by sliding the window in both time and batch ***,by constructing an adversarial domain adaptation graph convolutional network,the data from the source domain(original batches) and the target domain(similar batches) are mapped into a shared feature space,reducing the distribution discrepancy between the two ***,leveraging the labeled samples from the source domain,the model learns discriminative representations for the samples and performs classification on the target domain samples,enabling fault diagnosis for similar ***,the effectiveness of 2D-ADGCN is validated through industrial case studies of two Lactobacillus plantarum fed-batch fermentation processes,demonstrating its significant potential for fault diagnosis in low-volume and similar batch processes.

关键词： Fault diagnosis Similar batch process,Limited batches Two-dimensional modeling Adversarial Domain Adaptation Transfer Leaning Graph Convolutional Network

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The Influence of Kanban Agile Methodology on software Project Management: A Survey Method

The Influence of Kanban Agile Methodology on Software Projec...

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International Conference on Engineering and Emerging Technologies (ICEET)

作者： Irfan Ahmed Fatima Munir Fouzia Nafees Jabar Mahmood Shehzad Ashraf Chaudhry Maryam Javaid Khadija Batool Dept. of Computer Science University of Sialkot Sialkot Pakistan State Key Laboratory of Blockchain School of Cyber Science and Technology College of Computer Science and Technology Zhejiang University Hangzhou China Department of Computer Science and Information Technology College of Engineering Abu Dhabi University Abu Dhabi U.A.E Dept. of Computer Sciences International Institute of Science Arts and Technology Gujranwala Pakistan

ISBN: (数字)9798331532895

ISBN: (纸本)9798331532901

software project management (SPM) is an important process in the software development (SD) industry, that involves the planning, organizing, and overseeing software application development. Kanban agile (KA) methodology is a systematic project management approach that prioritizes workflow optimization, clear visibility, and continuous delivery in SD. It has become increasingly popular in software development for its ability to adapt to changing requirements and promote continuous delivery. It is examined that kanban affects critical project management factors. Data is gathered through a survey of software professionals who are experienced with kanban. The results indicate that kanban significantly improves team collaboration, workflow transparency, and overall project efficiency. This study investigates the impact of the KA methodology on SPM using a survey-based approach. However, the study also identifies challenges, including the initial learning curve and resistance to change within teams. These findings provide valuable insights for organizations considering kanban and contribute to the broader understanding of its role in SPM. We surveyed several software development companies, and the results show that almost all of them use agile methodology (Kanban), which improves SPM as compared to other agile methodologies like Scrum, extreme programming and dynamic systems development methods.

关键词： Surveys Industries Resistance Systematics Project management software Planning Scrum (software development) Programming profession software development management

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Teaching Activity Design Based on the Discipline key Competence of IT in Secondary Vocational School

Teaching Activity Design Based on the Discipline Key Compete...

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International Conference on Application of Intelligent Systems in Multi-modal Information Analytics, MMIA 2020

作者： Zhang, Penglong Zhang, Yongsheng Liu, Lining Sun, Wenqian School of Information Science and Engineering Shandong Normal University ShandongJinan250358 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology ShandongJinan250358 China

ISBN: (纸本)9783030514303

This paper, is based on the analysis of secondary vocational students learning of information technology, that’s what we call IT. In response to a new round of curriculum reform emphasizes the key competence, we sum high school IT discipline key competence and vocational education characteristics up to the secondary vocational discipline key competence, and design related teaching activity to change the secondary vocational school IT course "computer application foundation". So as to improve secondary vocational students’ IT discipline key competence, and to provide the reference for school IT teaching material writing, content design and classroom teaching. © 2021, Springer Nature Switzerland AG.

关键词： Students

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Dynamic inference in probabilistic graphical models 12

Dynamic inference in probabilistic graphical models

引用

12th Innovations in Theoretical computer science Conference, ITCS 2021

作者： Feng, Weiming He, Kun Sun, Xiaoming Yin, Yitong State Key Laboratory for Novel Software Technology Nanjing University China Shenzhen University China Shenzhen Institute of Computing Sciences China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783959771771

Probabilistic graphical models, such as Markov random fields (MRFs), are useful for describing high-dimensional distributions in terms of local dependence structures. The probabilistic inference is a fundamental problem related to graphical models, and sampling is a main approach for the problem. In this paper, we study probabilistic inference problems when the graphical model itself is changing dynamically with time. Such dynamic inference problems arise naturally in today’s application, e.g. multivariate time-series data analysis and practical learning procedures. We give a dynamic algorithm for sampling-based probabilistic inferences in MRFs, where each dynamic update can change the underlying graph and all parameters of the MRF simultaneously, as long as the total amount of changes is bounded. More precisely, suppose that the MRF has n variables and polylogarithmic-bounded maximum degree, and N(n) independent samples are sufficient for the inference for a polynomial function N(·). Our algorithm dynamically maintains an answer to the inference problem using Oe(nN(n)) space cost, and Oe(N(n) + n) incremental time cost upon each update to the MRF, as long as the Dobrushin-Shlosman condition is satisfied by the MRFs. This well-known condition has long been used for guaranteeing the efficiency of Markov chain Monte Carlo (MCMC) sampling in the traditional static setting. Compared to the static case, which requires Ω(nN(n)) time cost for redrawing all N(n) samples whenever the MRF changes, our dynamic algorithm gives a Ω(min e {n, N(n)})-factor speedup. Our approach relies on a novel dynamic sampling technique, which transforms local Markov chains (a.k.a. single-site dynamics) to dynamic sampling algorithms, and an "algorithmic Lipschitz" condition that we establish for sampling from graphical models, namely, when the MRF changes by a small difference, samples can be modified to reflect the new distribution, with cost proportional to the difference on MRF. © Weiming F

关键词： Sampling

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