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arXiv 2024年

作者： Sun, Haoxin Zhou, Xiaotian Zhang, Zhongzhi Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China

The forest matrix plays a crucial role in network science, opinion dynamics, and machine learning, offering deep insights into the structure of and dynamics on networks. In this paper, we study the problem of querying entries of the forest matrix in evolving graphs, which more accurately represent the dynamic nature of real-world networks compared to static graphs. To address the unique challenges posed by evolving graphs, we first introduce two approximation algorithms, SFQ and SFQPlus, for static graphs. SFQ employs a probabilistic interpretation of the forest matrix, while SFQPlus incorporates a novel variance reduction technique and is theoretically proven to offer enhanced accuracy. Based on these two algorithms, we further devise two dynamic algorithms centered around efficiently maintaining a list of spanning converging forests. This approach ensures O(1) runtime complexity for updates, including edge additions and deletions, as well as for querying matrix elements, and provides an unbiased estimation of forest matrix entries. Finally, through extensive experiments on various real-world networks, we demonstrate the efficiency and effectiveness of our algorithms. Particularly, our algorithms are scalable to massive graphs with more than forty million nodes. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph algorithms

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Enhanced Discriminant Analysis Based Fault Diagnosis Scheme for Marine Diesel Engines 11

Enhanced Discriminant Analysis Based Fault Diagnosis Scheme ...

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11th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2022

作者： Zhong, Kai Wang, Jiayi Pan, Donghui Institutes of Physical Science and Information Technology Anhui University Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education Anhui Hefei230601 China School of Mathematical Science Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui Hefei230601 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

Since the marine diesel engine is one of the most popular power equipments for modern shipping, accurate and timely diagnose the faults occurred in diesel engine is extraordinary important for long service life and high reliability. Note that Fisher discriminant analysis (FDA) has been widely applied in fault diagnosis field. However, it is quite susceptible to the small sample size (SSS) problem, which is very common in the running process of diesel engine. In response to the problem, this study pro-poses a new fault classification criterion based exponential discriminant analysis (EDA) scheme for diesel engine fault diagnosis, in which the exponential transformation is carried out first to enlarge the margin between different classes, thus the different fault classes are further separated and the diagnosis capability is enhanced. In addition, the theorems of matrix exponential guarantee that the matrix exp (Sw) is always full-rank, even though the Sw is singular. That means, the novel methodology is still appli-cable when confronts with the SSS problem. Finally, based on the more reasonable fault classification criterion, the simulation results on a real-world two-stroke six-cylinder low-speed marine diesel engine indicate that the EDA based diagnosis scheme achieves higher fault diagnosis accuracy with good robustness and practicability in real-world engineering applications. © 2022 IEEE.

关键词： Failure analysis

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GENERAL IMPLICIT SUBGRADIENT EXTRAGRADIENT METHODS FOR MONOTONE BILEVEL EQUILIBRIUM PROBLEMS

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2022年第3期84卷 3-20页

作者： Ceng, Lu-Chuan Zhao, Xiaopeng Zhu, Li-Jun Department of Mathematics Shanghai Normal University Shanghai200234 China School of Mathematical Sciences Tiangong University Tianjin300387 China The Key Laboratory of Intelligent Information and Big Data Processing of NingXia North Minzu University Yinchuan750021 China

In this paper, we introduce the general implicit subgradient extragradient method for solving the monotone bilevel equilibrium problem (MBEP) with a general system of variational inclusions (GSVI) and a common fixed-point problem of finitely many nonexpansive mappings and a strictly pseudocontractive mapping (CFPP) constraints. The strong convergence result for the proposed algorithm is established under the monotonicity assumption of the cost bifunctions with Lipschitz-type continuous conditions recently presented by Mastroeni in the auxiliary problem principle, and also applied for finding a common solution of variational inequality, variational inclusion and fixed-point problems. © 2022, Politechnica university of Bucharest. All rights reserved.

关键词： Variational techniques

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InferAligner: Inference-Time Alignment for Harmlessness through Cross-Model Guidance

arXiv

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arXiv 2024年

作者： Wang, Pengyu Zhang, Dong Li, Linyang Tan, Chenkun Wang, Xinghao Ren, Ke Jiang, Botian Qiu, Xipeng School of Computer Science Fudan University China Shanghai Key Laboratory of Intelligent Information Processing Fudan University China

With the rapid development of large language models (LLMs), they are not only used as general-purpose AI assistants but are also customized through further fine-tuning to meet the requirements of different applications. A pivotal factor in the success of current LLMs is the alignment process. Current alignment methods, such as supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF), focus on training-time alignment and are often complex and cumbersome to implement. Therefore, we develop InferAligner, a novel inference-time alignment method that utilizes cross-model guidance for harmlessness alignment. InferAligner utilizes safety steering vectors extracted from safety-aligned model to modify the activations of the target model when responding to harmful inputs, thereby guiding the target model to provide harmless responses. Experimental results show that our method can be very effectively applied to domain-specific models in finance, medicine, and mathematics, as well as to multimodal large language models (MLLMs) such as LLaVA. It significantly diminishes the Attack Success Rate (ASR) of both harmful instructions and jailbreak attacks, while maintaining almost unchanged performance in downstream tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Alignment

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A Multi-Stage Goal-Driven Network for Pedestrian Trajectory Prediction 5

A Multi-Stage Goal-Driven Network for Pedestrian Trajectory ...

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5th International Conference on Computer Vision, Image and Deep Learning, CVIDL 2024

作者： Wu, Xiuen Wang, Tao Cai, Yuanzheng Liang, Lingyu Papageorgiou, George Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China Fuzhou University College of Computer and Data Science Fuzhou China South China University of Technology School of Electronic and Information Engineering Guangzhou China European University Cyprus Department of Computer Science and Engineering Nicosia Cyprus

ISBN: (纸本)9798350373820

Pedestrian trajectory prediction plays a pivotal role in ensuring the safety and efficiency of various applications, including autonomous vehicles and traffic management systems. This paper proposes a novel method for pedestrian trajectory prediction, called multi-stage goal-driven network (MGNet). Diverging from prior approaches relying on stepwise recursive prediction and the singular forecasting of a long-term goal, MGNet directs trajectory generation by forecasting intermediate stage goals, thereby reducing prediction errors. The network comprises three main components: a conditional variational autoencoder (CVAE), an attention module, and a multi-stage goal evaluator. Trajectories are encoded using conditional variational autoencoders to acquire knowledge about the approximate distribution of pedestrians' future trajectories, and combined with an attention mechanism to capture the temporal dependency between trajectory sequences. The pivotal module is the multi-stage goal evaluator, which utilizes the encoded feature vectors to predict intermediate goals, effectively minimizing cumulative errors in the recursive inference process. The effectiveness of MGNet is demonstrated through comprehensive experiments on the JAAD and PIE datasets. Comparative evaluations against state-of-the-art algorithms reveal significant performance improvements achieved by our proposed method. © 2024 IEEE.

关键词： Trajectories

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Visualization and visual analysis of cyberspace metaphors based on continuous cartogram 2

Visualization and visual analysis of cyberspace metaphors ba...

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2023 2nd International Conference on Environmental Remote Sensing and Geographic information Technology, ERSGIT 2023

作者： Liu, Longhui Zhou, Yang Shi, Qunshan Hu, Xiaofei Shi, Shihao PLA Strategic Support Force Information Engineering University Institute of Geospatial Information Henan Zhengzhou450001 China Collaborative Innovation Center of Geo-Information Technology for Smart Central Plains Zhengzhou450001 China Key Laboratory of Spatiotemporal Perception and Intelligent Processing Ministry of Natural Resources Zhengzhou450001 China

ISBN: (纸本)9781510672949

Cyberspace mapping has emerged as a new and compelling area of research within the field of cartography. This paper addresses the challenge of overlapping points in cyberspace visualization, which poses difficulties for visual analysis. To tackle this issue, we propose a cyberspace metaphorical cartogram map that combines metaphorical mapping and continuous cartogram technology. Additionally, based on our method, we use our method to construct a cyberspace map using global IPv6 data from the Asia-Pacific Network information Center (APNIC) to analyze the current development pattern of the global IPv6 network. Experimental results demonstrate the effectiveness of our method in avoiding node duplication, aligning with the reading logic of geospatial maps, and providing an efficient visualization approach for data comprehension. © 2024 SPIE.

关键词： Visualization

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Exploring the Compositional Deficiency of Large Language Models in Mathematical Reasoning Through Trap Problems

arXiv

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arXiv 2024年

作者： Zhao, Jun Tong, Jingqi Mou, Yurong Zhang, Ming Zhang, Qi Huang, Xuanjing School of Computer Science Fudan University China Shanghai Key Laboratory of Intelligent Information Processing Fudan University China

Human cognition exhibits systematic compositionality, the algebraic ability to generate infinite novel combinations from finite learned components, which is the key to understanding and reasoning about complex logic. In this work, we investigate the compositionality of large language models (LLMs) in mathematical reasoning. Specifically, we construct a new dataset MATHTRAP‡ by introducing carefully designed logical traps into the problem descriptions of MATH and GSM8K. Since problems with logical flaws are quite rare in the real world, these represent "unseen" cases to LLMs. Solving these requires the models to systematically compose (1) the mathematical knowledge involved in the original problems with (2) knowledge related to the introduced traps. Our experiments show that while LLMs possess both components of requisite knowledge, they do not spontaneously combine them to handle these novel cases. We explore several methods to mitigate this deficiency, such as natural language prompts, few-shot demonstrations, and fine-tuning. Additionally, we test the recently released OpenAI o1 model and find that human-like ‘slow thinking’ helps improve the compositionality of LLMs. Overall, systematic compositionality remains an open challenge for large language models. Copyright © 2024, The Authors. All rights reserved.

关键词： Natural language processing systems

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Reciprocal collaboration network for 3D skeleton-based human motion prediction 3

Reciprocal collaboration network for 3D skeleton-based human...

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3rd International Conference on Computer Science and Communication Technology, ICCSCT 2022

作者： He, Zhiquan Zhang, Lujun Cao, Wenming College of Information Engineering Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen China Guangdong Multimedia Information Service Engineering Technology Research Center Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (纸本)9781510661240

Noticing that the human motion in both the positive and inverted time sequence are predictable, we design a reciprocal network which contains a forward and a backward prediction network to learn this information respectively, and then train the two networks in a collaborative and iterative way with the guidance of consistency constraint. Based on the reciprocal network, we establish an error compensation network to approximate the error of forward prediction network for making the forecast closer to the real pose. Extensive experiments show that our approach outperforms most recent methods in both short and long term motion predictions on Human 3.6M and CMU Mocap. © 2022 SPIE.

关键词： Error compensation

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Overview of indoor positioning technology 24

Overview of indoor positioning technology

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2024 International Conference on Image processing, intelligent Control and Computer Engineering, IPICE 2024

作者： Xu, Zeyu Huang, Fenghua The Academy of Digital China Fuzhou University China College of Artificial Intelligence Yango University China Fujian Key Laboratory of Spatial Information Perception and Intelligent Processing Yango University China Fujian University Engineering Research Center of Spatial Data Mining and Application Yango University China

ISBN: (纸本)9798400710285

This paper introduces the classification of indoor positioning technology, and elaborates the principles of three mainstream indoor positioning technologies based on external source, natural source and multi-source fusion and their development respectively. By analyzing the problems of existing indoor positioning technologies, it summarizes the difficulties and future trends of the current development of indoor positioning technologies at home and abroad. The comprehensive study shows that exploring combined positioning techniques and better positioning algorithms, and combining them with machine learning (including deep learning) to further improve indoor positioning accuracy, establishing a standard system and performance evaluation system for indoor positioning techniques, and focusing on user privacy are the main future development directions of indoor positioning techniques. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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A cycle-consistent reciprocal network for visual correspondence 3

A cycle-consistent reciprocal network for visual corresponde...

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3rd International Conference on Computer Science and Communication Technology, ICCSCT 2022

作者： He, Zhiquan Zheng, Donghong Cao, Wenming College of Information Engineering Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen China Guangdong Multimedia Information Service Engineering Technology Research Center Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (纸本)9781510661240

Visual correspondence refers to building dense correspondences between two or more images of the same category. Ideally, the predicted keypoints output by the model can be back to the source image's keypoints through the same type of network. However, in practical situations, the predicted keypoints usually do not perfectly map back to the source image keypoints. In order to strengthen the cycle-consistency of the model, we propose a cycle-consistent reciprocal network. The network uses joint loss functions to alternately train forward and inverse models, which makes the two models subject to cycle constraints and perform better with the help of each other. Experiment results demonstrate the performance of the model is improved on three popular benchmarks and set a new state-of-the-art on the benchmark of PF-WILLOW. © 2022 SPIE.

关键词： Benchmarking

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