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MixSA: Training-free Reference-based Sketch Extraction via Mixture-of-Self-Attention

IEEE Transactions on Visualization and Computer Graphics 2024年 PP卷 PP页

作者： Yang, Rui Wu, Xiaojun He, Shengfeng Shaanxi Normal University School of Computer Sciences Xi'an China Shaanxi Normal University School of Computer Science The Key Laboratory of Intelligent Computing and Service Technology for Folk Song The Ministry of Culture and Tourism Xi'an710119 China Singapore Management University School of Computing and Information Systems Singapore Singapore

Current sketch extraction methods either require extensive training or fail to capture a wide range of artistic styles, limiting their practical applicability and versatility. We introduce Mixture-of-Self-Attention (MixSA), a training-free sketch extraction method that leverages strong diffusion priors for enhanced sketch perception. At its core, MixSA employs a mixture-of-self-attention technique, which manipulates self-attention layers by substituting the keys and values with those from reference sketches. This allows for the seamless integration of brushstroke elements into initial outline images, offering precise control over texture density and enabling interpolation between styles to create novel, unseen styles. By aligning brushstroke styles with the texture and contours of colored images, particularly in late decoder layers handling local textures, MixSA addresses the common issue of color averaging by adjusting initial outlines. Evaluated with various perceptual metrics, MixSA demonstrates superior performance in sketch quality, flexibility, and applicability. This approach not only overcomes the limitations of existing methods but also empowers users to generate diverse, high-fidelity sketches that more accurately reflect a wide range of artistic expressions. © 1995-2012 IEEE.

关键词： Image texture

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A Weighted Ensemble Causal Discovery Method for Effective Connectivity Estimation

A Weighted Ensemble Causal Discovery Method for Effective Co...

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Smart World Congress (SWC), IEEE

作者： Qiqi Zhang Yingwei Zhang Yanhui Ding Yiqiang Chen Shuchao Song Shuang Wu School of Information Science and Engineering Shandong Normal University Jinan China Shandong Academy of Intelligent Computing Technology Jinan China Institute of Computing Technology Chinese Academy of Sciences Beijing China Beijing Key Laboratory of Mobile Computing and Pervasive Device Beijing China

Exploring and explaining the effective connectivity (EC) between brain regions can help us understand the mechanisms behind neurodegenerative diseases such as Alzheimer’s disease, thus helping us to diagnose patients better. Causal discovery techniques have been widely used in this domain recently, but diverse methods may produce conflicting outcomes because of the differences in underlying principles and search methods. To solve this problem, we integrate the causal relationships generated by different algorithms to obtain high-confidence inferences, resulting in a more reliable weighted ensemble causal discovery (WECD) method with higher accuracy. First of all, WECD defines a distribution metric function to measure the weights of different individual methods. Then, WECD adaptively chooses the critical threshold to obtain the confident edges set based on the generated directed edge confident metric curve. Finally, the directed acyclic graph condition is used to refine the confident edges set and acquire the final causal graph. We validate the effectiveness of WECD with both the simulated dataset and real functional near-infrared spectroscopy (fNIRS) dataset collected on AD patients, MCI patients and healthy controls. By comparing with other existing methods, experimental results demonstrate the potential of WECD as an effective tool for estimating brain EC.

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Accurate interpolation for scattered data through hierarchical residual refinement 23

Accurate interpolation for scattered data through hierarchic...

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Proceedings of the 37th International Conference on Neural information Processing Systems

作者： Shizhe Ding Boyang Xia Dongbo Bu Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China and University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China and University of Chinese Academy of Sciences Beijing China and Central China Institute for Artificial Intelligence Technologies Zhengzhou China

Accurate interpolation algorithms are highly desired in various theoretical and engineering scenarios. Unlike the traditional numerical algorithms that have exact zero-residual constraints on observed points, the neural network-based interpolation methods exhibit non-zero residuals at these points. These residuals, which provide observations of an underlying residual function, can guide predicting interpolation functions, but have not been exploited by the existing approaches. To fill this gap, we propose Hierarchical INTerpolation Network (HINT), which utilizes the residuals on observed points to guide target function estimation in a hierarchical fashion. HINT consists of several sequentially arranged lightweight interpolation blocks. The first interpolation block estimates the main component of the target function, while subsequent blocks predict the residual components using observed points residuals of the preceding blocks. The main component and residual components are accumulated to form the final interpolation results. Furthermore, under the assumption that finer residual prediction requires a more focused attention range on observed points, we utilize hierarchical local constraints in correlation modeling between observed and target points. Extensive experiments demonstrate that HINT outperforms existing interpolation algorithms significantly in terms of interpolation accuracy across a wide variety of datasets, which underscores its potential for practical scenarios.

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Observation of quantum oscillations near the Mott-Ioffe-Regel limit in CaAs3

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National Science Review 2024年第12期11卷 122-133页

作者： Yuxiang Wang Minhao Zhao Jinglei Zhang Wenbin Wu Shichao Li Yong Zhang Wenxiang Jiang Nesta Benno Joseph Liangcai Xu Yicheng Mou Yunkun Yang Pengliang Leng Yong Zhang Li Pi Alexey Suslov Mykhaylo Ozerov Jan Wyzula Milan Orlita Fengfeng Zhu Yi Zhang Xufeng Kou Zengwei Zhu Awadhesh Narayan Dong Qian Jinsheng Wen Xiang Yuan Faxian Xiu Cheng Zhang State Key Laboratory of Surface Physics and Institute for Nanoelectronic Devices and Quantum Computing Fudan University State Key Laboratory of Surface Physics and Department of Physics Fudan University Anhui Province Key Laboratory of Condensed Matter Physics at Extreme Conditions High Magnetic Field Laboratory of the Chinese Academy of Sciences State Key Laboratory of Precision Spectroscopy East China Normal University National Laboratory of Solid State Microstructures and Department of Physics Nanjing University Key Laboratory of Artificial Structures and Quantum Control(Ministry of Education) School of Physics and Astronomy Shanghai Jiao Tong University Solid State and Structural Chemistry Unit Indian Institute of Science Wuhan National High Magnetic Field Center and School of Physics Huazhong University of Science and Technology School of Information Science and Technology Shanghai Tech University National High Magnetic Field Laboratory LNCMI-CNRS UPR3228 Université Grenoble Alpes International Center for Quantum Materials School of Physics Peking University School of Physics and Electronic Science East China Normal University Shanghai Center of Brain-Inspired Intelligent Materials and Devices East China Normal University Shanghai Qi Zhi Institute Zhangjiang Fudan International Innovation Center Fudan University Shanghai Research Center for Quantum Sciences

The Mott-Ioffe-Regel limit sets the lower bound of the carrier mean free path for coherent quasiparticle transport. Metallicity beyond this limit is of great interest because it is often closely related to quantum criticality and unconventional superconductivity. Progress along this direction mainly focuses on the strange-metal behaviors originating from the evolution of the quasiparticle scattering rate, such as linear-in-temperature resistivity, while the quasiparticle coherence phenomena in this regime are much less explored due to the short mean free path at the diffusive bound. Here we report the observation of quantum oscillations from Landau quantization near the Mott-Ioffe-Regel limit in CaAs3. Despite the insulator-like temperature dependence of resistivity, CaAs3presents giant magnetoresistance and prominent Shubnikov–de Haas oscillations from Fermi surfaces, indicating highly coherent band transport. In contrast,quantum oscillation is absent in the magnetic torque. The quasiparticle effective mass increases systematically with magnetic fields, manifesting a much larger value than what is expected based on magneto-infrared spectroscopy. This suggests a strong many-body renormalization effect near the Fermi surface. We find that these unconventional behaviors may be explained by the interplay between the mobility edge and the van Hove singularity, which results in the formation of coherent cyclotron orbits emerging at the diffusive bound. Our results call for further study on the electron correlation effect of the van Hove singularity.

关键词： Mott-Ioffe-Regel limit quantum oscillations van Hove singularity mobility edge

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Two Heads Are Better Than One: Improving Fake News Video Detection by Correlating with Neighbors

arXiv

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arXiv 2023年

作者： Qi, Peng Zhao, Yuyang Shen, Yufeng Ji, Wei Cao, Juan Chua, Tat-Seng Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China National University of Singapore Singapore

The prevalence of short video platforms has spawned a lot of fake news videos, which have stronger propagation ability than textual fake news. Thus, automatically detecting fake news videos has been an important countermeasure in practice. Previous works commonly verify each news video individually with multimodal information. Nevertheless, news videos from different perspectives regarding the same event are commonly posted together, which contain complementary or contradictory information and thus can be used to evaluate each other mutually. To this end, we introduce a new and practical paradigm, i.e., cross-sample fake news video detection, and propose a novel framework, Neighbor-Enhanced fakE news video Detection (NEED), which integrates the neighborhood relationship of new videos belonging to the same event. NEED can be readily combined with existing single-sample detectors and further enhance their performances with the proposed graph aggregation (GA) and debunking rectification (DR) modules. Specifically, given the feature representations obtained from single-sample detectors, GA aggregates the neighborhood information with the dynamic graph to enrich the features of independent samples. After that, DR explicitly leverages the relationship between debunking videos and fake news videos to refute the candidate videos via textual and visual consistency. Extensive experiments on the public benchmark demonstrate that NEED greatly improves the performance of both single-modal (up to 8.34% in accuracy) and multimodal (up to 4.97% in accuracy) base detectors. Codes are available in https://***/ICTMCG/NEED. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

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Gaussian Label Distribution Learning for Spherical Image Object Detection

Gaussian Label Distribution Learning for Spherical Image Obj...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Hang Xu Xinyuan Liu Qiang Zhao Yike Ma Chenggang Yan Feng Dai Hangzhou Dianzi University Hangzhou China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing Technology Chinese Academy of Sciences Beijing China

Spherical image object detection emerges in many applications from virtual reality to robotics and automatic driving, while many existing detectors use $l_{n}$ -norms loss for regression of spherical bounding boxes. There are two intrinsic flaws for $l_{n}$ -norms loss, i.e., independent optimization of parameters and inconsistency between metric (dominated by IoU) and loss. These problems are common in planar image detection but more significant in spherical image detection. Solution for these problems has been extensively discussed in planar image detection by using IoU loss and related variants. However, these solutions cannot be migrated to spherical image object detection due to the undifferentiable of the Spherical IoU (SphIoU). In this paper, we design a simple but effective regression loss based on Gaussian Label Distribution Learning (GLDL) for spherical image object detection. Besides, we observe that the scale of the object in a spherical image varies greatly. The huge differences among objects from different categories make the sample selection strategy based on SphIoU challenging. Therefore, we propose GLDL-ATSS as a better training sample selection strategy for objects of the spherical image, which can alleviate the drawback of IoU threshold-based strategy of scale-sample imbalance. Extensive results on various two datasets with different baseline detectors show the effectiveness of our approach.

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Boosting Meaningful Dependency Mining with Clustering and Covariance Analysis

Boosting Meaningful Dependency Mining with Clustering and Co...

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International Conference on Data Engineering

作者： Xi Wang Ruochun Jin Wanrong Huang Yuhua Tang Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha China Department of Intelligent Data Science College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Functional dependencies (FDs) form a valuable ingredient for various data management tasks. However, existing methods can hardly discover practical and interpretable FDs, especially in large noisy real-life datasets. This paper studies the problem of discovering meaningful functional dependencies (FDms) that utilize support and error parameters to capture interesting dependencies in such datasets and proposes an efficient discovery algorithm called FDMε. In order to scale with large datasets, FDM ε employs an efficient sampling method with accuracy guarantees to capture the differences between tuple pairs and to quantify the connection between support/error of dependencies on samples and those on the entire dataset. Moreover, it adopts a clustering-based correlated attributes extraction to divide the exponentially large search space into multiple small sub-spaces and proposes an easy-first traversal strategy with covariance-based guidance that quickly detects candidate dependencies and validates them. Additionally, we prove a covariance lower bound as an additional pruning criterion to reduce the search space. Extensive experiments on real-life and synthetic datasets demonstrate that FDM ε is 14 times faster than existing discovery algorithms on average, up to 31 times, and scales to larger datasets with the least memory cost.

关键词： Accuracy Costs Clustering algorithms Search problems Data engineering Noise measurement Frequency division multiplexing

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A survey on cryptographic techniques for protecting big data security: present and forthcoming

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Science China(information Sciences) 2022年第10期65卷 73-106页

作者： Siqi LU Jianhua ZHENG Zhenfu CAO Yongjuan WANG Chunxiang GU School of Cyberspace Security Information Engineering University Henan Key Laboratory of Network Cryptography Technology Institute of Security Technology Shanghai Key Laboratory of Trustworthy Computing East China Normal University Cyberspace Security Research Center Peng Cheng Laboratory Shanghai Institute of Intelligent Science and Technology Tongji University

Big data drive multidimensional convergence and profound innovations among industries and provide novel ways of exploring the world. As they significantly create economic and social value, big data meaningfully impact the implementation and management of information security and privacy *** technologies are used to protect the security and entire life cycle of big data. The demand for this technology is multiplied when the data are stored in the cloud. They are stored in the cloud in the form of ciphertext, and the driving requirement for data retrieval, sharing, and manipulation places the security of data at risk. The all-or-nothing approach of traditional cryptography systems cannot realize the release and processing of data information with flexible and increasingly fine granularity. Consequently, dealing with the relationship between privacy protection and data utilization, as well as navigating the blurry boundaries between subverting either plaintext or ciphertext, has become a research focus of the current cryptographic trend for protecting big data security. Presently, there are many studies designed to solve these ***, security requirements and source encryption mode for future big data systems and applications are elaborated. Then, focusing on the practical security and functionality of the big data life cycle, including storage, retrieval, sharing, calculation, statistical analysis, and utilization, the research being conducted based on those functions is reviewed. For each cryptographic technology that meets the requirement of each type of big data security or application, security and efficiency comments and sufficient comparison analyses of cryptography schemes or protocols are provided; moreover, the current general problems and development trends are expounded. Because the current innovative research on cryptographic technology was primarily based on the development or improvement of a single solution, the study on

关键词： big data security cryptographic techniques ciphertext-based data sharing and computing authenticated encryption functional encryption homomorphic encryption secure multi-party computing

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Bidirectional Logits Tree: Pursuing Granularity Reconcilement in Fine-Grained Classification

arXiv

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arXiv 2024年

作者： Lu, Zhiguang Xu, Qianqian Bao, Shilong Yang, Zhiyong Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences China

This paper addresses the challenge of Granularity Competition in fine-grained classification tasks, which arises due to the semantic gap between multi-granularity labels. Existing approaches typically develop independent hierarchy-aware models based on shared features extracted from a common base encoder. However, because coarse-grained levels are inherently easier to learn than finer ones, the base encoder tends to prioritize coarse feature abstractions, which impedes the learning of fine-grained features. To overcome this challenge, we propose a novel framework called the Bidirectional Logits Tree (BiLT) for Granularity Reconcilement. The key idea is to develop classifiers sequentially from the finest to the coarsest granularities, rather than parallelly constructing a set of classifiers based on the same input features. In this setup, the outputs of finer-grained classifiers serve as inputs for coarser-grained ones, facilitating the flow of hierarchical semantic information across different granularities. On top of this, we further introduce an Adaptive Intra-Granularity Difference Learning (AIGDL) approach to uncover subtle semantic differences between classes within the same granularity. Extensive experiments demonstrate the effectiveness of our proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Coarse-grained modeling

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Dual arm coordination with coordination diagram based on teleoperation demonstration

Dual arm coordination with coordination diagram based on tel...

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2021 IEEE International Conference on Real-Time computing and Robotics, RCAR 2021

作者： Zuo, Guoyu Xu, Zichen Liu, Lu Gong, Daoxiong Li, Jianfeng Faculty of Information Technology Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China Faculty of Materials and Manufacturing Beijing University of Technology Beijing100124 China

ISBN: (纸本)9781665436786

Efficient and collision-free coordination of two robot arms is increasingly needed in various service-oriented robotic applications. This paper proposes a dual-arm coordination algorithm to improve the efficiency of coordination by considering both robot's actions and operating sequences for tasks that require multiple operations with both arms. Teleoperation demonstration is performed to obtain the robot's human-like motion trajectories, so as to reduce the probability of collisions of the two robotic arms. The coordination diagram in time domain is designed to more clearly represent the situations of trajectory collisions and find the collision-free coordination action law. Simultaneously, a coordination pair generator (CPG) is designed to reorganize the operating sequences according to the characteristics of input trajectories in addition to the action coordination. The effectiveness and efficiency of our method are verified on the experiment of the drug sorting task in nursing homes on the ABB YuMi robot model. © 2021 IEEE.

关键词： Remote control

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