536 research outputs found

    High Quality Image Interpolation via Local Autoregressive and Nonlocal 3-D Sparse Regularization

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    In this paper, we propose a novel image interpolation algorithm, which is formulated via combining both the local autoregressive (AR) model and the nonlocal adaptive 3-D sparse model as regularized constraints under the regularization framework. Estimating the high-resolution image by the local AR regularization is different from these conventional AR models, which weighted calculates the interpolation coefficients without considering the rough structural similarity between the low-resolution (LR) and high-resolution (HR) images. Then the nonlocal adaptive 3-D sparse model is formulated to regularize the interpolated HR image, which provides a way to modify these pixels with the problem of numerical stability caused by AR model. In addition, a new Split-Bregman based iterative algorithm is developed to solve the above optimization problem iteratively. Experiment results demonstrate that the proposed algorithm achieves significant performance improvements over the traditional algorithms in terms of both objective quality and visual perceptionComment: 4 pages, 5 figures, 2 tables, to be published at IEEE Visual Communications and Image Processing (VCIP) 201

    Can SAM Count Anything? An Empirical Study on SAM Counting

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    Meta AI recently released the Segment Anything model (SAM), which has garnered attention due to its impressive performance in class-agnostic segmenting. In this study, we explore the use of SAM for the challenging task of few-shot object counting, which involves counting objects of an unseen category by providing a few bounding boxes of examples. We compare SAM's performance with other few-shot counting methods and find that it is currently unsatisfactory without further fine-tuning, particularly for small and crowded objects. Code can be found at \url{https://github.com/Vision-Intelligence-and-Robots-Group/count-anything}.Comment: An empirical study on few-shot counting using Meta AI's segment anything mode

    A Review on Trajectory Datasets on Advanced Driver Assistance System

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    This paper presents a comprehensive review of trajectory data of Advanced Driver Assistance System equipped-vehicle, with the aim of precisely model of Autonomous Vehicles (AVs) behavior. This study emphasizes the importance of trajectory data in the development of AV models, especially in car-following scenarios. We introduce and evaluate several datasets: the OpenACC Dataset, the Connected & Autonomous Transportation Systems Laboratory Open Dataset, the Vanderbilt ACC Dataset, the Central Ohio Dataset, and the Waymo Open Dataset. Each dataset offers unique insights into AV behaviors, yet they share common challenges in terms of data availability, processing, and standardization. After a series of data cleaning, outlier removal and statistical analysis, this paper transforms datasets of varied formats into a uniform standard, thereby improving their applicability for modeling AV car-following behavior. Key contributions of this study include: 1. the transformation of all datasets into a unified standard format, enhancing their utility for broad research applications; 2. a comparative analysis of these datasets, highlighting their distinct characteristics and implications for car-following model development; 3. the provision of guidelines for future data collection projects, along with the open-source release of all processed data and code for use by the research community.Comment: 6 pages, 2 figure

    Spiking Semantic Communication for Feature Transmission with HARQ

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    In Collaborative Intelligence (CI), the Artificial Intelligence (AI) model is divided between the edge and the cloud, with intermediate features being sent from the edge to the cloud for inference. Several deep learning-based Semantic Communication (SC) models have been proposed to reduce feature transmission overhead and mitigate channel noise interference. Previous research has demonstrated that Spiking Neural Network (SNN)-based SC models exhibit greater robustness on digital channels compared to Deep Neural Network (DNN)-based SC models. However, the existing SNN-based SC models require fixed time steps, resulting in fixed transmission bandwidths that cannot be adaptively adjusted based on channel conditions. To address this issue, this paper introduces a novel SC model called SNN-SC-HARQ, which combines the SNN-based SC model with the Hybrid Automatic Repeat Request (HARQ) mechanism. SNN-SC-HARQ comprises an SNN-based SC model that supports the transmission of features at varying bandwidths, along with a policy model that determines the appropriate bandwidth. Experimental results show that SNN-SC-HARQ can dynamically adjust the bandwidth according to the channel conditions without performance loss

    Strongly Secure Authenticated Key Exchange from Ideal Lattices

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    In this paper, we propose an efficient and practical authenticated key exchange (AKE) protocol from ideal lattices, which is well-designed and has some similarity to the HMQV protocol. Using the hardness of the graded discrete logarithm (GDL) problem and graded decisional Diffie-Hellman (GCDH) problem, the proposed protocol is provably secure in the extended Canetti-Krawczyk model

    Two-party authenticated key exchange protocol using lattice-based cryptography

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    Authenticated key exchange (AKE) protocol is an important cryptographic primitive that assists communicating entities, who are communicating over an insecure network, to establish a shared session key to be used for protecting their subsequent communication. Lattice-based cryptographic primitives are believed to provide resilience against attacks from quantum computers. An efficient AKE protocol with smaller module over ideal lattices is constructed in this paper, which nicely inherits the design idea of the excellent high performance secure Diffie-Hellman protocol. Under the hard assumption of ring learning with errors (RLWE) hard assumption, the security of the proposed protocol is proved in the Bellare-Rogaway model

    Luteolin attenuates high glucose-induced cytotoxicity by suppressing TXNIP expression in neuronal cells

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    Purpose: To determine the potential effect of luteolin in neuroprotection using an in vitro model of diabetic neuropathy (DN) in PC12 cells by high glucose (HG)-induced neurotoxicity. Methods: PC12 cells were pretreated with HG media for 3, 6, 12, and 24 h, followed by treatment with increasing concentrations of luteolin (10, 25, and 50 ug/ml) for 24 hours. Following luteolin treatment, the cells were transfected with a plasmid expressing thioredoxin-interacting protein (TXNIP). To evaluate HG-induced cytotoxicity, the expression levels of the inflammatory markers interleukin (IL)-8, IL-6, and tumor necrosis factor-α (TNF-α) were evaluated by quantitative reverse transcription PCR (qRT-PCR) and ELISA. In addition, the apoptotic cells were assessed by flow cytometry. The expression levels of TXNIP protein and mRNA were determined by western blotting and qRT-PCR, respectively. Results: Luteolin decreased the expression levels of TNF-α, IL-1β, and IL-6 in a dose-dependent manner at both the protein and mRNA level. Luteolin also decreased HG-induced apoptosis in PC12 cells (p < 0.05). The expression of B-cell lymphoma 2 (BCL-2) was suppressed, whereas those of cleaved PARP and cleaved caspase-3 were increased following HG treatment. Luteolin treatment had the opposite effect in a dose-dependent manner (p < 0.05). Luteolin reduced HG-induced inflammation and apoptosis in PC12 cells by inhibiting TXNIP expression (p < 0.05). Conclusion: These data indicate that the neuroprotective effects of luteolin is probably exerted its antiapoptotic and anti-inflammatory activities via the TXNIP pathway
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