536 research outputs found
High Quality Image Interpolation via Local Autoregressive and Nonlocal 3-D Sparse Regularization
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
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
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
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
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
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
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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