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

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

PMET: Precise Model Editing in a Transformer

Model editing techniques modify a minor proportion of knowledge in Large Language Models (LLMs) at a relatively low cost, which have demonstrated notable success. Existing methods assume Transformer Layer (TL) hidden states are values of key-value memories of the Feed-Forward Network (FFN). They usually optimize the TL hidden states to memorize target knowledge and use it to update the weights of the FFN in LLMs. However, the information flow of TL hidden states comes from three parts: Multi-Head Self-Attention (MHSA), FFN, and residual connections. Existing methods neglect the fact that the TL hidden states contains information not specifically required for FFN. Consequently, the performance of model editing decreases. To achieve more precise model editing, we analyze hidden states of MHSA and FFN, finding that MHSA encodes certain general knowledge extraction patterns. This implies that MHSA weights do not require updating when new knowledge is introduced. Based on above findings, we introduce PMET, which simultaneously optimizes Transformer Component (TC, namely MHSA and FFN) hidden states, while only using the optimized TC hidden states of FFN to precisely update FFN weights. Our experiments demonstrate that PMET exhibits state-of-the-art performance on both the COUNTERFACT and zsRE datasets. Our ablation experiments substantiate the effectiveness of our enhancements, further reinforcing the finding that the MHSA encodes certain general knowledge extraction patterns and indicating its storage of a small amount of factual knowledge. Our code is available at https://github.com/xpq-tech/PMET.git.Comment: Preprint. Under revie