Party, State and Society: The Core Issue of Party Politics

The issue of relationship among party, state and society is the core problem of party politics. A clear understanding and handling of that relationship is basic requirement of party politics’ general principle, also the key to promote the smooth running of party politics

DiffLLE: Diffusion-guided Domain Calibration for Unsupervised Low-light Image Enhancement

Existing unsupervised low-light image enhancement methods lack enough effectiveness and generalization in practical applications. We suppose this is because of the absence of explicit supervision and the inherent gap between real-world scenarios and the training data domain. In this paper, we develop Diffusion-based domain calibration to realize more robust and effective unsupervised Low-Light Enhancement, called DiffLLE. Since the diffusion model performs impressive denoising capability and has been trained on massive clean images, we adopt it to bridge the gap between the real low-light domain and training degradation domain, while providing efficient priors of real-world content for unsupervised models. Specifically, we adopt a naive unsupervised enhancement algorithm to realize preliminary restoration and design two zero-shot plug-and-play modules based on diffusion model to improve generalization and effectiveness. The Diffusion-guided Degradation Calibration (DDC) module narrows the gap between real-world and training low-light degradation through diffusion-based domain calibration and a lightness enhancement curve, which makes the enhancement model perform robustly even in sophisticated wild degradation. Due to the limited enhancement effect of the unsupervised model, we further develop the Fine-grained Target domain Distillation (FTD) module to find a more visual-friendly solution space. It exploits the priors of the pre-trained diffusion model to generate pseudo-references, which shrinks the preliminary restored results from a coarse normal-light domain to a finer high-quality clean field, addressing the lack of strong explicit supervision for unsupervised methods. Benefiting from these, our approach even outperforms some supervised methods by using only a simple unsupervised baseline. Extensive experiments demonstrate the superior effectiveness of the proposed DiffLLE

Network Pruning Spaces

Network pruning techniques, including weight pruning and filter pruning, reveal that most state-of-the-art neural networks can be accelerated without a significant performance drop. This work focuses on filter pruning which enables accelerated inference with any off-the-shelf deep learning library and hardware. We propose the concept of \emph{network pruning spaces} that parametrize populations of subnetwork architectures. Based on this concept, we explore the structure aspect of subnetworks that result in minimal loss of accuracy in different pruning regimes and arrive at a series of observations by comparing subnetwork distributions. We conjecture through empirical studies that there exists an optimal FLOPs-to-parameter-bucket ratio related to the design of original network in a pruning regime. Statistically, the structure of a winning subnetwork guarantees an approximately optimal ratio in this regime. Upon our conjectures, we further refine the initial pruning space to reduce the cost of searching a good subnetwork architecture. Our experimental results on ImageNet show that the subnetwork we found is superior to those from the state-of-the-art pruning methods under comparable FLOPs

Improving the Rectangle Attack on GIFT-64

GIFT is a family of lightweight block ciphers based on SPN structure and composed of two versions named GIFT-64 and GIFT-128. In this paper, we reevaluate the security of GIFT-64 against the rectangle attack under the related-key setting. Investigating the previous rectangle key recovery attack on GIFT-64, we obtain the core idea of improving the attack——trading off the time complexity of each attack phase. We flexibly guess part of the involved subkey bits to balance the time cost of each phase so that the overall time complexity of the attack is reduced. Moreover, the reused subkey bits are identified according to the linear key schedule of GIFT-64 and bring additional advantages for our attacks. Furthermore, we incorporate the above ideas and propose a dedicated MILP model for finding the best rectangle key recovery attack on GIFT-64. As a result, we get the improved rectangle attacks on 26-round GIFT-64, which are the best attacks on it in terms of time complexity so far

Clinical efficacy of the combined use of levofloxacin and different courses of isoniazid and rifampicin in the treatment of mild spinal tuberculosis

Purpose: To investigate the clinical effectiveness of the combined use of levofloxacin and different courses of isoniazid and rifampicin in the treatment of mild spinal tuberculosis (TB). Methods: The clinic data of 100 patients with light spinal TB were retrospectively reviewed. A double-blind technique was used to divide the patients into 6-month treatment group (M6 group, n = 32), 12-month treatment group (M12 group, n = 34) and 18-month treatment group (M18 group, n = 34). All patients were given isoniazid and rifampicin, in combination with levofloxacin. The effects of the different treatment courses on mild spinal TB were determined. Results: There were significantly higher post-treatment levels of inflammatory factors in M6 group than in M12 and M18 groups (p < 0.001). Moreover, there were significantly higher Visual Analogue Scale (VAS) score and erythrocyte sedimentation rate (ESR), and larger focus size in M6 group than in M12 and M18 groups (p < 0.05). However, after treatment, M18 group had significantly higher total incidence of adverse reactions than M6 and M12 groups (p < 0.05). Conclusion: Compared with the short-course treatment, long-course treatment with isoniazid and rifampicin in combination with levofloxacin is more effective in reducing the levels of inflammatory factors and decreasing focus size in patients with mild spinal TB. However, patients given the 18-month treatment tend to develop more adverse reactions. Therefore, 12-month treatment with the combined therapy is a better therapeutic option

xQSM: Quantitative Susceptibility Mapping with Octave Convolutional and Noise Regularized Neural Networks

Quantitative susceptibility mapping (QSM) is a valuable magnetic resonance imaging (MRI) contrast mechanism that has demonstrated broad clinical applications. However, the image reconstruction of QSM is challenging due to its ill-posed dipole inversion process. In this study, a new deep learning method for QSM reconstruction, namely xQSM, was designed by introducing modified state-of-the-art octave convolutional layers into the U-net backbone. The xQSM method was compared with recentlyproposed U-net-based and conventional regularizationbased methods, using peak signal to noise ratio (PSNR), structural similarity (SSIM), and region-of-interest measurements. The results from a numerical phantom, a simulated human brain, four in vivo healthy human subjects, a multiple sclerosis patient, a glioblastoma patient, as well as a healthy mouse brain showed that the xQSM led to suppressed artifacts than the conventional methods, and enhanced susceptibility contrast, particularly in the ironrich deep grey matter region, than the original U-net, consistently. The xQSM method also substantially shortened the reconstruction time from minutes using conventional iterative methods to only a few seconds.Comment: 37 pages, 10 figures, 3 tabl

Graphene-Based Biosensors for Detection of Composite Vibrational Fingerprints in the Mid-Infrared Region.

In this study, a label-free multi-resonant graphene-based biosensor with periodic graphene nanoribbons is proposed for detection of composite vibrational fingerprints in the mid-infrared range. The multiple vibrational signals of biomolecules are simultaneously enhanced and detected by different resonances in the transmission spectrum. Each of the transmission dips can be independently tuned by altering the gating voltage applied on the corresponding graphene nanoribbon. Geometric parameters are investigated and optimized to obtain excellent sensing performance. Limit of detection is also evaluated in an approximation way. Besides, the biosensor can operate in a wide range of incident angles. Electric field intensity distributions are depicted to reveal the physical insight. Moreover, another biosensor based on periodic graphene nanodisks is further proposed, whose performance is insensitive to the polarization of incidence. Our research may have a potential for designing graphene-based biosensor used in many promising bioanalytical and pharmaceutical applications

Mechanism exploration and prognosis study of Astragali Radix-Spreading hedyotis herb for the treatment of lung adenocarcinoma based on bioinformatics approaches and molecular dynamics simulation

Background: Herb pair of Astragali Radix (AR) and Spreading Hedyotis Herb (SH) has been frequently prescribed in clinical for the treatment of lung cancer owing to its favorable efficacy. Yet, the mechanism under the therapeutic effects remained unveiled, which has limited its clinical applications, and new drug development for lung cancer.Methods: The bioactive ingredients of AR and SH were retrieved from the Traditional Chinese Medicine System Pharmacology Database, with the targets of obtained components predicted by Swiss Target Prediction. Genes related to lung adenocarcinoma (LUAD) were acquired from GeneCards, OMIM and CTD databases, with the hub genes of LUAD screened by CTD database. The intersected targets of LUAD and AR-SH were obtained by Venn, with David Database employed to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Survival analysis of the hub genes of LUAD was carried out using TCGA-LUAD dataset. Molecular docking of core proteins and active ingredients was performed by Auto-Dock Vina software, followed by molecular dynamics simulations of protein-ligand complexes with well-docked conformations.Results: 29 active ingredients were screened out with 422 corresponding targets predicted. It is revealed that AR-SH can act on various targets such as EGFR, MAPK1, and KARS by ursolic acid (UA), Astragaloside IV(ASIV), and Isomucronulatol 7,2′-di-O-glucoside (IDOG) to alleviate the symptoms of LUAD. Biological processes involved are protein phosphorylation, negative regulation of apoptotic process, and pathways involved are endocrine resistance, EGFR tyrosine kinase inhibitor resistance, PI3K-Akt, and HIF-1 pathway. Molecular docking analysis indicated that the binding energy of most of the screened active ingredients to proteins encoded by core genes was less than −5.6 kcal/mol, with some active ingredients showing even lower binding energy to EGFR than Gefitinib. Three ligand-receptor complexes including EGFR-UA, MAPK1-ASIV, and KRAS-IDOG were found to bind relatively stable by molecular dynamics simulation, which was consistent with the results of molecule docking.Conclusion: We suggested that the herb pair of AR-SH can act on targets like EGFR, MAPK1 and KRAS by UA, ASIV and IDOG, to play a vital role in the treatment and the enhancement of prognosis of LUAD