Wielding the sword: President Xi’s new anti-corruption campaign

A state achieves legitimacy through multiple sources, one of which is the effectiveness of its governance. Generations of scholars since Hobbes have identified the maintenance of peace and order as core functions of a legitimate state. In the modern world, economic prosperity, social stability and effective control of corruption often provide adequate compensation for a deficit of democracy. Corruption closely correlates with legitimacy. While a perceived pervasive, endemic corruption undermines the legitimacy of a regime, a successful anti-corruption campaign can allow a regime to recover from a crisis of legitimacy (Gilley 2009; Seligson and Booth 2009). This is the rationale behind the periodical campaigns against corruption that have been conducted by the Chinese Communist Party (‘Party’ or ‘CCP’) (Manion 2004; Wedeman 2012). Political leaders in China have found it expedient to use anti-corruption campaigns to remove their political foes, to rein in the bureaucracy and to restore public confidence in their ability to rule. Through anti-corruption campaigns, emerging political leaders consolidate their political power, secure loyalty from political factions and regional political forces, and enhance their legitimacy in the eyes of the general public. In an authoritarian state that experiences a high level of corruption, an anti-corruption campaign is a delicate political battle that addresses two significant concerns. The first concern is to orchestrate the campaign so that it is regime-reinforcing instead of regime-undermining. To remain credible, the regime must demonstrate its willingness and capacity to punish corrupt officials at the highest levels.preprin

Numerical modelling of mutual effect among nearby needles in a multi-needle configuration of an atmospheric air dielectric barrier discharge

A numerical study has been conducted to understand the mutual effect among nearby needles in a multi-needle electrode dielectric barrier discharge. In the present paper, a fluid-hydrodynamic model is adopted. In this model, the mutual effect among nearby needles in a multi-needle configuration of an atmospheric air dielectric barrier discharge are investigated using a fluid-hydrodynamic model including the continuity equations for electrons and positive and negative ions coupled with Poisson's equation. The electric fields at the streamer head of the middle needle (MN) and the side needles (SNs) in a three-needle model decreased under the influence of the mutual effects of nearby needles compared with that in the single-needle model. In addition, from the same comparison, the average propagation velocities of the streamers from MN and SNs, the electron average energy profile of MN and SNs (including those in the streamer channel, at the streamer head, and in the unbridged gap), and the electron densities at the streamer head of the MN and SNs also decreased. The results obtained in the current paper agreed well with the experimental and simulation results in the literature. © 2012 by the authors

Pix2HDR -- A pixel-wise acquisition and deep learning-based synthesis approach for high-speed HDR videos

Accurately capturing dynamic scenes with wide-ranging motion and light intensity is crucial for many vision applications. However, acquiring high-speed high dynamic range (HDR) video is challenging because the camera's frame rate restricts its dynamic range. Existing methods sacrifice speed to acquire multi-exposure frames. Yet, misaligned motion in these frames can still pose complications for HDR fusion algorithms, resulting in artifacts. Instead of frame-based exposures, we sample the videos using individual pixels at varying exposures and phase offsets. Implemented on a pixel-wise programmable image sensor, our sampling pattern simultaneously captures fast motion at a high dynamic range. We then transform pixel-wise outputs into an HDR video using end-to-end learned weights from deep neural networks, achieving high spatiotemporal resolution with minimized motion blurring. We demonstrate aliasing-free HDR video acquisition at 1000 FPS, resolving fast motion under low-light conditions and against bright backgrounds - both challenging conditions for conventional cameras. By combining the versatility of pixel-wise sampling patterns with the strength of deep neural networks at decoding complex scenes, our method greatly enhances the vision system's adaptability and performance in dynamic conditions.Comment: 14 pages, 14 figure

Emm type distribution of group A Streptococcus in China during 1990 and 2020: a systematic review and implications for vaccine coverage

BackgroundThe recent increase of group A Streptococcus (GAS) infections in Europe has aroused global concern. We aim to provide molecular biological data for GAS prevention and control in China by analyzing the temporal shift of emm type.MethodsWe collected studies reporting GAS emm types in China from 1990 to 2020 by PRISMA statement and established a summary database including emm types and literature quality assessment. Based on the database we analyzed the geographic distribution of emm types from 1990 to 2020 and assessed the coverage of the known GAS 30-valent vaccine. Outbreak-associated emm types that had been reported over the past 30 years were also included.Results47 high quality studies were included for a systematic analysis of emm type distribution. This generated a database including totally 12,347 GAS isolates and 85 emm types. Shift of dominant emm type was witnessed during the past 30 years in China. In mainland China, dominant types changed from emm3, emm1, emm4, emm12 in 1990s to emm12 and emm1 in 2000s and 2010s. Hong Kong and Taiwan were dominated by emm12, emm4 and emm1, of which emm4 reduced but emm12 increased in 2010s significantly. From 1990 to 2020, newly found emm types were increasingly reported in various regions of China. The reported 30-valent M protein vaccine covered 26 M types prevalent in China, including all dominant types

ViewFool: Evaluating the Robustness of Visual Recognition to Adversarial Viewpoints

Recent studies have demonstrated that visual recognition models lack robustness to distribution shift. However, current work mainly considers model robustness to 2D image transformations, leaving viewpoint changes in the 3D world less explored. In general, viewpoint changes are prevalent in various real-world applications (e.g., autonomous driving), making it imperative to evaluate viewpoint robustness. In this paper, we propose a novel method called ViewFool to find adversarial viewpoints that mislead visual recognition models. By encoding real-world objects as neural radiance fields (NeRF), ViewFool characterizes a distribution of diverse adversarial viewpoints under an entropic regularizer, which helps to handle the fluctuations of the real camera pose and mitigate the reality gap between the real objects and their neural representations. Experiments validate that the common image classifiers are extremely vulnerable to the generated adversarial viewpoints, which also exhibit high cross-model transferability. Based on ViewFool, we introduce ImageNet-V, a new out-of-distribution dataset for benchmarking viewpoint robustness of image classifiers. Evaluation results on 40 classifiers with diverse architectures, objective functions, and data augmentations reveal a significant drop in model performance when tested on ImageNet-V, which provides a possibility to leverage ViewFool as an effective data augmentation strategy to improve viewpoint robustness.Comment: NeurIPS 202

Case report: Squamous cell carcinoma of the prostate-a clinicopathological and genomic sequencing-based investigation

Squamous differentiation of prostate cancer, which accounts for less than 1% of all cases, is typically associated with androgen deprivation treatment (ADT) or radiotherapy. This entity is aggressive and exhibits poor prognosis due to limited response to traditional treatment. However, the underlying molecular mechanisms and etiology are not fully understood. Previous findings suggest that squamous cell differentiation may potentially arise from prostate adenocarcinoma (AC), but further validation is required to confirm this hypothesis. This paper presents a case of advanced prostate cancer with a combined histologic pattern, including keratinizing SCC and AC. The study utilized whole-exome sequencing (WES) data to analyze both subtypes and identified a significant overlap in driver gene mutations between them. This suggests that the two components shared a common origin of clones. These findings emphasize the importance of personalized clinical management for prostate SCC, and specific molecular findings can help optimize treatment strategies

Multisensor data fusion of operational sea ice observations

Multisensor data fusion (MDF) is a process/technique of combining observations from multiple sensors to provide a more robust, accurate and complete description of the concerned object, environment or process. In this paper we introduce a new MDF method, multisensor optimal data fusion (MODF), to fuse different operational sea ice observations around Svalbard. The overall MODF includes regridding, univariate multisensor optimal data merging (MODM), multivariate check of consistency, and generation of new variables. For MODF of operational sea ice observations around Svalbard, the AMSR2 sea ice concentration (SIC) is firstly merged with the Norwegian Meteorological Institute ice chart. Then the daily SMOS sea ice thickness (SIT) is merged with the weekly CS2SMOS SIT to form a daily CS2SMOS SIT, which is further refined to be consistent with the SIC through consistency check. Finally sea ice volume (SIV) and its uncertainty are calculated based on the merged SIC and fused SIT. The fused products provide an improved, united, consistent and multifaceted description for the operational sea ice observations, they also provide consistent descriptions of sea ice edge and marginal ice zone. We note that uncertainties may vary during the regridding process, and therefore correct determination of the observation uncertainties is critically important for MDF. This study provides a basic framework for managing multivariate multisensor observations

DIFFender: Diffusion-Based Adversarial Defense against Patch Attacks

Adversarial attacks, particularly patch attacks, pose significant threats to the robustness and reliability of deep learning models. Developing reliable defenses against patch attacks is crucial for real-world applications. This paper introduces DIFFender, a novel defense framework that harnesses the capabilities of a text-guided diffusion model to combat patch attacks. Central to our approach is the discovery of the Adversarial Anomaly Perception (AAP) phenomenon, which empowers the diffusion model to detect and localize adversarial patches through the analysis of distributional discrepancies. DIFFender integrates dual tasks of patch localization and restoration within a single diffusion model framework, utilizing their close interaction to enhance defense efficacy. Moreover, DIFFender utilizes vision-language pre-training coupled with an efficient few-shot prompt-tuning algorithm, which streamlines the adaptation of the pre-trained diffusion model to defense tasks, thus eliminating the need for extensive retraining. Our comprehensive evaluation spans image classification and face recognition tasks, extending to real-world scenarios, where DIFFender shows good robustness against adversarial attacks. The versatility and generalizability of DIFFender are evident across a variety of settings, classifiers, and attack methodologies, marking an advancement in adversarial patch defense strategies

Investigation of Electrode Electrochemical Reactions in CH3 NH3 PbBr3 Perovskite Single-Crystal Field-Effect Transistors.

Optoelectronic devices based on metal halide perovskites, including solar cells and light-emitting diodes, have attracted tremendous research attention globally in the last decade. Due to their potential to achieve high carrier mobilities, organic-inorganic hybrid perovskite materials can enable high-performance, solution-processed field-effect transistors (FETs) for next-generation, low-cost, flexible electronic circuits and displays. However, the performance of perovskite FETs is hampered predominantly by device instabilities, whose origin remains poorly understood. Here, perovskite single-crystal FETs based on methylammonium lead bromide are studied and device instabilities due to electrochemical reactions at the interface between the perovskite and gold source-drain top contacts are investigated. Despite forming the contacts by a gentle, soft lamination method, evidence is found that even at such "ideal" interfaces, a defective, intermixed layer is formed at the interface upon biasing of the device. Using a bottom-contact, bottom-gate architecture, it is shown that it is possible to minimize such a reaction through a chemical modification of the electrodes, and this enables fabrication of perovskite single-crystal FETs with high mobility of up to ≈15 cm2 V-1 s-1 at 80 K. This work addresses one of the key challenges toward the realization of high-performance solution-processed perovskite FETs