Towards Black-box Adversarial Example Detection: A Data Reconstruction-based Method

Adversarial example detection is known to be an effective adversarial defense method. Black-box attack, which is a more realistic threat and has led to various black-box adversarial training-based defense methods, however, does not attract considerable attention in adversarial example detection. In this paper, we fill this gap by positioning the problem of black-box adversarial example detection (BAD). Data analysis under the introduced BAD settings demonstrates (1) the incapability of existing detectors in addressing the black-box scenario and (2) the potential of exploring BAD solutions from a data perspective. To tackle the BAD problem, we propose a data reconstruction-based adversarial example detection method. Specifically, we use variational auto-encoder (VAE) to capture both pixel and frequency representations of normal examples. Then we use reconstruction error to detect adversarial examples. Compared with existing detection methods, the proposed method achieves substantially better detection performance in BAD, which helps promote the deployment of adversarial example detection-based defense solutions in real-world models.Comment: 14 pages, 8 figures, 13 table

Pre-training also Transfers Non-Robustness

Pre-training has enabled state-of-the-art results on many tasks. In spite of its recognized contribution to generalization, we observed in this study that pre-training also transfers adversarial non-robustness from pre-trained model into fine-tuned model in the downstream tasks. Using image classification as an example, we first conducted experiments on various datasets and network backbones to uncover the adversarial non-robustness in fine-tuned model. Further analysis was conducted on examining the learned knowledge of fine-tuned model and standard model, and revealed that the reason leading to the non-robustness is the non-robust features transferred from pre-trained model. Finally, we analyzed the preference for feature learning of the pre-trained model, explored the factors influencing robustness, and introduced a simple robust pre-traning solution

An open unified deep graph learning framework for discovering drug leads

Computational discovery of ideal lead compounds is a critical process for modern drug discovery. It comprises multiple stages: hit screening, molecular property prediction, and molecule optimization. Current efforts are disparate, involving the establishment of models for each stage, followed by multi-stage multi-model integration. However, this is non-ideal, as clumsy integration of incompatible models increases research overheads, and may even reduce success rates in drug discovery. Facilitating compatibilities requires establishing inherent model consistencies across lead discovery stages. Towards that effect, we propose an open deep graph learning (DGL) based pipeline: generative adversarial feature subspace enhancement (GAFSE), which first unifies the modeling of these stages into one learning framework. GAFSE also offers standardized modular design and streamlined interfaces for future expansions and community support. GAFSE combines adversarial/generative learning, graph attention network, graph reconstruction network, and optimizes the classification/regression loss, adversarial/generative loss, and reconstruction loss simultaneously. Convergence analysis theoretically guarantees model generalization performance. Exhaustive benchmarking demonstrates that the GAFSE pipeline achieves excellent performance across almost all lead discovery stages, while also providing valuable model interpretability. Hence, we believe this tool will enhance the efficiency and productivity of drug discovery researchers.Comment: This article is used as the preliminary studies for the application of Lee Kuan Yew Postdoctoral Fellowship (LKYPDF) 2023 in Singapore. All rights reserve

Elliptical-Core Highly Nonlinear Few-Mode Fiber Based OXC for WDM-MDM Networks

In order to realize an optical cross-connect (OXC) converting wavelengths and spatial modes into one-dimensional switching ports, we propose an active mode selective conversion without parasitic wavelength conversion, based on the intermodal four-wave mixing (FWM) arising in a few-mode fiber (FMF). First, we design a dispersion-engineered elliptical-core highly nonlinear FMF (e-HNL-FMF) with a graded refractive index (RI) profile, which can independently guide 3 linearly polarized (LP) spatial modes. Meanwhile, a high doping concentration of germanium in the core leads to relatively high intermodal nonlinear coefficients of 3.23 (W\ub7km)-1 between LP01 and LP11a modes and 3.14 (W\ub7km)-1 between LP01 and LP11b modes. Next, we propose an e-HNL-FMF based OXC scheme for wavelength division multiplexing-mode division multiplexing (WDM-MDM) networks. After optimizing both the e-HNL-FMF length and pump power, we can realize either active mode selective conversion over the designated wavelength-band or three-wavelength to three-mode superchannel conversion for 100 Gbaud 16-quadratic-amplitude modulation (16-QAM) signals over the C-band. Due to excellent characteristics of the e-HNL-FMF, both cost and configuration complexity of the OXC can be reduced, showing great potentials for all-optical signal processing in the future WDM-MDM networks

Efficacy of mirabegron for ureteral stones: a systematic review with meta-analysis of randomized controlled trials

Background: Medical expulsive therapy demonstrates efficacy in managing ureteral stones in patients amenable to conservative interventions. This meta-analysis aims to evaluate the effectiveness of mirabegron in the treatment of ureteral stones.Methods: From conception to November 2023, we examined PubMed databases, the Cochrane Library, Embase, Ovid, Scopus, and trial registries for this systematic review and meta-analysis. We chose relevant randomized controlled trials (RCTs) evaluating the efficacy of mirabegron as an expulsive treatment for ureteral stones. The Cochrane risk of bias method was used to assess the quality of the evidence. Outcome measures, which included the stone expulsion rate (SER), expulsion time, and pain episodes, were analyzed using RevMan 5.4 and Stata 17.Results: Seven RCTs (N = 701) had enough information and were ultimately included. In patients with ureteral stones, mirabegron-treated patients had a substantially higher SER [odds ratio (OR) = 2.57, 95% confidence interval (CI) = 1.41–4.68, p = 0.002] than placebo-treated patients. Subgroup analysis revealed that mirabegron was superior to placebo in patients with small ureteral stones (OR = 2.26, 95% CI = 1.05–4.87, p = 0.04), with no heterogeneity between studies (p = 0.54; I2 = 0%). Mirabegron patients had a higher SER than the control group for distal ureteral stones (DUSs) (OR = 2.48, 95% CI = 1.31–4.68, p = 0.005). However, there was no difference in stone ejection time or pain episodes between groups.Conclusion: Mirabegron considerably improves SER in patients with ureteral stones, and the effect appears to be more pronounced for small and DUSs. Nevertheless, mirabegron treatment was not associated with improved stone expulsion time or pain management

Vector mode inter-modal wavelength conversion in a dispersion tailored highly nonlinear few-mode fibre

We present the design and fabrication of a dispersion tailored highly nonlinear few-mode fibre with an inter-modal nonlinear coefficient of 2.81 (W \ub7 km)-1, the highest reported to date. Inter-modal wavelength conversion between the HE21 and TE01 vector modes is demonstrated in the fibre

Retrospective seroepidemiology indicated that human enterovirus 71 and coxsackievirus A16 circulated wildly in central and southern China before large-scale outbreaks from 2008

<p>Abstract</p> <p>Background</p> <p>Large nationwide outbreaks of hand, foot, and mouth disease (HFMD) occurred in China from 2008; most of the cases were in children under 5 years. This study aims to identify the situation of natural human enterovirus 71 (HEV71) and coxsackievirus A16 (CVA16) infections in children before 2008 in China.</p> <p>Results</p> <p>Retrospective seroepidemiologic studies of HEV71 and CVA16 were performed with 900 serum samples collected from children ≤5 years of age in 2005. The samples were collected from 6 different geographical areas (Anhui, Guangdong, Hunan, Xinjiang, Yunnan, and Heilongjiang provinces) in mainland China. Of the 900 samples, 288 were positive for HEV71; the total positive rate was 32.0% and the geometric mean titer (GMT) was 1:8.5. Guangdong (43.7% and 1:10.8), Xinjiang (45.4% and 1:11.1), and Yunnan (43.4% and 1:12.0) provinces had relatively high rates of infection, while Heilongjiang province (8.1% and 1:4.9) had the lowest rate of infection. On the other hand, 390 samples were positive for CVA16; the total positive rate was 43.4% and the GMT was 1:9.5. Anhui (62.2% and 1:16.0) and Hunan (61.1% and 1:23.1) had relatively high rates, while Heilongjiang (8.0% and 1:4.6) had the lowest rate. Although there is a geographical difference in HEV71 and CVA16 infections, low neutralizing antibody positive rate and titer of both viruses were found in all 6 provinces.</p> <p>Conclusions</p> <p>This report confirmed that HEV71 and CVA16 had wildly circulated in a couple provinces in China before the large-scale outbreaks from 2008. This finding also suggests that public health measures to control the spread of HEV71 and CVA16 should be devised according to the different regional characteristics.</p

Role of NLRP3 inflammasome in diabetes and COVID-19 role of NLRP3 inflammasome in the pathogenesis and treatment of COVID-19 and diabetes NLRP3 inflammasome in diabetes and COVID-19 intervention

2019 Coronavirus Disease (COVID-19) is a global pandemic caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). A “cytokine storm”, i.e., elevated levels of pro-inflammatory cytokines in the bloodstream, has been observed in severe cases of COVID-19. Normally, activation of the nucleotide-binding oligomeric domain-like receptor containing pyrin domain 3 (NLRP3) inflammatory vesicles induces cytokine production as an inflammatory response to viral infection. Recent studies have found an increased severity of necrobiosis infection in diabetic patients, and data from several countries have shown higher morbidity and mortality of necrobiosis in people with chronic metabolic diseases such as diabetes. In addition, COVID-19 may also predispose infected individuals to hyperglycemia. Therefore, in this review, we explore the potential relationship between NLRP3 inflammatory vesicles in diabetes and COVID-19. In contrast, we review the cellular/molecular mechanisms by which SARS-CoV-2 infection activates NLRP3 inflammatory vesicles. Finally, we propose several promising targeted NLRP3 inflammatory vesicle inhibitors with the aim of providing a basis for NLRP3-targeted drugs in diabetes combined with noncoronary pneumonia in the clinical management of patients