Adversarial Purification of Information Masking

Adversarial attacks meticulously generate minuscule, imperceptible perturbations to images to deceive neural networks. Counteracting these, adversarial purification methods seek to transform adversarial input samples into clean output images to defend against adversarial attacks. Nonetheless, extent generative models fail to effectively eliminate adversarial perturbations, yielding less-than-ideal purification results. We emphasize the potential threat of residual adversarial perturbations to target models, quantitatively establishing a relationship between perturbation scale and attack capability. Notably, the residual perturbations on the purified image primarily stem from the same-position patch and similar patches of the adversarial sample. We propose a novel adversarial purification approach named Information Mask Purification (IMPure), aims to extensively eliminate adversarial perturbations. To obtain an adversarial sample, we first mask part of the patches information, then reconstruct the patches to resist adversarial perturbations from the patches. We reconstruct all patches in parallel to obtain a cohesive image. Then, in order to protect the purified samples against potential similar regional perturbations, we simulate this risk by randomly mixing the purified samples with the input samples before inputting them into the feature extraction network. Finally, we establish a combined constraint of pixel loss and perceptual loss to augment the model's reconstruction adaptability. Extensive experiments on the ImageNet dataset with three classifier models demonstrate that our approach achieves state-of-the-art results against nine adversarial attack methods. Implementation code and pre-trained weights can be accessed at \textcolor{blue}{https://github.com/NoWindButRain/IMPure}

Acute suppurative thyroiditis with thyroid metastasis from oesophageal cancer

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Job Satisfaction Among Methadone Maintenance Treatment Clinic Service Providers in Jiangsu, China: A Cross-sectional Survey.

ObjectiveService providers' job satisfaction is critical to the stability of the work force and thereby the effectiveness of methadone maintenance treatment (MMT) programs. This study aimed to explore MMT clinic service providers' job satisfaction and associated factors in Jiangsu, China.MethodsThis secondary study used baseline data of a randomized interventional trial implemented in Jiangsu, China. A survey was conducted among 76 MMT service providers using the computer-assisted self-interview (CASI) method. Job satisfaction responses were assessed via a 30-item scale, with a higher score indicating a higher level of job satisfaction. Perceived institutional support and perceived stigma due to working with drug users were measured using a 9-item scale. Correlation and multiple linear regression analyses were performed to identify factors associated with job satisfaction.ResultsCorrelation analyses found a significant association between job satisfaction and having professional experience in the prevention and control of HIV, other sexually transmitted infections, or other infectious diseases (P = 0.046). Multiple regression analyses revealed that working at MMT clinics affiliated with Center for Disease Control and Prevention sites was associated with a lower level of job satisfaction (P = 0.014), and perception of greater institutional support (P = 0.001) was associated with a higher level of job satisfaction.ConclusionJob satisfaction among MMT clinic service providers was moderate in our study. Our findings suggest that institutional support for providers should be improved, and that acquisition of additional expertise should be encouraged

NF-Atlas: Multi-Volume Neural Feature Fields for Large Scale LiDAR Mapping

LiDAR Mapping has been a long-standing problem in robotics. Recent progress in neural implicit representation has brought new opportunities to robotic mapping. In this paper, we propose the multi-volume neural feature fields, called NF-Atlas, which bridge the neural feature volumes with pose graph optimization. By regarding the neural feature volume as pose graph nodes and the relative pose between volumes as pose graph edges, the entire neural feature field becomes both locally rigid and globally elastic. Locally, the neural feature volume employs a sparse feature Octree and a small MLP to encode the submap SDF with an option of semantics. Learning the map using this structure allows for end-to-end solving of maximum a posteriori (MAP) based probabilistic mapping. Globally, the map is built volume by volume independently, avoiding catastrophic forgetting when mapping incrementally. Furthermore, when a loop closure occurs, with the elastic pose graph based representation, only updating the origin of neural volumes is required without remapping. Finally, these functionalities of NF-Atlas are validated. Thanks to the sparsity and the optimization based formulation, NF-Atlas shows competitive performance in terms of accuracy, efficiency and memory usage on both simulation and real-world datasets

Ensemble Quadratic Assignment Network for Graph Matching

Graph matching is a commonly used technique in computer vision and pattern recognition. Recent data-driven approaches have improved the graph matching accuracy remarkably, whereas some traditional algorithm-based methods are more robust to feature noises, outlier nodes, and global transformation (e.g.~rotation). In this paper, we propose a graph neural network (GNN) based approach to combine the advantages of data-driven and traditional methods. In the GNN framework, we transform traditional graph-matching solvers as single-channel GNNs on the association graph and extend the single-channel architecture to the multi-channel network. The proposed model can be seen as an ensemble method that fuses multiple algorithms at every iteration. Instead of averaging the estimates at the end of the ensemble, in our approach, the independent iterations of the ensembled algorithms exchange their information after each iteration via a 1x1 channel-wise convolution layer. Experiments show that our model improves the performance of traditional algorithms significantly. In addition, we propose a random sampling strategy to reduce the computational complexity and GPU memory usage, so the model applies to matching graphs with thousands of nodes. We evaluate the performance of our method on three tasks: geometric graph matching, semantic feature matching, and few-shot 3D shape classification. The proposed model performs comparably or outperforms the best existing GNN-based methods.Comment: Accepted by IJCV in 202

A Substrate Scheduler for Compiling Arbitrary Fault-tolerant Graph States

Graph states are useful computational resources in quantum computing, particularly in measurement-based quantum computing models. However, compiling arbitrary graph states into executable form for fault-tolerant surface code execution and accurately estimating the compilation cost and the run-time resource cost remains an open problem. We introduce the Substrate Scheduler, a compiler module designed for fault-tolerant graph state compilation. The Substrate Scheduler aims to minimize the space-time volume cost of generating graph states. We show that Substrate Scheduler can efficiently compile graph states with thousands of vertices for "A Game of Surface Codes"-style patch-based surface code systems. Our results show that our module generates graph states with the lowest execution time complexity to date, achieving graph state generation time complexity that is at or below linear in the number of vertices and demonstrating specific types of graphs to have constant generation time complexity. Moreover, it provides a solid foundation for developing compilers that can handle a larger number of vertices, up to the millions or billions needed to accommodate a wide range of post-classical quantum computing applications.Comment: 11 pages, 11 figure

Secure Inter-domain Routing and Forwarding via Verifiable Forwarding Commitments

The Internet inter-domain routing system is vulnerable. On the control plane, the de facto Border Gateway Protocol (BGP) does not have built-in mechanisms to authenticate routing announcements, so an adversary can announce virtually arbitrary paths to hijack network traffic; on the data plane, it is difficult to ensure that actual forwarding path complies with the control plane decisions. The community has proposed significant research to secure the routing system. Yet, existing secure BGP protocols (e.g., BGPsec) are not incrementally deployable, and existing path authorization protocols are not compatible with the current Internet routing infrastructure. In this paper, we propose FC-BGP, the first secure Internet inter-domain routing system that can simultaneously authenticate BGP announcements and validate data plane forwarding in an efficient and incrementally-deployable manner. FC-BGP is built upon a novel primitive, name Forwarding Commitment, to certify an AS's routing intent on its directly connected hops. We analyze the security benefits of FC-BGP in the Internet at different deployment rates. Further, we implement a prototype of FC-BGP and extensively evaluate it over a large-scale overlay network with 100 virtual machines deployed globally. The results demonstrate that FC-BGP saves roughly 55% of the overhead required to validate BGP announcements compared with BGPsec, and meanwhile FC-BGP introduces a small overhead for building a globally-consistent view on the desirable forwarding paths.Comment: 16 pages, 17 figure

Golgi-associated LC3 lipidation requires V-ATPase in noncanonical autophagy

Autophagy is an evolutionarily conserved catabolic process by which cells degrade intracellular proteins and organelles in the lysosomes. Canonical autophagy requires all autophagy proteins (ATGs), whereas noncanonical autophagy is activated by diverse agents in which some of the essential autophagy proteins are dispensable. How noncanonical autophagy is induced and/or inhibited is still largely unclear. In this study, we demonstrated that AMDE-1, a recently identified chemical that can induce canonical autophagy, was able to elicit noncanonical autophagy that is independent of the ULK1 (unc-51-like kinase 1) complex and the Beclin1 complex. AMDE-1-induced noncanonical autophagy could be specifically suppressed by various V-ATPase (vacuolar-type H(+)-ATPase) inhibitors, but not by disturbance of the lysosome function or the intracellular ion redistribution. Similar findings were applicable to a diverse group of stimuli that can induce noncanonical autophagy in a FIP200-independent manner. AMDE-1-induced LC3 lipidation was colocalized with the Golgi complex, and was inhibited by the disturbance of Golgi complex. The integrity of the Golgi complex was also required for multiple other agents to stimulate noncanonical LC3 lipidation. These results suggest that the Golgi complex may serve as a membrane platform for noncanonical autophagy where V-ATPase is a key player. V-ATPase inhibitors could be useful tools for studying noncanonical autophagy

The altered intrinsic functional connectivity after acupuncture at shenmen (HT7) in acute sleep deprivation

IntroductionAccumulating evidence has shown that acupuncture could significantly improve the sleep quality and cognitive function of individuals suffering from insufficient sleep. Numerous animal studies have confirmed the effects and mechanisms of acupuncture on acute sleep deprivation (SD). However, the role of acupuncture on individuals after acute SD remains unclear.MethodsIn the current study, we recruited 30 healthy subjects with regular sleep. All subjects received resting-state fMRI scans during the rested wakefulness (RW) state and after 24 h of total SD. The scan after 24 h of total SD included two resting-state fMRI sessions before and after needling at Shenmen (HT7). Both edge-based and large-scale network FCs were calculated.ResultsThe edge-based results showed the suprathreshold edges with abnormal between-network FC involving all paired networks except somatosensory motor network (SMN)-SCN between the SD and RW state, while both decreased and increased between-network FC of edges involving all paired networks except frontoparietal network (FPN)-subcortical network (SCN) between before and after acupuncture at HT7. Compared with the RW state, the large-scale brain network results showed decreased between-network FC in SMN-Default Mode Network (DMN), SMN-FPN, and SMN-ventral attention network (VAN), and increased between-network FC in Dorsal Attention Network (DAN)-VAN, DAN-SMN between the RW state and after 24 h of total SD. After acupuncture at HT7, the large-scale brain network results showed decreased between-network FC in DAN-VAN and increased between-network FC in SMN-VAN.ConclusionAcupuncture could widely modulate extensive brain networks and reverse the specific between-network FC. The altered FC after acupuncture at HT7 may provide new evidence to interpret neuroimaging mechanisms of the acupuncture effect on acute SD