Weakly-Supervised Video Anomaly Detection with Snippet Anomalous Attention

With a focus on abnormal events contained within untrimmed videos, there is increasing interest among researchers in video anomaly detection. Among different video anomaly detection scenarios, weakly-supervised video anomaly detection poses a significant challenge as it lacks frame-wise labels during the training stage, only relying on video-level labels as coarse supervision. Previous methods have made attempts to either learn discriminative features in an end-to-end manner or employ a twostage self-training strategy to generate snippet-level pseudo labels. However, both approaches have certain limitations. The former tends to overlook informative features at the snippet level, while the latter can be susceptible to noises. In this paper, we propose an Anomalous Attention mechanism for weakly-supervised anomaly detection to tackle the aforementioned problems. Our approach takes into account snippet-level encoded features without the supervision of pseudo labels. Specifically, our approach first generates snippet-level anomalous attention and then feeds it together with original anomaly scores into a Multi-branch Supervision Module. The module learns different areas of the video, including areas that are challenging to detect, and also assists the attention optimization. Experiments on benchmark datasets XDViolence and UCF-Crime verify the effectiveness of our method. Besides, thanks to the proposed snippet-level attention, we obtain a more precise anomaly localization

Nesterov smoothing for sampling without smoothness

We study the problem of sampling from a target distribution in $\mathbb{R}^d$ whose potential is not smooth. Compared with the sampling problem with smooth potentials, this problem is much less well-understood due to the lack of smoothness. In this paper, we propose a novel sampling algorithm for a class of non-smooth potentials by first approximating them by smooth potentials using a technique that is akin to Nesterov smoothing. We then utilize sampling algorithms on the smooth potentials to generate approximate samples from the original non-smooth potentials. We select an appropriate smoothing intensity to ensure that the distance between the smoothed and un-smoothed distributions is minimal, thereby guaranteeing the algorithm's accuracy. Hence we obtain non-asymptotic convergence results based on existing analysis of smooth sampling. We verify our convergence result on a synthetic example and apply our method to improve the worst-case performance of Bayesian inference on a real-world example

SecureBoost Hyperparameter Tuning via Multi-Objective Federated Learning

SecureBoost is a tree-boosting algorithm leveraging homomorphic encryption to protect data privacy in vertical federated learning setting. It is widely used in fields such as finance and healthcare due to its interpretability, effectiveness, and privacy-preserving capability. However, SecureBoost suffers from high computational complexity and risk of label leakage. To harness the full potential of SecureBoost, hyperparameters of SecureBoost should be carefully chosen to strike an optimal balance between utility, efficiency, and privacy. Existing methods either set hyperparameters empirically or heuristically, which are far from optimal. To fill this gap, we propose a Constrained Multi-Objective SecureBoost (CMOSB) algorithm to find Pareto optimal solutions that each solution is a set of hyperparameters achieving optimal tradeoff between utility loss, training cost, and privacy leakage. We design measurements of the three objectives. In particular, the privacy leakage is measured using our proposed instance clustering attack. Experimental results demonstrate that the CMOSB yields not only hyperparameters superior to the baseline but also optimal sets of hyperparameters that can support the flexible requirements of FL participants.Comment: FL-ICAI'2

CryptoLight: An Electro-Optical Accelerator for Fully Homomorphic Encryption

Fully homomorphic encryption (FHE) protects data privacy in cloud computing by enabling computations to directly occur on ciphertexts. Although the speed of computationally expensive FHE operations can be significantly boosted by prior ASIC-based FHE accelerators, the performance of key-switching, the dominate primitive in various FHE operations, is seriously limited by their small bit-width datapaths and frequent matrix transpositions. In this paper, we present an electro-optical (EO) FHE accelerator, CryptoLight, to accelerate FHE operations. Its 512-bit datapath supporting 510-bit residues greatly reduces the key-switching cost. We also create an in-scratchpad-memory transpose unit to fast transpose matrices. Compared to prior FHE accelerators, on average, CryptoLight reduces the latency of various FHE applications by >94.4% and the energy consumption by >95%.Comment: 6 pages, 8 figure

Genomic and enzymatic evidence of acetogenesis by anaerobic methanotrophic archaea

Anaerobic oxidation of methane (AOM) mediated by anaerobic methanotrophic archaea (ANME) is the primary process that provides energy to cold seep ecosystems by converting methane into inorganic carbon. Notably, cold seep ecosystems are dominated by highly divergent heterotrophic microorganisms. The role of the AOM process in supporting heterotrophic population remains unknown. We investigate the acetogenic capacity of ANME-2a in a simulated cold seep ecosystem using high-pressure biotechnology, where both AOM activity and acetate production are detected. The production of acetate from methane is confirmed by isotope-labeling experiments. A complete archaeal acetogenesis pathway is identified in the ANME-2a genome, and apparent acetogenic activity of the key enzymes ADP-forming acetate-CoA ligase and acetyl-CoA synthetase is demonstrated. Here, we propose a modified model of carbon cycling in cold seeps: during AOM process, methane can be converted into organic carbon, such as acetate, which further fuels the heterotrophic community in the ecosystem. Ocean cold seeps are poorly understood relative to related systems like hydrothermal vents. Here the authors use high pressure bioreactors and microbial communities from a cold seep mud volcano and find a previously missing step of methane conversion to acetate that likely fuels heterotrophic communities

A Guanosine-Quadruplex Hydrogel as Cascade Reaction Container Consuming Endogenous Glucose for Infected Wound Treatment-A Study in Diabetic Mice

Diabetic foot ulcers infected with antibiotic‐resistant bacteria form a severe complication of diabetes. Antimicrobial‐loaded hydrogels are used as a dressing for infected wounds, but the ongoing rise in the number of antimicrobial‐resistant infections necessitates new, nonantibiotic based designs. Here, a guanosine‐quadruplex (G(4))‐hydrogel composed of guanosine, 2‐formylphenylboronic acid, and putrescine is designed and used as a cascade‐reaction container. The G(4)‐hydrogel is loaded with glucose‐oxidase and hemin. The first cascade‐reaction, initiated by glucose‐oxidase, transforms glucose and O(2) into gluconic acid and H(2)O(2). In vitro, this reaction is most influential on killing Staphylococcus aureus or Pseudomonas aeruginosa in suspension, but showed limited killing of bacteria in biofilm‐modes of growth. The second cascade‐reaction, however, transforming H(2)O(2) into reactive‐oxygen‐species (ROS), also enhances killing of biofilm bacteria due to hemin penetration into biofilms and interaction with eDNA G‐quadruplexes in the biofilm matrix. Therewith, the second cascade‐reaction generates ROS close to the target bacteria, facilitating killing despite the short life‐time of ROS. Healing of infected wounds in diabetic mice proceeds faster upon coverage by these G(4)‐hydrogels than by clinically common ciprofloxacin irrigation. Moreover, local glucose concentrations around infected wounds decrease. Concluding, a G(4)‐hydrogel loaded with glucose‐oxidase and hemin is a good candidate for infected wound dressings, particularly in diabetic patients

Ferroelastic-switching-driven colossal shear strain and piezoelectricity in a hybrid ferroelectric

Materials that can produce large controllable strains are widely used in shape memory devices, actuators and sensors. Great efforts have been made to improve the strain outputs of various material systems. Among them, ferroelastic transitions underpin giant reversible strains in electrically-driven ferro/piezoelectrics and thermally- or magneticallydriven shape memory alloys. However, large-strain ferroelastic switching in conventional ferroelectrics is very challenging while magnetic and thermal controls are not desirable for applications. Here, we demonstrate an unprecedentedly large shear strain up to 21.5 % in a hybrid ferroelectric, C6H5N(CH3)3CdCl3. The strain response is about two orders of magnitude higher than those of top-performing conventional ferroelectric polymers and oxides. It is achieved via inorganic bond switching and facilitated by the structural confinement of the large organic moieties, which prevents the undesired 180-degree polarization switching. Furthermore, Br substitution can effectively soften the bonds and result in giant shear piezoelectric coefficient (d35 ~ 4800 pm/V) in Br-rich end of the solid solution, C6H5N(CH3)3CdBr3xCl3(1-x). The superior electromechanical properties of the compounds promise their potential in lightweight and high energy density devices, and the strategy described here should inspire the development of next-generation piezoelectrics and electroactive materials based on hybrid ferroelectrics.Comment: 32 pages, 14 figures, 5 table

The CircHAS2/RPL23/MMP9 Axis Facilitates Brain Tumor Metastasis

Background: Circular RNAs (circRNAs) regulate tumor development by interacting with microRNAs. However, limited research has been conducted on the roles of circRNAs in gliomas. Therefore, we sought to demonstrate the function and molecular mechanism of circHAS2 in gliomas. Methods: CircHAS2, hsa-miR-508-3p, RPL23, and MMP9 mRNA levels were assessed with qRT-PCR. RPL23 and MMP9 protein levels were determined with western blotting and immunohistochemical staining. Glioma cell migration and invasion were assessed with Transwell assays. The interaction between hsa-miR-508-3p and circHAS2 or RPL23 was predicted with RNAhybrid and miRanda, and confirmed through luciferase reporter assays. The effects of circHAS2 on glioma cells were demonstrated in a nude mouse orthotopic xenograft glioma model. Results: We computationally analyzed the differentially expressed circRNAs in glioma tissues by using the GEO database. The screening indicated that circHAS2 was located primarily in the cytoplasm. Functionally, silencing of circHAS2 inhibited glioma migration and invasion. Mechanically, hsa-miR-508-3p was identified as a downstream target of circHAS2. CircHAS2 was found to regulate RPL23 and influence MMP9 via hsa-miR-508-3p, thereby promoting glioma migration and invasion. Moreover, inhibition of circHAS2 impeded the progression of U87 glioma cells in vivo. Conclusion: CircHAS2 regulates RPL23 and subsequent MMP9 expression by sponging hsa-miR508-3p in glioma cells