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

DisLoc: A Convex Partitioning Based Approach for Distributed 3-D Localization in Wireless Sensor Networks

Accurate localization in wireless sensor networks (WSNs) is fundamental to many applications, such as geographic routing and position-aware data processing. This, however, is challenging in large scale 3-D WSNs due to the irregular topology, such as holes in the path, of the network. The irregular topology may cause overestimated Euclidean distance between nodes as the communication path is bent and accordingly introduces severe errors in 3-D WSN localization. As an effort towards the issue, this paper develops a distributed algorithm to achieve accurate 3-D WSN localization. Our proposal is composed of two steps, segmentation and joint localization. In specific, the entire network is first divided into several subnetworks by applying the approximate convex partitioning. A spatial convex node recognition mechanism is developed to assist the network segmentation, which relies on the connectivity information only. After that, each subnetwork is accurately localized by using the multidimensional scaling-based algorithm. The proposed localization algorithm also applies a new 3-D coordinate transformation algorithm, which helps reduce the errors introduced by coordinate integration between subnetworks and improve the localization accuracy. Using extensive simulations, we show that our proposal can effectively segment a complex 3-D sensor network and significantly improve the localization rate in comparison with existing solutions

Analysis of Renewable Energy Research Hotspots and Trends Based on Bibliometric and Patent Survey

In recent years, renewable energy has taken on an increasingly important role as a result of the depletion of traditional fossil fuels and the pressure of climate change. Due to the advantages of clean energy production and wide availability, research on renewable energy has increased worldwide. We collected data from the Web of Science and the Derwent Innovations Index to analyze research trends in the field of renewable energy. It was found that the number of research achievements in this field has developed rapidly worldwide since 2005. The United States ranks first in the quantity and quality of literature and fourth in the number of authorized patents. China ranks second and first regarding the quantity of literature and authorized patents, respectively. Biomass energy, wind energy, and solar energy are trending research topics in various stages of development. China has maintained close cooperation with the United States, the United Kingdom, Australia, and other countries

Truthful Auctions for Automated Bidding in Online Advertising

Automated bidding, an emerging intelligent decision making paradigm powered by machine learning, has become popular in online advertising. Advertisers in automated bidding evaluate the cumulative utilities and have private financial constraints over multiple ad auctions in a long-term period. Based on these distinct features, we consider a new ad auction model for automated bidding: the values of advertisers are public while the financial constraints, such as budget and return on investment (ROI) rate, are private types. We derive the truthfulness conditions with respect to private constraints for this multi-dimensional setting, and demonstrate any feasible allocation rule could be equivalently reduced to a series of non-decreasing functions on budget. However, the resulted allocation mapped from these non-decreasing functions generally follows an irregular shape, making it difficult to obtain a closed-form expression for the auction objective. To overcome this design difficulty, we propose a family of truthful automated bidding auction with personalized rank scores, similar to the Generalized Second-Price (GSP) auction. The intuition behind our design is to leverage personalized rank scores as the criteria to allocate items, and compute a critical ROI to transform the constraints on budget to the same dimension as ROI. The experimental results demonstrate that the proposed auction mechanism outperforms the widely used ad auctions, such as first-price auction and second-price auction, in various automated bidding environments

Neurotransmitter system gene variants as biomarkers for the therapeutic efficacy of rTMS and SSRIs in obsessive-compulsive disorder

PurposeThis study aims to examine the potential influence of RS4680 (COMT), RS16965628 (SLC6A4), and RS1019385 (GRIN2B) polymorphisms on the therapeutic response to repetitive transcranial magnetic stimulation (rTMS) and selective serotonin reuptake inhibitors (SSRIs) in individuals with obsessive-compulsive disorder (OCD).Patients and methodsThirty-six untreated outpatients diagnosed with OCD were recruited and allocated to active or sham rTMS groups for two weeks. The mean age of the participants was 31.61, with 17 males (47.22%) and 19 females (52.78%). Peripheral blood samples (5 mL) were collected from each participant using ethylenediaminetetraacetic acid (EDTA) vacuum tubes for genotyping purposes, clinical evaluation was taken place at baseline and second week.ResultsThe A allele of RS4680, C allele of RS16965628, and GG allele of RS1019385 were identified as potential bio-markers for predicting treatment response to OCD treatments (rTMS & SSRIs).ConclusionThose genes may serve as bio-markers for the combined treatment of rTMS and SSRIs in OCD. The finding hold promise for further research and the potential implementation of precision treatment of OCD.Clinical trial registrationhttps://www.chictr.org.cn, identifier ChiCTR1900023641

IRGen: Generative Modeling for Image Retrieval

While generative modeling has been ubiquitous in natural language processing and computer vision, its application to image retrieval remains unexplored. In this paper, we recast image retrieval as a form of generative modeling by employing a sequence-to-sequence model, contributing to the current unified theme. Our framework, IRGen, is a unified model that enables end-to-end differentiable search, thus achieving superior performance thanks to direct optimization. While developing IRGen we tackle the key technical challenge of converting an image into quite a short sequence of semantic units in order to enable efficient and effective retrieval. Empirical experiments demonstrate that our model yields significant improvement over three commonly used benchmarks, for example, 22.9\% higher than the best baseline method in precision@10 on In-shop dataset with comparable recall@10 score

Differences between Chronically Hepatitis B Virus-Infected Pregnant Women with and without Intrafamilial Infection: From Viral Gene Sequences to Clinical Manifestations

Introduction: This study aimed to investigate the differences between pregnant women with chronic hepatitis B virus (HBV) infection and intrafamilial infection and those without intrafamilial infection. Methods: HBV-DNA was extracted from the sera of 16 pregnant women with chronic hepatitis B (CHB) and their family members for gene sequencing and phylogenetic analyses. A total of 74 pregnant women with CHB were followed up from the second trimester to 3 months postpartum. Viral markers and other laboratory indicators were compared between pregnant women with CHB with and without intrafamilial infection. Results: The phylogenetic tree showed that HBV lines in the mother-spread pedigree shared a node, whereas there was an unrelated genetic background for HBV lines in individuals without intrafamilial infection. From delivery to 3 months postpartum, compared with those without intrafamilial infection, pregnant women with intrafamilial infection were related negatively to HBV-DNA (β = −0.43, 95% confidence interval [CI]: −0.76 to −0.12, p = 0.009), HBeAg (β = −195.15, 95% CI: −366.35 to −23.96, p = 0.027), and hemoglobin changes (β = −8.09, 95% CI: −15.54 to −0.64, p = 0.035) and positively to changes in the levels of alanine aminotransferase (β = 73.9, 95% CI: 38.92–108.95, p < 0.001) and albumin (β = 2.73, 95% CI: 0.23–5.23, p = 0.033). Conclusion: The mother-spread pedigree spread model differs from that of non-intrafamilial infections. Pregnant women with intrafamilial HBV infection have less hepatitis flares and liver damage, but their HBV-DNA and HBeAg levels rebound faster after delivery, than those without intrafamilial infection by the virus

Regulation of serotonin production by specific microbes from piglet gut

Abstract Background Serotonin is an important signaling molecule that regulates secretory and sensory functions in the gut. Gut microbiota has been demonstrated to affect serotonin synthesis in rodent models. However, how gut microbes regulate intestinal serotonin production in piglets remains vague. To investigate the relationship between microbiota and serotonin specifically in the colon, microbial composition and serotonin concentration were analyzed in ileum-cannulated piglets subjected to antibiotic infusion from the ileum when comparing with saline infusion. Microbes that correlated positively with serotonin production were isolated from piglet colon and were further used to investigate the regulation mechanisms on serotonin production in IPEC-J2 and a putative enterochromaffin cell line RIN-14B cells. Results Antibiotic infusion increased quantities of Lactobacillus amylovorus (LA) that positively correlated with increased serotonin concentrations in the colon, while no effects observed for Limosilactobacillus reuteri (LR). To understand how microbes regulate serotonin, representative strains of LA, LR, and Streptococcus alactolyticus (SA, enriched in feces from prior observation) were selected for cell culture studies. Compared to the control group, LA, LR and SA supernatants significantly up-regulated tryptophan hydroxylase 1 (TPH1) expression and promoted serotonin production in IPEC-J2 cells, while in RIN-14B cells only LA exerted similar action. To investigate potential mechanisms mediated by microbe-derived molecules, microbial metabolites including lactate, acetate, glutamine, and γ-aminobutyric acid were selected for cell treatment based on computational and metabolite profiling in bacterial supernatant. Among these metabolites, acetate upregulated the expression of free fatty acid receptor 3 and TPH1 while downregulated indoleamine 2,3-dioxygenase 1. Similar effects were also recapitulated when treating the cells with AR420626, an agonist targeting free fatty acid receptor 3. Conclusions Overall, these results suggest that Lactobacillus amylovorus showed a positive correlation with serotonin production in the pig gut and exhibited a remarkable ability to regulate serotonin production in cell cultures. These findings provide evidence that microbial metabolites mediate the dialogue between microbes and host, which reveals a potential approach using microbial manipulation to regulate intestinal serotonin biosynthesis