Analysis On Illegal Crossing Behavior of Pedestrians At Signalized Intersections Based On Bayesian Network

Pedestrians do not always comply with the crossing rules of when and/or where to cross the road at signalized intersections. This risky behavior tends to undermine greatly the effectiveness of safety countermeasures at such locations. Thus, it is very important to understand illegal behavior to develop more effective and targeting measures. In order to address the problem, this paper aimed to analyze characteristics of illegal crossings and their impact on behavior choice. Firstly, illegal crossing behaviors at signalized intersections were classified into two categories, including “crossing at a red light” and “crossing outside of a crosswalk.” Secondly, two sets of data were collected to understand the behaviors. One set of data was collected from video-based observation conducted at 3 signalized intersections in Guangzhou, China, capturing 3334 valid illegal crossing cases in total. Another set of data, from a questionnaire survey conducted online, resulted in 275 valid responses. Finally, presentational characteristics of illegal crossings at signalized intersection were analyzed and two Bayesian network-based behavior models were developed to investigate the characteristics and their impacts on the two types of illegal crossing behaviors, “crossing at a red light” and “crossing outside of a crosswalk,” respectively. Findings reveal that, (i) illegal crossings occur at various types of signalized intersections, with a higher probability for “crossing outside of a crosswalk” compared to “crossing at a red light;” (ii) Arc routing crossing has the highest probability to occur at signalized intersections compared to other types of out-side-crosswalk crossings. (iii) The location of origin and destination of a pedestrian has a significant effect on crossing outside of a crosswalk, the location of origin and destination of “one is inside of a crosswalk and another is outside of a crosswalk” has a highest proportion. These findings provide better understanding of illegal crossings and their impact factors so that the effectiveness of management and control of pedestrians at signalized intersections can be improved

Optimal Subsampling Bootstrap for Massive Data

The bootstrap is a widely used procedure for statistical inference because of its simplicity and attractive statistical properties. However, the vanilla version of bootstrap is no longer feasible computationally for many modern massive datasets due to the need to repeatedly resample the entire data. Therefore, several improvements to the bootstrap method have been made in recent years, which assess the quality of estimators by subsampling the full dataset before resampling the subsamples. Naturally, the performance of these modern subsampling methods is influenced by tuning parameters such as the size of subsamples, the number of subsamples, and the number of resamples per subsample. In this paper, we develop a novel hyperparameter selection methodology for selecting these tuning parameters. Formulated as an optimization problem to find the optimal value of some measure of accuracy of an estimator subject to computational cost, our framework provides closed-form solutions for the optimal hyperparameter values for subsampled bootstrap, subsampled double bootstrap and bag of little bootstraps, at no or little extra time cost. Using the mean square errors as a proxy of the accuracy measure, we apply our methodology to study, compare and improve the performance of these modern versions of bootstrap developed for massive data through simulation study. The results are promising

A phage-displayed peptide recognizing porcine aminopeptidase N is a potent small molecule inhibitor of PEDV entry

Three phage-displayed peptides designated H, S and F that recognize porcine aminopeptidase N (pAPN), the cellular receptor of porcine transmissible gastroenteritis virus (TGEV) were able to inhibit cell infection by TGEV. These same peptides had no inhibitory effects on infection of Vero cells by porcine epidemic diarrhea virus (PEDV). However, when PEDV, TGEV and porcine pseudorabies virus were incubated with peptide H (HVTTTFAPPPPR), only infection of Vero cells by PEDV was inhibited. Immunofluorescence assays indicated that inhibition of PEDV infection by peptide H was independent of pAPN. Western blots demonstrated that peptide H interacted with PEDV spike protein and that pre-treatment of PEDV with peptide H led to a higher inhibition than synchronous incubation with cells. These results indicate direct interaction with the virus is necessary to inhibit infectivity. Temperature shift assays demonstrated that peptide H inhibited pre-attachment of the virus to the cells

Detecting Review Spam: Challenges and Opportunities

Abstract-Online customer reviews for both products or merchants have greatly affected others' decision making in purchase. Considering the easily accessibility of the reviews and the significant impacts to the retailers, there is an increasing incentive to manipulate the reviews, mostly profit driven. Without proper protection, spam reviews will cause gradual loss of credibility of the reviews and corrupt the entire online review systems eventually. Therefore, review spam detection is considered as the first step towards securing the online review systems. In this paper, we aim to overview existing detection approaches in a systematic way, define key research issues, and articulate future research challenges and opportunities for review spam detection. Index Terms-Review spam, review spammer, spam behav ior

Learning Incremental Triplet Margin for Person Re-identification

Person re-identification (ReID) aims to match people across multiple non-overlapping video cameras deployed at different locations. To address this challenging problem, many metric learning approaches have been proposed, among which triplet loss is one of the state-of-the-arts. In this work, we explore the margin between positive and negative pairs of triplets and prove that large margin is beneficial. In particular, we propose a novel multi-stage training strategy which learns incremental triplet margin and improves triplet loss effectively. Multiple levels of feature maps are exploited to make the learned features more discriminative. Besides, we introduce global hard identity searching method to sample hard identities when generating a training batch. Extensive experiments on Market-1501, CUHK03, and DukeMTMCreID show that our approach yields a performance boost and outperforms most existing state-of-the-art methods.Comment: accepted by AAAI19 as spotligh

Effect of fruit and vegetable concentrates on endothelial function in metabolic syndrome: A randomized controlled trial

<p>Abstract</p> <p>Background and Objective</p> <p>Dehydrated fruit and vegetable concentrates provide an accessible form of phytonutrient supplementation that may offer cardioprotective effects. This study assessed the effects of two blends of encapsulated juice powder concentrates (with and without added berry powders) on endothelial function in persons with metabolic syndrome, a risk factor for type 2 diabetes and cardiovascular disease.</p> <p>Methods</p> <p>Randomized, double blind, placebo controlled crossover clinical trial with three treatment arms. 64 adults with metabolic syndrome were enrolled and received 8-week sequences of each blend of the concentrates and placebo. The primary outcome measure was change in endothelial function (assessed as flow-mediated dilatation of the brachial artery) 2 hr after consuming a 75 g glucose load, after 8-weeks of daily consumption (sustained) or 2 hr after consumption of a single dose (acute). Secondary outcome measures included plasma glucose, serum insulin, serum lipids, and body weight.</p> <p>Results</p> <p>No significant between-group differences in endothelial function with daily treatment for 8 weeks were seen. No other significant treatment effects were discerned in glucose, insulin, lipids, and weight.</p> <p>Conclusion</p> <p>Encapsulated fruit and vegetable juice powder concentrates did not alter insulin or glucose measures in this sample of adults with metabolic syndrome.</p> <p>Trial Registration</p> <p>clinicaltrials.gov <a href="http://www.clinicaltrials.gov/ct2/show/NCT01224743">NCT01224743</a></p