Anomaly detection in spatiotemporal data via regularized non-negative tensor analysis

Anomaly detection in multidimensional data is a challenging task. Detecting anomalous mobility patterns in a city needs to take spatial, temporal, and traffic information into consideration. Although existing techniques are able to extract spatiotemporal features for anomaly analysis, few systematic analysis about how different factors contribute to or affect the anomalous patterns has been proposed. In this paper, we propose a novel technique to localize spatiotemporal anomalous events based on tensor decomposition. The proposed method employs a spatial-feature-temporal tensor model and analyzes latent mobility patterns through unsupervised learning. We first train the model based on historical data and then use the model to capture the anomalies, i.e., the mobility patterns that are significantly different from the normal patterns. The proposed technique is evaluated based on the yellow-cab dataset collected from New York City. The results show several interesting latent mobility patterns and traffic anomalies that can be deemed as anomalous events in the city, suggesting the effectiveness of the proposed anomaly detection method

Deep-learning-based identification, tracking, pose estimation and behaviour classification of interacting primates and mice in complex environments

The quantification of behaviors of interest from video data is commonly used to study brain function, the effects of pharmacological interventions, and genetic alterations. Existing approaches lack the capability to analyze the behavior of groups of animals in complex environments. We present a novel deep learning architecture for classifying individual and social animal behavior, even in complex environments directly from raw video frames, while requiring no intervention after initial human supervision. Our behavioral classifier is embedded in a pipeline (SIPEC) that performs segmentation, identification, pose-estimation, and classification of complex behavior, outperforming the state of the art. SIPEC successfully recognizes multiple behaviors of freely moving individual mice as well as socially interacting non-human primates in 3D, using data only from simple mono-vision cameras in home-cage setups

Effectiveness of Pandemic H1N1-2009 Vaccination in Reducing Laboratory Confirmed Influenza Infections among Military Recruits in Tropical Singapore

10.1371/journal.pone.0026572PLoS ONE610

A clinical diagnostic model for predicting influenza among young adult military personnel with febrile respiratory illness in Singapore

10.1371/journal.pone.0017468PLoS ONE63

The reliability, correlation with clinical symptoms and surgical outcomes of dural sac cross-sectional area, nerve root sedimentation sign and morphological grade for lumbar spinal stenosis

Abstract Background No study had directly compared the reliability, correlation with clinical symptoms, and surgical outcomes of dural sac cross-sectional area (DCSA), nerve root sedimentation sign (SedSign), and morphological grade for lumbar spinal stenosis (LSS). Methods From January 2017 to December 2020, 202 patients with LSS were retrospectively analyzed. The narrowest segments were assessed via T2-weighted cross-sectional images using DCSA, morphological grade, and SedSign by two independent observers. Three classifications’ reliabilities were evaluated. Correlations between three classifications and between each of the classifications and symptoms or surgical outcomes 12 months postoperatively were evaluated. Results There were 144 males and 58 females; 23, 52, and 127 patients had the narrowest segment in L2–3, L3–4, and L4–5, respectively. The intra-observer reliability of DCSA ranged from 0.91 to 0.93, and the inter-observer reliability was 0.90. The intra-observer reliability of SedSign ranged from 0.83 to 0.85, and the inter-observer reliability was 0.75. The intra-observer reliability of morphological grade ranged from 0.72 to 0.78, and the inter-observer reliability was 0.61. Each of these classifications was correlated with the other two (P < 0.01). For preoperative symptoms, DCSA was correlated with leg pain (LP) (r =  − 0.14), Oswestry Disability Index (ODI) (r =  − 0.17), and claudication (r =  − 0.19). Morphological grade was correlated with LP (r = 0.19) and claudication (r = 0.27). SedSign was correlated with ODI (r = 0.23). For postoperative outcomes, morphological grade was correlated with LP (r =  − 0.14), and SedSign was correlated with ODI (r = 0.17). Conclusions Substantial to almost perfect intra and inter-observer reliabilities for the three classifications were found; however, these classifications had either weak correlations with symptoms and surgical outcomes or none at all. Based on our findings, using one of them without conducting other tests for LSS will have limited or uncertain value in surgical decision-making or evaluating the prognostic value

Deep-learning-based identification, tracking, pose estimation and behaviour classification of interacting primates and mice in complex environments

ISSN:2522-583

SIPEC: the deep-learning Swiss knife for behavioral data analysis

Analysing the behavior of individuals or groups of animals in complex environments is an important, yet difficult computer vision task. Here we present a novel deep learning architecture for classifying animal behavior and demonstrate how this end-to-end approach can significantly outperform pose estimation-based approaches, whilst requiring no intervention after minimal training. Our behavioral classifier is embedded in a first-of-its-kind pipeline (SIPEC) which performs segmentation, identification, pose-estimation and classification of behavior all automatically. SIPEC successfully recognizes multiple behaviors of freely moving mice as well as socially interacting nonhuman primates in 3D, using data only from simple mono-vision cameras in home-cage setups

Relative risks for influenza infection after 2009-H1N1 vaccination program in Camp A, compared to the preceding period, by influenza subtype.

<p>BCI = Bayesian credible interval.</p