Contextual anomaly detection in crowded surveillance scenes

This work addresses the problem of detecting human behavioural anomalies in crowded surveillance environments. We focus in particular on the problem of detecting subtle anomalies in a behaviourally heterogeneous surveillance scene. To reach this goal we implement a novel unsupervised context-aware process. We propose and evaluate a method of utilising social context and scene context to improve behaviour analysis. We find that in a crowded scene the application of Mutual Information based social context permits the ability to prevent self-justifying groups and propagate anomalies in a social network, granting a greater anomaly detection capability. Scene context uniformly improves the detection of anomalies in both datasets. The strength of our contextual features is demonstrated by the detection of subtly abnormal behaviours, which otherwise remain indistinguishable from normal behaviour.

The changing epidemiology of Ebstein's anomaly and its relationship with maternal mental health conditions: a European registry-based study

OBJECTIVES: The aim of this study was to describe the epidemiology of Ebstein's anomaly in Europe and its association with maternal health and medication exposure during pregnancy.

DESIGN: We carried out a descriptive epidemiological analysis of population-based data.

SETTING: We included data from 15 European Surveillance of Congenital Anomalies Congenital Anomaly Registries in 12 European countries, with a population of 5.6 million births during 1982-2011. Participants Cases included live births, fetal deaths from 20 weeks gestation, and terminations of pregnancy for fetal anomaly. Main outcome measures We estimated total prevalence per 10,000 births. Odds ratios for exposure to maternal illnesses/medications in the first trimester of pregnancy were calculated by comparing Ebstein's anomaly cases with cardiac and non-cardiac malformed controls, excluding cases with genetic syndromes and adjusting for time period and country.

RESULTS: In total, 264 Ebstein's anomaly cases were recorded; 81% were live births, 2% of which were diagnosed after the 1st year of life; 54% of cases with Ebstein's anomaly or a co-existing congenital anomaly were prenatally diagnosed. Total prevalence rose over time from 0.29 (95% confidence interval (CI) 0.20-0.41) to 0.48 (95% CI 0.40-0.57) (p<0.01). In all, nine cases were exposed to maternal mental health conditions/medications (adjusted odds ratio (adjOR) 2.64, 95% CI 1.33-5.21) compared with cardiac controls. Cases were more likely to be exposed to maternal β-thalassemia (adjOR 10.5, 95% CI 3.13-35.3, n=3) and haemorrhage in early pregnancy (adjOR 1.77, 95% CI 0.93-3.38, n=11) compared with cardiac controls.

CONCLUSIONS: The increasing prevalence of Ebstein's anomaly may be related to better and earlier diagnosis. Our data suggest that Ebstein's anomaly is associated with maternal mental health problems generally rather than lithium or benzodiazepines specifically; therefore, changing or stopping medications may not be preventative. We found new associations requiring confirmation.

Feature Selection for Anomaly Detection Using Optical Emission Spectroscopy

To maintain the pace of development set by Moore's law, production processes in semiconductor manufacturing are becoming more and more complex. The development of efficient and interpretable anomaly detection systems is fundamental to keeping production costs low. As the dimension of process monitoring data can become extremely high anomaly detection systems are impacted by the curse of dimensionality, hence dimensionality reduction plays an important role. Classical dimensionality reduction approaches, such as Principal Component Analysis, generally involve transformations that seek to maximize the explained variance. In datasets with several clusters of correlated variables the contributions of isolated variables to explained variance may be insignificant, with the result that they may not be included in the reduced data representation. It is then not possible to detect an anomaly if it is only reflected in such isolated variables. In this paper we present a new dimensionality reduction technique that takes account of such isolated variables and demonstrate how it can be used to build an interpretable and robust anomaly detection system for Optical Emission Spectroscopy data.

State sum models for quantum gravity

This paper reviews the construction of quantum field theory on a 4-dimensional spacetime by combinatorial methods, and discusses the recent developments in the direction of a combinatorial construction of quantum gravity.

Anomaly Detection in Noisy Images

Finding rare events in multidimensional data is an important detection problem that has applications in many fields, such as risk estimation in insurance industry, finance, flood prediction, medical diagnosis, quality assurance, security, or safety in transportation. The occurrence of such anomalies is so infrequent that there is usually not enough training data to learn an accurate statistical model of the anomaly class. In some cases, such events may have never been observed, so the only information that is available is a set of normal samples and an assumed pairwise similarity function. Such metric may only be known up to a certain number of unspecified parameters, which would either need to be learned from training data, or fixed by a domain expert. Sometimes, the anomalous condition may be formulated algebraically, such as a measure exceeding a predefined threshold, but nuisance variables may complicate the estimation of such a measure. Change detection methods used in time series analysis are not easily extendable to the multidimensional case, where discontinuities are not localized to a single point. On the other hand, in higher dimensions, data exhibits more complex interdependencies, and there is redundancy that could be exploited to adaptively model the normal data. In the first part of this dissertation, we review the theoretical framework for anomaly detection in images and previous anomaly detection work done in the context of crack detection and detection of anomalous components in railway tracks. In the second part, we propose new anomaly detection algorithms. The fact that curvilinear discontinuities in images are sparse with respect to the frame of shearlets, allows us to pose this anomaly detection problem as basis pursuit optimization. Therefore, we pose the problem of detecting curvilinear anomalies in noisy textured images as a blind source separation problem under sparsity constraints, and propose an iterative shrinkage algorithm to solve it. Taking advantage of the parallel nature of this algorithm, we describe how this method can be accelerated using graphical processing units (GPU). Then, we propose a new method for finding defective components on railway tracks using cameras mounted on a train. We describe how to extract features and use a combination of classifiers to solve this problem. Then, we scale anomaly detection to bigger datasets with complex interdependencies. We show that the anomaly detection problem naturally fits in the multitask learning framework. The first task consists of learning a compact representation of the good samples, while the second task consists of learning the anomaly detector. Using deep convolutional neural networks, we show that it is possible to train a deep model with a limited number of anomalous examples. In sequential detection problems, the presence of time-variant nuisance parameters affect the detection performance. In the last part of this dissertation, we present a method for adaptively estimating the threshold of sequential detectors using Extreme Value Theory on a Bayesian framework. Finally, conclusions on the results obtained are provided, followed by a discussion of possible future work.

Geometrical measurements in three-dimensional quantum gravity

A set of observables is described for the topological quantum field theory which describes quantum gravity in three space-time dimensions with positive signature and positive cosmological constant. The simplest examples measure the distances between points, giving spectra and probabilities which have a geometrical interpretation. The observables are related to the evaluation of relativistic spin networks by a Fourier transform.

Monoamniotic twins discordant for body stalk anomaly

Body stalk anomaly is a rare malformation. This anomaly in monozygotic twins is extremely unusual. We describe a case of monoamniotic pregnancy discordant for body stalk anomaly diagnosed at 11 weeks. Ultrasound showed a fetus with a large anterior abdominal wall defect, anomaly of the spine and no evidence of lower extremities and other with a normal morphology. As far as our concern, only three monoamniotic pregnancies discordant for this malformation were reported. Our case represents the fourth reported monoamniotic pregnancy discordant for body stalk anomaly with diagnosis made by ultrasound and the second diagnosed in the first trimester.

Idealized modeling of the role of stability and shear on mesoscale gravity wave evolution

Mesoscale Gravity Waves (MGWs) are large pressure perturbations that form in the presence of a stable layer at the surface either behind Mesoscale Convective Systems (MCSs) in summer or over warm frontal surfaces behind elevated convection in winter. MGWs are associated with damaging winds, moderate to heavy precipitation, and occasional heat bursts at the surface. The forcing mechanism for MGWs in this study is hypothesized to be evaporative cooling occurring behind a convective line. This evaporatively-cooled air generates a downdraft that then depresses the surface-based stable layer and causes pressure decreases, strong wind speeds and MGW genesis. Using the Weather Research and Forecast Model (WRF) version 3.0, evaporative cooling is simulated using an imposed cold thermal. Sensitivity studies examine the response of MGW structure to different thermal and shear profiles where the strength and depth of the inversion are varied, as well as the amount of wind shear. MGWs are characterized in terms of response variables, such as wind speed perturbations (U'), temperature perturbations (T'), pressure perturbations (P'), potential temperature perturbations (Θ'), and the correlation coefficient (R) between U' and P'. Regime Diagrams portray the response of MGW to the above variables in order to better understand the formation, causes, and intensity of MGWs. The results of this study indicate that shallow, weak surface layers coupled with deep, neutral layers above favor the formation of waves of elevation. Conversely, deep strong surface layers coupled with deep, neutral layers above favor the formation of waves of depression. This is also the type of atmospheric setup that tends to produce substantial surface heating at the surface.

'Introducing Dendritic Cells as a Novel Immune-Inspired Algorithm for Anomaly Detection'

Abstract. Dendritic cells are antigen presenting cells that provide a vital link between the innate and adaptive immune system. Research into this family of cells has revealed that they perform the role of coordinating T-cell based immune responses, both reactive and for generating tolerance. We have derived an algorithm based on the functionality of these cells, and have used the signals and differentiation pathways to build a control mechanism for an artificial immune system. We present our algorithmic details in addition to some preliminary results, where the algorithm was applied for the purpose of anomaly detection. We hope that this algorithm will eventually become the key component within a large, distributed immune system, based on sound immunological concepts.

Off-diagonal ekpyrotic scenarios and equivalence of modified, massive and/or Einstein gravity

Using our anholonomic frame deformation method, we show how generic off-diagonal cosmological solutions depending, in general, on all spacetime coordinates and undergoing a phase of ultra-slow contraction can be constructed in massive gravity. In this paper, there are found and studied new classes of locally anisotropic and (in)homogeneous cosmological metrics with open and closed spatial geometries. The late time acceleration is present due to effective cosmological terms induced by nonlinear off-diagonal interactions and graviton mass. The off-diagonal cosmological metrics and related Stückelberg fields are constructed in explicit form up to nonholonomic frame transforms of the Friedmann–Lamaître–Robertson–Walker (FLRW) coordinates. We show that the solutions include matter, graviton mass and other effective sources modeling nonlinear gravitational and matter fields interactions in modified and/or massive gravity, with polarization of physical constants and deformations of metrics, which may explain certain dark energy and dark matter effects. There are stated and analyzed the conditions when such configurations mimic interesting solutions in general relativity and modifications and recast the general Painlevé–Gullstrand and FLRW metrics. Finally, we elaborate on a reconstruction procedure for a subclass of off-diagonal cosmological solutions which describe cyclic and ekpyrotic universes, with an emphasis on open issues and observable signatures.

Low-Volatility Anomaly in Helsinki Stock Exchange

International research shows that low-volatility stocks have beaten high-volatility stocks in terms of returns for decades on multiple markets. This abbreviation from traditional risk-return framework is known as low-volatility anomaly. This study focuses on explaining the anomaly and finding how strongly it appears in NASDAQ OMX Helsinki stock exchange. Data consists of all listed companies starting from 2001 and ending close to 2015. Methodology follows closely Baker and Haugen (2012) by sorting companies into deciles according to 3-month volatility and then calculating monthly returns for these different volatility groups. Annualized return for the lowest volatility decile is 8.85 %, while highest volatility decile destroys wealth at rate of -19.96 % per annum. Results are parallel also in quintiles that represent larger amount of companies and thus dilute outliers. Observation period captures financial crisis of 2007-2008 and European debt crisis, which embodies as low main index annual return of 1 %, but at the same time proves the success of low-volatility strategy. Low-volatility anomaly is driven by multiple reasons such as leverage constrained trading and managerial incentives which both prompt to invest in risky assets, but behavioral matters also have major weight in maintaining the anomaly.

Magnetic Theory and Applications in the Naples Bay (Southern Tyrrhenian Sea, Italy): Magnetic Anomaly Fields and Relationships with Morpho-Structural Lineaments

Magnetic theory and application to a complex volcanic area located in Southern Italy are here discussed showing the example of the Gulf of Naples, located at Southern Italy Tyrrhenian margin. A magnetic anomaly map of the Gulf of Naples has been constructed aimed at highlighting new knowledge on geophysics and volcanology of this area of the Eastern Tyrrhenian margin, characterized by a complex geophysical setting, strongly depending on sea bottom topography. The theoretical aspects of marine magnetometry and multibeam bathymetry have been discussed. Magnetic data processing included the correction of the data for the diurnal variation, the correction of the data for the offset and the leveling of the data as a function of the correction at the cross-points of the navigation lines. Multibeam and single-beam bathymetric data processing has been considered. Magnetic anomaly fields in the Naples Bay have been discussed through a detailed geological interpretation and correlated with main morpho-structural features recognized through morphobathymetric interpretation. Details of magnetic anomalies have been selected, represented and correlated with significant seismic profiles, recorded on the same navigation lines of magnetometry. They include the continental shelf offshore the Somma-Vesuvius volcanic complex, the outer shelf of the Gulf of Pozzuoli offshore the Phlegrean Fields volcanic complex, the relict volcanic banks of Pentapalummo, Nisida and Miseno, the Gaia volcanic bank on the Naples slope, the western slope of the Dohrn canyon, the Magnaghi canyon’s head and the magnetic anomalies among the Ischia and Procida islands.

Analyse des données de la mission Gravity Recovery And Climate Experiment (GRACE) sur des bassins versants choisis au Canada pour la caractérisation des eaux souterraines.

Résumé : Les eaux souterraines ont un impact majeur sur la vie terrestre, les besoins domestiques et le climat. Elles sont aussi un maillon essentiel du cycle hydrologique. Au Canada par exemple, plus de 30 % de la population est tributaire des eaux souterraines pour leur alimentation en eau potable. Ces ressources subissent de nombreuses pressions sous l’influence de certains facteurs comme la salinisation, la contamination et l’épuisement. La variabilité du climat et la demande croissante sur ces ressources imposent l'amélioration de nos connaissances sur les eaux souterraines. L’objectif principal du projet de recherche est d’exploiter les données d’anomalies (TWS) de la mission Gravity Recovery And Climate Experiment (GRACE) pour localiser, quantifier et analyser les variations des eaux souterraines à travers les bassins versants du Bas-Mackenzie, du Saint-Laurent, du Nord-Québec et du Labrador. Il s’agit aussi d’analyser l’influence des cycles d’accumulation et de fonte de neige sur les variations du niveau des eaux souterraines. Pour estimer les variations des eaux souterraines, la connaissance des autres paramètres du bilan hydrologique est nécessaire. Ces paramètres sont estimés à l’aide des sorties du modèles de surface CLM du Système Global d’Assimilation des Données de la Terre (GLDAS). Les données GRACE qui ont été utilisées sont celles acquises durant la période allant de mars 2002 à août 2012. Les résultats ont été évalués à partir d’enregistrements de niveaux piézométriques provenant de 1841 puits localisés dans les aquifères libres du bassin des réseaux de suivi des eaux souterraines au Canada. Les valeurs de rendements spécifiques des différents types d’aquifères de chaque puits et celles des variations mensuelles du niveau d’eau dans ces puits ont été utilisées pour estimer les variations des anomalies des eaux souterraines in-situ. L’étude de corrélation entre les variations des anomalies des eaux souterraines estimées à partir de la combinaison GRACE-GLDAS et celles issues de données in-situ révèle des concordances significatives avec des valeurs de