2D silhouette and 3D skeletal models for human detection and tracking

In this paper we propose a statistical model for detection and tracking of human silhouette and the corresponding 3D skeletal structure in gait sequences. We follow a point distribution model (PDM) approach using a Principal Component Analysis (PCA). The problem of non-lineal PCA is partially resolved by applying a different PDM depending of pose estimation; frontal, lateral and diagonal, estimated by Fisher's linear discriminant. Additionally, the fitting is carried out by selecting the closest allowable shape from the training set by means of a nearest neighbor classifier. To improve the performance of the model we develop a human gait analysis to take into account temporal dynamic to track the human body. The incorporation of temporal constraints on the model increase reliability and robustness.

Detection of a Red Supergiant Progenitor Star of a Type II-Plateau Supernova

We present the discovery of a red supergiant star that exploded as supernova 2003gd in the nearby spiral galaxy M74. The Hubble Space Telescope (HST) and the Gemini Telescope imaged this galaxy 6 to 9 months before the supernova explosion, and subsequent HST images confirm the positional coincidence of the supernova with a single resolved star that is a red supergiant of 8+4-2 solar masses. This confirms both stellar evolution models and supernova theories predicting that cool red supergiants are the immediate progenitor stars of type II-plateau supernovae.

Improved Process Monitoring Using Nonlinear Principal Component Models

This paper presents two new approaches for use in complete process monitoring. The firstconcerns the identification of nonlinear principal component models. This involves the application of linear
principal component analysis (PCA), prior to the identification of a modified autoassociative neural network (AAN) as the required nonlinear PCA (NLPCA) model. The benefits are that (i) the number of the reduced set of linear principal components (PCs) is smaller than the number of recorded process variables, and (ii) the set of PCs is better conditioned as redundant information is removed. The result is a new set of input data for a modified neural representation, referred to as a T2T network. The T2T NLPCA model is then used for complete process monitoring, involving fault detection, identification and isolation. The second approach introduces a new variable reconstruction algorithm, developed from the T2T NLPCA model. Variable reconstruction can enhance the findings of the contribution charts still widely used in industry by reconstructing the outputs from faulty sensors to produce more accurate fault isolation. These ideas are illustrated using recorded industrial data relating to developing cracks in an industrial glass melter process. A comparison of linear and nonlinear models, together with the combined use of contribution charts and variable reconstruction, is presented.

Application of auto-associative neural networks to transient fault detection in an IC engine

The tailpipe emissions from automotive engines have been subject to steadily reducing legislative limits. This reduction has been achieved through the addition of sub-systems to the basic four-stroke engine which thereby increases its complexity. To ensure the entire system functions correctly, each system and / or sub-systems needs to be continuously monitored for the presence of any faults or malfunctions. This is a requirement detailed within the On-Board Diagnostic (OBD) legislation. To date, a physical model approach has been adopted by me automotive industry for the monitoring requirement of OBD legislation. However, this approach has restrictions from the available knowledge base and computational load required. A neural network technique incorporating Multivariant Statistical Process Control (MSPC) has been proposed as an alternative method of building interrelationships between the measured variables and monitoring the correct operation of the engine. Building upon earlier work for steady state fault detection, this paper details the use of non-linear models based on an Auto-associate Neural Network (ANN) for fault detection under transient engine operation. The theory and use of the technique is shown in this paper with the application to the detection of air leaks within the inlet manifold system of a modern gasoline engine whilst operated on a pseudo-drive cycle. Copyright © 2007 by ASME.

PLS modelling and fault detection on the Tennessee Eastman benchmark

This paper describes the application of multivariate regression techniques to the Tennessee Eastman benchmark process for modelling and fault detection. Two methods are applied : linear partial least squares, and a nonlinear variant of this procedure using a radial basis function inner relation. The performance of the RBF networks is enhanced through the use of a recently developed training algorithm which uses quasi-Newton optimization to ensure an efficient and parsimonious network; details of this algorithm can be found in this paper. The PLS and PLS/RBF methods are then used to create on-line inferential models of delayed process measurements. As these measurements relate to the final product composition, these models suggest that on-line statistical quality control analysis should be possible for this plant. The generation of `soft sensors' for these measurements has the further effect of introducing a redundant element into the system, redundancy which can then be used to generate a fault detection and isolation scheme for these sensors. This is achieved by arranging the sensors and models in a manner comparable to the dedicated estimator scheme of Clarke et al. 1975, IEEE Trans. Pero. Elect. Sys., AES-14R, 465-473. The effectiveness of this scheme is demonstrated on a series of simulated sensor and process faults, with full detection and isolation shown to be possible for sensor malfunctions, and detection feasible in the case of process faults. Suggestions for enhancing the diagnostic capacity in the latter case are covered towards the end of the paper.

Ground-based detection of thermal emission from the exoplanet WASP-19b

We present an occultation of the newly discovered hot Jupiter system WASP-19, observed with the High Acuity Wide-field K-band Imager instrument on the VLT, in order to measure thermal emission from the planet's dayside at ~2µm. The light curve was analysed using a Markov Chain Monte Carlo method to find the eclipse depth and the central transit time. The transit depth was found to be 0.366 +/- 0.072 per cent, corresponding to a brightness temperature of 2540 +/- 180 K. This is significantly higher than the calculated (zero-albedo) equilibrium temperature and indicates that the planet shows poor redistribution of heat to the night side, consistent with models of highly irradiated planets. Further observations are needed to confirm the existence of a temperature inversion and possibly molecular emission lines. The central eclipse time was found to be consistent with a circular orbit.

THE DETECTION OF HOT ETHANOL IN G34.3+0.15

Hot molecular cores in star-forming regions are known to have gas-phase chemical compositions determined by the evaporation of material from the icy mantles of interstellar grains, followed by subsequent reactions in the gas phase. Current models suggest that the evaporated material is rich in hydrogenated species, such as water, methane and methanol. In this paper, we report the detection of 14 rotational transitions of ethanol in the submillimetre spectrum of the molecular cloud associated with the ultra-compact H II region G34.3+0.15. We derive a rotation temperature of 125 K and a beam-averaged column density of 2.0x10(15) cm(-2), corresponding to a fractional abundance on the order of 4x10(-9). This large abundance, which is a lower limit due to the likelihood of beam dilution, cannot be made by purely gas-phase processes, and we conclude that the ethanol must be formed efficiently in the grain surface chemistry. Since it has been argued previously that methanol is formed via surface chemistry, it appears that alcohol formation may be a natural by-product of surface reactions.

VLT DETECTION OF A RED SUPERGIANT PROGENITOR OF THE TYPE II-P SUPERNOVA 2008bk

We report the identification of a source coincident with the position of the nearby Type II-P supernova (SN) 2008bk in high-quality optical and near-infrared preexplosion images from the ESO Very Large Telescope (VLT). The SN position in the optical and near-infrared preexplosion images is identified to within about +/- 70 and +/- 40 mas, respectively, using postexplosion-band images obtained with the NAOS CONICA adaptive optics system K-s on the VLT. The preexplosion source detected in four different bands is precisely coincident with SN 2008bk and is consistent with being dominated by a single point source. We determine the nature of the point source using the STARS stellar evolutionary models and find that its colors and luminosity are consistent with the source being a red supergiant progenitor of SN 2008bk with an initial mass of 8.5 +/- 1.0 M-circle dot.

The WR content of IC10 - first detection of WC9 stars in a low metallicity environment?

We present deep, narrow-hand photometry of the Local Group starburst galaxy IC10. Our dedicated photometric system provides detection of 13 new Wolf-Rayet (WR) stars and allows spectral subtypes to be assigned. Three of these new stars appear to be WC9 subtypes. If confirmed, these would be the very first WC9 stars ever detected in a low metallicity environment, hence putting strong new constraints on the formation and evolution models of massive stars. Eight of the new WR stars are of the WC subtype, which does not significantly modify the anomalously high WC/WN ratio in IC10. However it is likely that a number of Wolf-Rayet stars of the WNE and WC spectral subtypes are still to be discovered in the heart of the galaxy.

Detection of soil compaction using seismic surface waves

Seismic geophysical methods have rarely been used in precision agriculture, predominantly due to the perception that they are slow and results require a complex evaluation. This paper explores the possibility of using a recently developed surface wave seismic geophysical approach, the multichannel analysis of surface waves (MASW) method, for assessment of agricultural compaction. This approach has the advantage of being non-intrusive, rapid and is able to produce 2D ground models with a relatively high density of spatial sampling points. The method, which was tested on a research site in Oakpark, Ireland, detected a significant difference in shear wave velocity between a heavily compacted headland and an uncompacted location. The results from this approach compared favourably with those obtained
from measurements of bulk density and penetrometer resistance and demonstrate that the MASW approach can distinguish between the extreme states of heavily compacted and uncompacted soil.

Modeling continuous self-report measures of perceived emotion using generalized additive mixed models

Emotion research has long been dominated by the “standard method” of displaying posed or acted static images of facial expressions of emotion. While this method has been useful it is unable to investigate the dynamic nature of emotion expression. Although continuous self-report traces have enabled the measurement of dynamic expressions of emotion, a consensus has not been reached on the correct statistical techniques that permit inferences to be made with such measures. We propose Generalized Additive Models and Generalized Additive Mixed Models as techniques that can account for the dynamic nature of such continuous measures. These models allow us to hold constant shared components of responses that are due to perceived emotion across time, while enabling inference concerning linear differences between groups. The mixed model GAMM approach is preferred as it can account for autocorrelation in time series data and allows emotion decoding participants to be modelled as random effects. To increase confidence in linear differences we assess the methods that address interactions between categorical variables and dynamic changes over time. In addition we provide comments on the use of Generalized Additive Models to assess the effect size of shared perceived emotion and discuss sample sizes. Finally we address additional uses, the inference of feature detection, continuous variable interactions, and measurement of ambiguity.

A J-band detection of the sub-stellar mass donor in SDSS J1433+1011

We present time-resolved J-band spectroscopy of the short-period cataclysmic variable SDSS J143317.78+101123.3. We detect absorption lines from the sub-stellar donor star in this system, which contributes 38 +/- 5 per cent to the J-band light. From the relative strengths of the absorption lines in the J band, we estimate the spectral type of the donor star to be L2 +/- 1. These data are the first spectroscopic detection of a donor with a confirmed sub-stellar mass in a cataclysmic variable, and the spectral type is consistent with that expected from semi-empirical evolutionary models.

Using skew mapping, we have been able to derive an estimate for the radial velocity of the donor of K-d = 520 +/- 60 km/s. This value is consistent with, though much less precise than, predictions from mass determinations found via photometric fitting of the eclipse light curves.

Characterization of Bose-Hubbard models with quantum nondemolition measurements

We propose a scheme for the detection of quantum phase transitions in the one-dimensional (1D) Bose-Hubbard (BH) and 1D Extended Bose-Hubbard (EBH) models, using the nondemolition measurement technique of quantum polarization spectroscopy. We use collective measurements of the effective total angular momentum of a particular spatial mode to characterize the Mott insulator to superfluid phase transition in the BH model and the transition to a density wave state in the EBH model. We extend the application of collective measurements to the ground states at various deformations of a superlattice potential.

Optical coherence tomography (OCT) for detection of macular oedema in patients with diabetic retinopathy

Background

Diabetic macular oedema (DMO) is a thickening of the central retina, or the macula, and is associated with long-term visual loss in people with diabetic retinopathy (DR). Clinically significant macular oedema (CSMO) is the most severe form of DMO. Almost 30 years ago, the Early Treatment Diabetic Retinopathy Study (ETDRS) found that CSMO, diagnosed by means of stereoscopic fundus photography, leads to moderate visual loss in one of four people within three years. It also showed that grid or focal laser photocoagulation to the macula halves this risk. Recently, intravitreal injection of antiangiogenic drugs has also been used to try to improve vision in people with macular oedema due to DR.Optical coherence tomography (OCT) is based on optical reflectivity and is able to image retinal thickness and structure producing cross-sectional and three-dimensional images of the central retina. It is widely used because it provides objective and quantitative assessment of macular oedema, unlike the subjectivity of fundus biomicroscopic assessment which is routinely used by ophthalmologists instead of photography. Optical coherence tomography is also used for quantitative follow-up of the effects of treatment of CSMO.

Objectives

To determine the diagnostic accuracy of OCT for detecting DMO and CSMO, defined according to ETDRS in 1985, in patients referred to ophthalmologists after DR is detected. In the update of this review we also aimed to assess whether OCT might be considered the new reference standard for detecting DMO.

Search methods

We searched the Cochrane Database of Systematic Reviews (CDSR), the Database of Abstracts of Reviews of Effects (DARE), the Health Technology Assessment Database (HTA) and the NHS Economic Evaluation Database (NHSEED) (The Cochrane Library 2013, Issue 5), Ovid MEDLINE, Ovid MEDLINE In-Process and Other Non-Indexed Citations, Ovid MEDLINE Daily, Ovid OLDMEDLINE (January 1946 to June 2013), EMBASE (January 1950 to June 2013), Web of Science Conference Proceedings Citation Index - Science (CPCI-S) (January 1990 to June 2013), BIOSIS Previews (January 1969 to June 2013), MEDION and the Aggressive Research Intelligence Facility database (ARIF). We did not use any date or language restrictions in the electronic searches for trials. We last searched the electronic databases on 25 June 2013. We checked bibliographies of relevant studies for additional references.

Selection Criteria

We selected studies that assessed the diagnostic accuracy of any OCT model for detecting DMO or CSMO in patients with DR who were referred to eye clinics. Diabetic macular oedema and CSMO were diagnosed by means of fundus biomicroscopy by ophthalmologists or stereophotography by ophthalmologists or other trained personnel.

Data collection and analysis

Three authors independently extracted data on study characteristics and measures of accuracy. We assessed data using random-effects hierarchical sROC meta-analysis models.

Main results

We included 10 studies (830 participants, 1387 eyes), published between 1998 and 2012. Prevalence of CSMO was 19% to 65% (median 50%) in nine studies with CSMO as the target condition. Study quality was often unclear or at high risk of bias for QUADAS 2 items, specifically regarding study population selection and the exclusion of participants with poor quality images. Applicablity was unclear in all studies since professionals referring patients and results of prior testing were not reported. There was a specific 'unit of analysis' issue because both eyes of the majority of participants were included in the analyses as if they were independent.In nine studies providing data on CSMO (759 participants, 1303 eyes), pooled sensitivity was 0.78 (95% confidence interval (CI) 0.72 to 0.83) and specificity was 0.86 (95% CI 0.76 to 0.93). The median central retinal thickness cut-off we selected for data extraction was 250 µm (range 230 µm to 300 µm). Central CSMO was the target condition in all but two studies and thus our results cannot be applied to non-central CSMO.Data from three studies reporting accuracy for detection of DMO (180 participants, 343 eyes) were not pooled. Sensitivities and specificities were about 0.80 in two studies and were both 1.00 in the third study.Since this review was conceived, the role of OCT has changed and has become a key ingredient of decision-making at all levels of ophthalmic care in this field. Moreover, disagreements between OCT and fundus examination are informative, especially false positives which are referred to as subclinical DMO and are at higher risk of developing clinical CSMO.

Authors' conclusions

Using retinal thickness thresholds lower than 300 µm and ophthalmologist's fundus assessment as reference standard, central retinal thickness measured with OCT was not sufficiently accurate to diagnose the central type of CSMO in patients with DR referred to retina clinics. However, at least OCT false positives are generally cases of subclinical DMO that cannot be detected clinically but still suffer from increased risk of disease progression. Therefore, the increasing availability of OCT devices, together with their precision and the ability to inform on retinal layer structure, now make OCT widely recognised as the new reference standard for assessment of DMO, even in some screening settings. Thus, this review will not be updated further.

A New Android Malware Detection Method Using Bayesian Classification

Mobile malware has been growing in scale and complexity as smartphone usage continues to rise. Android has surpassed other mobile platforms as the most popular whilst also witnessing a dramatic increase in malware targeting the platform. A worrying trend that is emerging is the increasing sophistication of Android malware to evade detection by traditional signature-based scanners. As such, Android app marketplaces remain at risk of hosting malicious apps that could evade detection before being downloaded by unsuspecting users. Hence, in this paper we present an effective approach to alleviate this problem based on Bayesian classification models obtained from static code analysis. The models are built from a collection of code and app characteristics that provide indicators of potential malicious activities. The models are evaluated with real malware samples in the wild and results of experiments are presented to demonstrate the effectiveness of the proposed approach.