Mitigating the effects of atmospheric distortion using DT-CWT fusion

This paper describes a new method for mitigating the effects of atmospheric distortion on observed images, particularly airborne turbulence which degrades a region of interest (ROI). In order to provide accurate detail from objects behind the dis-torting layer, a simple and efficient frame selection method is proposed to pick informative ROIs from only good-quality frames. We solve the space-variant distortion problem using region-based fusion based on the Dual Tree Complex Wavelet Transform (DT-CWT). We also propose an object alignment method for pre-processing the ROI since this can exhibit sig-nificant offsets and distortions between frames. Simple haze removal is used as the final step. The proposed method per-forms very well with atmospherically distorted videos and outperforms other existing methods. Index Terms — Image restoration, fusion, DT-CWT 1

The effects of non-unity lewis numbers on turbulent premixed flame interactions in a twin V-flame configuration

The influence of Lewis number on turbulent premixed flame interactions is investigated using Automatic Feature Extraction (AFE) applied to high-resolution flame simulation data. Premixed turbulent twin V-flames under identical turbulence conditions are simulated at global Lewis numbers of 0.4, 0.8, 1.0 and 1.2. Information on the position, frequency and magnitude of the interactions is compared, and the sensitivity of the results to sample interval is discussed. It is found that both the frequency and magnitude of normal type interactions increases with decreasing Lewis number. Counter-normal type interactions become more likely as the Lewis number increases. The variation in both the frequency and the magnitude of the interactions is found to be caused by large-scale changes in flame wrinkling resulting from differences in the thermo-diffusive stability of the flames. During flame interactions thermo-diffusive effects are found to be insignificant due to the separation of time scales.EPSRC funding through grant number EP/F028741/1, and funding from Rolls-Royce is acknowledged.This is an Accepted Manuscript of an article published by Taylor & Francis in Combustion Science and Technology on 16 May 2013, available online: http://wwww.tandfonline.com/10.1080/00102202.2013.763801

Flame interactions in turbulent premixed twin V-flames

Multiple flame-flame interactions in premixed combustion are investigated using Direct Numerical Simulations of twin turbulent V-flames for a range of turbulence intensities and length scales. Interactions are identified using a novel Automatic Feature Extraction (AFE) technique, based on data registration using the Dual-Tree Complex Wavelet Transform. Information on the time, position and type of interactions, and their influence on the flame area is extracted using AFE. Characteristic length and time scales for the interactions are identified. The effect of interactions on the flame brush is quantified through a global stretch rate, defined as the sum of flamelet stretch and interaction stretch contributions. The effects of each interaction type are discussed. It is found that the magnitude of the fluctuations in flamelet and interaction stretch are comparable, and a qualitative sensitivity to turbulence length scale is found for one interaction type. Implications for modelling are discussed.The authors would like to thank Professor R. S. Cant for the use of SENGA2. EPSRC funding through grant number EP/F028741/1, and funding from Rolls-Royce is acknowledged.This is an Accepted Manuscript of an article published by Taylor & Francis in Combustion Science and Technology on 16 January 2013, available online: http://wwww.tandfonline.com/10.1080/00102202.2012.713413

Improvements to deep convolutional neural networks for LVCSR

Deep Convolutional Neural Networks (CNNs) are more powerful than Deep Neural Networks (DNN), as they are able to better reduce spectral variation in the input signal. This has also been confirmed experimentally, with CNNs showing improvements in word error rate (WER) between 4-12% relative compared to DNNs across a variety of LVCSR tasks. In this paper, we describe different methods to further improve CNN performance. First, we conduct a deep analysis comparing limited weight sharing and full weight sharing with state-of-the-art features. Second, we apply various pooling strategies that have shown improvements in computer vision to an LVCSR speech task. Third, we introduce a method to effectively incorporate speaker adaptation, namely fMLLR, into log-mel features. Fourth, we introduce an effective strategy to use dropout during Hessian-free sequence training. We find that with these improvements, particularly with fMLLR and dropout, we are able to achieve an additional 2-3% relative improvement in WER on a 50-hour Broadcast News task over our previous best CNN baseline. On a larger 400-hour BN task, we find an additional 4-5% relative improvement over our previous best CNN baseline.Comment: 6 pages, 1 figur

Streptomycin-induced inflammation enhances Escherichia coli gut colonization through nitrate respiration.

UnlabelledTreatment with streptomycin enhances the growth of human commensal Escherichia coli isolates in the mouse intestine, suggesting that the resident microbial community (microbiota) can inhibit the growth of invading microbes, a phenomenon known as "colonization resistance." However, the precise mechanisms by which streptomycin treatment lowers colonization resistance remain obscure. Here we show that streptomycin treatment rendered mice more susceptible to the development of chemically induced colitis, raising the possibility that the antibiotic might lower colonization resistance by changing mucosal immune responses rather than by preventing microbe-microbe interactions. Investigation of the underlying mechanism revealed a mild inflammatory infiltrate in the cecal mucosa of streptomycin-treated mice, which was accompanied by elevated expression of Nos2, the gene that encodes inducible nitric oxide synthase. In turn, this inflammatory response enhanced the luminal growth of E. coli by nitrate respiration in a Nos2-dependent fashion. These data identify low-level intestinal inflammation as one of the factors responsible for the loss of resistance to E. coli colonization after streptomycin treatment.ImportanceOur intestine is host to a complex microbial community that confers benefits by educating the immune system and providing niche protection. Perturbation of intestinal communities by streptomycin treatment lowers "colonization resistance" through unknown mechanisms. Here we show that streptomycin increases the inflammatory tone of the intestinal mucosa, thereby making the bowel more susceptible to dextran sulfate sodium treatment and boosting the Nos2-dependent growth of commensal Escherichia coli by nitrate respiration. These data point to the generation of alternative electron acceptors as a by-product of the inflammatory host response as an important factor responsible for lowering resistance to colonization by facultative anaerobic bacteria such as E. coli

Multi-Resolution Dual-Tree Wavelet Scattering Network for Signal Classification

This paper introduces a Deep Scattering network that utilizes Dual-Tree complex wavelets to extract multi-scale translation invariant representations from an input signal. The computationally efficient Dual-Tree wavelets decompose the input signal into equally spaced representations over scales. Translation invariance is introduced in the representations by applying a non-linearity over a region followed by averaging. The discriminatory information from the equally spaced locally smooth signal representations aids the learning of the classi- fier. The proposed network is shown to outperform Mallat’s ScatterNet [1] on four datasets with different modalities, both for classification accuracy and computational efficiency.Cambridge Trus

PReS-FINAL-2161: Safety and effectiveness of adalimumab in children with polyarticular juvenile idiopathic arthritis aged 2 to <4 years or >=4 years weighing <15 kg

International audienceEn faisant le tour du monde (Mauritanie, Madagascar, Éthiopie, Burkina Faso, Cameroun, New-York, Nouvelle-Zélande, France... ) en passant par l’Internet, cet ouvrage fait le point sur les dernières innovations en matière de gestion des déchets. Considéré comme une ressource, le déchet révèle enfin sa valeur : il est créateur de revenus, de liens sociaux et de nouvelles technologies. C’est pourquoi il devient urgent de structurer son économie

Atmospheric Turbulence Mitigation using Complex Wavelet-based Fusion

Restoring a scene distorted by atmospheric turbulence is a challenging problem in video surveillance. The effect, caused by random, spatially varying, perturbations, makes a model-based solution difficult and in most cases, impractical. In this paper, we propose a novel method for mitigating the effects of atmospheric distortion on observed images, particularly airborne turbulence which can severely degrade a region of interest (ROI). In order to extract accurate detail about objects behind the distorting layer, a simple and efficient frame selection method is proposed to select informative ROIs only from good-quality frames. The ROIs in each frame are then registered to further reduce offsets and distortions. We solve the space-varying distortion problem using region-level fusion based on the dual tree complex wavelet transform. Finally, contrast enhancement is applied. We further propose a learning-based metric specifically for image quality assessment in the presence of atmospheric distortion. This is capable of estimating quality in both full-and no-reference scenarios. The proposed method is shown to significantly outperform existing methods, providing enhanced situational awareness in a range of surveillance scenarios. © 1992-2012 IEEE

A Hidden Markov Chain Model for the Term Structure of Bond Credit Risk Spreads

This paper provides a Markov chain model for the term structure and credit risk spreads of bond processes. It allows dependency between the stochastic process modeling the interest rate and the Markov chain process describing changes in the credit rating of the bonds by their mutual dependency on a hidden Markov chain. This Markov chain can be thought of as the underlying economic conditions. The model also allows a new interpretation of risk premia used in previous approaches. It also uses a linear programming approach to strip the bonds of their coupons in such a way as to guarantee there is no mis-pricing