A comparison of model validation techniques for audio-visual speech recognition

This paper implements and compares the performance of a number of techniques proposed for improving the accuracy of Automatic Speech Recognition (ASR) systems. As ASR that uses only speech can be contaminated by environmental noise, in some applications it may improve performance to employ Audio-Visual Speech Recognition (AVSR), in which recognition uses both audio information and mouth movements obtained from a video recording of the speaker’s face region. In this paper, model validation techniques, namely the holdout method, leave-one-out cross validation and bootstrap validation, are implemented to validate the performance of an AVSR system as well as to provide a comparison of the performance of the validation techniques themselves. A new speech data corpus is used, namely the Loughborough University Audio-Visual (LUNA-V) dataset that contains 10 speakers with five sets of samples uttered by each speaker. The database is divided into training and testing sets and processed in manners suitable for the validation techniques under investigation. The performance is evaluated using a range of different signal-to-noise ratio values using a variety of noise types obtained from the NOISEX-92 dataset

Mapping of household vulnerability and identification of adaptation strategies in dryland systems of South Asia, Research Report No. 67

Low amount and high variability of rainfall in South Asian dryland production system have affected the livelihood of small and marginal households. Therefore, a marginal change in the climate could challenge the livelihood resilience of millions of farmers and affect the healthy ecosystem function in South Asia. The CGIAR Consortium Research Program on Dryland Systems (DS) focuses on DS across the world in order to tackle these problems. The overall emphasis of the research involves understanding the problem, identifying and demonstrating technologies and searching for mechanisms to promote the adoption of promising technologies. In South Asia, the program selected six districts in Andhra Pradesh (Anantapur and Kurnool), Karnataka (Bijapur) and Western Rajasthan (Jaisalmer, Barmer, and Jodhpur) as action sites..

Climate Change, Gender and Adaptation Strategies in Dryland Systems of South Asia : A Household Level Analysis in Andhra Pradesh, Karnataka and Rajasthan States of India; Research Report No. 65

There is a blossoming literature on gender and climate change issues broadly focused on the adverse effects of climate condition. These studies are mostly focused on gender inequalities in agrarian communities of developing countries where the dependence on climatic conditions for living is more apparent. Frequently, the issues of climate change, poverty, gender and economic empowerment are addressed as the most prominent in developing regions. In some cases, the studies narrow down the focus to specific weather events like droughts, floods or natural disasters in rural communities. The agrarian communities of backward areas in South Asia are some of the representative examples which are frequently presented in climate change and gender studies..

Quantification of Risk Associated with Technology Adoption in Dryland Systems of South Asia : A Household Level Analysis in Andhra Pradesh, Karnataka and Rajasthan States of India; Research Report No. 66

Climate change increasingly becomes a challenge for smallholder farmers. Strategies that will help farmers to cope with vulnerability are important. These strategies comprise a variety of interventions ranging from technical, institutional to policy. This study is an in-depth analysis of household level climate change shocks, farmers’ perception of vulnerability, adaptation strategies they followed and risk in technology adoption. A baseline survey was conducted in the dryland system action sites in three states of India: Andhra Pradesh (Kurnool and Anantapur districts); Karnataka (Bijapur district) and Rajasthan (Jaisalmer, Barmer and Jodhpur districts) in 2012-13 for 2011-12 production season. A total of 1019 farmers were surveyed..

Empirical evaluation of sustainability of divergent farms in the dryland farming systems of India

The present study argues that there are heterogeneous farm systems within the drylands and each farm system is unique in terms of its livelihood asset and agricultural practice, and therefore in sustainability. Our method is based on household survey data collected from 500 farmers in Anantapur and Kurnool Districts, in Andhra Pradesh State of India, in 2013. We carried out principal component analysis (PCA) with subsequent hierarchical clustering methods to build farm typologies. To evaluate sustainability across these farm typologies, we adopted a framework consisting of economic, social and environmental sustainability pillars and associated indicators. We normalized values of target indicators and employed normative approach to assign different weights to these indicators. Composite sustainability indices (CSI) were then estimated by means of weighted sum of indicators, aggregated and integrated into farm typologies. The results suggested that there were five distinct farm typologies representing farming systems in the study area. The majority of farms (>70%) in the study area are small and extensive (typology 1); marginal and off farm based (typology 2). About 20% of the farms are irrigation based and intensive (typology 3); small and medium and off farm based (typology 4) and irrigation based semi-intensive (typology 5). There was apparent variability among farm typologies in terms of farm structure and functions and composite sustainability indices. Farm typologies 3 and 5 showed significantly higher performances for the social and economic indices, while typologies 2 and 4 had relatively stronger values for environment. These discrepancies support the relevance of integrated farm typology- and CSI approaches in assessing system sustainability and targeting technologies. Universally, for all farm typologies, composite sustainability indices for economic pillar was significantly lower than the social and environment pillars. More than 90% of farmers were in economically less-sustainable class. The correlations between sustainability indices for economic and environment were typology specific. It was strong and positive when aggregated for the whole study systems [all samples (r = 0.183; P < 0.001)] and for agriculture dependent farm typologies (e.g. typologies 1 and 3). This suggests the need to elevate farms economic performance and capacitate them to invest in the environment. These results provide information for policy makers to plan farm typology–context technological interventions and also create baseline information to evaluate sustainability performance in terms of progress made over time

Fast 2D/3D object representation with growing neural gas

This work presents the design of a real-time system to model visual objects with the use of self-organising networks. The architecture of the system addresses multiple computer vision tasks such as image segmentation, optimal parameter estimation and object representation. We first develop a framework for building non-rigid shapes using the growth mechanism of the self-organising maps, and then we define an optimal number of nodes without overfitting or underfitting the network based on the knowledge obtained from information-theoretic considerations. We present experimental results for hands and faces, and we quantitatively evaluate the matching capabilities of the proposed method with the topographic product. The proposed method is easily extensible to 3D objects, as it offers similar features for efficient mesh reconstruction

Citrullination facilitates cross-reactivity of rheumatoid factor with non-IgG1 Fc epitopes in rheumatoid arthritis

Rheumatoid factor (RF) and anti-citrullinated protein antibodies (ACPAs) are the two most prevalent autoantibodies in rheumatoid arthritis (RA), and are thought to have distinct autoantigen targets. Whilst RF targets the Fc region of antibodies, ACPAs target a far broader spectrum of citrullinated peptides. Here we demonstrate significant sequence and structural homology between proposed RF target epitopes in IgG1 Fc and the ACPA target fibrinogen. Two of the three homologous sequences were susceptible to citrullination, and this modification, which occurs extensively in RA, permitted significant cross-reactivity of RF+ patient sera with fibrinogen in both western blots and ELISAs. Crucially, this reactivity was specific to RF as it was absent in RF− patient and healthy control sera, and could be inhibited by pre-incubation with IgG1 Fc. These studies establish fibrinogen as a common target for both RF and ACPAs, and suggest a new mechanism in RF-mediated autoimmune diseases wherein RF may act as a precursor from which the ACPA response evolves

An Integrated Model of Multiple-Condition ChIP-Seq Data Reveals Predeterminants of Cdx2 Binding

Regulatory proteins can bind to different sets of genomic targets in various cell types or conditions. To reliably characterize such condition-specific regulatory binding we introduce MultiGPS, an integrated machine learning approach for the analysis of multiple related ChIP-seq experiments. MultiGPS is based on a generalized Expectation Maximization framework that shares information across multiple experiments for binding event discovery. We demonstrate that our framework enables the simultaneous modeling of sparse condition-specific binding changes, sequence dependence, and replicate-specific noise sources. MultiGPS encourages consistency in reported binding event locations across multiple-condition ChIP-seq datasets and provides accurate estimation of ChIP enrichment levels at each event. MultiGPS's multi-experiment modeling approach thus provides a reliable platform for detecting differential binding enrichment across experimental conditions. We demonstrate the advantages of MultiGPS with an analysis of Cdx2 binding in three distinct developmental contexts. By accurately characterizing condition-specific Cdx2 binding, MultiGPS enables novel insight into the mechanistic basis of Cdx2 site selectivity. Specifically, the condition-specific Cdx2 sites characterized by MultiGPS are highly associated with pre-existing genomic context, suggesting that such sites are pre-determined by cell-specific regulatory architecture. However, MultiGPS-defined condition-independent sites are not predicted by pre-existing regulatory signals, suggesting that Cdx2 can bind to a subset of locations regardless of genomic environment. A summary of this paper appears in the proceedings of the RECOMB 2014 conference, April 2–5.National Science Foundation (U.S.) (Graduate Research Fellowship under Grant 0645960)National Institutes of Health (U.S.) (grant P01 NS055923)Pennsylvania State University. Center for Eukaryotic Gene Regulatio