A fine-grained approach to scene text script identification

This paper focuses on the problem of script identification in unconstrained scenarios. Script identification is an important prerequisite to recognition, and an indispensable condition for automatic text understanding systems designed for multi-language environments. Although widely studied for document images and handwritten documents, it remains an almost unexplored territory for scene text images. We detail a novel method for script identification in natural images that combines convolutional features and the Naive-Bayes Nearest Neighbor classifier. The proposed framework efficiently exploits the discriminative power of small stroke-parts, in a fine-grained classification framework. In addition, we propose a new public benchmark dataset for the evaluation of joint text detection and script identification in natural scenes. Experiments done in this new dataset demonstrate that the proposed method yields state of the art results, while it generalizes well to different datasets and variable number of scripts. The evidence provided shows that multi-lingual scene text recognition in the wild is a viable proposition. Source code of the proposed method is made available online

"'Who are you?' - Learning person specific classifiers from video"

We investigate the problem of automatically labelling faces of characters in TV or movie material with their names, using only weak supervision from automaticallyaligned subtitle and script text. Our previous work (Everingham et al. [8]) demonstrated promising results on the task, but the coverage of the method (proportion of video labelled) and generalization was limited by a restriction to frontal faces and nearest neighbour classification. In this paper we build on that method, extending the coverage greatly by the detection and recognition of characters in profile views. In addition, we make the following contributions: (i) seamless tracking, integration and recognition of profile and frontal detections, and (ii) a character specific multiple kernel classifier which is able to learn the features best able to discriminate between the characters. We report results on seven episodes of the TV series “Buffy the Vampire Slayer”, demonstrating significantly increased coverage and performance with respect to previous methods on this material

A Upf3b-mutant mouse model with behavioral and neurogenesis defects.

Nonsense-mediated RNA decay (NMD) is a highly conserved and selective RNA degradation pathway that acts on RNAs terminating their reading frames in specific contexts. NMD is regulated in a tissue-specific and developmentally controlled manner, raising the possibility that it influences developmental events. Indeed, loss or depletion of NMD factors have been shown to disrupt developmental events in organisms spanning the phylogenetic scale. In humans, mutations in the NMD factor gene, UPF3B, cause intellectual disability (ID) and are strongly associated with autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD) and schizophrenia (SCZ). Here, we report the generation and characterization of mice harboring a null Upf3b allele. These Upf3b-null mice exhibit deficits in fear-conditioned learning, but not spatial learning. Upf3b-null mice also have a profound defect in prepulse inhibition (PPI), a measure of sensorimotor gating commonly deficient in individuals with SCZ and other brain disorders. Consistent with both their PPI and learning defects, cortical pyramidal neurons from Upf3b-null mice display deficient dendritic spine maturation in vivo. In addition, neural stem cells from Upf3b-null mice have impaired ability to undergo differentiation and require prolonged culture to give rise to functional neurons with electrical activity. RNA sequencing (RNAseq) analysis of the frontal cortex identified UPF3B-regulated RNAs, including direct NMD target transcripts encoding proteins with known functions in neural differentiation, maturation and disease. We suggest Upf3b-null mice serve as a novel model system to decipher cellular and molecular defects underlying ID and neurodevelopmental disorders

Integrity static analysis of COTS/SOUP

This paper describes the integrity static analysis approach developed to support the justification of commercial off-the-shelf software (COTS) used in a safety-related system. The static analysis was part of an overall software qualification programme, which also included the work reported in our paper presented at Safecomp 2002. Integrity static analysis focuses on unsafe language constructs and “covert” flows, where one thread can affect the data or control flow of another thread. The analysis addressed two main aspects: the internal integrity of the code (especially for the more critical functions), and the intra-component integrity, checking for covert channels. The analysis process was supported by an aggregation of tools, combined and engineered to support the checks done and to scale as necessary. Integrity static analysis is feasible for industrial scale software, did not require unreasonable resources and we provide data that illustrates its contribution to the software qualification programme