Maximum Relevance Minimum Redundancy Dropout with Informative Kernel Determinantal Point Process.

In recent years, deep neural networks have shown significant progress in computer vision due to their large generalization capacity; however, the overfitting problem ubiquitously threatens the learning process of these highly nonlinear architectures. Dropout is a recent solution to mitigate overfitting that has witnessed significant success in various classification applications. Recently, many efforts have been made to improve the Standard dropout using an unsupervised merit-based semantic selection of neurons in the latent space. However, these studies do not consider the task-relevant information quality and quantity and the diversity of the latent kernels. To solve the challenge of dropping less informative neurons in deep learning, we propose an efficient end-to-end dropout algorithm that selects the most informative neurons with the highest correlation with the target output considering the sparsity in its selection procedure. First, to promote activation diversity, we devise an approach to select the most diverse set of neurons by making use of determinantal point process (DPP) sampling. Furthermore, to incorporate task specificity into deep latent features, a mutual information (MI)-based merit function is developed. Leveraging the proposed MI with DPP sampling, we introduce the novel DPPMI dropout that adaptively adjusts the retention rate of neurons based on their contribution to the neural network task. Empirical studies on real-world classification benchmarks including, MNIST, SVHN, CIFAR10, CIFAR100, demonstrate the superiority of our proposed method over recent state-of-the-art dropout algorithms in the literature.

Fingerprint Liveness Detection in the Presence of Capable Intruders.

Fingerprint liveness detection methods have been developed as an attempt to overcome the vulnerability of fingerprint biometric systems to spoofing attacks. Traditional approaches have been quite optimistic about the behavior of the intruder assuming the use of a previously known material. This assumption has led to the use of supervised techniques to estimate the performance of the methods, using both live and spoof samples to train the predictive models and evaluate each type of fake samples individually. Additionally, the background was often included in the sample representation, completely distorting the decision process. Therefore, we propose that an automatic segmentation step should be performed to isolate the fingerprint from the background and truly decide on the liveness of the fingerprint and not on the characteristics of the background. Also, we argue that one cannot aim to model the fake samples completely since the material used by the intruder is unknown beforehand. We approach the design by modeling the distribution of the live samples and predicting as fake the samples very unlikely according to that model. Our experiments compare the performance of the supervised approaches with the semi-supervised ones that rely solely on the live samples. The results obtained differ from the ones obtained by the more standard approaches which reinforces our conviction that the results in the literature are misleadingly estimating the true vulnerability of the biometric system.

Recurrent duplications of the annexin A1 gene (ANXA1) in autism spectrum disorders.

BACKGROUND: Validating the potential pathogenicity of copy number variants (CNVs) identified in genome-wide studies of autism spectrum disorders (ASD) requires detailed assessment of case/control frequencies, inheritance patterns, clinical correlations, and functional impact. Here, we characterize a small recurrent duplication in the annexin A1 (ANXA1) gene, identified by the Autism Genome Project (AGP) study. METHODS: From the AGP CNV genomic screen in 2,147 ASD individuals, we selected for characterization an ANXA1 gene duplication that was absent in 4,964 population-based controls. We further screened the duplication in a follow-up sample including 1,496 patients and 410 controls, and evaluated clinical correlations and family segregation. Sequencing of exonic/downstream ANXA1 regions was performed in 490 ASD patients for identification of additional variants. RESULTS: The ANXA1 duplication, overlapping the last four exons and 3'UTR region, had an overall prevalence of 11/3,643 (0.30%) in unrelated ASD patients but was not identified in 5,374 controls. Duplication carriers presented no distinctive clinical phenotype. Family analysis showed neuropsychiatric deficits and ASD traits in multiple relatives carrying the duplication, suggestive of a complex genetic inheritance. Sequencing of exonic regions and the 3'UTR identified 11 novel changes, but no obvious variants with clinical significance. CONCLUSIONS: We provide multilevel evidence for a role of ANXA1 in ASD etiology. Given its important role as mediator of glucocorticoid function in a wide variety of brain processes, including neuroprotection, apoptosis, and control of the neuroendocrine system, the results add ANXA1 to the growing list of rare candidate genetic etiological factors for ASD.

Dysfunction of the Heteromeric KV7.3/KV7.5 Potassium Channel is Associated with Autism Spectrum Disorders.

Heterozygous mutations in the KCNQ3 gene on chromosome 8q24 encoding the voltage-gated potassium channel KV7.3 subunit have previously been associated with rolandic epilepsy and idiopathic generalized epilepsy (IGE) including benign neonatal convulsions. We identified a de novo t(3;8) (q21;q24) translocation truncating KCNQ3 in a boy with childhood autism. In addition, we identified a c.1720C > T [p.P574S] nucleotide change in three unrelated individuals with childhood autism and no history of convulsions. This nucleotide change was previously reported in patients with rolandic epilepsy or IGE and has now been annotated as a very rare SNP (rs74582884) in dbSNP. The p.P574S KV7.3 variant significantly reduced potassium current amplitude in Xenopus laevis oocytes when co-expressed with KV7.5 but not with KV7.2 or KV7.4. The nucleotide change did not affect trafficking of heteromeric mutant KV7.3/2, KV7.3/4, or KV7.3/5 channels in HEK 293 cells or primary rat hippocampal neurons. Our results suggest that dysfunction of the heteromeric KV7.3/5 channel is implicated in the pathogenesis of some forms of autism spectrum disorders, epilepsy, and possibly other psychiatric disorders and therefore, KCNQ3 and KCNQ5 are suggested as candidate genes for these disorders.

Genetic and functional analyses of SHANK2 mutations suggest a multiple hit model of autism spectrum disorders.

Autism spectrum disorders (ASD) are a heterogeneous group of neurodevelopmental disorders with a complex inheritance pattern. While many rare variants in synaptic proteins have been identified in patients with ASD, little is known about their effects at the synapse and their interactions with other genetic variations. Here, following the discovery of two de novo SHANK2 deletions by the Autism Genome Project, we identified a novel 421 kb de novo SHANK2 deletion in a patient with autism. We then sequenced SHANK2 in 455 patients with ASD and 431 controls and integrated these results with those reported by Berkel et al. 2010 (n = 396 patients and n = 659 controls). We observed a significant enrichment of variants affecting conserved amino acids in 29 of 851 (3.4%) patients and in 16 of 1,090 (1.5%) controls (P = 0.004, OR = 2.37, 95% CI = 1.23-4.70). In neuronal cell cultures, the variants identified in patients were associated with a reduced synaptic density at dendrites compared to the variants only detected in controls (P = 0.0013). Interestingly, the three patients with de novo SHANK2 deletions also carried inherited CNVs at 15q11-q13 previously associated with neuropsychiatric disorders. In two cases, the nicotinic receptor CHRNA7 was duplicated and in one case the synaptic translation repressor CYFIP1 was deleted. These results strengthen the role of synaptic gene dysfunction in ASD but also highlight the presence of putative modifier genes, which is in keeping with the "multiple hit model" for ASD. A better knowledge of these genetic interactions will be necessary to understand the complex inheritance pattern of ASD.