Transfer Learning for Speech Recognition on a Budget

End-to-end training of automated speech recognition (ASR) systems requires massive data and compute resources. We explore transfer learning based on model adaptation as an approach for training ASR models under constrained GPU memory, throughput and training data. We conduct several systematic experiments adapting a Wav2Letter convolutional neural network originally trained for English ASR to the German language. We show that this technique allows faster training on consumer-grade resources while requiring less training data in order to achieve the same accuracy, thereby lowering the cost of training ASR models in other languages. Model introspection revealed that small adaptations to the network's weights were sufficient for good performance, especially for inner layers.Comment: Accepted for 2nd ACL Workshop on Representation Learning for NL

Anxiety Associated Increased CpG Methylation in the Promoter of Asb1: A Translational Approach Evidenced by Epidemiological and Clinical Studies and a Murine Model

Epigenetic regulation in anxiety is suggested, but evidence from large studies is needed. We conducted an epigenome-wide association study (EWAS) on anxiety in a population-based cohort and validated our finding in a clinical cohort as well as a murine model. In the KORA cohort, participants (n= 1522, age 32–72 years) were administered the Generalized Anxiety Disorder (GAD-7) instrument, whole blood DNA methylation was measured (Illumina 450K BeadChip), and circulating levels of hs-CRP and IL-18 were assessed in the association between anxiety and methylation. DNA methylation was measured using the same instrument in a study of patients with anxiety disorders recruited at the Max Planck Institute of Psychiatry (MPIP, 131 non-medicated cases and 169 controls). To expand our mechanistic understanding, these findings were reverse translated in a mouse model of acute social defeat stress. In the KORA study, participants were classified according to mild, moderate, or severe levels of anxiety (29.4%/6.0%/1.5%, respectively). Severe anxiety was associated with 48.5% increased methylation at a single CpG site (cg12701571) located in the promoter of the gene encoding Asb1 (β-coefficient = 0.56 standard error (SE) =0.10, p (Bonferroni) = 0.005), a protein hypothetically involved in regulation of cytokine signaling. An interaction between IL-18 and severe anxiety with methylation of this CpG cite showed a tendency towards significance in the total population (p =0.083) and a significant interaction among women (p =0.014). Methylation of the same CpG was positively associated with Panic and Agoraphobia scale (PAS) scores (β= 0.005, SE= 0.002, p=0.021, n= 131) among cases in the MPIP study. In a murine model of acute social defeat stress, Asb1 gene expression was significantly upregulated in a tissue-specific manner (p= 0.006), which correlated with upregulation of the neuroimmunomodulating cytokine interleukin 1 beta. Our findings suggest epigenetic regulation of the stress-responsive Asb1 gene in anxiety-related phenotypes. Further studies are necessary to elucidate the causal direction of this association and the potential role of Asb1-mediated immune dysregulation in anxiety disorders

Predicting drug metabolism: experiment and/or computation?

Drug metabolism can produce metabolites with physicochemical and pharmacological properties that differ substantially from those of the parent drug, and consequently has important implications for both drug safety and efficacy. To reduce the risk of costly clinical-stage attrition due to the metabolic characteristics of drug candidates, there is a need for efficient and reliable ways to predict drug metabolism in vitro, in silico and in vivo. In this Perspective, we provide an overview of the state of the art of experimental and computational approaches for investigating drug metabolism. We highlight the scope and limitations of these methods, and indicate strategies to harvest the synergies that result from combining measurement and prediction of drug metabolism.This is the accepted manuscript of a paper published in Nature Reviews Drug Discovery (Kirchmair J, Göller AH, Lang D, Kunze J, Testa B, Wilson ID, Glen RC, Schneider G, Nature Reviews Drug Discovery, 2015, 14, 387–404, doi:10.1038/nrd4581). The final version is available at http://dx.doi.org/10.1038/nrd458

BLOOM: BLoom filter based oblivious outsourced matchings

Whole genome sequencing has become fast, accurate, and cheap, paving the way towards the large-scale collection and processing of human genome data. Unfortunately, this dawning genome era does not only promise tremendous advances in biomedical research but also causes unprecedented privacy risks for the many. Handling storage and processing of large genome datasets through cloud services greatly aggravates these concerns. Current research efforts thus investigate the use of strong cryptographic methods and protocols to implement privacy-preserving genomic computations

Формування конкурентних переваг підприємства в умовах зовнішньоекономічної діяльності

Abstract Background Whole genome sequencing has become fast, accurate, and cheap, paving the way towards the large-scale collection and processing of human genome data. Unfortunately, this dawning genome era does not only promise tremendous advances in biomedical research but also causes unprecedented privacy risks for the many. Handling storage and processing of large genome datasets through cloud services greatly aggravates these concerns. Current research efforts thus investigate the use of strong cryptographic methods and protocols to implement privacy-preserving genomic computations. Methods We propose Fhe-Bloom and Phe-Bloom, two efficient approaches for genetic disease testing using homomorphically encrypted Bloom filters. Both approaches allow the data owner to securely outsource storage and computation to an untrusted cloud. Fhe-Bloom is fully secure in the semi-honest model while Phe-Bloom slightly relaxes security guarantees in a trade-off for highly improved performance. Results We implement and evaluate both approaches on a large dataset of up to 50 patient genomes each with up to 1000000 variations (single nucleotide polymorphisms). For both implementations, overheads scale linearly in the number of patients and variations, while Phe-Bloom is faster by at least three orders of magnitude. For example, testing disease susceptibility of 50 patients with 100000 variations requires only a total of 308.31 s (σ=8.73 s) with our first approach and a mere 0.07 s (σ=0.00 s) with the second. We additionally discuss security guarantees of both approaches and their limitations as well as possible extensions towards more complex query types, e.g., fuzzy or range queries. Conclusions Both approaches handle practical problem sizes efficiently and are easily parallelized to scale with the elastic resources available in the cloud. The fully homomorphic scheme, Fhe-Bloom, realizes a comprehensive outsourcing to the cloud, while the partially homomorphic scheme, Phe-Bloom, trades a slight relaxation of security guarantees against performance improvements by at least three orders of magnitude

Transitions towards new economies? A transformative social innovation perspective

There are numerous social innovation networks and initiatives worldwide with the ambition to contribute to transformative change towards more sustainable, resilient and just societies. Many of these have a specific vision on the economy and relate to alternative visions of a ‘New Economy’. This paper highlights four prominent strands of new economy thinking in state-of-the-art discussions: degrowth, collaborative economy, solidarity economy, and social entrepreneurship. Taking a perspective of transformative social innovation, the paper draws on case studies of 12 social innovation initiatives to analyse how these relate to new economies and to transitions toward new economic arrangements. The 12 cases are analysed in terms of a) how they relate to narratives of change on new economies, b) how they renew social relations, and c) how their new economy arrangements hold potential to challenge established institutional constellations in the existing economy

Nanoparticulate dye-semiconductor hybrid materials formed by electrochemical self-assembly as electrodes in photoelectrochemical cells

Dye-sensitized zinc oxide thin films were prepared, characterized and optimized for applications as photoelectrochemically active electrodes. Conditions were established under which crystalline thin films of ZnO with a porous texture were formed by electrochemically induced crystallization controlled by structure-directing agents (SDA). Dye molecules were adsorbed either directly as SDA during this preparation step or, preferably, following desorption of a SDA. The external quantum efficiency (IPCE) could thereby be increased significantly. Particular emphasis was laid on dye molecules that absorb in the red part of the visible spectrum. Model experiments under ultrahigh vacuum (UHV) conditions with dye molecules adsorbed on defined crystal planes of single crystals aimed at a deeper understanding of the coupling of the chromophore electronic π-system within molecular aggregates and to the semiconductor surface. Detailed photoelectrochemical kinetic measurements were used to characterize and optimize the electrochemically prepared dye-sensitized ZnO films. Parallel electrical characterization in vacuum served to distinguish between contributions of charge transport within the ZnO semiconductor matrix and the ions of the electrolyte in the pore system of the electrode