SyGuS-Comp 2016: Results and Analysis

Syntax-Guided Synthesis (SyGuS) is the computational problem of finding an implementation f that meets both a semantic constraint given by a logical formula $\varphi$ in a background theory T, and a syntactic constraint given by a grammar G, which specifies the allowed set of candidate implementations. Such a synthesis problem can be formally defined in SyGuS-IF, a language that is built on top of SMT-LIB. The Syntax-Guided Synthesis Competition (SyGuS-Comp) is an effort to facilitate, bring together and accelerate research and development of efficient solvers for SyGuS by providing a platform for evaluating different synthesis techniques on a comprehensive set of benchmarks. In this year's competition we added a new track devoted to programming by examples. This track consisted of two categories, one using the theory of bit-vectors and one using the theory of strings. This paper presents and analyses the results of SyGuS-Comp'16.Comment: In Proceedings SYNT 2016, arXiv:1611.07178. arXiv admin note: text overlap with arXiv:1602.0117

What value do explicit high level concepts have in vision to language problems?

Much of the recent progress in Vision-to-Language (V2L) problems has been achieved through a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). This approach does not explicitly represent high-level semantic concepts, but rather seeks to progress directly from image features to text. We propose here a method of incorporating high-level concepts into the very successful CNN-RNN approach, and show that it achieves a significant improvement on the state-of-the-art performance in both image captioning and visual question answering. We also show that the same mechanism can be used to introduce external semantic information and that doing so further improves performance. In doing so we provide an analysis of the value of high level semantic information in V2L problems.Comment: Accepted to IEEE Conf. Computer Vision and Pattern Recognition 2016. Fixed titl

Parameterized Model-Checking for Timed-Systems with Conjunctive Guards (Extended Version)

In this work we extend the Emerson and Kahlon's cutoff theorems for process skeletons with conjunctive guards to Parameterized Networks of Timed Automata, i.e. systems obtained by an \emph{apriori} unknown number of Timed Automata instantiated from a finite set $U_1, \dots, U_n$ of Timed Automata templates. In this way we aim at giving a tool to universally verify software systems where an unknown number of software components (i.e. processes) interact with continuous time temporal constraints. It is often the case, indeed, that distributed algorithms show an heterogeneous nature, combining dynamic aspects with real-time aspects. In the paper we will also show how to model check a protocol that uses special variables storing identifiers of the participating processes (i.e. PIDs) in Timed Automata with conjunctive guards. This is non-trivial, since solutions to the parameterized verification problem often relies on the processes to be symmetric, i.e. indistinguishable. On the other side, many popular distributed algorithms make use of PIDs and thus cannot directly apply those solutions

Machine Learning for Fluid Mechanics

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. It outlines fundamental machine learning methodologies and discusses their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experimentation, and simulation. Machine learning provides a powerful information processing framework that can enrich, and possibly even transform, current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202

Hair cell maturation is differentially regulated along the tonotopic axis of the mammalian cochlea

Sound amplification within the mammalian cochlea depends upon specialized hair cells, the outer hair cells (OHCs), which possess both sensory and motile capabilities. In various altricial rodents, OHCs become functionally competent from around postnatal day 7 (P7), before the primary sensory inner hair cells (IHCs), which become competent at about the onset of hearing (P12). The mechanisms responsible for the maturation of OHCs and their synaptic specialization remain poorly understood. We report that spontaneous Ca2+ activity in the immature cochlea, which is generated by CaV1.3 Ca2+ channels, differentially regulates the maturation of hair cells along the cochlea. Under near‐physiological recording conditions we found that, similar to IHCs, immature OHCs elicited spontaneous Ca2+ action potentials (APs), but only during the first few postnatal days. Genetic ablation of these APs in vivo, using CaV1.3−/− mice, prevented the normal developmental acquisition of mature‐like basolateral membrane currents in low‐frequency (apical) hair cells, such as IK,n (carried by KCNQ4 channels), ISK2 and IACh (α9α10nAChRs) in OHCs and IK,n and IK,f (BK channels) in IHCs. Electromotility and prestin expression in OHCs were normal in CaV1.3−/− mice. The maturation of high‐frequency (basal) hair cells was also affected in CaV1.3−/− mice, but to a much lesser extent than apical cells. However, a characteristic feature in CaV1.3−/− mice was the reduced hair cell size irrespective of their cochlear location. We conclude that the development of low‐ and high‐frequency hair cells is differentially regulated during development, with apical cells being more strongly dependent on experience‐independent Ca2+ APs

Variation in Environmental Parameters in Research and Aquaculture: Effects on Behaviour, Physiology and Cell Biology of Teleost Fish

Over the last few years the increasing use of fish as animal models in scientific research and the increased fish breeding for human consumption have stressed the need for more knowledge on the effect of variations in environmental parameters on fish biology and on the welfare of specimens used both in research and aquaculture contexts. Experimental evidence shows that environmental variations can affect fish biology at various levels, from the molecular to that of the population, sometimes in a different way depending on the species considered. In order to achieve reproducible results in experiments involving fish it is necessary to set and maintain all environmental parameters constant at the optimal value to guarantee the wellness of the animal. The effects of the variation in environmental parameters on the behaviour, physiology and cell biology of teleosts are here discussed in order to provide useful information for research based on fish models