Deep Learning for the Generation of Heuristics in Answer Set Programming: A Case Study of Graph Coloring

Answer Set Programming (ASP) is a well-established declarative AI formalism for knowledge representation and reasoning. ASP systems were successfully applied to both industrial and academic problems. Nonetheless, their performance can be improved by embedding domain-specific heuristics into their solving process. However, the development of domain-specific heuristics often requires both a deep knowledge of the domain at hand and a good understanding of the fundamental working principles of the ASP solvers. In this paper, we investigate the use of deep learning techniques to automatically generate domain-specific heuristics for ASP solvers targeting the well-known graph coloring problem. Empirical results show that the idea is promising: the performance of the ASP solver wasp can be improved

Diet of a restocked population of the European pond turtle Emys orbicularis in NW Italy

Recently several projects have been implemented for the conservation of the European turtle Emys orbicularis, but few aspects of the captive-bred animals released into the wild have been described. In this note we report about the trophic habits of a small restocked population of the endemic subspecies E. o. ingauna that is now reproducing in NW Italy. Faecal contents from 25 individuals (10 females, 11 males and 4 juveniles) were obtained in June 2016. Overall, 11 taxonomic categories of invertebrates were identified, together with seeds and plant remains. Plant material was present in 24 out of 25 turtle faecal contents, suggesting that ingestion was deliberate. There were no differences between the dietary habits of females and males, and the trophic strategy of adult individuals was characterised by a relatively high specialization on dragonfly nymphae. These findings suggest that captive bred turtles are adapting well to the wild and that restocked individuals assumed an omnivorous diet, a trophic behaviour typical of other wild turtle populations living in similar habitats

So close so different: what makes the difference?

The introduction of alien fish species in wetland ecosystems could have a great impact on freshwater communities and ecological processes. Despite fish introduction has been noticed as one of the principal cause of freshwater extinctions, ecosystem processes alteration, and change in aquatic community assemblage, very few data about impact on freshwater reptiles are available. As study model we used two neighbour sub-populations of the endangered Sicilian pond turtle, Emys trinacris, inhabiting two small, close each other and very similar lakes, except for the presence of allocthonous fish, Cyprinus carpio and Gambusia hoolbroki in one of the two. The multi-year study allowed highlighting significant differences in abundance, growth and reproductive output between the two freshwater turtle sub-populations, suggesting their influence on phenotypic plasticity of the studied population. These results are discussed in the light of previous evidence about the impact of these alien species on abundance and assemblage of the invertebrate community with an evident impact on niche width, diet composition and therefore energy intake by Emys trinacris. These data may provide important information to address management strategies and conservation actions of small wetland areas inhabited by pond turtles, pointing out a threats never highlighted up to now

Comparison between Two Different Two-Stage Transperineal Approaches to Treat Urethral Strictures or Bladder Neck Contracture Associated with Severe Urinary Incontinence that Occurred after Pelvic Surgery: Report of Our Experience

Introduction. The recurrence of urethral/bladder neck stricture after multiple endoscopic procedures is a rare complication that can follow prostatic surgery and its treatment is still controversial. Material and Methods. We retrospectively analyzed our data on 17 patients, operated between September 2001 and January 2010, who presented severe urinary incontinence and urethral/bladder neck stricture after prostatic surgery and failure of at least four conservative endoscopic treatments. Six patients underwent a transperineal urethrovesical anastomosis and 11 patients a combined transperineal suprapubical (endoscopic) urethrovesical anastomosis. After six months the patients that presented complete incontinence and no urethral stricture underwent the implantation of an artificial urethral sphincter (AUS). Results. After six months 16 patients were completely incontinent and presented a patent, stable lumen, so that they underwent an AUS implantation. With a mean followup of 50.5 months, 14 patients are perfectly continent with no postvoid residual urine. Conclusions. Two-stage procedures are safe techniques to treat these challenging cases. In our opinion, these cases could be managed with a transperineal approach in patients who present a perfect operative field; on the contrary, in more difficult cases, it would be preferable to use the other technique, with a combined transperineal suprapubical access, to perform a pull-through procedure

A revised day +7 predictive score for transplant-related mortality: serum cholinesterase, total protein, blood urea nitrogen, γ glutamyl transferase, donor type and cell dose

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Betti numbers for numerical semigroup rings

We survey results related to the magnitude of the Betti numbers of numerical semigroup rings and of their tangent cones.Comment: 22 pages; v2: updated references. To appear in Multigraded Algebra and Applications (V. Ene, E. Miller Eds.

Deep Randomized Neural Networks

Randomized Neural Networks explore the behavior of neural systems where the majority of connections are fixed, either in a stochastic or a deterministic fashion. Typical examples of such systems consist of multi-layered neural network architectures where the connections to the hidden layer(s) are left untrained after initialization. Limiting the training algorithms to operate on a reduced set of weights inherently characterizes the class of Randomized Neural Networks with a number of intriguing features. Among them, the extreme efficiency of the resulting learning processes is undoubtedly a striking advantage with respect to fully trained architectures. Besides, despite the involved simplifications, randomized neural systems possess remarkable properties both in practice, achieving state-of-the-art results in multiple domains, and theoretically, allowing to analyze intrinsic properties of neural architectures (e.g. before training of the hidden layers' connections). In recent years, the study of Randomized Neural Networks has been extended towards deep architectures, opening new research directions to the design of effective yet extremely efficient deep learning models in vectorial as well as in more complex data domains. This chapter surveys all the major aspects regarding the design and analysis of Randomized Neural Networks, and some of the key results with respect to their approximation capabilities. In particular, we first introduce the fundamentals of randomized neural models in the context of feed-forward networks (i.e., Random Vector Functional Link and equivalent models) and convolutional filters, before moving to the case of recurrent systems (i.e., Reservoir Computing networks). For both, we focus specifically on recent results in the domain of deep randomized systems, and (for recurrent models) their application to structured domains

Tetrazine-Triggered Release of Carboxylic-Acid-Containing Molecules for Activation of an Anti-inflammatory Drug.

In addition to its use for the study of biomolecules in living systems, bioorthogonal chemistry has emerged as a promising strategy to enable protein or drug activation in a spatially and temporally controlled manner. This study demonstrates the application of a bioorthogonal inverse electron-demand Diels-Alder (iEDDA) reaction to cleave trans-cyclooctene (TCO) and vinyl protecting groups from carboxylic acid-containing molecules. The tetrazine-mediated decaging reaction proceeded under biocompatible conditions with fast reaction kinetics (<2 min). The anti-inflammatory activity of ketoprofen was successfully reinstated after decaging of the nontoxic TCOprodrug in live macrophages. Overall, this work expands the scope of functional groups and the application of decaging reactions to a new class of drugs

Deep Randomized Neural Networks

Randomized Neural Networks explore the behavior of neural systems where the majority of connections are fixed, either in a stochastic or a deterministic fashion. Typical examples of such systems consist of multi-layered neural network architectures where the connections to the hidden layer(s) are left untrained after initialization. Limiting the training algorithms to operate on a reduced set of weights inherently characterizes the class of Randomized Neural Networks with a number of intriguing features. Among them, the extreme efficiency of the resulting learning processes is undoubtedly a striking advantage with respect to fully trained architectures. Besides, despite the involved simplifications, randomized neural systems possess remarkable properties both in practice, achieving state-of-the-art results in multiple domains, and theoretically, allowing to analyze intrinsic properties of neural architectures (e.g. before training of the hidden layers’ connections). In recent years, the study of Randomized Neural Networks has been extended towards deep architectures, opening new research directions to the design of effective yet extremely efficient deep learning models in vectorial as well as in more complex data domains. This chapter surveys all the major aspects regarding the design and analysis of Randomized Neural Networks, and some of the key results with respect to their approximation capabilities. In particular, we first introduce the fundamentals of randomized neural models in the context of feed-forward networks (i.e., Random Vector Functional Link and equivalent models) and convolutional filters, before moving to the case of recurrent systems (i.e., Reservoir Computing networks). For both, we focus specifically on recent results in the domain of deep randomized systems, and (for recurrent models) their application to structured domains