Monthly, annual and quarterly frequencies: a comparison of models for tourism in Sardinia and bounded rationality

This paper constructs and estimates the demand for international tourism for the Italian Province of Sassari. The sample period under estimation is from 1972 to 1995. Three dynamic models are estimated at monthly, annual and quarterly data frequencies. Similarities and differences are explored amongst the three models, using recently developed econometric techniques. A "pre-modelling" data analysis is undertaken for the economic series of interest. By adopting the LSE "general-to-specific" methodology, dynamic estimations are run. A full range of diagnostic tests is provided. Short and long run income elasticities, negativity and substitutability are tested on the light of economic theory. On balance, evidence is found that the monthly and quarterly models present homogenous results in terms of seasonal and long run unit roots. Annual data show different and perhaps misleading results.

Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks

Deep neural networks have emerged as a widely used and effective means for tackling complex, real-world problems. However, a major obstacle in applying them to safety-critical systems is the great difficulty in providing formal guarantees about their behavior. We present a novel, scalable, and efficient technique for verifying properties of deep neural networks (or providing counter-examples). The technique is based on the simplex method, extended to handle the non-convex Rectified Linear Unit (ReLU) activation function, which is a crucial ingredient in many modern neural networks. The verification procedure tackles neural networks as a whole, without making any simplifying assumptions. We evaluated our technique on a prototype deep neural network implementation of the next-generation airborne collision avoidance system for unmanned aircraft (ACAS Xu). Results show that our technique can successfully prove properties of networks that are an order of magnitude larger than the largest networks verified using existing methods.Comment: This is the extended version of a paper with the same title that appeared at CAV 201

Translation-based approaches for solving disjunctive temporal problems with preferences

Disjunctive Temporal Problems (DTPs) with Preferences (DTPPs) extend DTPs with piece-wise constant preference functions associated to each constraint of the form l 64 x 12 y 64 u, where x, y are (real or integer) variables, and l, u are numeric constants. The goal is to find an assignment to the variables of the problem that maximizes the sum of the preference values of satisfied DTP constraints, where such values are obtained by aggregating the preference functions of the satisfied constraints in it under a \u201cmax\u201d semantic. The state-of-the-art approach in the field, implemented in the native DTPP solver Maxilitis, extends the approach of the native DTP solver Epilitis. In this paper we present alternative approaches that translate DTPPs to Maximum Satisfiability of a set of Boolean combination of constraints of the form l ./ x 12 y ./ u, ./ 08 {<, 64}, that extend previous work dealing with constant preference functions only. We prove correctness and completeness of the approaches. Results obtained with the Satisfiability Modulo Theories (SMT) solvers Yices and MathSAT on randomly generated DTPPs and DTPPs built from real-world benchmarks, show that one of our translation is competitive to, and can be faster than, Maxilitis

Property specification patterns at work: verification and inconsistency explanation

Property specification patterns (PSPs) have been proposed to ease the formalization of requirements, yet enable automated verification thereof. In particular, the internal consistency of specifications written with PSPs can be checked automatically with the use of, for example, linear temporal logic (LTL) satisfiability solvers. However, for most practical applications, the expressiveness of PSPs is too restricted to enable writing useful requirement specifications, and proving that a set of requirements is inconsistent can be worthless unless a minimal set of conflicting requirements is extracted to help designers to correct a wrong specification. In this paper, we extend PSPs by considering Boolean as well as atomic numerical assertions, we contribute an encoding from extended PSPs to LTL formulas, and we present an algorithm computing inconsistency explanations, i.e., irreducible inconsistent subsets of the original set of requirements. Our extension enables us to reason about the internal consistency of functional requirements which would not be captured by basic PSPs. Experimental results demonstrate that our approach can check and explain (in)consistencies in specifications with nearly two thousand requirements generated using a probabilistic model, and that it enables effective handling of real-world case studies

Infusion of casein hydrolizates into the mammary gland simulates the omission of one daily milking in goats

Suppression of one daily milking at weekends, even though socially desirable, may reduce milk yield. These losses have been attributed to a short-term mechanism: the filling of the cistern and ductal-alveolar system with milk which contains a peptide called feedback inhibitor of lactation (FIL) (Wilde and Peaker, 1990). The FIL probably reduces the synthesis and secretion of mammary cells by blocking the potassium channel of the apical membrane (Silanikove et al., 2000). Shamay et al. (2002) hypothesized that the FIL can be identified with the AA sequence 1-28, derived from the breakdown of β-casein by plasmin (PL). The aim of this work was to verify if the infusion of casein hydrolizates (CNH) into the mammary gland simulates the omission of one milking for two consecutive days

Impact of COVID-19 on Patients’ Attitudes and Perceptions of Dental Health Services: A Questionnaire Based Study in an Australian University Dental Clinic

COVID-19, the global pandemic, has significantly interrupted the provision of oral health care to many individuals. This study aims to evaluate patients’ attitudes to and perceptions of dental visits in the COVID-19 pandemic and assess if socio-economic status influences their perception of risk associated with dental visits. Patients attending the dental clinic were invited to participate in this study by completing a questionnaire administered in August 2021. Composite indicators for access, attitude, perception and socio-economic status were created based on subsets of questions. A total of 247 completed questionnaires were obtained. Analysis was performed with the perception, attitude and access indicators against the socio-economic status indicator. This study found that there is a statistically significant difference between socio-economic groups and their attitudes and perceptions around dental health care services in the current COVID-19 pandemic. Individuals from lower socio-economic status groups were less influenced by the pandemic. Participants from higher socio-economic status groups were found to be more cautious around COVID-19 and its risks

NNV: The Neural Network Verification Tool for Deep Neural Networks and Learning-Enabled Cyber-Physical Systems

This paper presents the Neural Network Verification (NNV) software tool, a set-based verification framework for deep neural networks (DNNs) and learning-enabled cyber-physical systems (CPS). The crux of NNV is a collection of reachability algorithms that make use of a variety of set representations, such as polyhedra, star sets, zonotopes, and abstract-domain representations. NNV supports both exact (sound and complete) and over-approximate (sound) reachability algorithms for verifying safety and robustness properties of feed-forward neural networks (FFNNs) with various activation functions. For learning-enabled CPS, such as closed-loop control systems incorporating neural networks, NNV provides exact and over-approximate reachability analysis schemes for linear plant models and FFNN controllers with piecewise-linear activation functions, such as ReLUs. For similar neural network control systems (NNCS) that instead have nonlinear plant models, NNV supports over-approximate analysis by combining the star set analysis used for FFNN controllers with zonotope-based analysis for nonlinear plant dynamics building on CORA. We evaluate NNV using two real-world case studies: the first is safety verification of ACAS Xu networks and the second deals with the safety verification of a deep learning-based adaptive cruise control system

Evaluating QBF Solvers: Quantifier Alternations Matter

We present an experimental study of the effects of quantifier alternations on the evaluation of quantified Boolean formula (QBF) solvers. The number of quantifier alternations in a QBF in prenex conjunctive normal form (PCNF) is directly related to the theoretical hardness of the respective QBF satisfiability problem in the polynomial hierarchy. We show empirically that the performance of solvers based on different solving paradigms substantially varies depending on the numbers of alternations in PCNFs. In related theoretical work, quantifier alternations have become the focus of understanding the strengths and weaknesses of various QBF proof systems implemented in solvers. Our results motivate the development of methods to evaluate orthogonal solving paradigms by taking quantifier alternations into account. This is necessary to showcase the broad range of existing QBF solving paradigms for practical QBF applications. Moreover, we highlight the potential of combining different approaches and QBF proof systems in solvers.Comment: preprint of a paper to be published at CP 2018, LNCS, Springer, including appendi

Down Syndrome: how to communicate the diagnosis

Communicating the diagnosis of Down Syndrome to a couple of parents is never easy, whether before or after birth. As doctors, we must certainly rely on our own relational skills, but it is also necessary to be confident in some general indications, which are often overlooked in the strict hospital routine. This article is intended as a summary of the main articles published on this subject in the international literature, collecting and summarising the most important indications that have emerged in years of medical practice all over the world as well as in our personal experience. The diffusion of these guidelines is essential to help the doctor in this difficult task, on which there is often little training, and above all to guarantee to the parents the least traumatic communication possible

Grain legume production in Europe for food, feed and meat-substitution

Partial shifts from animal-based to plant-based proteins in human diets could reduce environmental pressure from food systems and serve human health. Grain legumes can play an important role here. They are one of the few agricultural commodities for which Europe is not nearly self-sufficient. Here, we assessed area expansion and yield increases needed for European self-sufficiency of faba bean, pea and soybean. We show that such production could use substantially less cropland (4–8%) and reduce GHG emissions (7–22% current meat production) when substituting for animal-derived food proteins. We discuss changes required in food and agricultural systems to make grain legumes competitive with cereals for farmers and how their cultivation can help to increase sustainability of European cropping systems.</p