No abstract

Abstract. Execution of most of the modern DPLL-based SAT solvers is guided by a number of heuristics. Decisions made during the search process are usually driven by some fixed heuristic policies. Despite the outstanding progress in SAT solving in recent years, there is still an appealing lack of techniques for selecting policies appropriate for solving specific input formulae. In this paper we present a methodology for instancebased selection of solver's policies that uses a data-mining classification technique. The methodology also relies on analysis of relationships between formulae, their families, and their suitable solving strategies. The evaluation results are very good, demonstrate practical usability of the methodology, and encourage further efforts in this direction.

Most, if not all, state-of-the-art complete SAT solvers are complex variations of the DPLL procedure described in the early 1960's. Published descriptions of these modern algorithms and related data structures are given either as high-level (rule-based) transition systems or, informally, as (pseudo) programming language code. The former, although often accompanied with (informal) correctness proofs, are usually very abstract and do not specify many details crucial for efficient implementation. The latter usually do not involve any correctness argument and the given code is often hard to understand and modify. This paper aims at bridging this gap: we present SAT solving algorithms that are formally proved correct, but at the same time they contain information required for efficient implementation. We use a tutorial, top-down, approach and develop a SAT solver, starting from a simple design that is subsequently extended, step-by-step, with the requisite series of features. Heuristic parts of the solver are abstracted away, since they usually do not affect solver correctness (although they are very important for efficiency). All algorithms are given in pseudo-code. The code is accompanied with correctness conditions, given in Hoare logic style. Correctness proofs are formalized within the Isabelle theorem proving system and are available in the extended version of this paper. The given pseudo-code served as a basis for our SAT solver argo-sat.

No abstract

A high proportion of people are insufficiently physically active. The reasons for this are complex and in part relate to social determinants of health, lifestyle choices, and deleterious environmental conditions like climate change, loss of green and outdoor environments and a concomitant loss of biodiversity. Physiotherapists, and other health professionals, may have a positive impact on these global issues, through the encouragement of active transport, and advocacy to reduce barriers to its uptake and optimize exposure to health-giving outdoor spaces. In this paper, we demonstrate how physiotherapists can promote active transport as a planetary health intervention, and provide insight into the benefits and challenges of this planetary health intervention, with direct implications to physiotherapy practice.

The importance of algorithm portfolio techniques for SAT has long been noted, and a number of very successful systems have been devised, including the most successful one -SATzilla. However, all these systems are quite complex (to understand, reimplement, or modify). In this paper we present an algorithm portfolio for SAT that is extremely simple, but in the same time so efficient that it outperforms SATzilla. For a new SAT instance to be solved, our portfolio finds its k-nearest neighbors from the training set and invokes a solver that performs the best for those instances. The main distinguishing feature of our algorithm portfolio is the locality of the selection procedure -the selection of a SAT solver is based only on few instances similar to the input one. An open source tool that implements our approach is publicly available.

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Introduction:The relevance of ecosystems to physiotherapy has traditionally been overlooked, despite its potential for positive health impacts relevant to conditions often managed by physiotherapists. Purpose: The purpose of this article is to introduce the concept of ecosystem services to physiotherapists, and to discuss how understanding ecosystem services may improve patient care, and population and planetary health. Discussion and Conclusion:Physiotherapists with an understanding of ecosystem services may improve patient care by value-adding to management through patient education, empathy, advocacy, and broader population health approaches.Physiotherapists are also well placed to promote the conservation and restoration of ecosystem through participation, advocacy, and the development of public health measures, to the benefit of global sustainability and population health. Further research is required into how physiotherapists currently use nature-based interventions, and the barriers and enablers to their use. To be adequately prepared to meet the challenges that climate change and environmental degradation pose to patient care, population health and health systems, both current and future physiotherapists need to take a broader view of their practice. By including consideration of the potential role of the environment and green space exposure in particular on their patient's health, physiotherapists can ultimately contribute more to population and planetary health.