Statistical Mechanics Analysis of the Continuous Number Partitioning Problem

The number partitioning problem consists of partitioning a sequence of positive numbers ${a_1,a_2,..., a_N}$ into two disjoint sets, ${\cal A}$ and ${\cal B}$, such that the absolute value of the difference of the sums of $a_j$ over the two sets is minimized. We use statistical mechanics tools to study analytically the Linear Programming relaxation of this NP-complete integer programming. In particular, we calculate the probability distribution of the difference between the cardinalities of ${\cal A}$ and ${\cal B}$ and show that this difference is not self-averaging.Comment: 9 pages, 1 figur

Blackwell-Optimal Strategies in Priority Mean-Payoff Games

We examine perfect information stochastic mean-payoff games - a class of games containing as special sub-classes the usual mean-payoff games and parity games. We show that deterministic memoryless strategies that are optimal for discounted games with state-dependent discount factors close to 1 are optimal for priority mean-payoff games establishing a strong link between these two classes

Factors influencing pharmacists' clinical decision making in pharmacy practice

BackgroundPharmacists’ clinical decision-making is considered a core process of pharmaceutical care in pharmacy practice, but little is known about the factors influencing this process.ObjectiveTo identify factors influencing clinical decision-making among pharmacists working in pharmacy practice.MethodsSemi-structured interviews were conducted with pharmacists working in primary, secondary, and tertiary care settings in the Netherlands between August and December 2021. A thematic analysis was conducted using an inductive approach. The emerged themes were categorized into the Capability–Opportunity-Motivation–Behaviour (COM-B) model domains.ResultsIn total, 16 pharmacists working in primary care (n = 7), secondary care (n = 4) or tertiary care (n = 5) were interviewed. Factors influencing pharmacists' capability to make clinical decisions are a broad theoretical knowledge base, clinical experience, and skills, including contextualizing data, clinical reasoning, and clinical judgment. The pharmacy setting, data availability, rules and regulations, intra- and interprofessional collaboration, education, patient perspectives, and time are mentioned as factors influencing their opportunity. Factors influencing pharmacists’ motivation are confidence, curiosity, critical thinking, and responsibility.ConclusionsThe reported factors covered all domains of the COM-B model, implying that clinical decision-making is influenced by a combination of pharmacists' capability, opportunity, and motivation. Addressing these different factors in pharmacy practice and education may improve pharmacists’ clinical decision-making, thereby improving patient outcomes.</p

Deterministic Priority Mean-payoff Games as Limits of Discounted Games

International audienceInspired by the paper of de Alfaro, Henzinger and Majumdar about discounted $\mu$-calculus we show new surprising links between parity games and different classes of discounted games

Cognitive processes in pharmacists’ clinical decision-making

Background Pharmacists’ clinical decision-making is a core process in pharmaceutical care. However, the practical aspects and effective teaching methods of this process remain largely unexplored. Objective To examine the cognitive processes involved in pharmacists’ perceptions of how they make clinical decisions in pharmacy practice. Methods Semi-structured, face-to-face interviews were conducted with pharmacists working in community, outpatient, and hospital care in the Netherlands between August and December 2021. Participants were explicitly asked for examples when asked how they make clinical decisions in practice and how they teach this to others. After transcribing audio-recorded interviews, an inductive thematic analysis was conducted to identify cognitive processes. A theoretical model of clinical decision-making was then used and adapted to structure the identified processes. Results In total, 21 cognitive processes were identified from interviews with 16 pharmacists working in community (n = 5), outpatient (n = 2), and hospital care (n = 9). These cognitive processes were organized into 8 steps of the adapted theoretical model, i.e. problem and demand for care consideration, information collection, clinical reasoning, clinical judgment, shared decision-making, implementation, outcomes evaluation, and reflection. Pharmacists struggled to articulate their clinical decision-making and went back-and-forth in their explanations of this process. All pharmacists emphasized the importance of identifying the problem and described how they collect information through reviewing, gathering, recalling, and investigating. Clinical reasoning entailed various cognitive processes, of which comprehending the problem in the patient's context was deemed challenging at times. Pharmacists seemed least active in evaluating patient outcomes and reflecting on these outcomes. Conclusions Pharmacists use multiple cognitive processes when making clinical decisions in pharmacy practice, and their back-and-forth explanations emphasize its dynamic nature. This study adds to a greater understanding of how pharmacists make clinical decisions and to the development of a theoretical model that describes this process, which can be used in pharmacy practice and education

Algorithms for Game Metrics

Simulation and bisimulation metrics for stochastic systems provide a quantitative generalization of the classical simulation and bisimulation relations. These metrics capture the similarity of states with respect to quantitative specifications written in the quantitative {\mu}-calculus and related probabilistic logics. We first show that the metrics provide a bound for the difference in long-run average and discounted average behavior across states, indicating that the metrics can be used both in system verification, and in performance evaluation. For turn-based games and MDPs, we provide a polynomial-time algorithm for the computation of the one-step metric distance between states. The algorithm is based on linear programming; it improves on the previous known exponential-time algorithm based on a reduction to the theory of reals. We then present PSPACE algorithms for both the decision problem and the problem of approximating the metric distance between two states, matching the best known algorithms for Markov chains. For the bisimulation kernel of the metric our algorithm works in time O(n^4) for both turn-based games and MDPs; improving the previously best known O(n^9\cdot log(n)) time algorithm for MDPs. For a concurrent game G, we show that computing the exact distance between states is at least as hard as computing the value of concurrent reachability games and the square-root-sum problem in computational geometry. We show that checking whether the metric distance is bounded by a rational r, can be done via a reduction to the theory of real closed fields, involving a formula with three quantifier alternations, yielding O(|G|^O(|G|^5)) time complexity, improving the previously known reduction, which yielded O(|G|^O(|G|^7)) time complexity. These algorithms can be iterated to approximate the metrics using binary search.Comment: 27 pages. Full version of the paper accepted at FSTTCS 200

Graphical models for interactive POMDPs: representations and solutions

We develop new graphical representations for the problem of sequential decision making in partially observable multiagent environments, as formalized by interactive partially observable Markov decision processes (I-POMDPs). The graphical models called interactive inf uence diagrams (I-IDs) and their dynamic counterparts, interactive dynamic inf uence diagrams (I-DIDs), seek to explicitly model the structure that is often present in real-world problems by decomposing the situation into chance and decision variables, and the dependencies between the variables. I-DIDs generalize DIDs, which may be viewed as graphical representations of POMDPs, to multiagent settings in the same way that IPOMDPs generalize POMDPs. I-DIDs may be used to compute the policy of an agent given its belief as the agent acts and observes in a setting that is populated by other interacting agents. Using several examples, we show how I-IDs and I-DIDs may be applied and demonstrate their usefulness. We also show how the models may be solved using the standard algorithms that are applicable to DIDs. Solving I-DIDs exactly involves knowing the solutions of possible models of the other agents. The space of models grows exponentially with the number of time steps. We present a method of solving I-DIDs approximately by limiting the number of other agents’ candidate models at each time step to a constant. We do this by clustering models that are likely to be behaviorally equivalent and selecting a representative set from the clusters. We discuss the error bound of the approximation technique and demonstrate its empirical performance

Interlaboratory comparison of sample preparation methods, database expansions, and cutoff values for identification of yeasts by matrix-assisted laser desorption ionization-time of flight mass spectrometry using a yeast test panel

An interlaboratory study using matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS) to determine the identification of clinically important yeasts (n35) was performed at 11 clinical centers, one company, and one reference center using the Bruker Daltonics MALDI Biotyper system. The optimal cutoff for the MALDI-TOF MS score was investigated using receiver operating characteristic (ROC) curve analyses. The percentages of correct identifications were compared for different sample preparation methods and different databases. Logistic regression analysis was performed to analyze the association between the number of spectra in the database and the percentage of strains that were correctly identified. A total of 5,460 MALDI-TOF MS results were obtained. Using all results, the area under the ROC curve was 0.95 (95% confidence interval [CI], 0.94 to 0.96). With a sensitivity of 0.84 and a specificity of 0.97, a cutoff value of 1.7 was considered optimal. The overall percentage of correct identifications (formic acid-ethanol extraction method, score>1.7) was 61.5% when the commercial Bruker Daltonics database (BDAL) was used, and it increased to 86.8% by using an extended BDAL supplemented with a Centraalbureau voor Schimmelcultures (CBS)-KNAW Fungal Biodiversity Centre in-house database (BDALCBS in-house). A greater number of main spectra (MSP) in the database was associated with a higher percentage of correct identifications (odds ratio [OR], 1.10; 95% CI, 1.05 to 1.15; P<0.01). The results from the direct transfer method ranged from 0% to 82.9% correct identifications, with the results of the top four centers ranging from 71.4% to 82.9% correct identifications. This study supports the use of a cutoff value of 1.7 for the identification of yeasts using MALDI-TOF MS. The inclusion of enough isolates of the same species in the database can enhance the proportion of correctly identified strains. Further optimization of the preparation methods, especially of the direct transfer method, may contribute to improved diagnosis of yeast-related infections