Popularity, Mixed Matchings, and Self-Duality

Our input instance is a bipartite graph G where each vertex has a preference list ranking its neighbors in a strict order of preference. A matching M is popular if there is no matching N such that the number of vertices that prefer N to M outnumber those that prefer M to N. Each edge is associated with a utility and we consider the problem of matching vertices in a popular and utility-optimal manner. It is known that it is NP-hard to compute a max-utility popular matching. So we consider mixed matchings: a mixed matching is a probability distribution or a lottery over matchings. Our main result is that the popular fractional matching polytope PG is half-integral and in the special case where a stable matching in G is a perfect matching, this polytope is integral. This implies that there is always a max-utility popular mixed matching which is the average of two integral matchings. So in order to implement a max-utility popular mixed matching in G, we need just a single random bit. We analyze the popular fractional matching polytope whose description may have exponentially many constraints via an extended formulation with a linear number of constraints. The linear program that gives rise to this formulation has an unusual property: self-duality. The self-duality of this LP plays a crucial role in our proof. Our result implies that a max-utility popular half-integral matching in G and also in the roommates problem (where the input graph need not be bipartite) can be computed in polynomial time.

A Retrospective Analysis of Owner-reported Reasons for Canine Illness and Injury Visits in Veterinary General Practice: A List of the Top 50% of Complaints

Background Veterinarians are required to use critical thinking and communication skills to proficiently guide a client through available options for disease treatment or management. While multiple viable approaches exist for treating common problems, data describing the actual types of owner-reported complaints most often encountered in general veterinary practices is lacking. An understanding of the nature and distribution of common conditions, as reported by the owner, is a key step in providing the evidence-based foundation directing further efforts toward solutions to overcome barriers in general practice, veterinary-care delivery. Methods A retrospective analysis of common canine owner-reported complaints presented in general veterinary practice over a one-year period was performed. Data was collected from participating practices, cleaned, and analyzed to reflect the top 50% new presenting complaints of owners at all participating practices for canine patients with an illness or injury during their visit. Results The outcome is a comprehensive list ranking the top canine owner-reported presenting illnesses/injuries complaints seen by general veterinary practices included in the study. Conclusions These results provide evidence-based knowledge of the distribution of owner complaints potentially encountered in general practice, providing justification for the need to emphasize certain clinical case presentations in veterinary educational curriculums. Being prepared with such knowledge, veterinary students can be empowered with the necessary skills and tools required to provide informed, ethical, and affordable choices for canine care in the general veterinary practice setting. This information also provides the scientific foundation for canine conditions which may benefit from additional evidence-based trials to better identify the outcomes of spectrum of care interventions for benefits to the owner, the canine, and the veterinarian.

Pengaruh Pemeringkatan Terhadap Mental Siswa

Education in Indonesia is currently still using the ranking system. These government policies often become the talk and debate in Indonesia when taking report cards. At this time, schools do not list ranking on report cards. This is based on the understanding that education is not a place for experimentation. Children should not be focused on results but the process. However, in reality there are still many schools in Indonesia that give ranks on report cards. This study seeks to reveal the effect of ranking on students' mentality. Specifically this study aims (1) whether the importance of ranking, (2) how the influence of scoring for students' mentality, (3) whether ranking decreases learning motivation. This research uses a qualitative research method using a questionnaire through social media, ie through google form. Based on data analysis, the findings of this study can be stated as follows. First, ranking can affect the mentality of students who get low ranks and cause themselves to be inferior. Second, even high-ranking students possess a mental burden if their grades decline. Third, each student has a different way of responding to the ranking, there are those who get a low ranking makes learning motivation down, but there are also things that actually make the motivation for learning even harder.

Talent Identification in Youth Ice Hockey: Exploring “Intangible” Player Characteristics

The purpose of this study was to examine “intangible” characteristics that scouts consider when evaluating draft-eligible prospects for the Western Hockey League. Sixteen scouts participated in semistructured interviews that were subjected to an inductive thematic analysis and then organized around predetermined categories ofwhyintangibles were important,whatintangibles were valued, andhowscouts evaluated these intangibles. Intangibles helped scouts establish players’ fit with the organizational culture of teams and influenced scouts’ draft-list ranking of players. The key intangibles scouts sought were labeled compete, passion, character, and leadership/team player. Scouts noted red flags (i.e., selfish on-ice behaviors, bad body language, and poor parental behavior) that led them to question players’ suitability for their respective organizations. Finally, scouts used an investigative process to identify and evaluate these intangibles through direct observation; interviews with players, coaches, and trainers; and assessments of players’ social media activities. Implications for sport psychology consultants are discussed.

New BSP/CGM algorithms for spanning trees

Computing a spanning tree (ST) and a minimum ST (MST) of a graph are fundamental problems in graph theory and arise as a subproblem in many applications. In this article, we propose parallel algorithms to these problems. One of the steps of previous parallel MST algorithms relies on the heavy use of parallel list ranking which, though efficient in theory, is very time-consuming in practice. Using a different approach with a graph decomposition, we devised new parallel algorithms that do not make use of the list ranking procedure. We proved that our algorithms are correct, and for a graph [Formula: see text], [Formula: see text], and [Formula: see text], the algorithms can be executed on a Bulk Synchronous Parallel/Coarse Grained Multicomputer (BSP/CGM) model using [Formula: see text] communications rounds with [Formula: see text] computation time for each round. To show that our algorithms have good performance on real parallel machines, we have implemented them on graphics processing unit. The obtained speedups are competitive and showed that the BSP/CGM model is suitable for designing general purpose parallel algorithms.

Algoritmo Paralelo para Árvore Geradora Usando GPU

Neste trabalho, usando o modelo BSP/CGM, propomos um algoritmo paralelo, com uma implementação em CUDA, para obter uma árvore geradora de um grafo. Trabalhos anteriores para este problema são baseados na solução do problema de list ranking que, embora eficiente na teoria, não produz bons ganhos na prática. Num trabalho posterior, baseado na ideia do cálculo de uma estrutura chamada esteio, Cáceres et al. propuseram um algoritmo paralelo no modelo BSP/CGM para obter uma árvore geradora sem a utilização de list ranking. O cálculo do esteio é obtido com a utilização de um grafo bipartido auxiliar, com o uso de ordenação inteira. Neste artigo melhoramos aquele trabalho em vários aspectos. No algoritmo proposto, para implementação em GPGPU, não é mais necessário calcular o grafo bipartido, e a construção do esteio não necessita do algoritmo de ordenação. A eficiência e escalabilidade do algoritmo proposto são verificadas por experimentos.

A Respondent-friendly Method of Ranking Long Lists

This article illustrates a respondent-friendly approach to preference elicitation over large choice sets, which overcomes limitations of rating, full-list ranking, conjoint and choice-based approaches. This approach, HLm, requires respondents to identify the top and bottom m items from an overall list. Across respondents, the number of times an item appears in participants' L (low) list is subtracted from the number of times it appears in participants' H (high) list. These net scores are then used to order the total list. We illustrate the approach in three experiments, demonstrating that it compares favourably to familiar methods, while being much less demanding on survey participants. Experiment 1 had participants alphabetise words, suggesting the HLm method is easier than full ranking but less accurate if m does not increase with increases in list length. The objective of experiment 2 was to order US states by population. In this domain, where knowledge was imperfect, HLm outperformed full ranking. Experiment 3 involved eliciting respondents' personal tastes for fruit. HLm resulted in a final ranking that correlated highly with MaxDiff scaling. We argue that HLm is a viable method for obtaining aggregate order of preferences across large numbers of alternatives.