Tug-of-war model for the two-bandit problem: Nonlocally-correlated parallel exploration via resource conservation

We propose a model - the “tug-of-war (TOW) model” - to conduct unique parallel searches using many nonlocally-correlated search agents. The model is based on the property of a single-celled amoeba, the true slime mold Physarum, which maintains a constant intracellular resource volume while collecting environmental information by concurrently expanding and shrinking its branches. The conservation law entails a “nonlocal correlation” among the branches, i.e., volume increment in one branch is immediately compensated by volume decrement(s) in the other branch(es). This nonlocal correlation was shown to be useful for decision making in the case of a dilemma. The multi-armed bandit problem is to determine the optimal strategy for maximizing the total reward sum with incompatible demands, by either exploiting the rewards obtained using the already collected information or exploring new information for acquiring higher payoffs involving risks. Our model can efficiently manage the “exploration-exploitation dilemma” and exhibits good performances. The average accuracy rate of our model is higher than those of well-known algorithms such as the modified ?-greedy algorithm and modified softmax algorithm, especially, for solving relatively difficult problems. Moreover, our model flexibly adapts to changing environments, a property essential for living organisms surviving in uncertain environments.     

Spatial Modeling of Air Pollution in Studies of Its Short‐Term Health Effects

Summary In studies that estimate the short‐term effects of air pollution on health, daily measurements of pollution concentrations are often available from a number of monitoring locations within the study area. However, the health data are typically only available in the form of daily counts for the entire area, meaning that a corresponding single daily measure of pollution is required. The standard approach is to average the observed measurements at the monitoring locations, and use this in a log‐linear health model. However, as the pollution surface is spatially variable this simple summary is unlikely to be an accurate estimate of the average pollution concentration across the region, which may lead to bias in the resulting health effects. In this article, we propose an alternative approach that jointly models the pollution concentrations and their relationship with the health data using a Bayesian spatio‐temporal model. We compare this approach with the simple spatial average using a simulation study, by investigating the impact of spatial variation, monitor placement, and measurement error in the pollution data. An epidemiological study from Greater London is then presented, which estimates the relationship between respiratory mortality and four different pollutants.     

医师资格考试存在的问题分析及对策探讨

医师资格考试对于提高医师的素质,保证医疗质量至关重要。通过专家访谈,了解目前医师资格考试存在的问题,并提出相应的管理对策,以促进医师资格考试制度不断完善。     

Limit behaviour of spatially growing cell cultures

Growing cell cultures often exhibit branch patterns. The cultures are usually modelled as free boundary problems. Here, we review some of these models and investigate the appearance of branch patterns using methods from asymptotic analysis. Two extreme cases--large kinetics and small kinetics--are considered. For large kinetics the models reduce to the well-studied Stefan problem, which is known to exhibit a diffusive-instability and hence shows branch patterns. Considering small kinetics, small perturbations are smoothed out resulting in regular shapes of the cultures. The branching phenomenon in general can be understood as somewhere between these two extremes. Not relying on special assumptions, the presented analysis is very general.     

Livestock Predation—Insights From Problem Animal Control Registers in Botswana

Abstract: Problem Animal Control Registers, where farmers report livestock losses due to predators as a prerequisite for financial compensation, allow quantifying the human-predator conflict. We analyzed such registers from the Kweneng District of Botswana to assess the impact of native predators on livestock over 3 years. Leopards (Panthera pardus), lions (Panthera leo), wild dogs (Lycaon pictus), brown hyenas (Hyaena brunnea), and cheetahs (Acinonyx jubatus) altogether claimed 2,272 head of livestock. During 2002, the year with the highest impact, the number of animals reported depredated (954) represented 0.34% of the livestock in the district. Leopards and lions caused 64% of the losses. Leopard livestock predation mainly affected calves and was consistent over the district and over time. In contrast, lion predation concentrated on adult cattle, was characterized by local hot spots close to reserve borders, decreased with increasing distance to a reserve, and increased during 2002, an unusually dry year. Interviews with 60 farmers and herders within 30 km of Khutse and Central Kalahari Game Reserves revealed an annual loss of 2.2% of their livestock to predators. Here, small farms (max. 100 domestic animals) suffered relatively higher losses than large, commercial farms, not only due to predation (small farms: 11.7%; large farms: 1.0%) but also from other causes (small: 12.6%; large: 2.8%), even though herders on large farms guarded 5 times more livestock per person than those on small farms. To reduce livestock predation in most of the district where lions are absent, we recommend maternity corrals for pregnant females and calves to better protect vulnerable calves during day and night. In areas close to a reserve where lions roam, herders' incentives to keep all livestock protected in a corral at night have to be enhanced because, according to the registers, only 3 predation cases were reported to have happened inside a corral.     

A P system and a constructive membrane-inspired DNA algorithm for solving the Maximum Clique Problem

We present a P system with replicated rewriting to solve the Maximum Clique Problem for a graph. Strings representing cliques are built gradually. This involves the use of inhibitors that control the space of all generated solutions to the problem. Calculating the maximum clique for a graph is a highly relevant issue not only on purely computational grounds, but also because of its relationship to fundamental problems in genomics. We propose to implement the designed P system by means of a DNA algorithm. This algorithm is then compared with two standard papers that addressed the same problem and its DNA implementation in the past. This comparison is carried out on the basis of a series of computational and physical parameters. Our solution features a significantly lower cost in terms of time, the number and size of strands, as well as the simplicity of the biological implementation.     

Bayesian modeling of dynamic motion integration

The quality of the representation of an object's motion is limited by the noise in the sensory input as well as by an intrinsic ambiguity due to the spatial limitation of the visual motion analyzers (aperture problem). Perceptual and oculomotor data demonstrate that motion processing of extended objects is initially dominated by the local 1D motion cues, related to the object's edges and orthogonal to them, whereas 2D information, related to terminators (or edge-endings), takes progressively over and leads to the final correct representation of global motion. A Bayesian framework accounting for the sensory noise and general expectancies for object velocities has proven successful in explaining several experimental findings concerning early motion processing [Weiss, Y., Adelson, E., 1998. Slow and smooth: a Bayesian theory for the combination of local motion signals in human vision. MIT Technical report, A.I. Memo 1624]. In particular, these models provide a qualitative account for the initial bias induced by the 1D motion cue. However, a complete functional model, encompassing the dynamical evolution of object motion perception, including the integration of different motion cues, is still lacking. Here we outline several experimental observations concerning human smooth pursuit of moving objects and more particularly the time course of its initiation phase, which reflects the ongoing motion integration process. In addition, we propose a recursive extension of the Bayesian model, motivated and constrained by our oculomotor data, to describe the dynamical integration of 1D and 2D motion information. We compare the model predictions for object motion tracking with human oculomotor recordings.