Is Learning by Migrating to a Megalopolis Really Important? Evidence from Thailand

We examine the effects of learning by migrating on the productivity of migrants who move to a "megalopolis" from rural areas using the Thailand Labor Force Survey. The main contribution is to the development a simple framework to test for self-selection on migration decisions and learning by migrating into the urban labor market, focusing on experimental evidence in the observational data. The role of the urban labor market is examined. In conclusion, we find significant evidence for sorting: the self-selection effects test (1) is positive among new entrants from rural areas to the urban labor market; and (2) is negative among new exits that move to rural areas from the urban labor market. Further, estimated effects of learning by migrating into a "megalopolis" have a less significant impact. These results suggest the existence of a natural selection (i.e. survival of the fittest) mechanism in the urban labor market in a developing economy.

Pulp capping materials exert an effect on the secretion of IL-1β and IL-8 by migrating human neutrophils

Pulp repair is a complex process whose mechanisms are not yet fully understood. The first immune cells to reach the damaged pulp are neutrophils that play an important role in releasing cytokines and in phagocytosis. The objective of this study was to analyze the effect of different pulp-capping materials on the secretion of interleukin-1 beta (IL-1β) and interleukin-8 (IL-8) by migrating human neutrophils. Neutrophils were obtained from the blood of three healthy donors. The experimental groups were calcium hydroxide [Ca(OH)2], an adhesive system (Single Bond), and mineral trioxide aggregate (MTA). Untreated cells were used as control. Transwell chambers were used in performing the assays to mimic an in vivo situation of neutrophil chemotaxis. The pulp-capping materials were placed in the lower chamber and the human neutrophils, in the upper chamber. The cells were counted and the culture medium was assayed using ELISA kits for detecting and quantifying IL-1β and IL8. The data were compared by ANOVA followed by Tukey's test (p < 0.05). The secretion of IL-8 was significantly higher in all groups in comparison to the control group (p < 0.05). The adhesive system group showed higher IL-8 than the MTA group (p < 0.05). The secretion of IL-1β was significantly greater only in the MTA group (p < 0.001). It was concluded that only MTA is able to improve the secretion of IL-1β, and all materials tested increased IL-8 secretion. These results combined with all the other biological advantages of MTA indicate that it could be considered the material of choice for dental pulp capping.

Accelerating autonomous learning by using heuristic selection of actions

This paper investigates how to make improved action selection for online policy learning in robotic scenarios using reinforcement learning (RL) algorithms. Since finding control policies using any RL algorithm can be very time consuming, we propose to combine RL algorithms with heuristic functions for selecting promising actions during the learning process. With this aim, we investigate the use of heuristics for increasing the rate of convergence of RL algorithms and contribute with a new learning algorithm, Heuristically Accelerated Q-learning (HAQL), which incorporates heuristics for action selection to the Q-Learning algorithm. Experimental results on robot navigation show that the use of even very simple heuristic functions results in significant performance enhancement of the learning rate.

Learning by doing e curva de aprendizagem. Uma aplicação na esfera da Microeconomia

Trabalho apresentado no âmbito dos artigos 11º e 14º do Regulamento de Prestação de Serviço Docente do ISCAL

Learning by doing e Curva de aprendizagem. Um estudo empírico aplicado em Microeconomia

Trabalho apresentado no âmbito dos artigos 11º e 14º do Regulamento de Prestação de Serviço Docente do ISCAL

“Learning-by-producing” in the fashion value chain

Proceedings da AUTEX 2015, Bucareste, Roménia.

Spatial learning by rats across visually disconnected environments

Experiments were designed to examine some properties of spatial representations in rats. Adult subjects were trained to escape through a hole at a fixed position in a large circular arena (see Schenk 1989). The experiments were conducted in the dark, with a limited number of controlled visual light cues in order to assess the minimal cue requirement for place learning. Three identical light cues (shape, height and distance from the table) were used. Depending on the condition, they were either permanently on, or alternatively on or off, depending on the position of the rat in the field. Two questions were asked: a) how many identical visual cues were necessary for spatial discrimination in the dark, and b) could rats integrate the relative positions of separate cues, under conditions in which the rat was never allowed to perceive all three cues simultaneously. The results suggest that rats are able to achieve a place discrimination task even if the three cues necessary for efficient orientation can never be seen simultaneously. A dissociation between the discrimination of the spatial position of the goal and the capacity to reach it by a direct path suggests that a reduced number of cues might require prolonged locomotion to allow an accurate orientation in the environment.

Absence of snapshot memory of the target view interferes with place navigation learning by rats in the water maze

Contribution of visual and nonvisual mechanisms to spatial behavior of rats in the Morris water maze was studied with a computerized infrared tracking system, which switched off the room lights when the subject entered the inner circular area of the pool with an escape platform. Naive rats trained under light-dark conditions (L-D) found the escape platform more slowly than rats trained in permanent light (L). After group members were swapped, the L-pretrained rats found under L-D conditions the same target faster and eventually approached latencies attained during L navigation. Performance of L-D-trained rats deteriorated in permanent darkness (D) but improved with continued D training. Thus L-D navigation improves gradually by procedural learning (extrapolation of the start-target azimuth into the zero-visibility zone) but remains impaired by lack of immediate visual feedback rather than by absence of the snapshot memory of the target view.

Spatial learning by rats across visually disconnected environments

Two spatial tasks were designed to test specific properties of spatial representation in rats. In the first task, rats were trained to locate an escape hole at a fixed position in a visually homogeneous arena. This arena was connected with a periphery where a full view of the room environment existed. Therefore, rats were dependent on their memory trace of the previous position in the periphery to discriminate a position within the central region. Under these experimental conditions, the test animals showed a significant discrimination of the training position without a specific local view. In the second task, rats were trained in a radial maze consisting of tunnels that were transparent at their distal ends only. Because the central part of the maze was non-transparent, rats had to plan and execute appropriate trajectories without specific visual feedback from the environment. This situation was intended to encourage the reliance on prospective memory of the non-visited arms in selecting the following move. Our results show that acquisition performance was only slightly decreased compared to that shown in a completely transparent maze and considerably higher than in a translucent maze or in darkness. These two series of experiments indicate (1) that rats can learn about the relative position of different places with no common visual panorama, and (2) that they are able to plan and execute a sequence of visits to several places without direct visual feed-back about their relative position.

The 9-O-acetyl GD3 gangliosides are expressed by migrating chains of subventricular zone neurons in vitro

Neurons from the anterior subventricular zone (SVZ) of the cerebral cortex migrate tangentially to become interneurons in the olfactory bulb during development and in adult rodents. This migration was defined as neuronophilic, independent of a radial glial substrate. The cortical SVZ and the rostral migratory stream to the olfactory bulb were shown to be rich in 9-O-acetyl GD3 gangliosides (9-O-acGD3), which have been previously shown to be implicated in gliophilic migration in the rodent cerebral cortex and cerebellum. In the present study, we performed SVZ explant cultures using rats during their first postnatal week to analyze the expression of these gangliosides in chain migration of neuronal precursors. We characterized migrating chains of these neuroblasts through morphological analysis and immunocytochemistry for the neural cell adhesion molecule. By using the Jones monoclonal antibody which binds specifically to 9-O-acGD3 we showed that migrating chains from the SVZ explants express 9-O-acGD3 which is distributed in a punctate manner in individual cells. 9-O-acGD3 is also present in migrating chains that form in the absence of radial glia, typical of the neuronophilic chain migration of the SVZ. Our data indicate that 9-O-acetylated gangliosides may participate in neuronophilic as well as gliophilic migration.

Reinforcement Learning by Policy Search

One objective of artificial intelligence is to model the behavior of an intelligent agent interacting with its environment. The environment's transformations can be modeled as a Markov chain, whose state is partially observable to the agent and affected by its actions; such processes are known as partially observable Markov decision processes (POMDPs). While the environment's dynamics are assumed to obey certain rules, the agent does not know them and must learn. In this dissertation we focus on the agent's adaptation as captured by the reinforcement learning framework. This means learning a policy---a mapping of observations into actions---based on feedback from the environment. The learning can be viewed as browsing a set of policies while evaluating them by trial through interaction with the environment. The set of policies is constrained by the architecture of the agent's controller. POMDPs require a controller to have a memory. We investigate controllers with memory, including controllers with external memory, finite state controllers and distributed controllers for multi-agent systems. For these various controllers we work out the details of the algorithms which learn by ascending the gradient of expected cumulative reinforcement. Building on statistical learning theory and experiment design theory, a policy evaluation algorithm is developed for the case of experience re-use. We address the question of sufficient experience for uniform convergence of policy evaluation and obtain sample complexity bounds for various estimators. Finally, we demonstrate the performance of the proposed algorithms on several domains, the most complex of which is simulated adaptive packet routing in a telecommunication network.

Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks

Sigmoid type belief networks, a class of probabilistic neural networks, provide a natural framework for compactly representing probabilistic information in a variety of unsupervised and supervised learning problems. Often the parameters used in these networks need to be learned from examples. Unfortunately, estimating the parameters via exact probabilistic calculations (i.e, the EM-algorithm) is intractable even for networks with fairly small numbers of hidden units. We propose to avoid the infeasibility of the E step by bounding likelihoods instead of computing them exactly. We introduce extended and complementary representations for these networks and show that the estimation of the network parameters can be made fast (reduced to quadratic optimization) by performing the estimation in either of the alternative domains. The complementary networks can be used for continuous density estimation as well.

Cambio climático y mundo en desarrollo: las insidias del learning by doing… (Otros Temas)

No hay duda, el mundo parece estar de acuerdo en que la reunión de Copenhague, el pasado mes de diciembre, fue un fracaso rotundo. Hay quienes no rescatan nada de la cumbre, sencillamente todo acabó en una operación de fachada de última hora, en la que un puñado de países, en nombre de todos los demás y para sacar la cara frente al mundo, presentaba el famoso Acuerdo de Copenhague.