A Java simulator of Rescorla and Wagner's prediction error model and configural cue extensions

In this paper we present the "R&W Simulator" (version 3.0), a Java simulator of Rescorla and Wagner's prediction error model of learning. It is able to run whole experimental designs, and compute and display the associative values of elemental and compound stimuli simultaneously, as well as use extra configural cues in generating compound values; it also permits change of the US parameters across phases. The simulator produces both numerical and graphical outputs, and includes a functionality to export the results to a data processor spreadsheet. It is user-friendly, and built with a graphical interface designed to allow neuroscience researchers to input the data in their own "language". It is a cross-platform simulator, so it does not require any special equipment, operative system or support program, and does not need installation. The "R&W Simulator" (version 3.0) is available free.     

A case based reasoning model for multilingual language generation in dialogues

The process of Natural Language Generation for a Conversational Agent translates some semantic language to its surface form expressed in natural language. In this paper, we are going to show a Case Based Reasoning technique which is easily extensible and adaptable to multiple domains and languages, that generates coherent phrases and produces a natural outcome in the context of a Conversational Agent that maintains a dialogue with the user.     

Comments on “A novel dead time compensator for stable processes with long dead times” [Journal of Process Control 22 (2012) 612–625]

This short communication analyzes the results recently presented in the paper “On a novel dead time compensator for stable processes with long dead times” published in the Journal of Process Control. In the mentioned paper it is argued that the proposed strategy, called modified Smith predictor (MSP), gives better performance than the filtered Smith predictor (FSP) dead-time compensator for stable processes with dead time. In fact MSP has the same structure as FSP and only some specific tuning rules of the filters are proposed. Therefore, in this work some aspects of the comparative analysis and tuning rules presented in the referred paper are discussed to show that MSP is a particular case of FSP and that for some particular cases its tuning rule does not allow for a good closed-loop response. Some simulation case studies are used to illustrate these aspects.     

A learning result for continuous-time recurrent neural networks

The following learning problem is considered, for continuous-time recurrent neural networks having sigmoidal activation functions. Given a “black box” representing an unknown system, measurements of output derivatives are collected, for a set of randomly generated inputs, and a network is used to approximate the observed behavior. It is shown that the number of inputs needed for reliable generalization (the sample complexity of the learning problem) is upper bounded by an expression that grows polynomially with the dimension of the network and logarithmically with the number of output derivatives being matched.     

Packet Video Broadcasting with General-Purpose Operating Systems in an Ethernet

Video transmission with general-purpose PCs poses a number of requirements that radically differ from those of high-end dedicated video servers. We analyze the scenario of an Ethernet local area network in which a number of PCs are transmitting video streams, while other TCP/IP applications are also running concurrently. Our findings show that since the operating system clock resolution cannot cope with the transmission timing requirements the following holds: if the video transmission is performed with exact timing accuracy to maintain a constant rate then CPU load grows to 100%, thus blocking the PC for other user applications; on the other hand, if transmission is performed in a bursty manner, i.e. with sleep system calls, then CPU load decreases dramatically but the increased burstiness of the video stream has a negative impact on network performance (for example, capture effect in the Ethernet). Furthermore, the impact of video transmission over the rest of TCP/IP applications running on the same network depends heavily on the packet size. We provide an integrated analysis of operating system and network parameters to achieve video broadcasting while preserving timing requirements and minimizing the impact on other applications.     

Global stabilization of linear discrete-time systems with bounded feedback

This paper deals with the problem of global stabilization of linear discrete time systems by means of bounded feedback laws. The main result proved is an analog of one proved for the continuous time case by the authors, and shows that such stabilization is possible if and only if the system is stabilizable with arbitrary controls and the transition matrix has spectral radius less than or equal to one. The proof provides in principle an algorithm for the construction of such feedback laws, which can be implemented either as cascades or as parallel connections (“single hidden layer neural networks”) of simple saturation functions.     

Impact of Zinc on Oxidative Signaling Pathways in the Development of Pulmonary Vasoconstriction Induced by Hypobaric Hypoxia

Hypobaric hypoxia is a condition that occurs at high altitudes (>2500 m) where the partial pressure of gases, particularly oxygen (PO₂), decreases. This condition triggers several physiological and molecular responses. One of the principal responses is pulmonary vascular contraction, which seeks to optimize gas exchange under this condition, known as hypoxic pulmonary vasoconstriction (HPV); however, when this physiological response is exacerbated, it contributes to the development of high-altitude pulmonary hypertension (HAPH). Increased levels of zinc (Zn²⁺) and oxidative stress (known as the “ROS hypothesis”) have been demonstrated in the vasoconstriction process. Therefore, the aim of this review is to determine the relationship between molecular pathways associated with altered Zn²⁺ levels and oxidative stress in HPV in hypobaric hypoxic conditions. The results indicate an increased level of Zn²⁺, which is related to increasing mitochondrial ROS (mtROS), alterations in nitric oxide (NO), metallothionein (MT), zinc-regulated, iron-regulated transporter-like protein (ZIP), and nicotinamide adenine dinucleotide phosphate (NADPH) oxidase-induced protein kinase C epsilon (PKCε) activation in the development of HPV. In conclusion, there is an association between elevated Zn²⁺ levels and oxidative stress in HPV under different models of hypoxia, which contribute to understanding the molecular mechanism involved in HPV to prevent the development of HAPH.     

Learning Structure from Data and Its Application to Ozone Prediction

In this paper we propose an algorithm for structure learning in predictive expert systems based on a probabilistic network representation. The idea is to have the simplest structure (minimum number of links) with acceptable predictive capability. The algorithm starts by building a tree structure based on measuring mutual information between pairs of variables, and then it adds links as necessary to obtain certain predictive performance. We have applied this method for ozone prediction in México City, where the ozone level is used as a global indicator for the air quality in different parts of the city. It is important to predict the ozone level a day, or at least several hours in advance, to reduce the health hazards and industrial losses that occur when the ozone reaches emergency levels. We obtained as a first approximation a tree-structured dependency model for predicting ozone in one part of the city. We observe that even with only three parameters, its estimations are acceptable.A causal network representation and the structure learning techniques produced some very interesting results for the ozone prediction problem. Firstly, we got some insight into the dependence structure of the phenomena. Secondly, we got an indication of which are the important and not so important variables for ozone forecasting. Taking this into account, the measurement and computational costs for ozone prediction could be reduced. And thirdly, we have obtained satisfactory short term ozone predictions based on a small set of the most important parameters.     

Inductive transfer for learning Bayesian networks

In several domains it is common to have data from different, but closely related problems. For instance, in manufacturing, many products follow the same industrial process but with different conditions; or in industrial diagnosis, where there is equipment with similar specifications. In these cases it is common to have plenty of data for some scenarios but very little for others. In order to learn accurate models for rare cases, it is desirable to use data and knowledge from similar cases; a technique known as transfer learning. In this paper we propose an inductive transfer learning method for Bayesian networks, that considers both structure and parameter learning. For structure learning we use conditional independence tests, by combining measures from the target task with those obtained from one or more auxiliary tasks, using a novel weighted sum of the conditional independence measures. For parameter learning, we propose two variants of the linear pool for probability aggregation, combining the probability estimates from the target task with those from the auxiliary tasks. To validate our approach, we used three Bayesian networks models that are commonly used for evaluating learning techniques, and generated variants of each model by changing the structure as well as the parameters. We then learned one of the variants with a small dataset and combined it with information from the other variants. The experimental results show a significant improvement in terms of structure and parameters when we transfer knowledge from similar tasks. We also evaluated the method with real-world data from a manufacturing process considering several products, obtaining an improvement in terms of log-likelihood between the data and the model when we do transfer learning from related products.