An Artificial Synaptic Plasticity Mechanism for Classical Conditioning with Neural Networks

We present an artificial synaptic plasticity (ASP) mechanism that allows artificial systems to make associations between environmental stimuli and learn new skills at runtime. ASP builds on the classical neural network for simulating associative learning, which is induced through a conditioning-like procedure. Experiments in a simulated mobile robot demonstrate that ASP has successfully generated conditioned responses. The robot has learned during environmental exploration to use sensors added after training, improving its object-avoidance capabilities

Improving the predictive performance of SAFEL: A Situation-Aware FEar Learning model

In this paper, we optimize the predictive performance of a Situation-Aware FEar Learning model (SAFEL) by investigating the relationship between its parameters. SAFEL is a hybrid computational model based on the fear-learning system of the brain, which was developed to provide robots with the capability to predict threatening or undesirable situations based on temporal context. The main aim of this work is to improve SAFEL's emotional response. An emotional response coherent with environmental changes is essential not only for self-preservation and adaptation purposes, but also for improving the believability and interaction skills of companion robots. Experiments with a NAO humanoid robot show that adjusting the ratio between two parameters of SAFEL can significantly increase the predictive performance and reduce parameter settings

SAFEL - A Situation-aware Fear Learning Model

This thesis proposes a novel and robust online adaptation mechanism for threat prediction and prevention capable of taking into consideration complex contextual and temporal information in its internal learning processes. The proposed mechanism is a hybrid cognitive computational model named SAFEL (Situation-Aware FEar Learning), which integrates machine learning algorithms with concepts of situation-awareness from expert systems to simulate both the cued and contextual fear-conditioning phenomena. SAFEL is inspired by well-known neuroscience findings on the brain's mechanisms of fear learning and memory to provide autonomous robots with the ability to predict undesirable or threatening situations to themselves. SAFEL's ultimate goal is to allow autonomous robots to perceive intricate elements and relationships in their environment, learn with experience through autonomous environmental exploration, and adapt at execution time to environmental changes and threats. SAFEL consists of a hybrid architecture composed of three modules, each based on a different approach and inspired by a different region (or function) of the brain involved in fear learning. These modules are: the Amygdala Module (AM), the Hippocampus Module (HM) and the Working Memory Module (WMM). The AM learns and detects environmental threats while the HM makes sense of the robot's context. The WMM is responsible for combining and associating the two types of information processed by the AM and HM. More specifically, the AM simulates the cued conditioning phenomenon by creating associations between co-occurring aversive and neutral environmental stimuli. The AM represents the kernel of emotional appraisal and threat detection in SAFEL's architecture. The HM, in turn, handles environmental information at a higher level of abstraction and complexity than the AM, which depicts the robot's situation as a whole. The information managed by the HM embeds in a unified representation the temporal interactions of multiple stimuli in the environment. Finally, the WMM simulates the contextual conditioning phenomenon by creating associations between the contextual memory formed in the HM and the emotional memory formed in the AM, thus giving emotional meaning to the contextual information acquired in past experiences. Ultimately, any previously experienced pattern of contextual information triggers the retrieval of that stored contextual memory and its emotional meaning from the WMM, warning the robot that an undesirable situation is likely to happen in the near future. The main contribution of this work as compared to the state of the art is a domain-independent mechanism for online learning and adaptation that combines a fear-learning model with the concept of temporal context and is focused on real-world applications for autonomous robotics. SAFEL successfully integrates a symbolic rule-based paradigm for situation management with machine learning algorithms for memorizing and predicting environmental threats to the robot based on complex temporal context. SAFEL has been evaluated in several experiments, which analysed the performance of each module separately. Ultimately, we conducted a comprehensive case study in the robot soccer scenario to evaluate the collective work of all modules as a whole. This case study also analyses to which extent the emotional feedback of SAFEL can improve the intelligent behaviour of a robot in a practical real-world situation, where adaptive skills and fast/flexible decision-making are crucial

A Situation-Aware Fear Learning (SAFEL) Model for Robots

This work proposes a novel Situation-Aware FEar Learning (SAFEL) model for robots. SAFEL combines concepts of situation-aware expert systems with well-known neuroscientific findings on the brain fear-learning mechanism to allow companion robots to predict undesirable or threatening situations based on past experiences. One of the main objectives is to allow robots to learn complex temporal patterns of sensed environmental stimuli and create a representation of these patterns. This memory can be later associated with a negative or positive “emotion”, analogous to fear and confidence. Experiments with a real robot demonstrated SAFEL’s success in generating contextual fear conditioning behaviour with predictive capabilities based on situational information

CraftContext: A Test Platform for Context-Aware Applications

This paper presents a tool, called CraftContext, capable of leveraging the test phase of contextaware application development. CraftContext offers a 3D simulation environment, which is rich in details and resources, and is accessed by a robust and portable CORBA-based API. CraftContext excels most currentexisting testing tools due to its adaptability to different domains.Key words: context-awareness, test tool, CraftContext, JacORB, CORBA, minecraft

High yield expression of leptospirosis vaccine candidates LigA and LipL32 in the methylotrophic yeast Pichia pastoris

<p>Abstract</p> <p>Background</p> <p>Leptospirosis, a zoonosis caused by <it>Leptospira </it>spp., is recognized as an emergent infectious disease. Due to the lack of adequate diagnostic tools, vaccines are an attractive intervention strategy. Recombinant proteins produced in <it>Escherichia coli </it>have demonstrated promising results, albeit with variable efficacy. <it>Pichia pastoris </it>is an alternative host with several advantages for the production of recombinant proteins.</p> <p>Results</p> <p>The vaccine candidates LigANI and LipL32 were cloned and expressed in <it>P. pastoris </it>as secreted proteins. Large-scale expression resulted in a yield of 276 mg/L for LigANI and 285 mg/L for LipL32. The recombinant proteins were glycosylated and were recognized by antibodies present in the sera of patients with severe leptospirosis.</p> <p>Conclusions</p> <p>The expression of LigANI and LipL32 in <it>P. pastoris </it>resulted in a significant increase in yield compared to expression in <it>E. coli</it>. In addition, the proteins were secreted, allowing for easy purification, and retained the antigenic characteristics of the native proteins, demonstrating their potential application as subunit vaccine candidates.</p

Efeito do exercício físico e da suplementação com L-arginina em marcadores bioquímicos, antropométricos e de força em mulheres com Hipotireoidismo

Hypothyroidism is characterized by a dysfunction of the thyroid gland that has a high production of thyroid stimulating hormone (TSH) and fails to produce its hormones thyroxine (T4) and triiodothyronine (T3) correctly. Frequent in female populations, it is related to secondary dyslipidemias, endothelial dysfunction, and the reduction of the bioavailability of nitric oxide (NO). Objective: This study evaluated the effect of physical exercise and L-arginine supplementation on the levels of NO, lipid profile, anthropometric and physical data in 16 hypothyroid women residing in the city of Sarandi-RS. Materials and Methods: Characterized by a randomized clinical trial by intervention, divided into four groups. G1: exercise and supplementation; G2: exercise; G3: supplementation with L-arginine and G4: control. To evaluate the physical abilities of the volunteers, anthropometric data (weight, height, waist and hip measurements, skinfolds), 1RM strength tests, and VO2 max treadmill test were analyzed. Blood tests were performed on CT, HDL, LDL, TG, TSH, T3, T4 and NO. Results: The results obtained were significant for the reduction of% G and increase of lean mass for the G1 and G3 groups, as well as an increase of 1RM in G1 and G2. Other important results observed were the significant increase of NO to G1, and from T3 to G2. Conclusion: L-arginine supplementation may be beneficial to the studied population, and may improve quality of life, reducing CT and increasing bioavailability of NO, as well as physical exercise may be beneficial to increase T3O hipotireoidismo é caracterizado por uma disfunção da glândula tireoide que apresenta uma alta produção do hormônio tireo-estimulante (TSH) e deixa de produzir seus hormônios tiroxina (T4) e triiodotironina (T3) corretamente. Frequente em populações femininas, está relacionado a dislipidemias secundárias, disfunção endotelial e a redução da biodisponibilidade de óxido nítrico (NO). Objetivo: Esta pesquisa avaliou o efeito do exercício físico e da suplementação de L-arginina sobre os níveis de NO, perfil lipídico, dados antropométricos e físicos em 16 mulheres portadoras de hipotireoidismo residentes no município de Sarandi-RS. Materiais e Métodos: Este estudo é caracterizado por um estudo clínico randomizado por intervenção, dividido em quatro grupos. G1: exercício e suplementação; G2: exercício; G3: suplementação com L-arginina e G4: controle. Para avaliar as capacidades físicas das voluntárias foram analisados dados antropométricos (peso, altura, medidas de cintura e quadril, dobras cutâneas). Foram realizados testes de força de 1RM, e teste de VO2max em esteira rolante. Para análises sanguíneas foram realizados exames de CT, HDL, LDL, TG, TSH, T3, T4 e NO. Resultados: Os resultados obtidos foram significativos para a redução do %G e aumento de massa magra para os grupos G1 e G3, assim como foi verificado aumento de 1RM no G1 e G2. Outros resultados importantes verificados foram o aumento significativo de NO para G1, e de T3 para G2. Conclusão: A suplementação com L-arginina pode ser benéfica a população estudada, podendo melhorar a qualidade de vida, reduzindo o CT e aumentando a biodisponibilidade de NO, assim como exercício físico pode ser benéfico ao aumento de T3

Order and structure in syntax I: Word order and syntactic structure

This book reconsiders the role of order and structure in syntax, focusing on fundamental issues such as word order and grammatical functions. The first group of papers in the collection asks what word order can tell us about syntactic structure, using evidence from V2, object shift, word order gaps and different kinds of movement. The second group of papers all address the issue of subjecthood in some way, and examine how certain subject properties vary across languages: expression of subjects, expletive subjects, quirky and locative subjects. All of the papers address in some way the tension between modelling what can vary across languages whilst improving our understanding of what might be universal to human language. This book is complemented by Order and structure in syntax II: Subjecthood and argument structure &nbsp