Sequential Memory with ART: A Self-Organizing Network Capable of Learning Sequences of Patterns

A model which extends the adaptive resonance theory model to sequential memory is presented. This new model learns sequences of events and recalls a sequence when presented with parts of the sequence. A sequence can have repeated events and different sequences can share events. The ART model is modified by creating interconnected sublayers within ART's F2 layer. Nodes within F2 learn temporal patterns by forming recency gradients within LTM. Versions of the ART model like ART I, ART 2, and fuzzy ART can be used.

História e filosofia da ciência na formação inicial em ciências de educadores e professores do 1º e 2º ciclo do ensino básico : uma investigação-ação

Tese de doutoramento, Educação (Didática das Ciências), Universidade de Lisboa, Instituto de Educação, 2015

Ontogeny of manipulative behavior and nut-cracking in young tufted capuchin monkeys (Cebus apella): a Perception-action perspective

How do capuchin monkeys learn to use stones to crack open nuts? Perception-action theory posits that individuals explore producing varying spatial and force relations among objects and surfaces, thereby learning about affordances of such relations and how to produce them. Such learning supports the discovery of tool use. We present longitudinal developmental data from semifree-ranging tufted capuchin monkeys (Cebus apella) to evaluate predictions arising from Perception-action theory linking manipulative development and the onset of tool-using. Percussive actions bringing an object into contact with a surface appeared within the first year of life. Most infants readily struck nuts and other objects against stones or other surfaces from 6 months of age, but percussive actions alone were not sufficient to produce nut-cracking sequences. Placing the nut on the anvil surface and then releasing it, so that it could be struck with a stone, was the last element necessary for nut-cracking to appear in capuchins. Young chimpanzees may face a different challenge in learning to crack nuts: they readily place objects on surfaces and release them, but rarely vigorously strike objects against surfaces or other objects. Thus the challenges facing the two species in developing the same behavior (nut-cracking using a stone hammer and an anvil) may be quite different. Capuchins must inhibit a strong bias to hold nuts so that they can release them; chimpanzees must generate a percussive action rather than a gentle placing action. Generating the right actions may be as challenging as achieving the right sequence of actions in both species. Our analysis suggests a new direction for studies of social influence on young primates learning sequences of actions involving manipulation of objects in relation to surfaces.

Measuring Correlation Between HIV Sequence Variation and Disease Stage of HIV-Infected Individuals

With a virus such as Human Immunodeficiency Virus (HIV) that has infected millions of people worldwide, and with many unaware that they are infected, it becomes vital to understand how the virus works and how it functions at the molecular level. Because there currently is no vaccine and no way to eradicate the virus from an infected person, any information about how the virus interacts with its host greatly increases the chances of understanding how HIV works and brings scientists one step closer to being able to combat such a destructive virus. Thousands of HIV viruses have been sequenced and are available in many online databases for public use. Attributes that are linked to each sequence include the viral load within the host and how sick the patient is currently. Being able to predict the stage of infection for someone is a valuable resource, as it could potentially aid in treatment options and proper medication use. Our approach of analyzing region-specific amino acid composition for select genes has been able to predict patient disease state up to an accuracy of 85.4%. Moreover, we output a set of classification rules based on the sequence that may prove useful for diagnosing the expected clinical outcome of the infected patient.

Professor de dupla habilitação: (des)continuidades entre o 1º e 2ºciclos do ensino básico

Relatório de estágio apresentado à Escola Superior de Educação de Paula Frassinetti para a obtenção do grau de Mestre em Ensino do 1.º e 2.º Ciclo do Ensino Básico

O papel da observação na compreensão da natureza do conhecimento científico : uma proposta didática baseada nas reações de combustão

Com vista a abandonar a ideia tradicional de que a observação é objetiva e que, decorrente da objetividade dos factos empíricos é possível estabelecer uma correspondência com a realidade e a verdade, procura-se, através de uma situação experimental problemática, inserida num contexto de História da Ciência, criar um contexto didático apropriado para a compreensão da relação da observação com a teoria que a interpreta. Os resultados obtidos no decurso do Projeto lbero-americano de Avaliação de Atitudes Relativas a Ciência, Tecnologia e Sociedade, através da aplicação do Cuestionario de Opiniones sobre Ciencia-Tecnologia-Sociedad mostraram que a conceção que os alunos têm sobre a Natureza da Ciência e Tecnologia e sobre a relação entre observação e teoria não é a mais adequada. A Sequência de Ensino-Aprendizagem proposta baseia-se na realização de uma atividade experimental problemática e na discussão dos resultados, relacionando-os com a análise e debate de dois textos sobre a Teoria do Flogisto e a Teoria do Oxigénio.

Bi-criteria single machine scheduling problem with a learning effect: Aneja-Nair method to obtain the set of optimal sequences

In this paper, we consider the bi-criteria single machine scheduling problem of n jobs with a learning effect. The two objectives considered are the total completion time (TC) and total absolute differences in completion times (TADC). The objective is to find a sequence that performs well with respect to both the objectives: the total completion time and the total absolute differences in completion times. In an earlier study, a method of solving bi-criteria transportation problem is presented. In this paper, we use the methodology of solvin bi-criteria transportation problem, to our bi-criteria single machine scheduling problem with a learning effect, and obtain the set of optimal sequences,. Numerical examples are presented for illustrating the applicability and ease of understanding.

Learning and Production of Movement Sequences: Behavioral, Neurophysiological, and Modeling Perspectives

A growing wave of behavioral studies, using a wide variety of paradigms that were introduced or greatly refined in recent years, has generated a new wealth of parametric observations about serial order behavior. What was a mere trickle of neurophysiological studies has grown to a more steady stream of probes of neural sites and mechanisms underlying sequential behavior. Moreover, simulation models of serial behavior generation have begun to open a channel to link cellular dynamics with cognitive and behavioral dynamics. Here we summarize the major results from prominent sequence learning and performance tasks, namely immediate serial recall, typing, 2XN, discrete sequence production, and serial reaction time. These populate a continuum from higher to lower degrees of internal control of sequential organization. The main movement classes covered are speech and keypressing, both involving small amplitude movements that are very amenable to parametric study. A brief synopsis of classes of serial order models, vis-à-vis the detailing of major effects found in the behavioral data, leads to a focus on competitive queuing (CQ) models. Recently, the many behavioral predictive successes of CQ models have been joined by successful prediction of distinctively patterend electrophysiological recordings in prefrontal cortex, wherein parallel activation dynamics of multiple neural ensembles strikingly matches the parallel dynamics predicted by CQ theory. An extended CQ simulation model-the N-STREAMS neural network model-is then examined to highlight issues in ongoing attemptes to accomodate a broader range of behavioral and neurophysiological data within a CQ-consistent theory. Important contemporary issues such as the nature of working memory representations for sequential behavior, and the development and role of chunks in hierarchial control are prominent throughout.

An examination of the effects on learning seen in children afforded the opportunity to control the order of repetitions for three novel spatiotemporal sequences

Children were afforded the opportunity to control the order of repetitions for three novel spatiotemporal sequences. The following was predicted: a) children and adults in the self-regulated (SELF) groups would produce faster movement (MT) and reaction times (R T) and greater recall success (RS) during retention compared to the age-matched yoked (YOKE) groups; b) children would choose to switch sequences less often than adults; c) adults would produce faster MT and RT and greater RS than the children during acquisition and retention, independent of experimental group. During acquisition, no effects were seen for RS, however for MT and RT there was a main effect for age as well as block. During retention a main effect for practice condition was seen for RS and failed to reach statistical significance for MT and RT, thus partially supporting our first and second hypotheses. The third hypothesis was not supported.

Online Learning of Non-stationary Sequences

We consider an online learning scenario in which the learner can make predictions on the basis of a fixed set of experts. The performance of each expert may change over time in a manner unknown to the learner. We formulate a class of universal learning algorithms for this problem by expressing them as simple Bayesian algorithms operating on models analogous to Hidden Markov Models (HMMs). We derive a new performance bound for such algorithms which is considerably simpler than existing bounds. The bound provides the basis for learning the rate at which the identity of the optimal expert switches over time. We find an analytic expression for the a priori resolution at which we need to learn the rate parameter. We extend our scalar switching-rate result to models of the switching-rate that are governed by a matrix of parameters, i.e. arbitrary homogeneous HMMs. We apply and examine our algorithm in the context of the problem of energy management in wireless networks. We analyze the new results in the framework of Information Theory.

People Recognition in Image Sequences by Supervised Learning

We describe a system that learns from examples to recognize people in images taken indoors. Images of people are represented by color-based and shape-based features. Recognition is carried out through combinations of Support Vector Machine classifiers (SVMs). Different types of multiclass strategies based on SVMs are explored and compared to k-Nearest Neighbors classifiers (kNNs). The system works in real time and shows high performance rates for people recognition throughout one day.

Learning HMMs for nucleotide sequences from amino acid alignments

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)