8,049 research outputs found
Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-Organization
Artificial autonomous agents and robots interacting in complex environments
are required to continually acquire and fine-tune knowledge over sustained
periods of time. The ability to learn from continuous streams of information is
referred to as lifelong learning and represents a long-standing challenge for
neural network models due to catastrophic forgetting. Computational models of
lifelong learning typically alleviate catastrophic forgetting in experimental
scenarios with given datasets of static images and limited complexity, thereby
differing significantly from the conditions artificial agents are exposed to.
In more natural settings, sequential information may become progressively
available over time and access to previous experience may be restricted. In
this paper, we propose a dual-memory self-organizing architecture for lifelong
learning scenarios. The architecture comprises two growing recurrent networks
with the complementary tasks of learning object instances (episodic memory) and
categories (semantic memory). Both growing networks can expand in response to
novel sensory experience: the episodic memory learns fine-grained
spatiotemporal representations of object instances in an unsupervised fashion
while the semantic memory uses task-relevant signals to regulate structural
plasticity levels and develop more compact representations from episodic
experience. For the consolidation of knowledge in the absence of external
sensory input, the episodic memory periodically replays trajectories of neural
reactivations. We evaluate the proposed model on the CORe50 benchmark dataset
for continuous object recognition, showing that we significantly outperform
current methods of lifelong learning in three different incremental learning
scenario
The Overlooked Potential of Generalized Linear Models in Astronomy - I: Binomial Regression
Revealing hidden patterns in astronomical data is often the path to
fundamental scientific breakthroughs; meanwhile the complexity of scientific
inquiry increases as more subtle relationships are sought. Contemporary data
analysis problems often elude the capabilities of classical statistical
techniques, suggesting the use of cutting edge statistical methods. In this
light, astronomers have overlooked a whole family of statistical techniques for
exploratory data analysis and robust regression, the so-called Generalized
Linear Models (GLMs). In this paper -- the first in a series aimed at
illustrating the power of these methods in astronomical applications -- we
elucidate the potential of a particular class of GLMs for handling
binary/binomial data, the so-called logit and probit regression techniques,
from both a maximum likelihood and a Bayesian perspective. As a case in point,
we present the use of these GLMs to explore the conditions of star formation
activity and metal enrichment in primordial minihaloes from cosmological
hydro-simulations including detailed chemistry, gas physics, and stellar
feedback. We predict that for a dark mini-halo with metallicity , an increase of in the gas
molecular fraction, increases the probability of star formation occurrence by a
factor of 75%. Finally, we highlight the use of receiver operating
characteristic curves as a diagnostic for binary classifiers, and ultimately we
use these to demonstrate the competitive predictive performance of GLMs against
the popular technique of artificial neural networks.Comment: 20 pages, 10 figures, 3 tables, accepted for publication in Astronomy
and Computin
Aerospace medicine and biology: A continuing bibliography with indexes (supplement 309)
This bibliography lists 136 reports, articles and other documents introduced into the NASA scientific and technical information system in February, 1988
Bessel Functions in Mass Action. Modeling of Memories and Remembrances
Data from experimental observations of a class of neurological processes
(Freeman K-sets) present functional distribution reproducing Bessel function
behavior. We model such processes with couples of damped/amplified oscillators
which provide time dependent representation of Bessel equation. The root loci
of poles and zeros conform to solutions of K-sets. Some light is shed on the
problem of filling the gap between the cellular level dynamics and the brain
functional activity. Breakdown of time-reversal symmetry is related with the
cortex thermodynamic features. This provides a possible mechanism to deduce
lifetime of recorded memory.Comment: 16 pages, 9 figures, Physics Letters A, 2015 in pres
A contribution to exchange rate forecasting based on machine learning techniques
El propòsit d'aquesta tesi és examinar les aportacions a l'estudi de la predicció de la taxa de canvi basada en l'ús de tècniques d'aprenentatge automà tic. Aquestes aportacions es veuen facilitades i millorades per l'ús de variables econòmiques, indicadors tècnics i variables de tipus ‘business and consumer survey’. Aquesta investigació s’organitza entorn d’una recopilació de quatre articles. L'objectiu de cadascun dels quatre treballs de recerca d'aquesta tesi és el de contribuir a l'avanç del coneixement sobre els efectes i mecanismes mitjançant els quals l'ús de variables econòmiques, indicadors tècnics, variables de tipus ‘business and consumer survey’, i la selecció dels parà metres de models predictius són capaços de millorar les prediccions de la taxa de canvi.
Fent ús d'una tècnica de predicció no lineal, el primer article d'aquesta tesi es centra majorità riament en l'impacte que tenen l'ús de variables econòmiques i la selecció dels parà metres dels models en les prediccions de la taxa de canvi per a dos països. L'últim experiment d'aquest primer article fa ús de la taxa de canvi del perÃode anterior i d'indicadors econòmics com a variables d'entrada en els models predictius.
El segon article d'aquesta tesi analitza com la combinació de mitjanes mòbils, variables de tipus ‘business and consumer survey’ i la selecció dels parà metres dels models milloren les prediccions del canvi per a dos països. A diferència del primer article, aquest segon treball de recerca afegeix mitjanes mòbils i variables de tipus ‘business and consumer survey’ com a variables d'entrada en els models predictius, i descarta l'ús de variables econòmiques. Un dels objectius d'aquest segon article és determinar el possible impacte de les variables de tipus ‘business and consumer survey’ en les taxes de canvi.
El tercer article d'aquesta tesi té els mateixos objectius que el segon, però amb l'excepció que l'anà lisi abasta les taxes de canvi de set països. El quart article de la tesi compta amb els mateixos objectius que l'article anterior, però amb la diferència que fa ús d'un sol indicador tècnic.
En general, l'enfocament d'aquesta tesi pretén examinar diferents alternatives per a millorar les prediccions del tipus de canvi a través de l'ús de mà quines de suport vectorial. Una combinació de variables i la selecció dels parà metres dels models predictius ajudaran a aconseguir aquest propòsit.El propósito de esta tesis es examinar las aportaciones al estudio de la predicción de la tasa de cambio basada en el uso de técnicas de aprendizaje automático. Dichas aportaciones se ven facilitadas y mejoradas por el uso de variables económicas, indicadores técnicos y variables de tipo ‘business and consumer survey’. Esta investigación está organizada en un compendio de cuatro artÃculos. El objetivo de cada uno de los cuatro trabajos de investigación de esta tesis es el de contribuir al avance del conocimiento sobre los efectos y mecanismos mediante los cuales el uso de variables económicas, indicadores técnicos, variables de tipo ‘business and consumer survey’, y la selección de los parámetros de modelos predictivos son capaces de mejorar las predicciones de la tasa de cambio.
Haciendo uso de una técnica de predicción no lineal, el primer artÃculo de esta tesis se centra mayoritariamente en el impacto que tienen el uso de variables económicas y la selección de los parámetros de los modelos en las predicciones de la tasa de cambio para dos paÃses. El último experimento de este primer artÃculo hace uso de la tasa de cambio del periodo anterior y de indicadores económicos como variables de entrada en los modelos predictivos. El segundo artÃculo de esta tesis analiza cómo la combinación de medias móviles, variables de tipo ‘business and consumer survey’ y la selección de los parámetros de los modelos mejoran las predicciones del cambio para dos paÃses. A diferencia del primer artÃculo, este segundo trabajo de investigación añade medias móviles y variables de tipo ‘business and consumer survey’ como variables de entrada en los modelos predictivos, y descarta el uso de variables económicas. Uno de los objetivos de este segundo artÃculo es determinar el posible impacto de las variables de tipo ‘business and consumer survey’ en las tasas de cambio. El tercer artÃculo de esta tesis tiene los mismos objetivos que el segundo, pero con la salvedad de que el análisis abarca las tasas de cambio de siete paÃses. El cuarto artÃculo de esta tesis cuenta con los mismos objetivos que el artÃculo anterior, pero con la diferencia de que hace uso de un solo indicador técnico.
En general, el enfoque de esta tesis pretende examinar diferentes alternativas para mejorar las predicciones del tipo de cambio a través del uso de máquinas de soporte vectorial. Una combinación de variables y la selección de los parámetros de los modelos predictivos ayudarán a conseguir este propósito.The purpose of this thesis is to examine the contribution made by machine learning techniques on exchange rate forecasting. Such contributions are facilitated and enhanced by the use of fundamental economic variables, technical indicators and business and consumer survey variables as inputs in the forecasting models selected. This research has been organized in a compendium of four articles. The aim of each of these four articles is to contribute to advance our knowledge on the effects and means by which the use of fundamental economic variables, technical indicators, business and consumer surveys, and a model’s free-parameters selection is capable of improving exchange rate predictions. Through the use of a non-linear forecasting technique, one research paper examines the effect of fundamental economic variables and a model’s parameters selection on exchange rate forecasts, whereas the other three articles concentrate on the effect of technical indicators, a model’s parameters selection and business and consumer surveys variables on exchange rate forecasting.
The first paper of this thesis has the objective of examining fundamental economic variables and a forecasting model’s parameters in an effort to understand the possible advantages or disadvantages these variables may bring to the exchange rate predictions in terms of forecasting performance and accuracy. The second paper of this thesis analyses how the combination of moving averages, business and consumer surveys and a forecasting model’s parameters improves exchange rate predictions. Compared to the first paper, this second paper adds moving averages and business and consumer surveys variables as inputs to the forecasting model, and disregards the use of fundamental economic variables. One of the goals of this paper is to determine the possible effects of business and consumer surveys on exchange rates.
The third paper of this thesis has the same objectives as the second paper, but its analysis is expanded by taking into account the exchange rates of 7 countries. The fourth paper in this thesis takes a similar approach as the second and third papers, but makes use of a single technical indicator. In general, this thesis focuses on the improvement of exchange rate predictions through the use of support vector machines. A combination of variables and a model’s parameters selection enhances the way to achieve this purpose
Aerospace medicine and biology: A continuing bibliography with indexes
This bibliography lists 148 reports, articles and other documents introduced into the NASA scientific and technical information system in December 1984
Electronic nose coupled with linear and nonlinear supervised learning methods for rapid discriminating quality grades of superior java cocoa beans
An electronic nose (E-nose), comprising eight metal oxide semiconductor (MOS) gas sensors and a moisture-temperature sensor, was used for classifying three quality grades of superior java cocoa beans, namely fine cocoa dark bean 60%, and bulk cocoa bean that is a harder task compared to the discrimination of high versus low-quality cocoa beans. The E-nose signals were pre-processed using the maximum value method. The capability for discriminating the quality grade of the cocoa beans was checked by applying multivariate statistical tools, namely, linear discriminant analysis (LDA), support vector machine (SVM) and artificial neural networks (ANN). For this, the experimental dataset was split into two subsets, one for training (i.e., establishing the classification models) and the other for external-validation purposes. Furthermore, hyperparameter optimization and K-fold cross-validation variant were implemented during the model training procedure to select the best classification models and to avoid over-fitting issues. The best predictive classification performance was obtained with the E-nose-MLP-ANN procedure, which allowed 99% of correct classifications (overall accuracy) for the training dataset and 95% of correct classifications (overall accuracy) for the external-validation dataset. The satisfactory results clearly demonstrated that the E-nose could be applied as a quality control tool in the cocoa industry, requiring minimum and simple sample preparation. © Intelligent Network and Systems Society.The authors thank the Directorate of Research and Community Service, Ministry of Research,
Technology and Higher Education, the Republic of Indonesia for providing research grants of PTUPT
2019 (Contract No. 2688/UN1.DITLIT/DITLIT/LT/2019). The authors also like to acknowledge the financial support given by Associate Laboratory LSRE-LCM-UID/EQU/50020/2019, strategic
funding UID/BIO/04469/2019-CEB, BioTecNorte operation (NORTE-01-0145-FEDER-000004) and
strategic project PEst-OE/AGR/UI0690/2014 – CIMO, all funded by national funds through
FCT/MCTES (PIDDAC).info:eu-repo/semantics/publishedVersio
NASA supported research programs
A summary of the scientific NASA grants and achievements accomplished by the University of California, Los Angles, is presented. The development of planetary and space sciences as a major curriculum of the University, and statistical data on graduate programs in aerospace sciences are discussed. An interdisciplinary approach to aerospace science education is emphasized. Various research programs and scientific publications that are a direct result of NASA grants are listed
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