704 research outputs found
The Impact of Employment Web Sites' Traffic on Unemployment: A Cross Country Comparison
Although employment web sites have recently become the main source for re-
cruitment and selection process, the relation between those sites and unemploy-
ment rates is seldom addressed. Deriving data from 32 countries and 427 web
sites, this study explores the correlation between unemployment rates of
European countries and the attractiveness of country specific employment web
sites. It also compares the changes in unemployment rates and traffic on all
the aforementioned web sites. The results showed that there is a strong
correlation between web sites traffic and unemployment rates.Comment: 9 page
Estimating Material Properties of Interacting Objects Using Sum-GP-UCB
Robots need to estimate the material and dynamic properties of objects from
observations in order to simulate them accurately. We present a Bayesian
optimization approach to identifying the material property parameters of
objects based on a set of observations. Our focus is on estimating these
properties based on observations of scenes with different sets of interacting
objects. We propose an approach that exploits the structure of the reward
function by modeling the reward for each observation separately and using only
the parameters of the objects in that scene as inputs. The resulting
lower-dimensional models generalize better over the parameter space, which in
turn results in a faster optimization. To speed up the optimization process
further, and reduce the number of simulation runs needed to find good parameter
values, we also propose partial evaluations of the reward function, wherein the
selected parameters are only evaluated on a subset of real world evaluations.
The approach was successfully evaluated on a set of scenes with a wide range of
object interactions, and we showed that our method can effectively perform
incremental learning without resetting the rewards of the gathered
observations
High Frequency Scattering from Arbitrarily Oriented Dielectric Disks
Calculations have been made of electromagnetic wave scattering from dielectric disks of arbitrary shape and orientation in the high frequency (physical optics) regime. The solution is obtained by approximating the fields inside the disk with the fields induced inside an identically oriented slab (i.e. infinite parallel planes) with the same thickness and dielectric properties. The fields inside the disk excite conduction and polarization currents which are used to calculate the scattered fields by integrating the radiation from these sources over the volume of the disk. This computation has been executed for observers in the far field of the disk in the case of disks with arbitrary orientation and for arbitrary polarization of the incident radiation. The results have been expressed in the form of a dyadic scattering amplitude for the disk. The results apply to disks whose diameter is large compared to wavelength and whose thickness is small compared to diameter, but the thickness need not be small compared to wavelength. Examples of the dependence of the scattering amplitude on frequency, dielectric properties of the disk and disk orientation are presented for disks of circular cross section
Entropy-based fault detection approach for motor vibration signals under accelerated aging process
The purpose of this study is to analyze motor vibration signals due to the bearing fault, which is artificially generated by aging process. Vibration signal data recorded by the experimental setup has been conditioned by a high-pass filter (Butterworth type) to reach the regarding frequency components of the bearing failure. Spectral analysis has been applied to realize the degradation on the bearing and the power spectral density figures revealed that the magnitudes of frequency components between 1.5-4 kHz bandwidth increased after every aging cycle. Vibration signals were investigated statistically by examining four main statistical parameters: mean value, standard deviation, skewness and kurtosis. Evaluation of these parameters indicated that significant variance occurred on standard deviation. At this point Shannon entropy became an approach to analyze the variance on the standard deviation. The probability of the aging cycles has been defined as a function of standard deviation values for each aging cycle. Entropy definition, which is a function of probability, determines the uncertainty level on the data and it has been examined to identify the effect of the aging progress on the bearing by examining the transferred entropy amount between aging cycles
A Quantisation of Cognitive Learning Process by Computer Graphics-Games: Towards More Efficient Learning Models
Research group of Computer Sciences at DMU, Psychology Research Group at University of Birmingham and Reseach Group of Computer Science at University of Northumbria.With the latest developments in computer technologies and artificial intelligence (AI) techniques, more opportunities of cognitive data acquisition and stimulation via game-based systems have become available for computer scientists and psychologists. This may lead to more efficient cognitive learning model developments to be used in different fields of cognitive psychology than in the past. The increasing popularity of computer games among a broad range of age groups leads scientists and experts to seek game domain solutions to cognitive based learning abnormalities, especially for younger age groups and children. One of the major advantages of computer graphics and using game-based techniques over the traditional face-to-face therapies is that individuals, especially children immerse in the game’s virtual environment and consequently feel more open to share their cognitive behavioural characteristics naturally. The aim of this work is to investigate the effects of graphical agents on cognitive behaviours to generate more efficient cognitive models
Imitation and Mirror Systems in Robots through Deep Modality Blending Networks
Learning to interact with the environment not only empowers the agent with
manipulation capability but also generates information to facilitate building
of action understanding and imitation capabilities. This seems to be a strategy
adopted by biological systems, in particular primates, as evidenced by the
existence of mirror neurons that seem to be involved in multi-modal action
understanding. How to benefit from the interaction experience of the robots to
enable understanding actions and goals of other agents is still a challenging
question. In this study, we propose a novel method, deep modality blending
networks (DMBN), that creates a common latent space from multi-modal experience
of a robot by blending multi-modal signals with a stochastic weighting
mechanism. We show for the first time that deep learning, when combined with a
novel modality blending scheme, can facilitate action recognition and produce
structures to sustain anatomical and effect-based imitation capabilities. Our
proposed system, can be conditioned on any desired sensory/motor value at any
time-step, and can generate a complete multi-modal trajectory consistent with
the desired conditioning in parallel avoiding accumulation of prediction
errors. We further showed that given desired images from different
perspectives, i.e. images generated by the observation of other robots placed
on different sides of the table, our system could generate image and joint
angle sequences that correspond to either anatomical or effect based imitation
behavior. Overall, the proposed DMBN architecture not only serves as a
computational model for sustaining mirror neuron-like capabilities, but also
stands as a powerful machine learning architecture for high-dimensional
multi-modal temporal data with robust retrieval capabilities operating with
partial information in one or multiple modalities
DeepSym: Deep Symbol Generation and Rule Learning from Unsupervised Continuous Robot Interaction for Planning
Autonomous discovery of discrete symbols and rules from continuous
interaction experience is a crucial building block of robot AI, but remains a
challenging problem. Solving it will overcome the limitations in scalability,
flexibility, and robustness of manually-designed symbols and rules, and will
constitute a substantial advance towards autonomous robots that can learn and
reason at abstract levels in open-ended environments. Towards this goal, we
propose a novel and general method that finds action-grounded, discrete object
and effect categories and builds probabilistic rules over them that can be used
in complex action planning. Our robot interacts with single and multiple
objects using a given action repertoire and observes the effects created in the
environment. In order to form action-grounded object, effect, and relational
categories, we employ a binarized bottleneck layer of a predictive, deep
encoder-decoder network that takes as input the image of the scene and the
action applied, and generates the resulting object displacements in the scene
(action effects) in pixel coordinates. The binary latent vector represents a
learned, action-driven categorization of objects. To distill the knowledge
represented by the neural network into rules useful for symbolic reasoning, we
train a decision tree to reproduce its decoder function. From its branches we
extract probabilistic rules and represent them in PPDDL, allowing off-the-shelf
planners to operate on the robot's sensorimotor experience. Our system is
verified in a physics-based 3d simulation environment where a robot arm-hand
system learned symbols that can be interpreted as 'rollable', 'insertable',
'larger-than' from its push and stack actions; and generated effective plans to
achieve goals such as building towers from given cubes, balls, and cups using
off-the-shelf probabilistic planners
FEPDS: A Proposal for the Extraction of Fuzzy Emerging Patterns in Data Streams
Nowadays, most data is generated by devices that produce data continuously. These kinds of data can be categorised as data streams and valuable insights can be extracted from them. In particular, the insights extracted by emerging patterns are interesting in a data stream context as easy, fast, reliable decisions can be made. However, their extraction is a challenge due to the necessary response time, memory and continuous model updates.
In this paper, an approach for the extraction of emerging patterns in data streams is presented. It processes the instances by means of batches following an adaptive approach. The learning algorithm is an evolutionary fuzzy system where previous knowledge is employed in order to adapt to concept drift. A wide experimental study has been performed in order to show both the suitability of the approach in combating concept drift and the quality of the knowledge extracted. Finally, the proposal is applied to a case study related to the continuous determination of the profiles of New York City cab customers according to their fare amount, in order to show its potential
Twenty-seven mutations with three novel pathologenic variants causing biotinidase deficiency: a report of 203 patients from the southeastern part of Turkey
BACKGROUND: Biotinidase deficiency (BD) is an autosomal recessive inborn error of metabolism characterized by neurologic and cutaneous symptoms and can be detected by newborn screening. Newborn screening for BD was implemented in Turkey at the end of 2008. METHODS: In total, 203 patients who were identified among the infants detected by the newborn screening were later confirmed to have BD through measurement of serum biotinidase activity. We also performed BTD mutation analysis to characterize the genetic profile. RESULTS: Twenty-seven mutations were identified. The most commonly found variants were c.1330G>C (p.D444H), c.1595C>T (p.T532M), c.470G>A (p.R157H), and c.198_104delGCGGCTGinsTCC (p.C33Ffs ) with allele frequencies of 0.387, 0.175, 0.165 and 0.049, respectively. Three novel pathogenic and likely pathogenic variants were identified: p.W140* (c.419G>A), p.S319F (c.956C>T) and p.L69Hfs*24 (c.192_193insCATC). We also identified three mutations reported in just one patient in the past (p.V442Sfs*59 [c.1324delG], p.H447R [c.1340A>G] and p.198delV [c.592_594delGTC]). Although all of the patients were asymptomatic under the treatment of biotin, only one patient, who had the novel c.419G>A homozygous mutation became symptomatic during an episode of acute gastroenteritis with a presentation of ketosis and metabolic acidosis. Among the screened patients, 156 had partial and 47 had profound BD. CONCLUSIONS: We determined the mutation spectra of BD from the southeastern part of Turkey. The results of this study add three more mutations to the total number of mutations described as causing BD
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