215 research outputs found
Using Strategy Improvement to Stay Alive
We design a novel algorithm for solving Mean-Payoff Games (MPGs). Besides
solving an MPG in the usual sense, our algorithm computes more information
about the game, information that is important with respect to applications. The
weights of the edges of an MPG can be thought of as a gained/consumed energy --
depending on the sign. For each vertex, our algorithm computes the minimum
amount of initial energy that is sufficient for player Max to ensure that in a
play starting from the vertex, the energy level never goes below zero. Our
algorithm is not the first algorithm that computes the minimum sufficient
initial energies, but according to our experimental study it is the fastest
algorithm that computes them. The reason is that it utilizes the strategy
improvement technique which is very efficient in practice
Improved model identification for non-linear systems using a random subsampling and multifold modelling (RSMM) approach
In non-linear system identification, the available observed data are conventionally partitioned into two parts: the training data that are used for model identification and the test data that are used for model performance testing. This sort of 'hold-out' or 'split-sample' data partitioning method is convenient and the associated model identification procedure is in general easy to implement. The resultant model obtained from such a once-partitioned single training dataset, however, may occasionally lack robustness and generalisation to represent future unseen data, because the performance of the identified model may be highly dependent on how the data partition is made. To overcome the drawback of the hold-out data partitioning method, this study presents a new random subsampling and multifold modelling (RSMM) approach to produce less biased or preferably unbiased models. The basic idea and the associated procedure are as follows. First, generate K training datasets (and also K validation datasets), using a K-fold random subsampling method. Secondly, detect significant model terms and identify a common model structure that fits all the K datasets using a new proposed common model selection approach, called the multiple orthogonal search algorithm. Finally, estimate and refine the model parameters for the identified common-structured model using a multifold parameter estimation method. The proposed method can produce robust models with better generalisation performance
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On the Interpretation of Diamagnetic Loop Measurements for a Current-Carrying Plasma Column in a Conducting Chamber
A general expression is derived for the signal of a magnetic loop encircling a plasma column inside a conducting chamber with nonuniform current distribution over the plasma cross-section. The ratio of the paramagnetic component to the diamagnetic component of the signal is shown to be independent of the loop radius. Both components increases the loop radius decreases from the chamber radius to the plasma radius. From the derived expressions, the paramagnetic component of the signal is calculated numerically for several current distributions including those of interest for the experiments. At a given total current, the paramagnetic component of the signal may vary considerably, which generally has to be taken into account in interpreting experimental data. The results of the calculations are used to process the data obtained in the experiments on the SPIN plasma device
Simulation of the Zero Temperature Behavior of a 3-Dimensional Elastic Medium
We have performed numerical simulation of a 3-dimensional elastic medium,
with scalar displacements, subject to quenched disorder. We applied an
efficient combinatorial optimization algorithm to generate exact ground states
for an interface representation. Our results indicate that this Bragg glass is
characterized by power law divergences in the structure factor . We have found numerically consistent values of the coefficient for
two lattice discretizations of the medium, supporting universality for in
the isotropic systems considered here. We also examine the response of the
ground state to the change in boundary conditions that corresponds to
introducing a single dislocation loop encircling the system. Our results
indicate that the domain walls formed by this change are highly convoluted,
with a fractal dimension . We also discuss the implications of the
domain wall energetics for the stability of the Bragg glass phase. As in other
disordered systems, perturbations of relative strength introduce a new
length scale beyond which the perturbed ground
state becomes uncorrelated with the reference (unperturbed) ground state. We
have performed scaling analysis of the response of the ground state to the
perturbations and obtain . This value is consistent with the
scaling relation , where characterizes the
scaling of the energy fluctuations of low energy excitations.Comment: 20 pages, 13 figure
Specific-Heat Exponent of Random-Field Systems via Ground-State Calculations
Exact ground states of three-dimensional random field Ising magnets (RFIM)
with Gaussian distribution of the disorder are calculated using
graph-theoretical algorithms. Systems for different strengths h of the random
fields and sizes up to N=96^3 are considered. By numerically differentiating
the bond-energy with respect to h a specific-heat like quantity is obtained,
which does not appear to diverge at the critical point but rather exhibits a
cusp. We also consider the effect of a small uniform magnetic field, which
allows us to calculate the T=0 susceptibility. From a finite-size scaling
analysis, we obtain the critical exponents \nu=1.32(7), \alpha=-0.63(7),
\eta=0.50(3) and find that the critical strength of the random field is
h_c=2.28(1). We discuss the significance of the result that \alpha appears to
be strongly negative.Comment: 9 pages, 9 figures, 1 table, revtex revised version, slightly
extende
Using Scaling Methods to Improve Support Vector Regression’s Performance for Travel Time and Volume Predictions
Long queues often happen on toll roads, especially at the tollgates. These create many problems, including having an impact on the regular roads nearby. If travel time and traffic volume at the tollgates can be predicted accurately in advance, this would allow traffic authorities to take appropriate measures to improve traffic flow and the safety of road users. This paper describes a novel combination of scaling methods with Support Vector Machines for Regression (SVR) for travel time and tollgate volume prediction tasks, as part of the Knowledge Discovery and Data Mining (KDD) Cup 2017. A new method is introduced to handle missing data by utilising the structure of the road network. Moreover, experiments with reduced data were conducted to evaluate whether conclusions from combining scaling methods with SVR could be generalised
Ground state numerical study of the three-dimensional random field Ising model
The random field Ising model in three dimensions with Gaussian random fields
is studied at zero temperature for system sizes up to 60^3. For each
realization of the normalized random fields, the strength of the random field,
Delta and a uniform external, H is adjusted to find the finite-size critical
point. The finite-size critical point is identified as the point in the H-Delta
plane where three degenerate ground states have the largest discontinuities in
the magnetization. The discontinuities in the magnetization and bond energy
between these ground states are used to calculate the magnetization and
specific heat critical exponents and both exponents are found to be near zero.Comment: 10 pages, 6 figures; new references and small changes to tex
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Disorder-specific functional abnormalities during sustained attention in youth with Attention Deficit Hyperactivity Disorder (ADHD) and with Autism
Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) are often comorbid and share behavioural-cognitive abnormalities in sustained attention. A key question is whether this shared cognitive phenotype is based on common or different underlying pathophysiologies. To elucidate this question, we compared 20 boys with ADHD to 20 age and IQ matched ASD and 20 healthy boys using functional magnetic resonance imaging (fMRI) during a parametrically modulated vigilance task with a progressively increasing load of sustained attention. ADHD and ASD boys had significantly reduced activation relative to controls in bilateral striato–thalamic regions, left dorsolateral prefrontal cortex (DLPFC) and superior parietal cortex. Both groups also displayed significantly increased precuneus activation relative to controls. Precuneus was negatively correlated with the DLPFC activation, and progressively more deactivated with increasing attention load in controls, but not patients, suggesting problems with deactivation of a task-related default mode network in both disorders. However, left DLPFC underactivation was significantly more pronounced in ADHD relative to ASD boys, which furthermore was associated with sustained performance measures that were only impaired in ADHD patients. ASD boys, on the other hand, had disorder-specific enhanced cerebellar activation relative to both ADHD and control boys, presumably reflecting compensation. The findings show that ADHD and ASD boys have both shared and disorder-specific abnormalities in brain function during sustained attention. Shared deficits were in fronto–striato–parietal activation and default mode suppression. Differences were a more severe DLPFC dysfunction in ADHD and a disorder-specific fronto–striato–cerebellar dysregulation in ASD
Forecasting Government Bond Spreads with Heuristic Models:Evidence from the Eurozone Periphery
This study investigates the predictability of European long-term government bond spreads through the application of heuristic and metaheuristic support vector regression (SVR) hybrid structures. Genetic, krill herd and sine–cosine algorithms are applied to the parameterization process of the SVR and locally weighted SVR (LSVR) methods. The inputs of the SVR models are selected from a large pool of linear and non-linear individual predictors. The statistical performance of the main models is evaluated against a random walk, an Autoregressive Moving Average, the best individual prediction model and the traditional SVR and LSVR structures. All models are applied to forecast daily and weekly government bond spreads of Greece, Ireland, Italy, Portugal and Spain over the sample period 2000–2017. The results show that the sine–cosine LSVR is outperforming its counterparts in terms of statistical accuracy, while metaheuristic approaches seem to benefit the parameterization process more than the heuristic ones
An Ensemble Analysis of Electromyographic Activity during Whole Body Pointing with the Use of Support Vector Machines
We explored the use of support vector machines (SVM) in order to analyze the ensemble activities of 24 postural and focal muscles recorded during a whole body pointing task. Because of the large number of variables involved in motor control studies, such multivariate methods have much to offer over the standard univariate techniques that are currently employed in the field to detect modifications. The SVM was used to uncover the principle differences underlying several variations of the task. Five variants of the task were used. An unconstrained reaching, two constrained at the focal level and two at the postural level. Using the electromyographic (EMG) data, the SVM proved capable of distinguishing all the unconstrained from the constrained conditions with a success of approximately 80% or above. In all cases, including those with focal constraints, the collective postural muscle EMGs were as good as or better than those from focal muscles for discriminating between conditions. This was unexpected especially in the case with focal constraints. In trying to rank the importance of particular features of the postural EMGs we found the maximum amplitude rather than the moment at which it occurred to be more discriminative. A classification using the muscles one at a time permitted us to identify some of the postural muscles that are significantly altered between conditions. In this case, the use of a multivariate method also permitted the use of the entire muscle EMG waveform rather than the difficult process of defining and extracting any particular variable. The best accuracy was obtained from muscles of the leg rather than from the trunk. By identifying the features that are important in discrimination, the use of the SVM permitted us to identify some of the features that are adapted when constraints are placed on a complex motor task
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