A simulation as a service cloud middleware

Many seemingly simple questions that individual users face in their daily lives may actually require substantial number of computing resources to identify the right answers. For example, a user may want to determine the right thermostat settings for different rooms of a house based on a tolerance range such that the energy consumption and costs can be maximally reduced while still offering comfortable temperatures in the house. Such answers can be determined through simulations. However, some simulation models as in this example are stochastic, which require the execution of a large number of simulation tasks and aggregation of results to ascertain if the outcomes lie within specified confidence intervals. Some other simulation models, such as the study of traffic conditions using simulations may need multiple instances to be executed for a number of different parameters. Cloud computing has opened up new avenues for individuals and organizations with limited resources to obtain answers to problems that hitherto required expensive and computationally-intensive resources. This paper presents SIMaaS, which is a cloud-based Simulation-as-a-Service to address these challenges. We demonstrate how lightweight solutions using Linux containers (e.g., Docker) are better suited to support such services instead of heavyweight hypervisor-based solutions, which are shown to incur substantial overhead in provisioning virtual machines on-demand. Empirical results validating our claims are presented in the context of two case studies.     

Collinear stimuli regulate visual responses depending on cell's contrast threshold

Neurons in the primary visual cortex are selective for the size, orientation and direction of motion of patterns falling within a restricted region of visual space known as the receptive field. The response to stimuli presented within the receptive field can be facilitated or suppressed by other stimuli falling outside the receptive field which, when presented in isolation, fail to activate the cell. Whether this interaction is facilitative or suppressive depends on the relative orientation of pattern elements inside and outside the receptive field. Here we show that neuronal facilitation preferentially occurs when a near-threshold stimulus inside the receptive field is flanked by higher-contrast, collinear elements located in surrounding regions of visual space. Collinear flanks and orthogonally oriented flanks, however, both act to reduce the response to high-contrast stimuli presented within the receptive field. The observed pattern of facilitation and suppression may be the cellular basis for the observation in humans that the detectability of an oriented pattern is enhanced by collinear flanking elements. Modulation of neuronal responses by stimuli falling outside their receptive fields may thus represent an early neural mechanism for encoding objects and enhancing their perceptual saliency.     

Three—body collisions involving Na（3P）,Rb（5S） and buffer gas atoms

Energy pooling in the Na-Rb vapor mixture has been investigated.While some kind of buffer gas is introduced into the cell the peculiar features appear.The buffer gas enhances the energy transfer betwwen Na(3P) and Rb(5S),which can be detected through the effects induced on the highly excited states populated by the Na(3P)/Rb(5P) and Rb(5P)/Rb(5P) collisions.     

Learning Intelligent Behavior in a Non-stationary and Partially Observable Environment

Individual learning in an environment where more than one agent exist is a chal-lengingtask. In this paper, a single learning agent situated in an environment where multipleagents exist is modeled based on reinforcement learning. The environment is non-stationaryand partially accessible from an agents' point of view. Therefore, learning activities of anagent is influenced by actions of other cooperative or competitive agents in the environment.A prey-hunter capture game that has the above characteristics is defined and experimentedto simulate the learning process of individual agents. Experimental results show that thereare no strict rules for reinforcement learning. We suggest two new methods to improve theperformance of agents. These methods decrease the number of states while keeping as muchstate as necessary.     

Similarity-based attribute weighting methods via clustering algorithms in the classification of imbalanced medical datasets

In the fields of pattern recognition and machine learning, the use of data preprocessing algorithms has been increasing in recent years to achieve high classification performance. In particular, it has become inevitable to use the data preprocessing method prior to classification algorithms in classifying medical datasets with the nonlinear and imbalanced data distribution. In this study, a new data preprocessing method has been proposed for the classification of Parkinson, hepatitis, Pima Indians, single proton emission computed tomography (SPECT) heart, and thoracic surgery medical datasets with the nonlinear and imbalanced data distribution. These datasets were taken from UCI machine learning repository. The proposed data preprocessing method consists of three steps. In the first step, the cluster centers of each attribute were calculated using k-means, fuzzy c-means, and mean shift clustering algorithms in medical datasets including Parkinson, hepatitis, Pima Indians, SPECT heart, and thoracic surgery medical datasets. In the second step, the absolute differences between the data in each attribute and the cluster centers are calculated, and then, the average of these differences is calculated for each attribute. In the final step, the weighting coefficients are calculated by dividing the mean value of the difference to the cluster centers, and then, weighting is performed by multiplying the obtained weight coefficients by the attribute values in the dataset. Three different attribute weighting methods have been proposed: (1) similarity-based attribute weighting in k-means clustering, (2) similarity-based attribute weighting in fuzzy c-means clustering, and (3) similarity-based attribute weighting in mean shift clustering. In this paper, we aimed to aggregate the data in each class together with the proposed attribute weighting methods and to reduce the variance value within the class. Thus, by reducing the value of variance in each class, we have put together the data in each class and at the same time, we have further increased the discrimination between the classes. To compare with other methods in the literature, the random subsampling has been used to handle the imbalanced dataset classification. After attribute weighting process, four classification algorithms including linear discriminant analysis, k-nearest neighbor classifier, support vector machine, and random forest classifier have been used to classify imbalanced medical datasets. To evaluate the performance of the proposed models, the classification accuracy, precision, recall, area under the ROC curve, κ value, and F-measure have been used. In the training and testing of the classifier models, three different methods including the 50–50% train–test holdout, the 60–40% train–test holdout, and tenfold cross-validation have been used. The experimental results have shown that the proposed attribute weighting methods have obtained higher classification performance than random subsampling method in the handling of classifying of the imbalanced medical datasets.

     

Temperature effects on the electrical characteristics of $$\mathrm{Al}/\mathrm{PTh}-\mathrm{SiO}_{2}/\mathrm{p\hbox {-}Si}$$ structure

The temperature-dependent current–voltage (\(I\text {--}V\)) and capacitance–voltage (\(C\text {--}V\)) characteristics of the fabricated Al/p-Si Schottky diodes with the polythiopene–SiO\(_{2}\) nanocomposite (\(\hbox {PTh--SiO}_{2}\)) interlayer were investigated. The ideality factor of \(\hbox {Al}/\hbox {PTh--SiO}_{2}/{p}\text {-Si}\) Schottky diodes has decreased with increasing temperature and the barrier height has increased with increasing temperature. The change in the barrier height and ideality factor values with temperature was attributed to inhomogeneties of the zero-bias barrier height. Richardson plot has exhibited curved behaviour due to temperature dependence of barrier height. The activation energy and effective Richardson constant were calculated as 0.16 eV and \(1.79 \times 10^{-8} \hbox {A\,cm}^{-2} \,\hbox {K}^{-2}\) from linear part of Richardson plots, respectively. The barrier height values determined from capacitance–voltage–temperature (\(C\text {--}V\text {--}T\)) measurements decrease with increasing temperature on the contrary of barrier height values obtained from \(I\text {--}V\text {--}T\) measurements.     

Regional soft error vulnerability and error propagation analysis for GPGPU applications

The Journal of Supercomputing - The wide use of GPUs for general-purpose computations as well as graphics programs makes soft errors a critical concern. Evaluating the soft error vulnerability of...     

Wide band fractal‐based perfect energy absorber and power harvester

In this article, a novel wide band polarization and incident angle independent metamaterial absorber (MA) and energy harvesting applications which operates at C (4GHz‐8 GHz) and X (8GHz‐12 GHz) is proposed. The unit‐cell of the proposed structure based on fractal circle loop. Four lumped resistors are mounted the structure to obtain a broad band absorption characteristics. Resistors increase the absorption characteristic of proposed MA significantly at mentioned frequency ranges. In addition, under favor of the resistors proposed MA can convert absorbed energy from incident wave to appearing power.     

Locally adaptive <Emphasis Type="Italic">k</Emphasis> parameter selection for nearest neighbor classifier: one nearest cluster

The k nearest neighbors (k-NN) classification technique has a worldly wide fame due to its simplicity, effectiveness, and robustness. As a lazy learner, k-NN is a versatile algorithm and is used in many fields. In this classifier, the k parameter is generally chosen by the user, and the optimal k value is found by experiments. The chosen constant k value is used during the whole classification phase. The same k value used for each test sample can decrease the overall prediction performance. The optimal k value for each test sample should vary from others in order to have more accurate predictions. In this study, a dynamic k value selection method for each instance is proposed. This improved classification method employs a simple clustering procedure. In the experiments, more accurate results are found. The reasons of success have also been understood and presented.