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To make clear the mechanism of the visual movement is important in he visual system. The prominent feature is the nonlinear characteristics as the squaring and rectification functions, which are observed in the retinal and visual cortex networks. Many popular models for motion processing in cortex, is the use of symmetric quadrature functions with Gabor filters. This paper proposes a new motion sensing...
Idle periods on different processes of Message Passing applications are unavoidable. While the origin of idle periods on a single process is well understood as the effect of system and architectural random delays, yet it is unclear how these idle periods propagate from one process to another. It is important to understand idle period propagation in Message Passing applications as it allows application...
Simulation is widely used to predict the performance of complex systems. The main drawback of simulation is that it is slow in execution and the related compute experiments can be very expensive. On the other hand, analytical methods are used to rapidly provide performance estimates, but they are often approximate because of their restrictive assumptions. Recently, Extended Kernel Regression (EKR)...
Classical molecular dynamics simulations have been the preferred method to cope with the characteristic sizes and time scales of complex life-science systems. However, while classical methods have well known limitations, such as that their accuracy strongly depends on empirical tuning, the practical use of far more accurate methods that rely on quantum Hamiltonians, has been limited by the current...
Most of the intrusion detection systems analyze all network traffic features to identify intrusions with different classification techniques. Any intrusion detection model developed has to provide maximum accuracy with minimal false alarms. Identifying the optimal feature subset for classification is an important task for improved classification. In this paper, consistency based feature selection...
In this paper, we propose an efficient and robust gross outlier removal method, called the Conceptual Space based Gross Outlier Removal (CSGOR) method, to remove gross outliers for geometric model fitting. In the proposed method, each data point is mapped to a conceptual space by computing the preference of "good" model hypotheses. In the conceptual space, the distributions of inliers and...
In this research study, we propose an automatic group activity recognition approach by modelling the interdependencies of group activity features over time. Unlike in simple human activity recognition approaches, the distinguishing characteristics of group activities are often determined by how the movement of people are influenced by one another. We propose to model the group interdependences in...
The aim of this presentation is to show how various ideas coming from the nonlinear stability theory of functional differential systems, stochastic modeling, and machine learning, can be put together in order to create an approximating model that explains the working mechanisms behind a certain type of reservoir computers. Reservoir computing is a recently introduced brain-inspired machine learning...
Monte Carlo simulations are used to tackle a wide range of exciting and complex problems, such as option pricing and biophotonic modelling. Since Monte Carlo simulations are both computationally expensive and highly parallelizable, they are ideally suited for acceleration through GPUs and FPGAs. Alongside these accelerators, Multilevel Monte Carlo techniques can be harnessed to further hasten simulations...
Dynamic neural field (DNF) is a popular mesoscopic model for cortical column interactions. It is widely studied analytically and successfully applied to physiological modelling, bioinspired computation and robotics. DNF behavior emerges from distributed and decentralized interactions between computing units which makes it an interesting candidate as a cellular building-block for unconventional computations...
The energy efficiency of computing systems can be enhanced via power models that provide insights into how the systems consume power. However, there are no application-general, fine-grained and validated power models which can provide insights into how a given application running on an ultra-low power (ULP) embedded system consumes power. In this study, we devise new fine-grained power models that...
We present a model for time series consisting of an infinite mixture of basis functions, whereby the bases and the mixing process are modelled as posterior means of latent Gaussian processes (GPs). Conditional to observed data, the bases and the mixing process are learnt using a parametric approximation based on pseudo-observations, where the complexity and accuracy of the method are controlled by...
A method to reduce the dynamic order of linear parameter-varying (LPV) systems in grid representation is developed in this paper. It approximates balancing and truncation by an oblique projection onto a dominant subspace. The approach is novel in its use of a parameter-varying kernel to define the direction of this projection. Parameter-varying state transformations in general lead to parameter rate...
In driving process, providing accurate collision warning and effective advice about acceleration or deceleration in advance is beneficial to traffic safety. Furthermore, it will reduce the probability of vehicle collision. Much studies based on model or infrastructure have been proposed to solve this problem. However, their prediction accuracy is limited and few work utilize the large amount of historical...
We present an object oriented design for a reusable nonlinear model predictive controller framework. The framework balances reusability with flexibility and performance. Separation of interface from implementation simplifies development and facilitates use of dynamic polymorphism to change the controller behaviour at runtime. The work is presented in language agnostic terms, but the success of a particular...
The reaction kernel for MPD-RDME, the GPU-accelerated reaction-diffusion master equation solver found in Lattice Microbes uses a large number of kinetic parameters to describe a biochemical network. Many of these parameters are required to compute the system's total reaction propensity, which is used to stochastically evaluate whether a reaction event takes place. In this paper, we examine two techniques...
Numerical approach to frequency response problems usually requires that the system governing equation is solved repeatedly at many frequencies. The computational efficiency of the overall process can be increased by departing from traditional sequential computing model in favor of utilizing the parallel processing capability commonly offered by modern hardware. In this paper, we consider a hybrid...
Sparse coding models have been widely used to decompose monocular images into linear combinations of small numbers of basis vectors drawn from an overcomplete set. However, little work has examined sparse coding in the context of stereopsis. In this paper, we demonstrate that sparse coding facilitates better depth inference with sparse activations than comparable feed-forward networks of the same...
With the aggressive scaling of integrated circuit technology, analog performance modeling is facing enormous challenges due to high-dimensional variation space and expensive transistor-level simulation. In this paper, we propose a kernel density based sparse regression algorithm (KDSR) to accurately fit analog performance models where the modeling error is not simply Gaussian due to strong nonlinearity...
Feature selection is an important step in many Machine Learning classification problems. It reduces the dimensionality of the feature space by removing noisy, irrelevant and redundant data, such that classification accuracy is enhanced while computational time remains affordable. In this paper, we present a new wrapper feature subset selection model based on Skewed Variable Neighborhood Search (SVNS)...
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