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In this paper, an existing numerical algorithm for high-accurate computation of exact Carson formulas based on piecewise linear approximation is improved. Carson formulas are used for computing of per-unit-length (pul) self and mutual impedances of infinitely long parallel conductors. The proposed algorithm is based on piecewise quadratic approximation of kernel function and analytical integrations...
In this paper, a new numerical algorithm for high-accurate computation of exact Carson formulas is developed. Carson formulas are used for computing of per-unit-length (pul) self and mutual impedances of infinitely long parallel conductors. The proposed algorithm is based on piecewise linear approximation of kernel function and analytical integrations of piecewise-linearized kernel function multiplied...
The paper considers the class of discrete-time, single-input, single-output, nonlinear dynamical systems described by a polynomial difference equation. This class, call polynomial time-invariant, is a proper generalization of the linear time-invariant model class. The identification data is assumed to be generated in the errors-in-variables setting, where the input and the output noise is zero mean,...
In this paper, we consider the scalar transverse magnetic (TM), two-dimensional, time-harmonic, lossless inverse scattering problem. The goal of this problem is to determine an unknown permittivity contrast within some domain from field measurements taken outside that domain. It is well known that this problem is both non-linear and ill-posed in the classical sense, i.e., the solution is non-unique...
We introduce a kernel formulation of the recently proposed minimum density hyperplane approach to clustering. This enables the identification of clusters that are not linearly separable in the input space by mapping them into a feature space. This mapping also extends the applicability of the minimum density hyperplane to datasets whose features are not necessarily continuous. The location of minimum...
This paper presents large-scale naturalistic and spontaneous facial expression classification on uncontrolled webcam data. We describe an active learning approach that helped us efficiently acquire and hand-label hundreds of thousands of non-neutral spontaneous and natural expressions from thousands of different individuals. With the increased numbers of training samples a classic RBF SVM classifier,...
Kernel methods constitute a family of powerful machine learning algorithms, which have found wide use in remote sensing and geosciences. However, kernel methods are still not widely adopted because of the high computational cost when dealing with large scale problems, such as the inversion of radiative transfer models. This paper introduces the method of random kitchen sinks (RKS) for fast statistical...
Based on the definition of local spectral subspace, we propose a novel approach called LOSP for local overlapping community detection. Using the power method for a few steps, LOSP finds an approximate invariant subspace, which depicts the embedding of the local neighborhood structure around the seeds of interest. LOSP then identifies the local community expanded from the given seeds by seeking a sparse...
Clustering is a task of finding natural groups in datasets based on measured or perceived similarity between data points. Spectral clustering is a well-known graph-theoretic approach, which is capable of capturing non-convex geometries of datasets. However, it generally becomes infeasible for analyzing large datasets due to relatively high time and space complexity. In this paper, we propose Multi-level...
Quantitative microscopy (QM) became a key tool in systems-level drug discovery and disease diagnosis such as cancers and neurodegenerative disorders. However, to date QM is limited to epifluorescence microscopy which requires chemical labels, special imaging modality and often causes phototoxicity. Differential Interference Contrast (DIC) microscopy is label free and is low-phototoxic, thus it has...
Recognition, Mining, and Synthesis (RMS) applications are expected to make up much of the computing workloads of the future. Many of these applications (e.g., recommender systems and search engine) are formulated as finding eigenvalues/vectors of large-scale matrices. These applications are inherently error-tolerant, and it is often unnecessary, sometimes even impossible, to calculate all the eigenpairs...
A general approach for anomaly detection or novelty detection consists in estimating high density regions or Minimum Volume (MV) sets. The One-Class Support Vector Machine (OCSVM) is a state-of-the-art algorithm for estimating such regions from high dimensional data. Yet it suffers from practical limitations. When applied to a limited number of samples it can lead to poor performance even when picking...
We study the satisfiability of ordering constraint satisfaction problems (CSPs) above average. We prove the conjecture of Gutin, van Iersel, Mnich, and Yeo that the satisfiability above average of ordering CSPs of arity k is fixed-parameter tractable for every k. Previously, this was only known for k=2 and k=3. We also generalize this result to more general classes of CSPs, including CSPs with predicates...
In this paper, we provide a new viewpoint of sequential random processes of the kind F(x), where x is a multivariate vector of covariates, in terms of a smoothing operation governed by a covariance function. By exploiting the eigenvalues and eigenvectors of the covariance function, we represent the smooth function in terms of an orthogonal series over basis functions where the basis function weights...
Methods of optimizing a single trajectory are mature enough for planning in many applications. Yet such optimization methods applied to high Degree-Of-Freedom robots either consume too much time to be real-time or approximate the dynamics such that they lack physical consistency. In this paper, we present a method of precomputing optimized trajectories and compressing the information to get a compact...
Maintaining clearance, or distance from obstacles and sampling efficient enough configurations on the medial axises are a vital component for successful motion planning. Maintaining high clearance often creates safer paths for robots. Having bias for sampling on medial axis also offers higher possibility to find a path in complex environment where the feasible configuration space only occupies a small...
Stream processing is a compute paradigm that promises safe and efficient parallelism. Its realization requires optimization of multiple parameters such as kernel placement and communications. Most techniques to optimize streaming systems use queueing network models or network flow models, which often require estimates of the execution rate of each compute kernel. This is known as the non-blocking...
Approximate Bayesian computation (ABC) filtration of state-space models replaces popular particle filters in cases where the observation models (i.e. likelihoods) are either computationally too demanding or completely intractable, but it is still possible to simulate from them. These sequential Monte Carlo methods evaluate importance weights based on the distance between the true observation and the...
General state space valued optimal stochastic control problems are often computationally intractable. On the other hand, for finite state-action models, there exist powerful computational and simulation tools for computing optimal strategies. With this motivation, we consider finite state and action space approximations of discrete time Markov decision processes with discounted and average costs and...
This paper presents novel means for estimating the polynomial static nonlinearity coefficients of a Wiener system in absence of a priori information about the linear block. To capture the system structure, the identification is performed with respect to a Volterra series model, whose kernels are parameterized in terms of Laguerre functions. A property of the resulting Volterra-Laguerre model is exploited...
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