The critical points (also known as phase singularities) in the heart reflect the pathological change of the heart tissue, and hence can be used to describe and analyze the dynamics of the cardiac electrical activity. As a result, the detection of these critical points can lead to correct understanding and effective therapy of the tachycardia. In this paper, we propose a novel approach to addres...

[1] We discuss the polar field precursor method of solar activity forecasting, first developed 3 decades ago. Using this method the peak amplitude of the next solar cycle (24) is estimated at 124 ± 30 in terms of smoothed F10.7 Radio Flux and 80 ± 30 in terms of smoothed international or Zurich Sunspot number (Ri or Rz). This may be regarded as a ‘‘fair space weather’’ long term forecast. To su...

Effective local search methods for finding satisfying assignments of CNF formulae exhibit several systematic characteristics in their search. We identify a series of measurable characteristics of local search behavior that are predictive of problem solving efficiency. These measures are shown to be useful for diagnosing inefficiencies in given search procedures, tuning parameters, and predictin...

Exploiting the information from multiple views can improve clustering accuracy. However, most existing multi-view clustering algorithms are nonconvex and are thus prone to becoming stuck into bad local minima, especially when there are outliers and missing data. To overcome this problem, we present a new multi-view self-paced learning (MSPL) algorithm for clustering, that learns the multi-view ...

Stochastic gradient descent (SGD) is widely used in machine learning. Although being commonly viewed as a fast but not accurate version of gradient descent (GD), it always finds better solutions than GD for modern neural networks. In order to understand this phenomenon, we take an alternative view that SGD is working on the convolved (thus smoothed) version of the loss function. We show that, e...

In this paper, we propose a frozen Gaussian approximation (FGA)-based multilevel particle swarm optimization (MLPSO) method for seismic inversion of highfrequency wave data. The method addresses two challenges in it: First, the optimization problem is highly non-convex, which makes hard for gradient-based methods to reach global minima. This is tackled by MLPSO which can escape from undesired l...

Noise spectrum estimation is a fundamental component of speech enhancement and speech recognition systems. In this paper, we present an improved minima controlled recursive averaging (IMCRA) approach, for noise estimation in adverse environments involving nonstationary noise, weak speech components, and low input signal-to-noise ratio (SNR). The noise estimate is obtained by averaging past spec...

We present a general theory of atomistic dynamical response in surface probe microscopy when two solid surfaces move with respect to each other in close proximity, when atomic instabilities are likely to occur. These instabilities result in a bistable potential energy surface, leading to temperature dependent atomic scale topography and damping (dissipation) images. The theory is illustrated on...

We use smoothed analysis techniques to provide guarantees on the training loss of Multilayer Neural Networks (MNNs) at differentiable local minima. Specifically, we examine MNNs with piecewise linear activation functions, quadratic loss and a single output, under mild over-parametrization. We prove that for a MNN with one hidden layer, the training error is zero at every differentiable local mi...