We present a Modular Neural Network (MNN) for phoneme recognition within the framework of a hybrid system (neural networks and HMMs) for speakerindependent single word recognition. With this approach, we are taking the computational effort into account which is used as an additional criterion for assessing the system performance. The main idea of the proposed MNN is the distribution of the comp...

In this paper, inspired by the mechanism of memory and prediction in our brains [2], we propose a straightforward and effective memorybased gait recognition method (MGR) to realize the memory and recognition process of the gait sequences. Because of various covariates including carrying, clothing, surface and view angle, we extract the robust 2D joint location information via the joint extracti...

We examine the thermal origin of detected “saw-tooth” shaped Hall resistance ([Formula: see text]) signals in spin–orbit torque switching experiment for antiferromagnetic MnN. Compared with results heavy metal/antiferromagnet bilayers (MnN/Ta), qualitatively same also appear samples metal layer alone (either Ta or Pt) without MnN layer. In addition, [Formula: text] signal changes oppositely dev...

The nearest neighbor classification/regression technique, besides its simplicity, is one of the most widely applied and well studied techniques for pattern recognition in machine learning. A nearest neighbor classifier assumes class conditional probabilities to be locally smooth. This assumption is often invalid in high dimensions and significant bias can be introduced when using the nearest ne...

We study clustering algorithms based on neighborhood graphs on a random sample of data points. The question we ask is how such a graph should be constructed in order to obtain optimal clustering results. Which type of neighborhood graph should one choose, mutual k-nearest neighbor or symmetric k-nearest neighbor? What is the optimal parameter k? In our setting, clusters are defined as connected...

K nearest neighbor classifier (K-NN) is widely discussed and applied in pattern recognition and machine learning, however, as a similar lazy classifier using local information for recognizing a new test, neighborhood classifier, few literatures are reported on. In this paper, we introduce neighborhood rough set model as a uniform framework to understand and implement neighborhood classifiers. T...

Nearest neighbor techniques are commonly used in remote sensing, pattern recognition and statistics to classify objects into a predefined number of categories based on a given set of predictors. These techniques are especially useful for highly nonlinear relationship between the variables. In most studies the distance measure is adopted a priori. In contrast we propose a general procedure to fi...

Using density functional calculations, it is shown that pure MnN is stable in zincblende rather than in the experimentally reported distorted rocksalt structure. However, about 4% N vacancies are found to stabilize the rocksalt structure. It is found that C and O substitutions on N and Ga on Mn also favor rocksalt but Ni, SN, FeMn, and CoMn stabilize zincblende. This suggests that zincblende Mn...

We introduce the Larger than Life family of two-dimensional two-state cellular automata that generalize certain nearest neighbor outer totalistic cellular automaton rules to large neighborhoods. We describe linear and quadratic rescalings of John Conway’s celebrated Game of Life to these large neighborhood cellular automaton rules and present corresponding generalizations of Life’s famous glide...