Abstract-The description of a novel type of rn-gram language model is given. The model offers, via a nonlinear recursive procedure, a com-putation and space efficient solution to the problem of estimating prob-abilities from sparse data. This solution compares favorably to other proposed methods

Yaroslavsky. His wide knowledge, patience and personal guidance throughout this work have been a great value for me. I feel very fortunate for having a supervisor like you. I wish to express my sincere gratitude to Barak Fishbain, for constructive and interesting discussions and for valuable advices and friendly help. i A common distortion in videos is image instability in the form of chaotic global and local displacements of image frames caused by camera instability, fluctuations in the refraction index of the light propagation media and similar factors. Such videos that very frequently present moving objects on a stable background contain tremendous redundancy that potentially can be used for image stabilization and perfecting provided reliable separation of stable background from true moving objects. Recently, it was proposed to use this redundancy for resolution enhancement of video through elastic registration, with sub-pixel accuracy, of segments of video frames which represent stable scenes.

and systematic historic data, yet this is a sine qua non for almost all proposed methods: statistical, machine learning or calibration of existing models. In this paper we describe our sparse data method based upon a pairwise comparison technique and Saaty's Analytic Hierarchy Process. Our minimum

Robust parameters estimation of sparse data is generally applied to the test cases of time-consuming or high cost data collection. This study concerns with the problem in small sample size which is often encountered in the client data processing for speaker verification. We found that there always

and systematic historic data, yet this is a sine qua non for almost all proposed methods: statistical, machine learning or calibration of existing models. In this paper we describe our sparse data method (SDM) based upon a pairwise comparison technique and Saaty's Analytic Hierarchy Process (AHP). Our

show how explicitly incorporating the notion of 'sparseness' improves the found decompositions. Additionally, we provide complete MATLAB code both for standard NMF and for our extension. Our hope is that this will further the application of these methods to solving novel data

A common trend in object recognition is to detect and leverage the use of sparse, informative feature points. The use of such features makes the problem more manageable while providing increased robustness to noise and pose variation. In this work we develop an extension of these ideas

Abstract — This paper presents and analyzes an architecture that exploits the serendipitous movement of mobile agents in an environment to collect sensor data in sparse sensor networks. The mobile entities, called MULEs, pick up data from sensors when in close range, buffer it, and drop off

that have no non-trivial standard data stream algorithms. However, even though optimal schemes are now known for several basic problems, such optimality holds only for streams whose length is commensurate with the size of the data universe. In contrast, many real-world data sets are relatively sparse

Layout methods for dense and sparse data are often seen as two separate prob-lems with its own particular techniques. However, they are based on the same basic concepts. This paper studies how to integrate automatic data-layout and partition techniques for both dense and sparse data structures