Abstract not found

In this paper, we introduce SPIRIT (Streaming Pattern dIscoveRy in multIple Timeseries) . Given n numerical data streams, all of whose values we observe at each time tick t, SPIRIT can incrementally find correlations and hidden variables, which summarise the key trends in the entire stream collection.

How do we find patterns in author-keyword associations, evolving over time? Or in DataCubes, with product-branchcustomer sales information? Matrix decompositions, like principal component analysis (PCA) and variants, are invaluable tools for mining, dimensionality reduction, feature selection, rule identification in numerous settings like streaming data, text, graphs, social networks and many more. However, they have only two orders, like author and keyword, in the above example. We propose to envision such higher order data as tensors, and tap the vast literature on the topic. However, these methods do not necessarily scale up, let alone operate on semi-infinite streams. Thus, we introduce the dynamic tensor analysis (DTA) method, and its variants. DTA provides a compact summary for high-order and high-dimensional data, and it also reveals the hidden correlations. Algorithmically, we designed DTA very carefully so that it is (a) scalable, (b) space efficient (it does not need to store the past) and (c) fully automatic with no need for user defined parameters. Moreover, we propose STA, a streaming tensor analysis method, which provides a fast, streaming approximation to DTA. We implemented all our methods, and applied them in two real settings, namely, anomaly detection and multi-way latent semantic indexing. We used two real, large datasets, one on network flow data (100GB over 1 month) and one from DBLP (200MB over 25 years). Our experiments show that our methods are fast, accurate and that they find interesting patterns and outliers on the real datasets. 1.

Electronically steerable arrays of microphones have a variety of uses in speech data acquisition systems. Applications include teleconferencing, speech recognition and speaker identification, sound capture in adverse environments, and biomedical devices for the hearing impaired. An array of microphones has a number of advantages over a single-microphone system. It may be electronically aimed to provide a high-quality signal from a desired source location while simultaneously attenuating interfering talkers and ambient noise, does not necessitate local placement of transducers or encumber the talker with a hand-held or head-mounted microphone, and does not require physical movement to alter its direction of reception. Additionally, it has capabilities that a single microphone does not; namely automatic detection, localization, and tracking of active talkers in its receptive area. This paper addresses the specific application of source localization algorithms for estimating the position ...

Electronically steerable arrays of microphones have avariety of uses in speech data ac-quisition systems. Applications include teleconferencing, speech recognition and speaker identification, sound capture in adverse environments, and biomedical devices for the hear-ing impaired. An array of microphones has a number of advantages over a single-microphone system. It may be electronically aimed to provide a high-quality signal from a desired source location while simultaneously attenuating interfering talkers and ambient noise, does not necessitate local placement of transducers or encumber the talker with a hand-held or head-mounted microphone, and does not require physical movement to alter its direction of reception. Additionally, it has capabilities that a single microphone does not; namely automatic detection, localization, and tracking of active talkers in its receptive area. A fundamental requirement of sensor array systems is the ability to locate and track a speech source. An accurate fix on the primary talker, as well as knowledge of any interfering talkers or coherent noise sources, is necessary to effectively steer the array. Source location data may also be used for purposes other than beamforming; e.g. aiming a camera in a video-conferencing system. In addition to high accuracy, the location estimator must be

This paper presents a new method based on adaptive filtering theory for super-resolution restoration of continuous image sequences. The proposed methodology suggests least squares (LS) estimators which adapt in time, based on adaptive filters (LMS or RLS). The adaptation enables the treatment of linear space and time variant blurring and arbitrary motion, both of them assumed known. The proposed new approach is shown to be of relatively low computational requirements. Simulations demonstrating the super-resolution restoration algorithms are presented. HP Israel Science Center, Technion City, Haifa 32000, Israel, elad@hp.technion.ac.il. y The Electrical Engineering Department, Technion City, Haifa 32000, Israel, feuer@ee.technion.ac.il. 1 1 Introduction Signal restoration - linear deblurring and noise suppression - is widely treated in the literature for a variety of applications [1, 2, 3]. Single image restoration has become a classic chapter in image processing theory [1],...

In an earlier work we have introduced the problem of reconstructing a super-resolution image sequence from a given low resolution sequence. We proposed two iterative algorithms, the R-SD and the R-LMS, to generate the desired image sequence. These algorithms assume the knowledge of the blur, the down-sampling, the sequences motion, and the measurements noise characteristics, and apply a sequential reconstruction process. It has been shown that the computational complexity of these two algorithms makes both of them practically applicable. In this paper we re-derive these algorithms as approximations of the Kalman filter and then carry out a thorough analysis of their performance. For each algorithm we calculate a bound on its deviation from the Kalman filter performance. We also show that the propagated information matrix within the R-SD algorithm remains sparse in time - thus ensuring the applicability of this algorithm. To support these analytical results we present some computer simulations on synthetic sequences, which also show the computational feasibility of these algorithms. Index Terms: Image restoration, Super resolution, Dynamic Estimation, Kalman filter, Adaptive filters, Recursive Least Squares (RLS), Least Mean Squares (LMS), Steepest Descent (SD).

Loosely-coupled distributed systems have significant scale and cost advantages over more traditional architectures, but the availability of the nodes in these systems varies widely. Availability modeling is crucial for predicting per-machine resource burdens and understanding emergent, system-wide phenomena. We present new techniques for predicting availability and test them using traces taken from three distributed systems. We then describe three applications of availability prediction. The first, availability-guided replica placement, reduces object copying in a distributed data store while increasing data availability. The second shows how availability prediction can improve routing in delay-tolerant networks. The third combines availability prediction with virus modeling to improve forecasts of global infection dynamics.

The present paper has two aims. One is to survey briefly the state of the art of parameter estimation in meteorology and oceanography in view of applications of 4-D variational data assimilation techniques to inverse parameter estimation problems, which bear promise of serious positive impact on improving model prediction. The other aim is to present crucial aspects of identifiability and stability essential for validating results of optimal parameter estimation and which have not been addressed so far in either the meteorological or the oceanographic literature. As noted by Yeh (1986, Water Resour. Res. 22, 95-108) in the context of ground water flow parameter estimation the inverse or parameter estimation problem is often ill-posed and beset by instability and nonuniqueness, particularly if one seeks parameters distributed in space-time domain. This approach will allow one to assess and rigorously validate results of parameter estimation, i.e. do they indeed represent a real identification of physical model parameters or just compensate model errors? A brief survey of other approaches for solving the problem of optimal parameter estimation in meteorology and oceanography is finally presented. 1997 Elsevier Science B.V.

We present a non-linear kernel-based version of the Recursive Least Squares (RLS) algorithm. Our Kernel-RLS (KRLS) algorithm performs linear regression in the feature space induced by a Mercer kernel, and can therefore be used to recursively construct the minimum mean squared -error regressor. Sparsity of the solution is achieved by a sequential sparsification process that admits into the kernel representation a new input sample only if its feature space image cannot be suffciently well approximated by combining the images of previously admitted samples. This sparsification procedure is crucial to the operation of KRLS, as it allows it to operate on-line, and by effectively regularizing its solutions. A theoretical analysis of the sparsification method reveals its close affinity to kernel PCA, and a data-dependent loss bound is presented, quantifying the generalization performance of the KRLS algorithm. We demonstrate the performance and scaling properties of KRLS and compare it to a stateof -the-art Support Vector Regression algorithm, using both synthetic and real data. We additionally test KRLS on two signal processing problems in which the use of traditional least-squares methods is commonplace: Time series prediction and channel equalization.