Sliding-window cross-correlation is a common method to esimate time-varying correlations between signals (Laurent and Davidowitz, 1994; Laurent et al., 1996; Macleod and Laurent, 1996; Stopfer and Laurent, 1997; Wehr, 1999 (p. 96)). It produces a correlation value betwen two signals (positive or negative) for every (time,lag) pair of values. In principle, the expected value of the correlation f...

Pathogen incidence rate prediction, which can be considered as time series modeling, is an important task for infectious disease incidence rate prediction and for public health. This paper investigates applying a genetic computation technique, namely GEP, for pathogen incidence rate prediction. To overcome the shortcomings of traditional sliding windows in GEP based time series modeling, the pa...

Stream processing is gaining importance as more data becomes available in the form of continuous streams and companies compete to promptly extract insights from them. In such applications, sliding-window aggregation is a central operator, and incremental aggregation helps avoid the performance penalty of re-aggregating from scratch for each window change. This paper presents Reactive Aggregator...

This paper introduces an orthonormal version of the sliding-window Projection Approximation Subspace Tracker (PAST). The new algorithm guarantees the orthonormality of the signal subspace basis at each iteration. Moreover, it has the same complexity as the original PAST algorithm, and like the more computationally demanding natural power (NP) method, it satisfies a global convergence property, ...

A sliding-window dynamic frameproof code is a scheme for discouraging the piracy of digital broadcasts through the use of digital fingerprinting. In this paper we formally define sliding-window dynamic frameproof codes and provide optimal constructions for a certain class of these schemes. We also discuss bounds on the number of users such schemes

The sliding window concept is a common method for computing a profile of a document with unknown structure. This paper outlines an experiment with stylometric word-based feature in order to determine an optimal size of the sliding window. It was conducted for a vocabulary richness method called ’average word frequency class’ using the PAN 2015 source retrieval training corpus for plagiarism det...

In the context of Knowledge Discovery in Databases, data reduction is a pre-processing step delivering succinct yet meaningful data to sequent stages. If the target of mining are data streams, then it is crucial to suitably reduce them, since often analyses on such data require multiple scans. In this chapter, we propose a histogram-based approach to reducing sliding windows supporting approxim...

Sliding Window is the most popular data model in processing data streams as it captures finite and relevant subset of an infinite stream. This paper studies different Mathematical operators used for querying and mining of data streams. The focus of our study is on operators, operating on the whole data set. These are termed as blocking operators. We have classified these operators according to ...

BACKGROUND The demand of improved dose conformity of the tumor has been increased in radiation therapy with the advent of recent imaging facilities and efficient computer technologies. AIM We compared the intensity modulated radiotherapy (IMRT) plans delivered with the sliding window (SW IMRT) and step and shoot (SS IMRT) techniques. MATERIALS AND METHODS Thirteen patients were planned on 1...

Aggregating information across multiple variants in a gene or region can improve power for rare variant association testing. Power is maximized when the aggregation region contains many causal variants and few neutral variants. In this paper, we present a method for the localization of the association signal in a region using a sliding-window based approach to rare variant association testing i...