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This paper is a continuation of a previous study in which we formulated the problem of estimating clinical data from OTC sales in terms of optimal LMS linear and Finite Impulse Response (FIR) filters.
- Our mechanistic model for AV dynamics is a stochastic model, which we formulated in an attempt to understand the fluctuations and considerable dynamic range of behavior observed in single-cell experiments.
Our new model aimed at identifying and measuring the departure of observed allele fractions from germline expectations (0, 0.5 and 1), which we formulated as: This model was able to incorporate known population genetics information and sequencing data characteristics to aid mosaic detection.
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In the proofs of Theorems 1.2 and 1.3 we use some standard argumentation, which we formulate here as lemmas.
Section 2 describes the theoretical aspects presented in previous literature based on which we formulate empirical hypotheses.
The following result, which we formulate here for equation (1.1), is known as the Bohl-Perron theorem.
The extreme value distributions along the founded directions are efficiently assessed in the frequency domain, from which we formulate the distribution of √J2,a.
The possible seeds are known to the reader such that it can construct S. As a response to a query, the reader receives a superposition of signature sequences (mathbf {s}_{a_{k}}) that are weighted with the respective channel coefficients h k, which we formulate as CS measurement mathbf{z} = sumlimits_{k=1}^{K} mathbf{s}_{a_{k}} h_{k} +mathbf{w} = mathbf{Sx} + mathbf{w}, (5).
In this paper, we present an efficient and scalable multi-camera multi-people tracking system with a three-layer architecture, in which we formulate the overall task (i.e., tracking all people using all available cameras) as a vision based state estimation problem and aim to maximize utility and sharing of available sensing and processing resources.
The way in which we formulate opinions on the events of last week, on world issues, political candidates, and culture are influenced not only by what appears in our social feeds but by how our network presents that information.
Theorem 2 immediately implies the Theorem of Varga and Shazmary [ 23] which we formulate as follows: Corollary.
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