Mathematics

The performance of maximum likelihood sequence estimation (MLSE) is bounded (and often approximated well) by the matched filter bound (MFB). In this paper, we first show how the GMSK modulation of GSM can be linearized and reformulated to have a real symbol constellation, leading to a two-channel aspect due to the in-phase and in-quadrature components of the received signal. More channels can be added by oversampling and/or the use of multiple antennas. We present the MFB for this model in the presence of colored noise (interferers) for MLSE that employs noise correlation information (that in general may differ from the true one). We show that by properly taking into account the noise correlation leads to a much improved MFB

In this paper we are interested in the prediction of preterm birth based on diagnosis codes from longitudinal EHR. We formulate the prediction problem as a supervised classification with noisy labels. Our base classifier is a Recurrent Neural Network with an attention mechanism. We assume the availability of a data subset with both noisy and clean labels. For the cohort definition, most of the diagnosis codes on mothers' records related to pregnancy are ambiguous for the definition of full-term and preterm classes. On the other hand, diagnosis codes on babies' records provide fine-grained information on prematurity. Due to data de-identification, the links between mothers and babies are not available. We developed a heuristic based on admission and discharge times to match babies to their mothers and hence enrich mothers' records with additional information on delivery status. The obtained additional dataset from the matching heuristic has noisy labels and was used to le...

Latent variable mixture modeling is part of a latent variable modeling framework and is flexible with regard to the type of data that can be analyzed. Observed variables used to determine latent classes (LCs) can be continuous, censored, binary, ordered/unordered categorical counts, or combinations of these variable types, and the data can be collected in a cross-sectional and/or longitudinal manner. Consequently, a diverse array of research questions involving LCs can be investigated. For example, hypotheses can focus on predicting class membership, identifying mean differences in outcomes across LCs, or describing the extent to which LC membership moderates the relationship between two or more variables. The literature has used many names to describe mixture modeling, or finite mixture modeling as it is known in the statistics literature. Names vary according to the type of data used for indicators (continuous vs. categorical, akin to cross-sectional latent profile analysis vs. la...

According to classical test theory (CTT) (or true score theory), reliability is defined as the ratio of true-score variance to observed score variance. The observed score is the score that is obtained by the measure itself. The true score is the hypothetical amount of the variable of interest that is specific to the test taker. Measurement error is the difference between the amount of true score and the observed score. The most common conceptualization of reliability is that it is the proportion of observed score variance that is attributable to true score variance.

Publisher Summary This chapter focuses on two types of piecewise quadratic spaces and their order of accuracy for Poisson's equation. Error estimates are described, and the results of several numerical experiments are discussed. The results for the vertex method in the chapter, which are exact only for linear polynomials provide an example of the consequence of using a quadrature scheme of insufficient accuracy in relation to the finite element space being used.

Probabilistic topology optimization has gained significant research attention recently. This interest stems from the realization that the achieved high-performance designs resulted from deterministic topology optimization algorithms may become suboptimal under real-world conditions that are often accompanied with uncertainties. Among sources of these uncertainties, the ones that define structural characteristics, such as geometry, are numerically challenging to treat as they lead to stochastic structural stiffness. To date, research on developing efficient probabilistic topology optimization under stochastic stiffness is mainly focused on displacement-based objectives. However, in the design of structures, stress is also a primary design criterion that needs to be directly controlled for. A robust stress-based topology optimization methodology for frame structures under geometric uncertainty is proposed in this work. Assuming that such uncertainties are small relative to frame membe...

The author defends a combination of Strawson’s account of definite descriptions as devices of singular reference par excellence with the Russellian truth-evaluation of utterances of sentences with descriptions. The complex Russellian proposition is, according to the author’s view, introduced by such utterances into communication as a by-product of the instrumental side of an attempt to make a singular statement. This, precisely like the instrumental aspects of similar attempts exploiting names or demonstratives has to be reflected by analysis but should not be confused with the communicative function of utterances. The success of all these attempts depends on the fulfilment of empirical conditions of various types, given by semantics of descriptions, names or demonstratives (unless the relevance of these conditions is neutralized by another identificatory factor dominating in given context). But their communicative function does not consist in claiming that these conditions are fulfilled. The author agrees with Strawson that the first two conjuncts of the complex Russellian proposition are introduced into communication as presuppositions: but argues in favour of defining presupposition (in pragmatic sense) in normative, rather the intentional terms.

This paper considers the problem of output-feedback control of stochastic nonlinear systems. A predictive controller is designed for which stability and feasibility (in a probabilistic sense) are guaranteed from an explicitly characterized region of attraction. The controller's performance is characterized by its risk of allowing destabilizing system behavior. Since the controller design relies on certain convergence properties of the state observer, a compatible stochastic high-gain type observer design is also presented. Finally, simulation results illustrating the efficacy of the design are presented.

In this thesis, we report advances to the theory of stabilization of control systems. A first contribution of this thesis is to utilize the concept of stability regions to derive control laws to stabilize nonlinear systems with stochastic disturbances and subject to lack of availability of state measurements. In this direction, the probability of destabilizing behavior is approximated that incorporates the effect of the observation error. In a second contribution, we determine, for any given input-constrained nonlinear control system, the largest region from which stabilization can be achieved, termed the null controllable region (NCR). Indeed, a key result of this thesis is to propose a control law that induces stabilization from the entire NCR. In this direction, two methods to construct NCRs are proposed using completely different principles. In one major contribution, the NCR is given by special smooth optimal trajectories integrated in reverse-time using Pontryagin’s Maximum Pr...

... 9/27). Tam mükellefiyette tabi kişi Kırgızistan içinde ve dışında elde ettiği kazançları tamamı üzerinde vergilendirildiği halde, dar mükellefiyette tabi olanlar ise sadece Kırgızistan'da elde ettikleri kazançlar üzerinde vergilendirilirler (KCVK mad. 74/3,4). ...

We consider a network whose nodes are electromagnetic cavities, each coupled to a single three-level atom. The nodes are connected by optical fibers. Each atom is addressed by a control laser, which along with the cavity field drives atomic transitions. The network can be in the form of chain or two and three dimensional arrays of $N$-cavities connected by $N_B$ fibres. Following the work on two-cavity system by Pellizzari, we find that under certain conditions, the system possesses two kinds of dark states. The first kind are $N$ states corresponding to atomic excitations at each node and these are our logical states for quantum processing. The second kind are $N_B$ degenerate dark states on pairs of sites connected by a fiber. By manipulating intensities and phases of control lasers on the cavities, one can pass adiabatically among these dark states due to their degeneracy. This network operates as a $N$-level quantum system in which one can generate computationally useful states ...