| 2941. |
- Mishchenko, Kateryna, 1973-
(författare)
-
Numerical Algorithms for Optimization Problems in Genetical Analysis
- 2008
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The focus of this thesis is on numerical algorithms for efficient solution of QTL analysis problem in genetics.Firstly, we consider QTL mapping problems where a standard least-squares model is used for computing the model fit. We develop optimization methods for the local problems in a hybrid global-local optimization scheme for determining the optimal set of QTL locations. Here, the local problems have constant bound constraints and may be non-convex and/or flat in one or more directions. We propose an enhanced quasi-Newton method and also implement several schemes for constrained optimization. The algorithms are adopted to the QTL optimization problems. We show that it is possible to use the new schemes to solve problems with up to 6 QTLs efficiently and accurately, and that the work is reduced with up to two orders magnitude compared to using only global optimization.Secondly, we study numerical methods for QTL mapping where variance component estimation and a REML model is used. This results in a non-linear optimization problem for computing the model fit in each set of QTL locations. Here, we compare different optimization schemes and adopt them for the specifics of the problem. The results show that our version of the active set method is efficient and robust, which is not the case for methods used earlier. We also study the matrix operations performed inside the optimization loop, and develop more efficient algorithms for the REML computations. We develop a scheme for reducing the number of objective function evaluations, and we accelerate the computations of the derivatives of the log-likelihood by introducing an efficient scheme for computing the inverse of the variance-covariance matrix and other components of the derivatives of the log-likelihood.
|
|
| 2942. |
- Mishchenko, Kateryna
(författare)
-
Runge--Kutta methods in financial engineering
- 2011
-
Ingår i: Exploring the world of financial engineering. - Västerås : Mälardalen University. ; , s. 66-79
-
Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Nowadays, a number of mathematical problems that pertain to the area of financial engineering are formulated and solved through the systems of ordinary differential equations. Apart from that, it appears to be impossible to derive close form solutions for some of those systems. Therefore,numerical methods for solving differential equations are a unique tool for finding approximate solutions. The paper is devoted to some of the most common and well-studied numerical method, namely a family of explicit Runge--Kutta methods. The paper touches upon both theoretical aspects such as derivation of Runge--Kutta schemes of 2- 4 order of accuracy, and the numerical implementation of the Runge--Kutta method for approximating a solution of some specific problem in financial engineering.
|
|
| 2943. |
- Misheva, Vessela, et al.
(författare)
-
Framing the “exceptions to the rule” in analyses of responses to the first wave of the COVID-19 pandemic : A study of European poles involving Sweden and Bulgaria
- 2023
-
Ingår i: Crisis and the Culture of Fear and Anxiety in Contemporary Europe. - : Taylor & Francis. - 9781000916836 - 9781032268606 ; , s. 203-223
-
Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Against the background of the great variety of interpretations of the diversity of responses to the first wave of the COVID-19 pandemic, this chapter focuses on those responses that have been identified in research as “exceptions to the rule.” It comprises a two-country comparative study of the qualitative aspects of factors that have been responsible for the implementation of two radically different anti-pandemic policies that are termed here “liberal” and “restrictive.” The sample countries—Sweden and Bulgaria—were selected not simply as European democracies representative of the polar differences between persistent geo-political identities and cultural zones, but also as marking the poles of a continuum of measures capable of accommodating the entire variety of pandemic responses, from the most liberal to the most restrictive. Based on this analysis, a theoretical framework is developed that helps explain the underlying rationale of exceptions-to-the-rule in respect to anti-pandemic policies and approaches. This chapter identifies cultural and societal orientations, the levels and orientations of social and epistemic trust, and the values and frames that shape the perception of pandemic reality as the primary elements that need to be taken into account when forging anti-pandemic policies suitable for democracies.
|
|
| 2944. |
- Modarres Razavi, Sara, et al.
(författare)
-
Production planning for district heating networks (DHN)
- 2014. - 8
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- District Heating Networks (DHN) can provide higher efficiencies and better pollution control compared to local heat generation. However, there are still many areas, which can be improved and optimized in these systems. A DHN is a complex distributed system of different customer substations and components such as boilers, accumulators, pipes, and in many cases also turbines for electricity production. How to schedule the components with the objective of maximizing the profit of heat and electricity production over a finite time horizon is receiving increased attention, and is the problem that has been dealt with in this work. This mixed integer linear programming (MILP) problem has been formulated as a unit commitment problem (UCP), which involves finding the most profitable unit dispatch regarding production costs and heat and electricity sell prices, while simultaneously meeting the predicted district heating demands and satisfying network operational constraints. The heating demands within the optimization time horizon are predicted based on season and weather forecasts. In this work, the district heating plant in Uppsala, Sweden, owned by Vattenfall AB, has been considered as a reference plant for modeling and optimization. The optimization model is formulated in Python using Pyomo modeling language, and solved by Gurobi and GLPK solvers. An hourly- based data of five consecutive days is used as the time horizon. The results demonstrate the fact that with an accurate model of the DHN, it is possible to significantly increase the revenue of the DHN by finding the most economical way to dispatch different production components.
|
|
| 2945. |
|
|
| 2946. |
|
|
| 2947. |
|
|
| 2948. |
- Mohammadi, Samaneh
(författare)
-
Balancing Privacy and Performance in Emerging Applications of Federated Learning
- 2023
-
Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Federated Learning (FL) has emerged as a novel paradigm within machine learning (ML) that allows multiple devices to collaboratively train a shared ML model without sharing their private data with a central server. FL has gained popularity across various applications by eliminating the necessity for centralized data storage, thereby improving the confidentiality of sensitive information. Among the new FL applications, this thesis focuses on Speech Emotion Recognition (SER), which involves the analysis of audio signals from human speech to identify patterns and classify the conveyed emotions. When SER is implemented within a FL framework, even though speech data remains on local devices, new privacy challenges emerge during the training phase and the exchange of SER model update parameters between servers and clients. These challenges encompass the potential for privacy leakage and adversarial attacks, including model inversion attacks and membership or property inference attacks, which can be conducted by unauthorized or malicious parties to exploit the shared SER model, compromising client data confidentiality and revealing sensitive information.While several privacy-preserving solutions have been developed to mitigate potential breaches in FL architectures, those are too generic to be easily integrated into specific applications. Furthermore, incorporating existing privacy-preserving mechanisms into the FL framework can increase communication and computational overheads, which may, in turn, compromise data utility and learning performance.This thesis aims to propose privacy-preserving methods in FL for emerging security-critical applications such as SER while addressing the challenges related to their effect on performance. First, we categorize and analyze recent research on privacy-preserving mechanisms in FL, with a focus on assessing their effects on FL performance and how to balance privacy and performance across various applications. Second, we design an optimized FL setup tailored to SER applications in order to evaluate effects on performance and overhead. Third, we design and develop privacy-preserving mechanisms within FL to safeguard against potential privacy threats while ensuring the confidentiality of clients' data. Finally, we propose and evaluate new methods for FL in SER and integrate them with appropriate privacy-preserving mechanisms to achieve an optimal balance of privacy with efficiency, accuracy, as well as communication and computation overhead.
|
|
| 2949. |
|
|
| 2950. |
|
|
