IAMINSTITUTE OF APPLIED MATHEMATICS

Reliability and Mean Residual Life Functions of Coherent Systems in an Active Redundancy

Konul Bayramoglu Kavlak

Department of Actuarial Sciences

Hacettepe University

Invited by: Bülent Karasözen

Place: IAM S212

Date/Time: 02.05.2017 -15.40

Abstract: In this talk the reliability and the mean residual life (MRL) functions of a system with active redundancies at the component and system levels are investigated. In active redundancy at the component level, the original and redundant components are working together and lifetime of the system is determined by the maximum of lifetime of the original components and their spares. In the active redundancy at the system level,the system has a spare, and the original and redundant systems work together. The lifetime of such a system is then the maximum of lifetimes of the system and its spare. The lifetimes of the original component and the spare are assumed to be dependent random variables. Key words. Coherent system, reliability function, active redundancy, bivariate order statistics, MRL functions.

External Imbalances in Turkish Economy

Esma Gaygısız

Department of Economics

Middle East Technical University



Place: IAM S212

Date/Time: 18.04.2017 -15.40

Abstract:External imbalances have a crucial role in Turkish economy characterized by chronic current account and financial account deficits. The magnitudes of these crucial deficits, especially relative to the capability of the economy to cover these deficits, have had an increasing trend since 2002. The increasing dependency of the economy on external funds stands in stark contrast to its stagnant and even declining capability to pay back its obligations. This study investigates the compositions of Turkey’s external imbalances and relates these imbalances to the changing sectoral configuration and patterns of its production structures as well as its consumption directions.

Short Biography of the Presenter

Esma Gaygısız is an associate professor in the Department of Economics in Middle East Technical University (METU) in Ankara. She obtained her Doctor of Philosophy Degree in Economics and Econometrics from University of Manchester in United Kingdom. She received Master of Economics and Baccalaureate degrees from the Department of Economics, METU. Recently, she visited Norwegian University of Science and Technology between 2012-2014. In a crisis prone world, she concentrates on the links between real sectors and financial structures which constitute a vital dimension in understanding all economies in good and bad times. She studies on how real sector activities shape the financial outcomes and how distinct financial structures affect real sectors’ production characteristics and quantities.

Bounded Component Analysis: An Algorithmic Framework  for Blind Separation of Independent and Dependent Sources

Alper Erdoğan

Department of Electrical-Electronics Engineering 

Koç University

Invited by: Bülent Karasözen

Place: IAM S212

Date/Time: 11.04.2017 -15.40

Abstract: In many scientific experiments and engineering applications, measurements can be modeled as linear mixtures of  desired source signals, while the mixing system is unknown. The goal in Blind Source Separation (BSS) is to process these measurement sequences in an unsupervised manner to learn the inverse of the mixing  system, potentially by ​exploiting some side information about the sources. Most popular solution to BSS problem is Independent Component Analysis (ICA) which assumes that the sources are mutuallly statistically independent.  The fact that strong independence assumption may not hold in various scenarios has led to search for new frameworks that are capable of separating dependent sources.

 

Bounded Component Analysis (BCA) is a recent algorithmic BSS framework introduced in this direction. BCA can be considered as an extension of  the ICA framework  where the boundedness of sources is exploited to replace independence assumption with a weaker ``domain separability'' assumption. As a result BCA algorithms can be used to separate dependent as well as independent  signals from their linear mixtures.  In this talk, I'll introduce a geometric approach for developing instantaneous and convolutive BCA algorithms.  Furthermore, I'll illustrate the potential benefits of the corresponding BCA algorithms through different application examples.

 

This is a joint work with Huseyin A. Inan. and Eren Babatas


Short Bio


Alper T, Erdogan received his B.S.(93) in EE from Middle East Technical University in Turkey, M.S. (95) and Ph.D.(99) in EE from Stanford University. He worked as a principal research engineer in Globespan-Virata (formerly Excess Bandwidth) in Santa Clara CA during 1999-2001. In 2002, he joined Electrical-Electronics Engineering Department of Koc University in Istanbul, Turkey where he is currently an associate professor. Dr. Erdogan served as an associate editor for IEEE Transactions on Signal Processing and as a member of IEEE Signal Processing Theory and Methods Technical Committee. He is a recipient of TUBITAK Encouragement Award, Werner Von Siemens Award and Turkish Academy of Sciences Outstanding Young Scholar Award. His research interest is on Adaptive Signal Processing, Machine Learning, Communication Systems, Computational Neuroscience and Convex Optimization Applications.

A Statistical Approach in Turbine Heat Transfer

Harika Kahveci

Department of Aerospace Engineering

METU

Invited by: Bülent Karasözen

Place: IAM S212

Date/Time: 07.03.2017 -15.40

Abstract: A high-quality extensive database is very critical to the gas turbine industry for improving the capabilities of the current state-of-the-art design of these machines so that more efficient cooled designs with extended turbine life can be accomplished. A series of experiments was performed at the OSU Gas Turbine Laboratory involving a rotating rig with a cooled 1-1/2 stage high-pressure transonic turbine operating at design corrected conditions with the goal of providing the turbine designer with such high-quality data. The turbine stage used is a modern 3-D design consisting of a cooled high-pressure vane, an uncooled high-pressure rotor, and a low-pressure vane. The work investigates the influence of different vane inlet temperature profiles and cooling flow rates on heat transfer of the full-stage turbine. A novel application of a traditional statistical method is introduced to the analysis to assign confidence limits to measurements in the absence of repeat runs. This approach is later incorporated into a CFD validation effort for blade heat transfer predictions in order to quantify the overall predictive uncertainty due to the variation in the inlet temperature profile, gauge position, and surface roughness. Presented data analysis highlights important turbine flow regions that are highly complex and are still not well understood today

Short bio:

Dr. Harika S. Kahveci is an Assistant Professor at Aerospace Engineering Department at METU. She holds a B.S. degree in Aeronautical Engineering from METU (2002), an M.Sc. degree in Aerospace Engineering from Penn State University (2004), and a Ph.D degree in Mechanical Engineering from The Ohio State University (2010). Before joining METU, she was employed at General Electric Company for eleven years where she held several positions in engineering and management.  She has expertise in hot gas path aero design and thermal design of gas turbine engines. During her Ph.D, Dr. Kahveci worked at the short-duration rotating rig facility of the Gas Turbine Lab at The Ohio State University investigating the film-cooled first-stage turbine vane heat transfer. In June 2015, Dr. Kahveci received the Gas Turbine Award, which is the prestigious ASME gas turbine industry award that recognizes outstanding contributions to the literature.

Enumeration of irreducible polynomials with prescribed coefficients

Emrah Sercan Yılmaz

College Dublin University

Place: IAM S212

Date/Time: 10.01.2017 -15.30

Abstract: In this seminar, we will give the general theory of enumeration of irreducible polynomials with prescribed coefficients from beginning. We will explain how these numbers are related with (fibre products of) supersingular curves.

A Parametric Simplex Algorithm for Linear Vector Optimization Problems

Firdevs Ulus

Department of Industrial Engineering
Bilkent University
Invited by: Murat Manguoğlu
Place: IAM-S209

Date / Time: 03.01.2017 / 15.40

Abstract. A parametric simplex algorithm for solving linear vector optimization problems (LVOPs) is presented. This algorithm can be seen s a variant of the multi-objective simplex algorithm. Different from it, the proposed algorithm works in the parameter space and does not aim to find the set of all efficient solutions. Instead, it finds a ‘solution’ which is a subset of efficient solutions that allows to generate the whole efficient frontier. In that sense, it can also be seen as a generalization of the parametric self-dual simplex algorithm, which originally is designed for solving single objective linear ptimization problems, and is modified to solve two objective bounded LVOPs with the positive orthant as the ordering cone. The algorithm proposed here works for any dimension, any solid pointed polyhedral ordering cone and for bounded as well as unbounded problems.Numerical results are provided to compare the proposed algorithm with an objective space based LVOP (Benson's) algorithm and with the multiobjective simplex (the Evans-Steuer) algorithm. The results show that for non-degenerate problems the proposed algorithm outperforms Benson's algorithm and is on par with the Evan-Steuer algorithm. For highly degenerate problems Benson's algorithm outperforms the simplex-type algorithms; however, the parametric simplex algorithm is computationally much more efficient than the Evans-Steuer algorithm for these problems.

Em Algorithm for Markov Chain Observed Via Gaussian Noise and Point Processes Information

Zehra Eksi-Altay

Vienna University of Economics and Business
Invited by: Yeliz Yolcu Okur
Place: IAM-S209

Date / Time: 27.12.2016 / 15.40

Abstract. In this paper we deal with the parameter estimation of a  finite-state Markov chain observed via Gaussian noise and point processes information. To this, we use the Expectation Maximization (EM) algorithm. This amounts to the derivation of finite-dimensional filters for the related quantities. In this context, we obtain both exact and unnormalized filters. Next, we compute discretized robust versions of the unnormalized filters. Moreover, we introduce a novel goodness of fit test to check how well the estimated model explains the given data set. Finally, we run a simulation study to test speed and accuracy of the algorithm. In particular, we provide a comparison for the estimates resulting from the robust and naive discretization and the value of point process information.


Efficient methods to generate cryptographically good binary linear transformations 

Tolga Sakallı
Trakya University
Department of Computer Engineering

Place: IAM-S209

Date / Time: 20.12.2016 / 15.40

Abstract. In this presentation, we propose new methods using a divide-and-conquer strategy to generate $n \times n$ binary matrices (for composite $n$) with a high/maximum branch number and the same Hamming weight in each row and column. We introduce new types of binary matrices, namely $(BHwC)_{t,m}$ and $(BCwC)_{q,m}$ types, which are a combination of Hadamard and circulant matrices, and the recursive use of circulant matrices, respectively. With the help of these hybrid structures, the search space to generate a binary matrix with a high/maximum branch number is drastically reduced. By using the proposed methods, we focus on generating $12 \times 12$, $16 \times 16$ and $32 \times 32$ binary matrices with a maximum or maximum achievable branch number and low implementation costs to be used in block ciphers. Then, we discuss the implementation properties of binary matrices generated and present experimental results for binary matrices in these sizes. Finally, we apply the proposed methods to larger sizes, i.e., $48 \times 48$, $64 \times 64$ and $80 \times 80$ binary matrices having some applications in secure multi-party computation and fully homomorphic encryption.


Protein-Protein Interaction Network’s Data

Vilda Purutçuoğlu

Department of Statistics
METU
Invited by:Gerhard Wilhelm Weber
Place: IAM-S209

Date / Time: 13.12.2016 / 15.40

Abstract. In systems biology, the protein-protein interaction network is one of the common types of network structures. There are a number of different approaches to describe these complex systems under various assumptions. In this talk, we initially present a well-known modelling approach, called the Gaussian graphical model (GGM) to explain the steady-state behavior of the systems. This model basically represents the interactions between proteins via the precision matrix whose entries are defined under the conditional independency of the states. Although GGM and its major inference method, glasso, are successful in the description of the small and moderate systems, it has certain drawbacks such as the strike normality assumption of the model, high computational demand in the estimation, particularly, under high dimensional systems. Hence, here, we suggest several alternatives to overcome these challenges. In order to solve the problem of normality and high computational inefficiency, we propose a new non-parametric model based on the lasso regression. Then, to deal with the restriction of dimension of the systems, we further propose copula GGM and Bayesian inference of the model parameters. Finally, we investigate the possibility to improve the raw data and exclude the batch effect as the pre-processing step before any modelling. We evaluate the performance of all suggested models and underlying normalizing steps via real and simulate datasets.


The Political Economy of Financialization

Ali Tarhan

Independent Researcher Political Economy
Invited by: Bülent Karasözen

Place: IAM-S209

Date / Time: 06.12.2016 / 15.40

Abstract. Financialization is the key operating vehicle of neoliberalism for nearly four decades. With Gerald Epstein’s words it “refers to the increasing importance of financial markets, financial motives, financial institutions, and financial elites in the operations of the economy and its governing institutions, both at the national and international levels.” First two decades of this process constitute the political preparation stage. The latter two decades, marked with the “Gramm-Leach-Bliley act of 1999” portray the advancement phase of financialization. This whole era has different impacts on financialized core countries (The United States and The United Kingdom), and peripheral countries. While financialization has gone hand in hand with deindustrialization in the former group, it has created a financial Dutch disease by decreasing the savings ratio and promoting consumerism with abundance of readily available foreign funds in the latter. Consequently, lenders and borrowers of the global financial system have closely tied with financial threads which eased the spread of financial crises. These intertwined financial interactions of the core and periphery have also established a new network of power relations between these two realms. Experiences after the 2008 crisis show that still unregulated financial industry of the core states, especially of the US, has gained more political power than it had in the pre-crisis epoch. On the other hand, the peripheral states caught by unprepared by the last crisis have lost their financial stabilities. Therefore, the purpose of this seminar is to discuss financialization beginning with its early stages with a special emphasis on center-periphery relations