378 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Tradition and Innovation in Construction Project Management

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    This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings

    Analysis and forecasting of asset quality, risk management and financial stability for the Greek banking system

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    The increase in non-performing loans (NPLs) during the financial crisis of 2008, which has been converted into a fiscal crisis, as well as the risk of a medium-term increase due to the COVID-19 pandemic has put into question the robustness of many banks and the financial stability of the whole sector. As far as the banking sector is concerned, the management of non-performing loans represents the most significant challenge as their stock reached unprecedented levels, with the deterioration in asset quality being widespread. Addressing the problem of non-performing loans with the assistance of credit risk modeling is important from both a micro and a macro-prudential perspective, since it would not only improve the financial soundness and the capital adequacy of the banking sector, but also free-up funds to be directed to other more productive sectors of the economy. This Thesis extends earlier research by employing a short-term monitoring system with the aim to forecast ā€œfailuresā€ i.e. NPL creation. The creation of such a monitoring system allows the risk of a ā€œfailureā€ to change over time, measuring the likelihood of ā€œfailureā€ given the survival time and a set of explanatory variables. The application of Cox proportional hazards models and survival trees to forecast NPLs can be usefully employed in the Greek corporate sectors. The research aim of this thesis consists of two domains: The first aim is the investigation of the determinants that contribute to the NPLs formation. Two GAMLSS models are being tested, a linear GAMLSS model and a nonlinear semi-parametric GAMLSS model which includes smoothing functions that capture potential nonlinear relationships between the explanatory variables to model the parameters favorably. The explanatory variables of the models consist of credit risk variables, macroeconomic variables, bank-specific variables and supervisory and market variables, while the response variable is the non-performing loans. The second aim is to provide answers on whether proportional hazards Cox models and survival tree models can forecast NPLs of loans that are provided in specific corporate sectors in Greece by the use of the most granular data set of corporate borrowers. By evaluating a series of Cox models, a short-term monitoring system has been created with the aim to forecast ā€œfailuresā€ i.e. NPL creation. The Cox proportional hazards regression models are incorporating time-to-event, involving a timeline, described by the survival function, indicating the probability that a loan becomes an NPL until time t. The time period counts from the origination of the loan until the ā€œdeathā€ of the loan, i.e. its termination, incorporating an ā€œin betweenā€ observation point. The event is when the loan is initially being ā€œinfectedā€, i.e. has become NPL. Regarding survival trees, the data set was divided into more subsets, which are easier to model separately and hence yield an improved overall performance. Such models are then beneficial to implement with different machine learning techniques. Predictors (or covariates) are defined as the sectors of the Greek economy and the model is fitted both for the whole sample and for the sample of early terminated loans. The Thesis is organized as follows: Chapter 1 - Introduction addresses the role of banks in financial intermediation, the evolution of credit risk and some issues regarding the Greek banking sector. Chapter 2 constitutes a literature review on research focused on improving the predictive performance of different credit risk assessment methods. Chapter 3 outlines the competitive conditions in the banking sector to demonstrate whether the increase in concentration had affected the competitive conditions in the Greek banking system. In Chapter 4, the funding and the liquidity conditions in the Greek banking sector are being addressed. Chapter 5 contains the selection of aggregate sample, results and analysis of GAMLSS models that have been used for determining NPLs. Chapter 6 provides an introduction to the granular database on Large Exposures, which is used for deriving the panel sample of corporate borrowers whereby models of forecasting and prediction are being employed. Chapter 7 contains the application of Cox models and decision trees, the estimation procedure, parameters, model fit, estimation results and empirical findings. Chapter 8 provides an evaluation and applicability of models as well as the implications for further research. Finally, a conclusion is provided by summarizing my contribution to the research community and my recommendations to the banking industr

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

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    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    Optimal Rate-Matrix Pruning For Large-Scale Heterogeneous Systems

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    We present an analysis of large-scale load balancing systems, where the processing time distribution of tasks depends on both the task and server types. Our study focuses on the asymptotic regime, where the number of servers and task types tend to infinity in proportion. In heterogeneous environments, commonly used load balancing policies such as Join Fastest Idle Queue and Join Fastest Shortest Queue exhibit poor performance and even shrink the stability region. Interestingly, prior to this work, finding a scalable policy with a provable performance guarantee in this setup remained an open question. To address this gap, we propose and analyze two asymptotically delay-optimal dynamic load balancing policies. The first policy efficiently reserves the processing capacity of each server for ``good" tasks and routes tasks using the vanilla Join Idle Queue policy. The second policy, called the speed-priority policy, significantly increases the likelihood of assigning tasks to the respective ``good" servers capable of processing them at high speeds. By leveraging a framework inspired by the graphon literature and employing the mean-field method and stochastic coupling arguments, we demonstrate that both policies achieve asymptotic zero queuing. Specifically, as the system scales, the probability of a typical task being assigned to an idle server approaches 1

    Exponential Tail Bounds on Queues: A Confluence of Non-Asymptotic Heavy Traffic and Large Deviations

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    In general, obtaining the exact steady-state distribution of queue lengths is not feasible. Therefore, we establish bounds for the tail probabilities of queue lengths. Specifically, we examine queueing systems under Heavy-Traffic (HT) conditions and provide exponentially decaying bounds for the probability P(Ļµq>x)\mathbb P(\epsilon q > x), where Ļµ\epsilon is the HT parameter denoting how far the load is from the maximum allowed load. Our bounds are not limited to asymptotic cases and are applicable even for finite values of Ļµ\epsilon, and they get sharper as Ļµā†’0\epsilon \to 0. Consequently, we derive non-asymptotic convergence rates for the tail probabilities. Unlike other approaches such as moment bounds based on drift arguments and bounds on Wasserstein distance using Stein's method, our method yields sharper tail bounds. Furthermore, our results offer bounds on the exponential rate of decay of the tail, given by āˆ’1xlogā”P(Ļµq>x)-\frac{1}{x} \log \mathbb P(\epsilon q > x) for any finite value of xx. These can be interpreted as non-asymptotic versions of Large Deviation (LD) results. We demonstrate our approach by presenting tail bounds for: (i) a continuous time Join-the-shortest queue (JSQ) load balancing system, (ii) a discrete time single-server queue and (iii) an M/M/nM/M/n queue. We not only bridge the gap between classical-HT and LD regimes but also explore the large system HT regimes for JSQ and M/M/nM/M/n systems. In these regimes, both the system size and the system load increase simultaneously. Our results also close a gap in the existing literature on the limiting distribution of JSQ in the super-NDS (a.k.a. super slowdown) regime. This contribution is of an independent interest. Here, a key ingredient is a more refined characterization of state space collapse for JSQ system, achieved by using an exponential Lyapunov function designed to approximate the ā„“āˆž\ell_{\infty} norm.Comment: 37 pages, 1 figur

    Fundamentals

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    Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters

    Learning a model is paramount for sample efficiency in reinforcement learning control of PDEs

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    The goal of this paper is to make a strong point for the usage of dynamical models when using reinforcement learning (RL) for feedback control of dynamical systems governed by partial differential equations (PDEs). To breach the gap between the immense promises we see in RL and the applicability in complex engineering systems, the main challenges are the massive requirements in terms of the training data, as well as the lack of performance guarantees. We present a solution for the first issue using a data-driven surrogate model in the form of a convolutional LSTM with actuation. We demonstrate that learning an actuated model in parallel to training the RL agent significantly reduces the total amount of required data sampled from the real system. Furthermore, we show that iteratively updating the model is of major importance to avoid biases in the RL training. Detailed ablation studies reveal the most important ingredients of the modeling process. We use the chaotic Kuramoto-Sivashinsky equation do demonstarte our findings

    Covid-19 and Capitalism

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    This open access book provides a comprehensive analysis of the socioeconomic determinants of Covid-19. From the end of 2019 until presently, the world has been ravaged by the Covid-19 pandemic. Although the cause of this is (obviously) a virus, the extent to which this virus spread, and therefore the number of infections and deaths, was largely determined by socio-economic factors. From this, it follows that the course of the pandemic varies greatly from one country to another. This observation applies both to countriesā€™ resilience to such a pandemic (which is mainly rooted in the period preceding the outbreak of the virus) and to the way in which countries have reacted to the virus (including the political choices on how to respond). Meanwhile, research has made it clear that the nature of this response (e.g., elimination policy, mitigation policy, and proceeding herd immunity) was, on the one hand, strongly determined by political and ideological factors and, on the other hand, was highly influential in the factors of success or failure in combating the pandemic. The book focuses on the situation in a number of Western regions (notably the USA, the UK, and the EU and its Member States). The author addresses the reasons why in many Western countries both pandemic prevention and response policies to Covid-19 have failed. The book concludes with recommendations concerning the rearrangement of the socio-economic order that could increase the resilience of (Western) societies against such pandemics
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