Financial Sector Deepening and Economic Growth: Evidence from Turkey

This paper analyzes the effects of financial sector deepening on economic growth using a province-level data set for 1996-2001 on Turkey. This period is associated with a weakly regulated and relatively unsupervised expansion of the banking sector which led to the 2001 financial crisis. Contrary to findings in the previous literature, our results indicate a strong negative relationship between financial deepening-both public and private-and economic growth. In light of the developments in the period of analysis, this result is not surprising, as the main function of the banking sector at that time was to provide financing for the Turkish Treasury, which channeled these funds to the government-albeit mainly for rent distribution purposes. However, it is important to note that the growth of private banking sector needs yet to be examined separately, as government ownership of banks may distort the development of the banking sector as a whole. Yet, it is possible to conclude that financial development may not always contribute to economic growth, and the conditions under which such a contribution takes place should be investigated further.Financial sector; Economic growth; Panel data; GMM; Turkey

Enabling adaptive scientific workflows via trigger detection

Next generation architectures necessitate a shift away from traditional workflows in which the simulation state is saved at prescribed frequencies for post-processing analysis. While the need to shift to in~situ workflows has been acknowledged for some time, much of the current research is focused on static workflows, where the analysis that would have been done as a post-process is performed concurrently with the simulation at user-prescribed frequencies. Recently, research efforts are striving to enable adaptive workflows, in which the frequency, composition, and execution of computational and data manipulation steps dynamically depend on the state of the simulation. Adapting the workflow to the state of simulation in such a data-driven fashion puts extremely strict efficiency requirements on the analysis capabilities that are used to identify the transitions in the workflow. In this paper we build upon earlier work on trigger detection using sublinear techniques to drive adaptive workflows. Here we propose a methodology to detect the time when sudden heat release occurs in simulations of turbulent combustion. Our proposed method provides an alternative metric that can be used along with our former metric to increase the robustness of trigger detection. We show the effectiveness of our metric empirically for predicting heat release for two use cases.Comment: arXiv admin note: substantial text overlap with arXiv:1506.0825

The Robust Network Loading Problem under Hose Demand Uncertainty: Formulation, Polyhedral Analysis, and Computations

Cataloged from PDF version of article.We consider the network loading problem (NLP) under a polyhedral uncertainty description of traffic demands. After giving a compact multicommodity flow formulation of the problem, we state a decomposition property obtained from projecting out the flow variables. This property considerably simplifies the resulting polyhedral analysis and computations by doing away with metric inequalities. Then we focus on a specific choice of the uncertainty description, called the “hose model,” which specifies aggregate traffic upper bounds for selected endpoints of the network. We study the polyhedral aspects of the NLP under hose demand uncertainty and use the results as the basis of an efficient branch-and-cut algorithm. The results of extensive computational experiments on well-known network design instances are reported

Rekabet hukuku ile haksız rekabet hukuku ilişkisi

Cataloged from PDF version of article.Türkiye, piyasa ekonomisi prensibine dayalı ekonomik bir sisteme sahiptir. Türkiye’de hukuki reformların en önemli sebebi 1995’te Avrupa Birliği ile Türkiye arasında gerçekleşen Gümrük Birliğidir. Günümüzde Türkiye, serbest ve dürüst rekabetin düzenlenmesi için gerekli hukuki alt yapısını esas itibarıyla tamamlamıştır. Türk hukukunda rekabete ilişkin hükümlerin mehazı AB hukukudur. Ayrıca bu hükümlerin uygulamasında AB hukukunun ölçütleri de dikkate alınmaktadır. Rekabet hukuku ve haksız rekabet hukukunun, piyasada serbest ve dürüst, bir diğer ifade ile bozulmamış bir rekabetin sağlanması amacına hizmet ettiği kabul edilmektedir. Türk hukukunda özellikle de piyasa aktörleri tarafından rekabet hukuku ile haksız rekabet hukuku sık sık birbirine karıştırılmaktadır. Piyasa düzeninde serbestlik ve iktisadi faaliyetlerde dürüstlüğün korunması ve sağlanması amacıyla piyasada gerçekleşen ihlâllere karşı kamu ve tüm katılımcıların yararına rekabetin korunmasını temin için, rekabet hukuku ile haksız rekabet hukukunun birbirini tamamladığının kabulü doğru bir yaklaşım olacaktır. Zira sadece meşru sınırlar içinde gerçekleştiği takdirde bir rekabet serbestîsinden bahsetmek mümkün olabilecektir

Exploiting Cognitive Structure for Adaptive Learning

Adaptive learning, also known as adaptive teaching, relies on learning path recommendation, which sequentially recommends personalized learning items (e.g., lectures, exercises) to satisfy the unique needs of each learner. Although it is well known that modeling the cognitive structure including knowledge level of learners and knowledge structure (e.g., the prerequisite relations) of learning items is important for learning path recommendation, existing methods for adaptive learning often separately focus on either knowledge levels of learners or knowledge structure of learning items. To fully exploit the multifaceted cognitive structure for learning path recommendation, we propose a Cognitive Structure Enhanced framework for Adaptive Learning, named CSEAL. By viewing path recommendation as a Markov Decision Process and applying an actor-critic algorithm, CSEAL can sequentially identify the right learning items to different learners. Specifically, we first utilize a recurrent neural network to trace the evolving knowledge levels of learners at each learning step. Then, we design a navigation algorithm on the knowledge structure to ensure the logicality of learning paths, which reduces the search space in the decision process. Finally, the actor-critic algorithm is used to determine what to learn next and whose parameters are dynamically updated along the learning path. Extensive experiments on real-world data demonstrate the effectiveness and robustness of CSEAL.Comment: Accepted by KDD 2019 Research Track. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'19

Tissue-type plasminogen activator-primed human iPSC-derived neural progenitor cells promote motor recovery after severe spinal cord injury.

The goal of stem cell therapy for spinal cord injury (SCI) is to restore motor function without exacerbating pain. Induced pluripotent stem cells (iPSC) may be administered by autologous transplantation, avoiding immunologic challenges. Identifying strategies to optimize iPSC-derived neural progenitor cells (hiNPC) for cell transplantation is an important objective. Herein, we report a method that takes advantage of the growth factor-like and anti-inflammatory activities of the fibrinolysis protease, tissue plasminogen activator tPA, without effects on hemostasis. We demonstrate that conditioning hiNPC with enzymatically-inactive tissue-type plasminogen activator (EI-tPA), prior to grafting into a T3 lesion site in a clinically relevant severe SCI model, significantly improves motor outcomes. EI-tPA-primed hiNPC grafted into lesion sites survived, differentiated, acquired markers of motor neuron maturation, and extended βIII-tubulin-positive axons several spinal segments below the lesion. Importantly, only SCI rats that received EI-tPA primed hiNPC demonstrated significantly improved motor function, without exacerbating pain. When hiNPC were treated with EI-tPA in culture, NMDA-R-dependent cell signaling was initiated, expression of genes associated with stemness (Nestin, Sox2) was regulated, and thrombin-induced cell death was prevented. EI-tPA emerges as a novel agent capable of improving the efficacy of stem cell therapy in SCI

Restricted robust uniform matroid maximization under interval uncertainty

For the problem of selecting p items with interval objective function coefficients so as to maximize total profit, we introduce the r-restricted robust deviation criterion and seek solutions that minimize the r-restricted robust deviation. This new criterion increases the modeling power of the robust deviation (minmax regret) criterion by reducing the level of conservatism of the robust solution. It is shown that r-restricted robust deviation solutions can be computed efficiently. Results of experiments and comparisons with absolute robustness, robust deviation and restricted absolute robustness criteria are reported. © Springer-Verlag 2007