1,029 research outputs found
Payments and finance problems in the Commonwealth of Independent States
No full textPayments problems constrained interstate trade among the Commonwealth of Independent States (CIS) countries in 1992-95, especially during the prolonged demise of the ruble zone. Two kinds of solutions should be sought: 1) more effective stabilization measures to improve the prospects of currency convertibility among CIS countries; and 2) strengthening of institutional arrangements to permit payments and settlements through correspondent bank accounts. Strengthening institutions will require not only strengthening commercial banks but liberalizing foreign exchange markets and promoting the use of letters of credit and other mechanisms to increase the security of trade transactions. A multilateral clearing arrangement operated among central banks would have been a useful alternative to the chaotic payments prevailing earlier, but such arrangements are no longer needed as considerable progress has been made toward convertibility. Nor is a payments union desirable. Trade deficits are likely to persist in such countries as Belarus and Ukraine. Surplus countries such as Russia and Turkmenistan must develop transparent means of trade financing that take into account the recipient countries'ability to pay. External financing will remain important for practically all CIS countries. The best way to mobilize private financing will be to establish macroeconomic stability and stable, transparent rules on private capital inflows. Improving the flow of public resources requires improving countries'capacity to quickly absorb the large amounts already committed. Donors need to expedite procurement and other procedures and recipient countries must address governance problems and institutional weaknesses that delay disbursements. Certain smaller CIS countries face significant debt servicing problems and often the creditors are other CIS countries that themselves need additional financing. The smaller countries need debt relief on concessional terms, which is possible only if external assistance allows local creditors to offer such relief.Environmental Economics&Policies,Payment Systems&Infrastructure,Economic Theory&Research,Trade Policy,Financial Intermediation,Economic Theory&Research,Environmental Economics&Policies,TF054105-DONOR FUNDED OPERATION ADMINISTRATION FEE INCOME AND EXPENSE ACCOUNT,Trade Policy,Financial Intermediation
Solving linear partial differential equations via semidefinite optimization
No full textThesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.Includes bibliographical references (p. 49-51).Using recent progress on moment problems, and their connections with semidefinite optimization, we present in this thesis a new methodology based on semidefinite optimization, to obtain a hierarchy of upper and lower bounds on both linear and nonlinear functionals defined on solutions of linear partial differential equations. We apply the proposed methods to examples of PDEs in one and two dimensions with very encouraging results. We also provide computational evidence that the semidefinite constraints are critically important in improving the quality of the bounds, that is without them the bounds are weak.by Constantine Caramanis.S.M
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Methods for matrix completion
No full textIn this paper, we are going to provide major results for two sorts of Matrix Completion problems. One involves the recovery of a low rank matrix and the other involves the recovery of an approximately low rank matrix based on a small number of observed entries. In the end of the paper, we are going to demonstrate the feasibility of the recovery methods discussed on randomly generated low rank and approximately low rank matrices.Computer Scienc
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Using machine learning to detect web application attacks
No full textWith the increased ease in cloud deployment platforms, web applications have become an easy target for cyber-criminals and state-sponsored hackers. In this paper, I propose a detection solution to help identify network traffic generated by web application attacks. My experimental results reveal that the gradient boosting models, like LightGBM and XGBoost, all yielded extremely high ROC-AUC scores above .98. When compared to traditional anomaly detection models, like K-nearest neighbors, the ROC-AUC scores are higher and training times are much faster. Finally, I also identified several network data features like the window length in bytes, packet length, and flow duration that are critical when identifying web application attacks using network traffic data.Electrical and Computer Engineerin
Adaptable optimization : theory and algorithms
No full textThesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 189-200).Optimization under uncertainty is a central ingredient for analyzing and designing systems with incomplete information. This thesis addresses uncertainty in optimization, in a dynamic framework where information is revealed sequentially, and future decisions are adaptable, i.e., they depend functionally on the information revealed in the past. Such problems arise in applications where actions are repeated over a time horizon (e.g., portfolio management, or dynamic scheduling problems), or that have multiple planning stages (e.g., network design). The first part of the thesis focuses on the robust optimization approach to systems with uncertainty. Unlike the probability-driven stochastic programming approach, robust optimization is built on deterministic set-based formulations of uncertainty. This thesis seeks to place Robust Optimization within a dynamic framework. In particular, we introduce the notion of finite adaptability. Using geometric results, we characterize the benefits of adaptability, and use these theoretical results to design efficient algorithms for finding near-optimal protocols. Among the novel contributions of the work are the capacity to accommodate discrete variables, and the development of a hierarchy of adaptability.(cont.) The second part of the thesis takes a data-driven view to uncertainty. The central questions are (a) how can we construct adaptability in multi-stage optimization problems given only data, and (b) what feasibility guarantees can we provide. Multi-stage Stochastic Optimization typically requires exponentially many data points. Robust Optimization, on the other hand, has a very limited ability to address multi-stage optimization in an adaptable manner. We present a hybrid sample-based robust optimization methodology for constructing adaptability in multi-stage optimization problems, that is both tractable and also flexible, offering a hierarchy of adaptability. We prove polynomial upper bounds on sample complexity. We further extend our results to multi-stage problems with integer variables in the future stages. We illustrate the ideas above on several problems in Network Design, and Portfolio Optimization. The last part of the thesis focuses on an application of adaptability, in particular, the ideas of finite adaptability from the first part of the thesis, to the problem of air traffic control. The main problem is to sequentially schedule the departures, routes, ground-holding, and air-holding, for every flight over the national air space (NAS).(cont.) The schedule seeks to minimize the aggregate delay incurred, while satisfying capacity constraints that specify the maximum number of flights that can take off or land at a particular airport, or fly over the same sector of the NAS at any given time. These capacities are impacted by the weather conditions. Since we receive an initial weather forecast, and then updates throughout the day, we naturally have a multistage optimization problem, with sequentially revealed uncertainty. We show that finite adaptability is natural, since the scheduling problem is inherently finite, and furthermore the uncertainty set is low-dimensional. We illustrate both the applicability of finite adaptability, and also its effectiveness, through several examples.by Constantine Caramanis.Ph.D
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Comparison of algorithms for Twitter sentiment analysis
No full textSentiment Analysis has gained attention in recent years owing to the massive increase in personal statements made at the individual level, spread across vast geographic and demographic ranges. That data has become vastly more accessible as micro-blog sites such as Twitter and Facebook have released public, free interfaces. This research seeks to understand the processes behind Sentiment Analysis and to compare statistical methodologies for classifying Twitter sentiments.Electrical and Computer Engineerin
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Intelligent vision marketplace : feasibility study
No full textRecent years have been very generous for machine learning, as we have seen a tremendous growth in that industry. And this advancement can be further boosted by making it easily accessible to the general public. The goal of this report is to explore if the infrastructure and tooling available today would allow for such a platform to exist. This report looks into currently available image/video processing applications and dive into the latest and greatest tool available. The outcome of this study will be a prototype of this marketplace along with my findings on availability of polished tools and an insight into the performance expectation and cost of running this platform.Electrical and Computer Engineerin
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Numeric image classification with TensorFlow
No full textRecognition of alphanumeric data using a machine learning algorithm is a problem with practical applications in license plate, traffic sign, and street number recognition. TensorFlow, an open source software library originally developed by the Google Brain Team, offers a flexible architecture and an easy to learn interface that allows for rapid implementation of and evaluation of different machine learning algorithms and data structures. This paper covers predictive analysis of the SVHN, or Street View House Numbers, dataset using a Convolution Neural Network model developed on the TensorFlow platform. The goal of the paper is to improve the training speed and validation accuracy of an existing CIFAR-10 neural network model implemented in TensorFlow by changing its activation functions, regularization measures, number of convolution layers, loss optimizers, and architectural organization.Electrical and Computer Engineerin
Constantine and the Christian Empire
No full textUnder Constantine, Christianity was transformed from a persecuted cult into an established religion, and pagan Rome became the Christian empire of Byzantine times. This biography is a detailed, comprehensive, and compelling portrayal of the life and times of arguably the greatest of Roman emperors. In a seamless combination of vivid narrative and historical analysis, the crisis of the Roman Empire and the Great persecution, Constantine\u27s political maneuvers and military campaigns, his conversion to and patronage of Christianity, and his church-building programs in Rome, Jerusalem, and Constantinople are brought to life and made understandable for modern readers. The author\u27s comprehensive knowledge of the literary sources, and his extensive research into the material remains of Constantine\u27s reign, mean that this volume provides a more rounded and accurate portrait of the emperor than ever before. Extensively illustrated and fully documented, Constantine and the Christian Empire is a landmark publication in Roman imperial, early Christian, and Byzantine history.https://scholarworks.boisestate.edu/fac_books/1489/thumbnail.jp
The why's the limit: curtailing self-enhancement with explanatory introspection
No full textSelf-enhancement is linked to psychological gains (e.g., subjective well-being, persistence in adversity) but also to intrapersonal and interpersonal costs (e.g., excessive risk taking, antisocial behavior). Thus, constraints on self-enhancement may sometimes afford intrapersonal and interpersonal advantages. We tested whether explanatory introspection (i.e., generating reasons for why one might or might not possess personality traits) constitutes one such constraint. Experiment 1 demonstrated that explanatory introspection curtails self-enhancement. Experiment 2 clarified that the underlying mechanism must (a) involve explanatory questioning rather than descriptive imagining, (b) invoke the self rather than another person, and (c) feature written expression rather than unaided contemplation. Finally, Experiment 3 obtained evidence that an increase in uncertainty about oneself mediates the effect
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