An Introduction to Algebraic Geometry codes

We present an introduction to the theory of algebraic geometry codes. Starting from evaluation codes and codes from order and weight functions, special attention is given to one-point codes and, in particular, to the family of Castle codes

Portfolio Optimization and Long-Term Dependence

Whilst emphasis has been given to short-term dependence of financial returns, long-term dependence remains overlooked. Despite financial literature provides evidence of long-term’s memory existence, serial-independence assumption prevails. This document’s long-term dependence assessment relies on rescaled range analysis (R/S), a popular and robust methodology designed for Geophysics but extensively used in financial literature. Results correspond to most of the previous evidence of significant long-term dependence, particularly for small and illiquid markets, where persistence is its most common kind. Persistence conveys that the range of possible future values of the variable will be wider than the range of purely random and independent variables. Ahead of R/S financial literature, authors estimate an adjusted Hurst exponent in order to properly estimate the covariance matrix at higher investment horizons, avoiding the traditional -independence reliant- square-root-of-time rule. Ignoring long-term dependence within the mean-variance portfolio optimization results in concealed risk taking; conversely, by adjusting for long-term dependence the weight of high (low) persistence risk factors decreases (increases) as the investment horizon widens. This alleviates some well-known shortcomings of conventional portfolio optimization for long-term investors (e.g. central banks, pension funds and sovereign wealth managers), such as excessive risk taking in long-term portfolios, extreme weights, home bias, and reluctance to hold foreign currency-denominated assets.Portfolio optimization, Hurst exponent, long-term dependence, biased random walk, rescaled range analysis. Classification JEL: G11, G32, G20, C14.

Designing an expert knowledge-based Systemic Importance Index for financial institutions

Defining whether a financial institution is systemically important (or not) is challenging due to (i) the inevitability of combining complex importance criteria such as institutions’ size, connectedness and substitutability; (ii) the ambiguity of what an appropriate threshold for those criteria may be; and (iii) the involvement of expert knowledge as a key input for combining those criteria. The proposed method, a Fuzzy Logic Inference System, uses four key systemic importance indicators that capture institutions’ size, connectedness and substitutability, and a convenient deconstruction of expert knowledge to obtain a Systemic Importance Index. This method allows for combining dissimilar concepts in a non-linear, consistent and intuitive manner, whilst considering them as continuous –non binary- functions. Results reveal that the method imitates the way experts them-selves think about the decision process regarding what a systemically important financial institution is within the financial system under analysis. The Index is a comprehensive relative assessment of each financial institution’s systemic importance. It may serve financial authorities as a quantitative tool for focusing their attention and resources where the severity resulting from an institution failing or near-failing is estimated to be the greatest. It may also serve for enhanced policy-making (e.g. prudential regulation, oversight and supervision) and decision-making (e.g. resolving, restructuring or providing emergency liquidity).Systemic Importance, Systemic Risk, Fuzzy Logic, Approximate Reasoning, Too-connected-to-fail, Too-big-to-fail. Classification JEL: D85, C63, E58, G28.

Operational Risk Management using a Fuzzy Logic Inference System

Operational Risk (OR) results from endogenous and exogenous risk factors, as diverse and complex to assess as human resources and technology, which may not be properly measured using traditional quantitative approaches. Engineering has faced the same challenges when designing practical solutions to complex multifactor and non-linear systems where human reasoning, expert knowledge or imprecise information are valuable inputs. One of the solutions provided by engineering is a Fuzzy Logic Inference System (FLIS). Despite the goal of the FLIS model for OR is its assessment, it is not an end in itself. The choice of a FLIS results in a convenient and sound use of qualitative and quantitative inputs, capable of effectively articulating risk management's identification, assessment, monitoring and mitigation stages. Different from traditional approaches, the proposed model allows evaluating mitigation efforts ex-ante, thus avoiding concealed OR sources from system complexity build-up and optimizing risk management resources. Furthermore, because the model contrasts effective with expected OR data, it is able to constantly validate its outcome, recognize environment shifts and issue warning signals.Operational Risk, Fuzzy Logic, Risk Management Classification JEL:G32, C63, D80

Efficient Portfolio Optimization in the Wealth Creation and Maximum Drawdown Space

First developed by Markowitz (1952), the mean-variance framework is the most widespread theoretical approximation to the portfolio problem. Nevertheless, successful application in the investment community has been limited. Assumptions such as normality of returns and a static correlation matrix could partially account for this. To overcome some of the limitations of the mean-variance framework, mainly the choice of the risk metric and the inconvenience of using an estimated correlation matrix typical of tranquil or euphoria periods, this paper proposes an alternative risk measure: the maximum drawdown (MDD), and combines it with a wealth creation measure to define a new portfolio optimization space. Like other market practitioners’ measures, MDD lacks of a complete and solid theoretical foundation. In an effort to contribute to its theoretical foundation, this paper uses common sense and financial intuition to introduce such measure, followed by a review of its technical advantages and coherence for risk management. Finally, an application of a MDD risk metric based portfolio optimization model is presented. The main findings indicate this proposal may effectively help overcome some of the traditional mean-variance shortcomings and provide some useful tools for portfolio optimization in practice. For long-term performance driven portfolios, such as pension funds, this approach may yield interesting results because it focuses on wealth creation over the long run.Portfolio Optimization, Asset Allocation, Downside Risk, Maximum Drawdown, mean-variance Criteria, Diversification. Classification JEL: G11; G23; G32; D81.

Improving Knowledge Retrieval in Digital Libraries Applying Intelligent Techniques

Nowadays an enormous quantity of heterogeneous and distributed information is stored in the digital University. Exploring online collections to find knowledge relevant to a user’s interests is a challenging work. The artificial intelligence and Semantic Web provide a common framework that allows knowledge to be shared and reused in an efficient way. In this work we propose a comprehensive approach for discovering E-learning objects in large digital collections based on analysis of recorded semantic metadata in those objects and the application of expert system technologies. We have used Case Based-Reasoning methodology to develop a prototype for supporting efficient retrieval knowledge from online repositories. We suggest a conceptual architecture for a semantic search engine. OntoUS is a collaborative effort that proposes a new form of interaction between users and digital libraries, where the latter are adapted to users and their surroundings

Entanglement swapping between spacelike separated atoms

We show a mechanism that projects a pair of neutral two-level atoms from an initially uncorrelated state to a maximally entangled state while they remain spacelike separated. The atoms begin both excited in a common electromagnetic vacuum, and the radiation is collected with a partial Bell-state analyzer. If the interaction time is short enough and a certain two-photon Bell state is detected after the interaction, a high degree of entanglement, even maximal, can be generated while one atom is outside the light cone of the other, for arbitrary large interatomic distances.Comment: v2: version accepted in Phys. Rev.

Generation of atom-atom correlations inside and outside the mutual light cone

We analyze whether a pair of neutral two level atoms can become entangled in a finite time while they remain causally disconnected. The interaction with the e. m. field is treated perturbatively in the electric dipole approximation. We start from an initial vacuum state and obtain the final atomic correlations for the cases where n = 0, 1, or 2 photons are produced in a time t, and also when the final field state is unknown. Our results show that correlations are sizable inside and outside the mutual light cone for n= 1 and 2, and also that quantum correlations become classical by tracing over the field state. For n = 0 we obtain entanglement generation by photon propagation between the atoms, the correlations come from the indistinguishability of the source for n = 1, and may give rise to entanglement swapping for n = 2.Comment: v2: Minor changes, references added. v3: full revision, appendix added. v4: Minor changes. Accepted in Phys. Rev.

Intelligent Integrated Management for Telecommunication Networks

As the size of communication networks keeps on growing, faster connections, cooperating technologies and the divergence of equipment and data communications, the management of the resulting networks gets additional important and time-critical. More advanced tools are needed to support this activity. In this article we describe the design and implementation of a management platform using Artificial Intelligent reasoning technique. For this goal we make use of an expert system. This study focuses on an intelligent framework and a language for formalizing knowledge management descriptions and combining them with existing OSI management model. We propose a new paradigm where the intelligent network management is integrated into the conceptual repository of management information called Managed Information Base (MIB). This paper outlines the development of an expert system prototype based in our propose GDMO+ standard and describes the most important facets, advantages and drawbacks that were found after prototyping our proposal

Tendencias en bibliotecas universitarias

Mercedes Aguilar Gómez, de la Biblioteca de Ingeniería, mostró los futuros caminos por los que trabajarán - trabajan ya- las bibliotecas universitarias, adaptadas a los tiempos, a la tecnología y con el objetivo de prestar un mejor servicio a los usuarios. Estas tendencias de futuro, presentadas de forma resumida, se derivan de los distintos informes que acaban de realizarse en la BUS para apoyar la redacción de nuestro próximo Plan Estratégico