Modeling interbank relations during the international financial crisis

This paper examines the effects of the current financial crisis on the correlations of four international banking stocks. We find that in the beginning of the crisis banks generally show a transition to a higher correlation followed by a dramatic decline towards the end of 2008. These findings are consistent with both traditional contagion theory and the more recent network theory of contagion.Financial Crises, Contagion, Interbank Markets

Scalable aggregation predictive analytics: a query-driven machine learning approach

We introduce a predictive modeling solution that provides high quality predictive analytics over aggregation queries in Big Data environments. Our predictive methodology is generally applicable in environments in which large-scale data owners may or may not restrict access to their data and allow only aggregation operators like COUNT to be executed over their data. In this context, our methodology is based on historical queries and their answers to accurately predict ad-hoc queries’ answers. We focus on the widely used set-cardinality, i.e., COUNT, aggregation query, as COUNT is a fundamental operator for both internal data system optimizations and for aggregation-oriented data exploration and predictive analytics. We contribute a novel, query-driven Machine Learning (ML) model whose goals are to: (i) learn the query-answer space from past issued queries, (ii) associate the query space with local linear regression & associative function estimators, (iii) define query similarity, and (iv) predict the cardinality of the answer set of unseen incoming queries, referred to the Set Cardinality Prediction (SCP) problem. Our ML model incorporates incremental ML algorithms for ensuring high quality prediction results. The significance of contribution lies in that it (i) is the only query-driven solution applicable over general Big Data environments, which include restricted-access data, (ii) offers incremental learning adjusted for arriving ad-hoc queries, which is well suited for query-driven data exploration, and (iii) offers a performance (in terms of scalability, SCP accuracy, processing time, and memory requirements) that is superior to data-centric approaches. We provide a comprehensive performance evaluation of our model evaluating its sensitivity, scalability and efficiency for quality predictive analytics. In addition, we report on the development and incorporation of our ML model in Spark showing its superior performance compared to the Spark’s COUNT method

Aggregate Query Prediction under Dynamic Workloads

Large organizations have seamlessly incorporated data-driven decision making in their operations. However, as data volumes increase, expensive big data infrastructures are called to rescue. In this setting, analytics tasks become very costly in terms of query response time, resource consumption, and money in cloud deployments, especially when base data are stored across geographically distributed data centers. Therefore, we introduce an adaptive Machine Learning mechanism which is light-weight, stored client-side, can estimate the answers of a variety of aggregate queries and can avoid the big data backend. The estimations are performed in milliseconds and are inexepensive as the mechanism learns from past analytical-query patterns. However, as analytic queries are ad-hoc and analysts’ interests change over time we develop solutions that can swiftly and accurately detect such changes and adapt to new query patterns. The capabilities of our approach are demonstrated using extensive evaluation with real and synthetic datasets

SuRF: Identification of Interesting Data Regions with Surrogate Models

Several data mining tasks focus on repeatedly inspecting multidimensional data regions summarized by a statistic. The value of this statistic (e.g., region-population sizes, order moments) is used to classify the region’s interesting-ness. These regions can be naively extracted from the entire dataspace – however, this is extremely time-consuming and compute-resource demanding. This paper studies the reverse problem: analysts provide a cut-off value for a statistic of interest and in turn our proposed framework efficiently identifies multidimensional regions whose statistic exceeds (or is below) the given cut-off value (according to user’s needs). However, as data dimensions and size increase, such task inevitably becomes laborious and costly. To alleviate this cost, our solution, coined SuRF (SUrrogate Region Finder), leverages historical region evaluations to train surrogate models that learn to approximate the distribution of the statistic of interest. It then makes use of evolutionary multi-modal optimization to effectively and efficiently identify regions of interest regardless of data size and dimensionality. The accuracy, efficiency, and scalability of our approach are demonstrated with experiments using synthetic and real-world datasets and compared with other methods

A Coronavirus Asset Pricing Model: The Role of Skewness

We study an equilibrium risk and return model to explore the effects of the coronavirus crisis and associated skewness. We derive the moment and equilibrium equations, specifying skew-ness price of risk as an additive component of the effect of variance on mean expected return. We estimate our model using the flexible skewed generalized error distribution, for which we derive the distribution of returns and the likelihood function. Using S&P 500 Index returns from January 1990 to mid-May 2020, our results show that the coronavirus crisis generated the most negative reaction in the skewness price of risk, more negative even than the subprime crisis

Explaining Aggregates for Exploratory Analytics

Analysts wishing to explore multivariate data spaces, typically pose queries involving selection operators, i.e., range or radius queries, which define data subspaces of possible interest and then use aggregation functions, the results of which determine their exploratory analytics interests. However, such aggregate query (AQ) results are simple scalars and as such, convey limited information about the queried subspaces for exploratory analysis.We address this shortcoming aiding analysts to explore and understand data subspaces by contributing a novel explanation mechanism coined XAXA: eXplaining Aggregates for eXploratory Analytics. XAXA’s novel AQ explanations are represented using functions obtained by a three-fold joint optimization problem. Explanations assume the form of a set of parametric piecewise-linear functions acquired through a statistical learning model. A key feature of the proposed solution is that model training is performed by only monitoring AQs and their answers on-line. In XAXA, explanations for future AQs can be computed without any database (DB) access and can be used to further explore the queried data subspaces, without issuing any more queries to the DB. We evaluate the explanation accuracy and efficiency of XAXA through theoretically grounded metrics over real-world and synthetic datasets and query workloads

Structural studies of the bacteriophage lambda holin and M. tuberculosis secA translocase

Double stranded DNA bacteriophages achieve release of phage progeny by disrupting the cell envelope of the host cell. This is accomplished by two phage-encoded proteins, the holin and the endolysin. In bacteriophage lambda, the S holin is a small three TMD membrane protein that creates a lesion in the inner membrane of the host at a specific time, programmed in its primary structure. Lesion formation permits the cytoplasmic endolysin R access to the murein cell wall for degradation and cell lysis. Although it has been shown that S oligomerizes in the membrane, the structural nature of this complex has not been elucidated. In this study the S holin was purified using a mild non-ionic detergent and the structure of a ring complex formed by the holin was determined by electron microscopy and single particle analysis at a resolution of 2.6 nm. Biochemical characterization of the rings suggests that such a complex might represent the assembly formed by S in the membrane. Protein translocation in all organisms allows the export of proteins destined for localization outside the cytoplasm. In eubacteria, newly synthesized proteins are directed to the heterotrimeric membrane complex SecYEG by signals embedded in their sequence. The driving force through this complex is provided by the cytoplasmic ATPase SecA which combines ATP hydrolysis to mechanically insert proteins through the protein conducting channel. Using electron microscopy and single particle analysis we have obtained the structure of SecA from M. tuberculosis. The structure indicates that four SecA monomers assemble to form an elongated molecule with D2 symmetry. Docking of the EM map to the crystal structure of tb SecA confirms this arrangement of the subunits. This finding, that M. tuberculosis SecA forms a tetramer raises intriguing possibilities about SecA function

Lessons that can be learnt using Action Research strategies within TfL

This study explores what strategies have been implemented in reducing safety risks, where improvements could be made, what challenges have been encountered and how they have been addressed. Furthermore, this study explores how these strategies have been institutionalised, and uses actionable knowledge to develop implications for practice within a wider organisational context. This study uses a stakeholder theory to gain a broader view by using all the perspectives of the delivery stakeholders