Strong implementation with partially honest individuals

In this paper we provide sufficient conditions for a social choice rule to be implementable in strong Nash equilibrium in the presence of partially honest agents, that is, agents who break ties in favor of a truthful message when they face indifference between outcomes. In this way, we achieve a relaxation in the condition of Korpela (2013), namely the Axiom of Sufficient Reason. Our new condition, Weak Pareto Dominance, is shown to be sufficient along with Weak Pareto Optimality and Universally Worst Alternative. We finally provide applications of our result in pure matching and bargaining environments

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

Wetting of prototypical one- and two-dimensional systems: Thermodynamics and density functional theory.

Consider a two-dimensional capped capillary pore formed by capping two parallel planar walls with a third wall orthogonal to the two planar walls. This system reduces to a slit pore sufficiently far from the capping wall and to a single planar wall when the side walls are far apart. Not surprisingly, wetting of capped capillaries is related to wetting of slit pores and planar walls. For example, the wetting temperature of the capped capillary provides the boundary between first-order and continuous transitions to condensation. We present a numerical investigation of adsorption in capped capillaries of mesoscopic widths based on density functional theory. The fluid-fluid and fluid-substrate interactions are given by the pairwise Lennard-Jones potential. We also perform a parametric study of wetting in capped capillaries by a liquid phase by varying the applied chemical potential, temperature, and pore width. This allows us to construct surface phase diagrams and investigate the complicated interplay of wetting mechanisms specific to each system, in particular, the dependence of capillary wetting temperature on the pore width

Two-dimensional droplet spreading over random topographical substrates

We examine theoretically the effects of random topographical substrates on the motion of two-dimensional droplets via appropriate statistical approaches. Different random substrate families are represented as stationary random functions. The variance of the droplet shift at both early times and in the long-time limit is deduced and the droplet footprint is found to be a normal random variable at all times. It is shown that substrate roughness decreases droplet wetting, illustrating also the tendency of the droplet to slide without spreading as equilibrium is approached. Our theoretical predictions are verified by numerical experiments.Comment: 12 pages, 5 figure

Unexpected correlations between gene expression and codon usage bias from microarray data for the whole Escherichia coli K-12 genome

Escherichia coli has long been regarded as a model organism in the study of codon usage bias (CUB). However, most studies in this organism regarding this topic have been computational or, when experimental, restricted to small datasets; particularly poor attention has been given to genes with low CUB. In this work, correspondence analysis on codon usage is used to classify E.coli genes into three groups, and the relationship between them and expression levels from microarray experiments is studied. These groups are: group 1, highly biased genes; group 2, moderately biased genes; and group 3, AT-rich genes with low CUB. It is shown that, surprisingly, there is a negative correlation between codon bias and expression levels for group 3 genes, i.e. genes with extremely low codon adaptation index (CAI) values are highly expressed, while group 2 show the lowest average expression levels and group 1 show the usual expected positive correlation between CAI and expression. This trend is maintained over all functional gene groups, seeming to contradict the E.coli–yeast paradigm on CUB. It is argued that these findings are still compatible with the mutation–selection balance hypothesis of codon usage and that E.coli genes form a dynamic system shaped by these factors