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

Image Representations and New Domains in Neural Image Captioning

We examine the possibility that recent promising results in automatic caption generation are due primarily to language models. By varying image representation quality produced by a convolutional neural network, we find that a state-of-the-art neural captioning algorithm is able to produce quality captions even when provided with surprisingly poor image representations. We replicate this result in a new, fine-grained, transfer learned captioning domain, consisting of 66K recipe image/title pairs. We also provide some experiments regarding the appropriateness of datasets for automatic captioning, and find that having multiple captions per image is beneficial, but not an absolute requirement.Comment: 11 Pages, 5 Images, To appear at EMNLP 2015's Vision + Learning worksho

A comparison of slip, disjoining pressure, and interface formation models for contact line motion through asymptotic analysis of thin two-dimensional droplet spreading

The motion of a contact line is examined, and comparisons drawn, for a variety of models proposed in the literature. Pressure and stress behaviours at the contact line are examined in the prototype system of quasistatic spreading of a thin two-dimensional droplet on a planar substrate. The models analysed include three disjoining pressure models based on van der Waals interactions, a model introduced for polar fluids, and a liquid-gas diffuse-interface model; Navier-slip and two non-linear slip models are investigated, with three microscopic contact angle boundary conditions imposed (two of these contact angle conditions having a contact line velocity dependence); and the interface formation model is also considered. In certain parameter regimes it is shown that all of the models predict the same quasistatic droplet spreading behaviour.Comment: 29 pages, 3 figures, J. Eng. Math. 201

Architecturally diverse proteins converge on an analogous mechanism to inactivate Uracil-DNA glycosylase

Uracil-DNA glycosylase (UDG) compromises the replication strategies of diverse viruses from unrelated lineages. Virally encoded proteins therefore exist to limit, inhibit or target UDG activity for proteolysis. Viral proteins targeting UDG, such as the bacteriophage proteins ugi, and p56, and the HIV-1 protein Vpr, share no sequence similarity, and are not structurally homologous. Such diversity has hindered identification of known or expected UDG-inhibitory activities in other genomes. The structural basis for UDG inhibition by ugi is well characterized; yet, paradoxically, the structure of the unbound p56 protein is enigmatically unrevealing of its mechanism. To resolve this conundrum, we determined the structure of a p56 dimer bound to UDG. A helix from one of the subunits of p56 occupies the UDG DNA-binding cleft, whereas the dimer interface forms a hydrophobic box to trap a mechanistically important UDG residue. Surprisingly, these p56 inhibitory elements are unexpectedly analogous to features used by ugi despite profound architectural disparity. Contacts from B-DNA to UDG are mimicked by residues of the p56 helix, echoing the role of ugi’s inhibitory beta strand. Using mutagenesis, we propose that DNA mimicry by p56 is a targeting and specificity mechanism supporting tight inhibition via hydrophobic sequestration