Papers.

Tweeting Apart: Applying Network Analysis to Detect Selective Exposure Clusters in Twitter

Fast Fuzzy Inference in Octave

Fuzzy relations are simple mathematical structures that enable a very general representation of fuzzy knowledge, and fuzzy relational calculus offers a powerful machinery for approximate reasoning.However, one of the most relevant limitations of approximate reasoning is the efficiency bottleneck.In this paper, we present two implementations for fast fuzzy inference through relational composition, with the twofold objective of being general and efficient.The two implementations are capable of working on full and sparse representations respectively.Further, a wrapper procedure is capable of automatically selecting the best implementation on the basis of the input features.We implemented the code in GNU Octave because it is a high-level language targeted to numerical computations.Experimental results show the impressive performance gain when the proposed implementation is used.

Inferring Semantic Facets of a Music Folksonomy with Wikipedia

Automated reconstruction of ancient languages using probabilistic models of sound change

Terahertz generation by tilted-front laser pulses in weakly and strongly nonlinear regimes

Unlocking Open Educational Resources (OERs) Interaction Data

Quantitative estimation of activity and quality for collections of functional genetic elements

MatchTree: Flexible, scalable, and fault-tolerant wide-area resource discovery with distributed matchmaking and aggregation

The role of trader positions in spot and futures prices for WTI

An adaptive risk management and access control framework to mitigate insider threats

User behaviors in private BitTorrent communities

Hybrid music information retrieval

Personalised Information Retrieval: survey and classification

A structure-guided approach for protein pocket modeling and affinity prediction

Binding affinity prediction is frequently addressed using computational models constructed solely with molecular structure and activity data. We present a hybrid structure-guided strategy that combines molecular similarity, docking, and multiple-instance learning such that information from protein structures can be used to inform models of structure–activity relationships. The Surflex-QMOD approach has been shown to produce accurate predictions of binding affinity by constructing an interpretable physical model of a binding site with no experimental binding site structural information. We introduce a method to integrate protein structure information into the model induction process in order to construct more robust physical models. The structure-guided models accurately predict binding affinities over a broad range of compounds while producing more accurate representations of the protein pockets and ligand binding modes. Structure-guidance for the QMOD method yielded significant performance improvements, both for affinity and pose prediction, especially in cases where predictions were made on ligands very different from those used for model induction.

Implementing crowdsourcing-based relevance experimentation: an industrial perspective

On a conjecture of Butler and Graham.

Automatic keyphrase extraction from scientific articles

Learning with multi-resolution overlapping communities

Quantifying eventual consistency with PBS

Here be Dragons: The Unexplored Continents of the CMSSM

Learning How to Count: A High Multiplicity Search for the LHC

Duplicate News Story Detection Revisited

Similarity in Web Search

One Half or 50%? An Eye-Tracking Study of Number Representation Readability

Frequent Words Improve Readability and Short Words Improve Understandability for People with Dyslexia

A Machine-Checked Proof of the Odd Order Theorem

Composition and Reuse with Compiled Domain-Specific Languages

Multilingual vs. Monolingual User Models for Personalized Multilingual Information Retrieval

Distributed Applications and Interoperable Systems - 13th IFIP WG 6.1 International Conference, DAIS 2013, Held as Part of the 8th International Federated Conference on Distributed Computing Techniques, DisCoTec 2013, Florence, Italy, June 3-5, 2013. Proceedings

Fast Grid-Based Path Finding for Video Games.

Constructing a Focused Taxonomy from a Document Collection

The Impact of Lexical Simplification by Verbal Paraphrases for People with and without Dyslexia

Using Imageability and Topic Chaining to Locate Metaphors in Linguistic Corpora

Setting the Direction for Big Data Benchmark Standards

How to Survive and Thrive in a Private BitTorrent Community

An Evaluation of Multi-way Joins for Relational Database Systems.

A Virtual Player for “Who Wants to Be a Millionaire?” based on Question Answering

Automatic construction of lexicons, taxonomies, ontologies, and other knowledge structures

Protein Function Annotation By Local Binding Site Surface Similarity

Hundreds of protein crystal structures exist for proteins whose function cannot be confidently determined from sequence similarity. Surflex‐PSIM, a previously reported surface‐based protein similarity algorithm, provides an alternative method for hypothesizing function for such proteins. The method now supports fully automatic binding site detection and is fast enough to screen comprehensive databases of protein binding sites. The binding site detection methodology was validated on apo/holo cognate protein pairs, correctly identifying 91% of ligand binding sites in holo structures and 88% in apo structures where corresponding sites existed. For correctly detected apo binding sites, the cognate holo site was the most similar binding site 87% of the time. PSIM was used to screen a set of proteins that had poorly characterized functions at the time of crystallization, but were later biochemically annotated. Using a fully automated protocol, this set of 8 proteins was screened against ∼60,000 ligand binding sites from the PDB. PSIM correctly identified functional matches that predated query protein biochemical annotation for five out of the eight query proteins. A panel of 12 currently unannotated proteins was also screened, resulting in a large number of statistically significant binding site matches, some of which suggest likely functions for the podorly characterized proteins. Proteins 2014; 82:679–694. © 2013 Wiley Periodicals, Inc.

Dayside auroral hiss observed at South Pole Station

Optimization and Analysis of High‐Power Hydrogen/Bromine‐Flow Batteries for Grid‐Scale Energy Storage

A Functional Approach to Standard Binary Heaps

Exploring the Dark Side of the Top Yukawa

Inference, Sampling, and Learning in Copula Cumulative Distribution Networks

The cumulative distribution network (CDN) is a recently developed class of probabilistic graphical models (PGMs) permitting a copula factorization, in which the CDF, rather than the density, is factored. Despite there being much recent interest within the machine learning community about copula representations, there has been scarce research into the CDN, its amalgamation with copula theory, and no evaluation of its performance. Algorithms for inference, sampling, and learning in these models are underdeveloped compared those of other PGMs, hindering widerspread use. One advantage of the CDN is that it allows the factors to be parameterized as copulae, combining the benefits of graphical models with those of copula theory. In brief, the use of a copula parameterization enables greater modelling flexibility by separating representation of the marginals from the dependence structure, permitting more efficient and robust learning. Another advantage is that the CDN permits the representation of implicit latent variables, whose parameterization and connectivity are not required to be specified. Unfortunately, that the model can encode only latent relationships between variables severely limits its utility. In this thesis, we present inference, learning, and sampling for CDNs, and further the state-of-the-art. First, we explain the basics of copula theory and the representation of copula CDNs. Then, we discuss inference in the models, and develop the first sampling algorithm. We explain standard learning methods, propose an algorithm for learning from data missing completely at random (MCAR), and develop a novel algorithm for learning models of arbitrary treewidth and size. Properties of the models and algorithms are investigated through Monte Carlo simulations. We conclude with further discussion of the advantages and limitations of CDNs, and suggest future work.

A Comparison of Future Proton Colliders Using SUSY Simplified Models: A Snowmass Whitepaper

Caracterizando la Web Chilena

Policy as Types

Adaptive Crowdsourcing Algorithms for the Bandit Survey Problem

Very recently crowdsourcing has become the de facto platform for distributing and collecting human computation for a wide range of tasks and applications such as information retrieval, natural language processing and machine learning. Current crowdsourcing platforms have some limitations in the area of quality control. Most of the effort to ensure good quality has to be done by the experimenter who has to manage the number of workers needed to reach good results. We propose a simple model for adaptive quality control in crowdsourced multiple-choice tasks which we call the bandit survey problem. This model is related to, but technically different from the well-known multi-armed bandit problem. We present several algorithms for this problem, and support them with analysis and simulations. Our approach is based in our experience conducting relevance evaluation for a large commercial search engine.

Highly Available Transactions: Virtues and Limitations (Extended Version)

Zero-Shot Learning Through Cross-Modal Transfer

This work introduces a model that can recognize objects in images even if no training data is available for the object class. The only necessary knowledge about unseen visual categories comes from unsupervised text corpora. Unlike previous zero-shot learning models, which can only differentiate between unseen classes, our model can operate on a mixture of seen and unseen classes, simultaneously obtaining state of the art performance on classes with thousands of training images and reasonable performance on unseen classes. This is achieved by seeing the distributions of words in texts as a semantic space for understanding what objects look like. Our deep learning model does not require any manually defined semantic or visual features for either words or images. Images are mapped to be close to semantic word vectors corresponding to their classes, and the resulting image embeddings can be used to distinguish whether an image is of a seen or unseen class. We then use novelty detection methods to differentiate unseen classes from seen classes. We demonstrate two novelty detection strategies; the first gives high accuracy on unseen classes, while the second is conservative in its prediction of novelty and keeps the seen classes' accuracy high.