Identifying which outlet in social media leads the rest in disseminating novel information on specific topics is an interesting challenge for information analysts and social scientists. In this work, we hypothesize that novel ideas are disseminated through the creation and propagation of new or newly emphasized key words, and therefore lead/lag of outlets can be estimated by tracking word usage...

Abstract. We consider the Q-state Potts model on Z, Q ≥ 3, d ≥ 2, with Kac ferromagnetic interactions and scaling parameter γ. We prove the existence of a first order phase transition for large but finite potential ranges. More precisely we prove that for γ small enough there is a value of the temperature at which coexist Q+1 Gibbs states. The proof is obtained by a perturbation around mean-fie...

This paper studies nearest neighbor classification in a model where unlabeled data points arrive in a stream, and the learner decides, for each one, whether to ask for its label. Are there generic ways to augment or modify any selective sampling strategy so as to ensure the consistency of the resulting nearest neighbor classifier?

Models 1. 5-Nearest Neighbor with Dynamic Time Warping (DTW)? 2. 5-Nearest Neighbor with Complexity-Invariant Distance (CID)? 3. Regression on features (Reg) Method 1. 10 fold cross validation 2. Lowest quality training cases duplicated 3. Bayesian approach using Gibbs sampling 4. Model from best algorithm used to classify 90,631 cases 5. Labels correlated with 30-day outcomes (4) Times Series ...

Using nearest neighbor classification with fault dictionaries to resolve inexact signature matches in digital circuit diagnosis is inadequate. Nearest neighbor focuses on the possible diagnoses rather than on the tests. Our alternative—the information flow model—focuses on test information in the fault dictionary to provide more accurate diagnostics.

abstract saturated hydraulic conductivity (ks) is needed for many studies related to water and solute transport, but often cannot be measured because of practical and/or cost-related reasons. nonparametric approaches are being used in various fields to estimate continuous variables. one type of the nonparametric lazy learning algorithms, a k-nearest neighbor (k-nn) algorithm, was introduced and...

The k-d tree was one of the first spatial data structures proposed for nearest neighbor search. Its efficacy is diminished in high-dimensional spaces, but several variants, with randomization and overlapping cells, have proved to be successful in practice. We analyze three such schemes. We show that the probability that they fail to find the nearest neighbor, for any data set and any query poin...

We propose two new semiparametric specification tests which test whether a vector of conditional moment conditions is satisfied for any vector of parameter values θ0. Unlike most existing tests, our tests are asymptotically valid under weak and/or partial identification and can accomodate discontinuities in the conditional moment functions. Our tests are moreover consistent provided that identi...

WHAT: a probabilistic model to infer binary latent variables that preserve neighbourhood structure of the data • WHY: to perform a nearest neighbour search for the purpose of retrieval • WHEN: in dynamic and streaming nature of the Internet data • HOW: the Indian Buffet Process prior coupled with a preference relation • WHERE: dynamic extension of hash codes Motivating Example: Dynamic Hash Cod...