Power distribution grids are currently challenged by observability issues due to limited metering infrastructure. On the other hand, smart meter data, including local voltage magnitudes and power injections, are collected at grid nodes with renewable generation and demand-response programs. A power flow-based approach using these data is put forth here to infer the unknown power injections at non-metered grid nodes. Exploiting the control capabilities of smart inverters and the relative time-invariance of conventional loads, the idea is to solve the non-linear power flow equations jointly over two system realizations. An intuitive condition pertaining to the graph of the underlying grid is shown to be necessary and sufficient for the local identifiability of this task. The derived graph theoretic criterion can be checked efficiently and is numerically verified under realistic scenarios on the IEEE 13-bus feeder.

This paper describes a highly scalable state of the art record aggregation system and the backbone infrastructure developed to support it. The system, called PeopleMap, allows legal professionals to effectively and efficiently explore a broad spectrum of public records databases by way of a single person-centric search. The backbone support system, called Concord, is a toolkit that allows developers to economically create record resolution solutions. The PeopleMap system is capable of linking billions of public records to a master data set consisting of hundreds of millions of person records. It was constructed using successive applications of Concord to link disparate public record data sets to a central person authority file. To our knowledge, the PeopleMap system is the largest of its kind. In contrast, the Concord support system is a novel record linkage tool that uses a new semi-supervised training technique called `surrogate learning' to enable the rapid development of record resolution solutions.

We formalize the notion of style context, which accounts for the increased accuracy of the field classifiers reported in this journal recently. We argue that style context forms the basis of all order-independent field classification schemes. We distinguish between intraclass style, which underlies most adaptive classifiers, and interclass style, which is a manifestation of interpattern dependence between the features of the patterns of a field. We show how style-constrained classifiers can be optimized either for field error (useful for short fields like zip codes) or for singlet error (for long fields, like business letters). We derive bounds on the reduction of error rate with field length and show that the error rate of the optimal style-constrained field classifier converges asymptotically to the error rate of a style-aware Bayesian singlet classifier.

The general approach for automatically driving data collection using information from previously acquired data is called active learning. Traditional active learning addresses the problem of choosing the unlabeled examples for which the class labels are queried with the goal of learning a classifier. In contrast we address the problem of active feature sampling for detecting useless features. We propose a strategy to actively sample the values of new features on class-labeled examples, with the objective of feature relevance assessment. We derive an active feature sampling algorithm from an information theoretic and statistical formulation of the problem. We present experimental results on synthetic, UCI and real world datasets to demonstrate that our active sampling algorithm can provide accurate estimates of feature relevance with lower data acquisition costs than random sampling and other previously proposed sampling algorithms. 1.

The proliferation of text documents on the web as well as within institutions necessitates their convenient organization to enable efficient retrieval of information. Although text corpora are frequently organized into concept hierarchies or taxonomies, the classification of the documents into the hierarchy is expensive in terms human effort. We present a novel and simple hierarchical Dirichlet generative model for text corpora and derive an efficient algorithm for the estimation of model parameters and the unsupervised classification of text documents into a given hierarchy. The class conditional feature means are assumed to be inter-related due to the hierarchical Bayesian structure of the model. We show that the algorithm provides robust estimates of the classification parameters by performing smoothing or regularization. We present experimental evidence on real web data that our algorithm achieves significant gains in accuracy over simpler models.

Patterns often occur as homogeneous groups or fields generated by the same source. In multisource recognition problems, such isogeny induces statistical dependencies between patterns (termed style context). We model these dependencies by second-order statistics and formulate the optimal classifier for normally distributed styles. We show that model parameters estimated only from pairs of classes suffice to train classifiers for any test field length. Although computationally expensive, the style-conscious classifier reduces the field error rate by up to 20 percent on quadruples of handwritten digits from standard NIST data sets.

We propose further improvement of a handwriting recognition method that avoids segmentation while able to recognize words that were never seen before in handwritten form. This method is based on the fact that few pairs of English words share exactly the same set of letter bigrams and even fewer share longer n-grams. The lexical n-gram matches between every word in a lexicon and a set of reference words can be precomputed. A position-based match function then detects the matches between the handwritten signal of a query word and each reference word. We show that with a reasonable set of reference words, the recognition of lexicon words exceeds 90%.

In style-constrained classification often there are only a few samples of each style and class, and the correspondences between styles in the training set and the test set are unknown. To avoid gross misestimates of the classifier parameters it is therefore important to model the pattern distributions accurately. We offer empirical evidence for intuitively appealing assumptions, in feature spaces appropriate for symbolic patterns, for (1) tetrahedral configurations of class means that suggests linear style-adaptive classification, (2) improved estimates of classification boundaries by taking into account the asymmetric configuration of the patterns with respect to the directions toward other classes, and (3) pattern-correlated style variability.

In knowledge discovery applications, where new features are to be added, an acquisition policy can help select the features to be acquired based on their relevance and the cost of extraction. This can be posed as a feature selection problem where the feature values are not known in advance. We propose a technique to actively sample the feature values with the ultimate goal of choosing between alternative candidate features with minimum sampling cost. Our heuristic algorithm is based on extracting candidate features in a region of the instance space where the feature value is likely to alter our knowledge the most. An experimental evaluation on a standard database shows that it is possible outperform a random subsampling policy in terms of the accuracy in feature selection.

Very few pairs of English words share exactly the same letter bigrams. This linguistic property can be exploited to bring lexical context into the classification stage of a word recognition system. The lexical n-gram matches between every word in a lexicon and a subset of reference words can be precomputed. If a match function can detect matching segments of at least n-gram length from the feature representation of words, then an unknown word can be recognized by determining the subset of reference words having an n-gram match at the feature level with the unknown word. We show that with a reasonable number of reference words, bigrams represent the best compromise between the recall ability of single letters and the precision of trigrams. Our simulations indicate that using a longer reference list can compensate errors in feature extraction. The algorithm is fast enough, even with a slow processor, for human-computer interaction.