@article {12495,
	title = {Adaptive Threshold Estimation via Extreme Value Theory},
	journal = {Signal Processing, IEEE Transactions on},
	volume = {58},
	year = {2010},
	month = {2010/02//},
	pages = {490 - 500},
	abstract = {Determining a detection threshold to automatically maintain a low false alarm rate is a challenging problem. In a number of different applications, the underlying parametric assumptions of most automatic target detection algorithms are invalid. Therefore, thresholds derived using these incorrect distribution assumptions do not produce desirable results when applied to real sensor data. Monte Carlo methods for threshold determination work well but tend to perform poorly when targets are present. In order to mitigate these effects, we propose an algorithm using extreme value theory through the use of the generalized Pareto distribution (GPD) and a Kolmogorov-Smirnov statistical test. Unlike previous work based on GPD estimates, this algorithm incorporates a way to adaptively maintain low false alarm rates in the presence of targets. Both synthetic and real-world detection results demonstrate the usefulness of this algorithm.},
	keywords = {detection;, detection;Pareto, distribution;adaptive, distribution;signal, estimation;extreme, estimation;signal, Kolmogorov-Smirnov, Pareto, statistical, test;adaptive, theory;generalized, threshold, value},
	isbn = {1053-587X},
	doi = {10.1109/TSP.2009.2031285},
	author = {Broadwater, J.B. and Chellapa, Rama}
}
@conference {17752,
	title = {Aggregate Query Answering under Uncertain Schema Mappings},
	booktitle = {Data Engineering, 2009. ICDE {\textquoteright}09. IEEE 25th International Conference on},
	year = {2009},
	month = {2009/04/29/2},
	pages = {940 - 951},
	abstract = {Recent interest in managing uncertainty in data integration has led to the introduction of probabilistic schema mappings and the use of probabilistic methods to answer queries across multiple databases using two semantics: by-table and by-tuple. In this paper, we develop three possible semantics for aggregate queries: the range, distribution, and expected value semantics, and show that these three semantics combine with the by-table and by-tuple semantics in six ways. We present algorithms to process COUNT, AVG, SUM, MIN, and MAX queries under all six semantics and develop results on the complexity of processing such queries under all six semantics. We show that computing COUNT is in PTIME for all six semantics and computing SUM is in PTIME for all but the by-tuple/distribution semantics. Finally, we show that AVG, MIN, and MAX are PTIME computable for all by-table semantics and for the by-tuple/range semantics.We developed a prototype implementation and experimented with both real-world traces and simulated data. We show that, as expected, naive processing of aggregates does not scale beyond small databases with a small number of mappings. The results also show that the polynomial time algorithms are scalable up to several million tuples as well as with a large number of mappings.},
	keywords = {aggregate, algorithm;probabilistic, answering;by-table, complexity;data, complexity;distributed, database;polynomial, databases;, databases;probability;query, integration;distribution, mapping;computational, mapping;query, processing;range, processing;statistical, query, schema, semantics;by-tuple, semantics;computational, semantics;expected, semantics;multiple, semantics;uncertain, TIME, value},
	doi = {10.1109/ICDE.2009.55},
	author = {Gal,A. and Martinez,M. V and Simari,G. I and V.S. Subrahmanian}
}
@conference {16834,
	title = {3. Similarity searching: Indexing, nearest neighbor finding, dimensionality reduction, and embedding methods for applications in multimedia databases},
	booktitle = {Pattern Recognition, 2008. ICPR 2008. 19th International Conference on},
	year = {2008},
	month = {2008/12//},
	pages = {1 - 1},
	abstract = {Similarity searching is usually achieved by means of nearest neighbor finding. Existing methods for handling similarity search in this setting fall into one of two classes. The first is based on mapping to a low-dimensional vector space which is then indexed using representations such as k-d trees, R-trees, quadtrees, etc. The second directly indexes the the objects based on distances using representations such as the vp-tree, M-tree, etc. Mapping from a high-dimensional space into a low-dimensional space is known as dimensionality reduction and is achieved using SVD, DFT, etc. At times, when we just have distance information, the data objects are embedded in a vector space so that the distances of the embedded objects as measured by the distance metric in the embedding space approximate the actual distance. The search in the embedding space uses conventional indexing methods which are often coupled with dimensionality reduction. Some commonly known embedding methods are multidimensional scaling, Lipschitz embeddings, and Fast Map. This tutorial is organized into five parts that cover the five basic concepts outlined above: indexing low and high dimensional spaces, distance-based indexing, dimensionality reduction, embedding methods, and nearest neighbor searching.},
	keywords = {database;nearest, databases;singular, decomposition;, DFT;FastMap, EMBEDDING, embedding;Lipschitz, finding;similarity, Fourier, indexing;discrete, indexing;multidimensional, method;multimedia, method;SVD;distance-based, neighbor, scaling, searching;database, transforms;multimedia, value},
	doi = {10.1109/ICPR.2008.4760937},
	author = {Samet, Hanan}
}
@conference {15029,
	title = {Strategies for exploring large scale data},
	booktitle = {Parallel Architectures, Algorithms and Networks, 2004. Proceedings. 7th International Symposium on},
	year = {2004},
	month = {2004/05//},
	pages = {2 - 2},
	abstract = {We consider the problem of querying large scale multidimensional time series data to discover events of interest, test and validate hypotheses, or to associate temporal patterns with specific events. This type of data currently dominates most other types of available data, and will very likely become even more prevalent in the future given the current trends in collecting time series of business, scientific, demographic, and simulation data. The ability to explore such collections interactively, even at a coarse level, will be critical in discovering the information and knowledge embedded in such collections. We develop indexing techniques and search algorithms to efficiently handle temporal range value querying of multidimensional time series data. Our indexing uses linear space data structures that enable the handling of queries in I/O time that is essentially the same as that of handling a single time slice, assuming the availability of a logarithmic number of processors as a function of the temporal window. A data structure with provably almost optimal asymptotic bounds is also presented for the case when the number of multidimensional objects is relatively small. These techniques improve significantly over standard techniques for either serial or parallel processing, and are evaluated by extensive experimental results that confirm their superior performance.},
	keywords = {algorithms;, association;, asymptotic, bounds;, business, data, data;, database, databases;, demographic, discovery;, Indexing, indexing;, information, knowledge, large, linear, mining;, multidimensional, objects;, optimal, Parallel, pattern, processing;, query, querying;, range, scale, scientific, search, serial, series, series;, simulation, space, structure;, structures;, techniques;, temporal, TIME, value, very, window;},
	doi = {10.1109/ISPAN.2004.1300447},
	author = {JaJa, Joseph F.}
}
@article {16897,
	title = {Properties of embedding methods for similarity searching in metric spaces},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {25},
	year = {2003},
	month = {2003/05//},
	pages = {530 - 549},
	abstract = {Complex data types-such as images, documents, DNA sequences, etc.-are becoming increasingly important in modern database applications. A typical query in many of these applications seeks to find objects that are similar to some target object, where (dis)similarity is defined by some distance function. Often, the cost of evaluating the distance between two objects is very high. Thus, the number of distance evaluations should be kept at a minimum, while (ideally) maintaining the quality of the result. One way to approach this goal is to embed the data objects in a vector space so that the distances of the embedded objects approximates the actual distances. Thus, queries can be performed (for the most part) on the embedded objects. We are especially interested in examining the issue of whether or not the embedding methods will ensure that no relevant objects are left out. Particular attention is paid to the SparseMap, FastMap, and MetricMap embedding methods. SparseMap is a variant of Lipschitz embeddings, while FastMap and MetricMap are inspired by dimension reduction methods for Euclidean spaces. We show that, in general, none of these embedding methods guarantee that queries on the embedded objects have no false dismissals, while also demonstrating the limited cases in which the guarantee does hold. Moreover, we describe a variant of SparseMap that allows queries with no false dismissals. In addition, we show that with FastMap and MetricMap, the distances of the embedded objects can be much greater than the actual distances. This makes it impossible (or at least impractical) to modify FastMap and MetricMap to guarantee no false dismissals.},
	keywords = {complex, contractiveness;, data, databases;, decomposition;, dimension, distance, distortion;, DNA, documents;, EMBEDDING, embeddings;, Euclidean, evaluations;, FastMap;, images;, Lipschitz, methods;, metric, MetricMap;, multimedia, processing;, query, reduction, search;, searching;, sequences;, similarity, singular, spaces;, SparseMap;, types;, value},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2003.1195989},
	author = {Hjaltason,G. R and Samet, Hanan}
}
@conference {12718,
	title = {Rank constrained recognition under unknown illuminations},
	booktitle = {Analysis and Modeling of Faces and Gestures, 2003. AMFG 2003. IEEE International Workshop on},
	year = {2003},
	month = {2003/10//},
	pages = {11 - 18},
	abstract = {Recognition under illumination variations is a challenging problem. The key is to successfully separate the illumination source from the observed appearance. Once separated, what remains is invariant to illuminant and appropriate for recognition. Most current efforts employ a Lambertian reflectance model with varying albedo field ignoring both attached and cast shadows, but restrict themselves by using object-specific samples, which undesirably deprives them of recognizing new objects not in the training samples. Using rank constraints on the albedo and the surface normal, we accomplish illumination separation in a more general setting, e.g., with class-specific samples via a factorization approach. In addition, we handle shadows (both attached and cast ones) by treating them as missing values, and resolve the ambiguities in the factorization method by enforcing integrability. As far as recognition is concerned, a bootstrap set which is just a collection of two-dimensional image observations can be utilized to avoid the explicit requirement that three-dimensional information be available. Our approaches produce good recognition results as shown in our experiments using the PIE database.},
	keywords = {albedo, approach;, constrained, database;, databases;, decomposition;, factorization, field;, illumination, image;, information;, Lambertian, lighting;, model;, object, object-specific, PIE, rank, recognition;, reflectance, samples;, singular, three-dimensional, two-dimensional, value, variations;, visual},
	doi = {10.1109/AMFG.2003.1240818},
	author = {Zhou, S. and Chellapa, Rama}
}
@article {17735,
	title = {The decompositional approach to matrix computation},
	journal = {Computing in Science Engineering},
	volume = {2},
	year = {2000},
	month = {2000/02//jan},
	pages = {50 - 59},
	abstract = {The introduction of matrix decomposition into numerical linear algebra revolutionized matrix computations. The article outlines the decompositional approach, comments on its history, and surveys the six most widely used decompositions: Cholesky decomposition; pivoted LU decomposition; QR decomposition; spectral decomposition; Schur decomposition; and singular value decomposition},
	keywords = {Algebra, approach;history;matrix, Cholesky, computation;matrix, computations;pivoted, computing;matrix, decomposition;, decomposition;bibliographies;history;mathematics, decomposition;decompositional, decomposition;numerical, decomposition;QR, decomposition;Schur, decomposition;singular, decomposition;spectral, linear, LU, matrix, revolutionized, value},
	isbn = {1521-9615},
	doi = {10.1109/5992.814658},
	author = {Stewart, G.W.}
}
@article {15371,
	title = {A new point of view on the mathematical structure of Maxwell{\textquoteright}s equations},
	journal = {Magnetics, IEEE Transactions on},
	volume = {29},
	year = {1993},
	month = {1993/03//},
	pages = {1315 - 1320},
	abstract = {It is emphasized that the Maxwell equations have a peculiar mathematical structure which leads to boundary value problems with an overspecified number of partial differential equations and an underspecified number of boundary conditions. An attempt is made to reduce these boundary value problems to standard mathematical formulations with equal numbers of partial differential equations and boundary conditions. It is demonstrated that these formulations have some attractive features which appreciably facilitate their finite element discretizations. A numerical example involving scattering of plane electromagnetic waves from a perfect conducting thin plate illustrates the discussion},
	keywords = {analysis;partial, conditions;boundary, conducting, differential, discretizations;mathematical, element, EM, equations;, equations;boundary, equations;boundary-value, equations;perfect, example;partial, Maxwell{\textquoteright}s, plate;plane, problems;electromagnetic, problems;finite, scattering;finite, scattering;Maxwell, structure;numerical, thin, value, wave},
	isbn = {0018-9464},
	doi = {10.1109/20.250640},
	author = {Mayergoyz, Issak D and D{\textquoteright}Angelo,J.}
}
