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Articles 1351 - 1380 of 2141

Full-Text Articles in Engineering

How To Tell When A Product Of Two Partially Ordered Spaces Has A Certain Property: General Results With Application To Fuzzy Logic, Francisco Zapata, Olga Kosheleva, Karen Villaverde Dec 2010

Towards Optimal Sensor Placement In Multi-Zone Measurements, Octavio Lerma, Craig Tweedie, Vladik Kreinovich Dec 2010

Computing The Range Of Variance-To-Mean Ratio Under Interval And Fuzzy Uncertainty, Sio-Long Lo, Gang Xiang Dec 2010

Towards Chemical Applications Of Dempster-Shafer-Type Approach: Case Of Variant Ligands, Jaime Nava Dec 2010

Why Curvature In L-Curve: Combining Soft Constraints, Uram Anibal Sosa Aguirre, Martine Ceberio, Vladik Kreinovich Dec 2010

Why Curvature In L-Curve: Combining Soft Constraints, Uram Anibal Sosa Aguirre, Martine Ceberio, Vladik Kreinovich

Departmental Technical Reports (CS)

In solving inverse problems, one of the successful methods of determining the appropriate value of the regularization parameter is the L-curve method of combining the corresponding soft constraints, when we plot the curve describing the dependence of the logarithm $x$ of the mean square difference on the logarithm $y$ of the mean square non-smoothness, and select a point on this curve at which the curvature is the largest. This method is empirically successful, but from the theoretical viewpoint, it is not clear why we should use curvature and not some other criterion. In this paper, we show that reasonable scale-invariance …


How To Bargain: An Interval Approach, Vladik Kreinovich, Hung T. Nguyen, Songsak Sriboonchitta Dec 2010

Why L2 Topology In Quantum Physics, Chris Culellar, Evan Longpre, Vladik Kreinovich Nov 2010

Cleanjava: A Formal Notation For Functional Program Verification, Yoonsik Cheon, Cesar Yeep, Melisa Vela Nov 2010

Power Vs. Performance Evaluation Of Synthetic Aperture Radar Image-Formation Algorithms And Implementations For Embedded Hec Environments (Ongoing Study), Ricardo Portillo, Sarala Arunagiri, Patricia J. Teller Nov 2010

A New Answer To Pauli's Question: Almost All Quantum States Can Be Uniquely Determined By Measuring Location And Momentum, Don Jackson, Olga Kosheleva Nov 2010

Visualization Queries, Nicholas Del Rio, Paulo Pinheiro Da Silva Nov 2010

Uncertainty In Partially Ordered Sets As A Natural Generalization Of Intervals: Negative Information Is Sufficient, Positive Is Not, David Mireles, Olga Kosheleva Oct 2010

Why Feynman Path Integration?, Jaime Nava, Juan Ferret, Vladik Kreinovich, Gloria Berumen, Sandra Griffin, Edgar Padilla Oct 2010

Expanding Algorithmic Randomness To The Algebraic Approach To Quantum Physics: Kolmogorov Complexity And Quantum Logics, Vladik Kreinovich Oct 2010

Expanding Algorithmic Randomness To The Algebraic Approach To Quantum Physics: Kolmogorov Complexity And Quantum Logics, Vladik Kreinovich

Departmental Technical Reports (CS)

Physicists usually assume that events with a very small probability cannot occur. Kolmogorov complexity formalizes this idea for non-quantum events. We show how this formalization can be extended to quantum events as well.


Strings Lead To Lattice-Type Causality, Francisco Zapata, Essau Ramirez, Joel A. Lopez, Olga Kosheleva Oct 2010

Towards Simpler Description Of Properties Like Commutativity And Associativity: Using Expression Fragments, Shubhra Datta, Valeria Fierro, Krasen Petrov, Jessica Romo, Gesuri Ramirez, Cesar Valenzuela Oct 2010

Equivalence Of Gian-Carlo Rota Poset Approach And Taylor Series Approach Extended To Variant Ligands, Jaime Nava, Vladik Kreinovich Oct 2010

Towards A Fast, Practical Alternative To Joint Inversion Of Multiple Datasets: Model Fusion, Omar Ochoa, Aaron A. Velasco, Christian Servin Oct 2010

A Use Case-Guided Comparison Of Opm And Pml, Paulo Pinheiro Da Silva, Steve Roach Oct 2010

A Tutorial On Functional Program Verification, Yoonsik Cheon, Melisa Vela Sep 2010

From Interval And Probabilistic Granules To Granules Of Higher Order, Vladik Kreinovich Sep 2010

From Interval And Probabilistic Granules To Granules Of Higher Order, Vladik Kreinovich

Departmental Technical Reports (CS)

Constructive mathematics, mathematics in which the existence of an object means that that we can actually construct this object, started as a heavily restricted version of mathematics, a version in which many commonly used mathematical techniques (like the Law of Excluded Middle) were forbidden to maintain constructivity. Eventually, it turned out that not only constructive mathematics is not a weakened version of the classical one -- as it was originally perceived -- but that, vice versa, classical mathematics can be viewed as a particular (thus, weaker) case of the constructive one. Crucial results in this direction were obtained by M. …


Visualization To Support The Discovery Of Prosodic Contours Related To Turn-Taking, Nigel Ward, Joshua L. Mccartney Sep 2010

Visualization To Support The Discovery Of Prosodic Contours Related To Turn-Taking, Nigel Ward, Joshua L. Mccartney

Departmental Technical Reports (CS)

Some meaningful prosodic patterns can be usefully represented with pitch contours, however the development of such descriptions is a labor-intensive process. To assist in the discovery of contours, visualization tools may be helpful. Edlund et al. (2009) presented the idea of superimposing hundreds of pitch curves from a corpus as a way to see the overall patterns. In this paper we refine and extend this method and illustrate its utility in the discovery of a prosodic cue to back-channels in Chinese. We also discuss issues in relating a contour-based description to one in terms of a conjunction of features, and …


Computing Standard-Deviation-To-Mean And Variance-To-Mean Ratios Under Interval Uncertainty Is Np-Hard, Sio-Long Lo Sep 2010

Computing Standard-Deviation-To-Mean And Variance-To-Mean Ratios Under Interval Uncertainty Is Np-Hard, Sio-Long Lo

Departmental Technical Reports (CS)

Once we have a collection of values x1, ..., ,xn corresponding a class of objects, a usual way to decide whether a new object with the value x of the corresponding property belongs to this class is to check whether the value x belongs to interval [E - k0 * s, E + k0 * s], where E = (1/n) * (x1 + ... + xn) is the sample mean, V = s^2 = (1/n) * ((x1 - E)^2 + ... + (xn - E)^2) is the sample variance, and the parameter k0 is determined by the degree of confidence …


Cantor's Paradise Regained: Constructive Mathematics From Brouwer To Kolmogorov To Gelfond, Vladik Kreinovich Aug 2010

Cantor's Paradise Regained: Constructive Mathematics From Brouwer To Kolmogorov To Gelfond, Vladik Kreinovich

Departmental Technical Reports (CS)

Constructive mathematics, mathematics in which the existence of an object means that that we can actually construct this object, started as a heavily restricted version of mathematics, a version in which many commonly used mathematical techniques (like the Law of Excluded Middle) were forbidden to maintain constructivity. Eventually, it turned out that not only constructive mathematics is not a weakened version of the classical one -- as it was originally perceived -- but that, vice versa, classical mathematics can be viewed as a particular (thus, weaker) case of the constructive one. Crucial results in this direction were obtained by M. …


Constraint-Related Reinterpretation Of Fundamental Physical Equations Can Serve As A Built-In Regularization, Vladik Kreinovich, Juan Ferret, Martine Ceberio Aug 2010

Constraint-Related Reinterpretation Of Fundamental Physical Equations Can Serve As A Built-In Regularization, Vladik Kreinovich, Juan Ferret, Martine Ceberio

Departmental Technical Reports (CS)

Many traditional physical problems are known to be ill-defined: a tiny change in the initial condition can lead to drastic changes in the resulting solutions. To solve this problem, practitioners regularize these problem, i.e., impose explicit constraints on possible solutions (e.g., constraints on the squares of gradients). Applying the Lagrange multiplier techniques to the corresponding constrained optimization problems is equivalent to adding terms proportional to squares of gradients to the corresponding optimized functionals. It turns out that many optimized functionals of fundamental physics already have such squares-of-gradients terms. We therefore propose to re-interpret these equations -- by claiming that they …


Studies In The Use Of Time Into Utterance As A Predictive Feature For Language Modeling, Nigel Ward, Alejandro Vega Aug 2010

Studies In The Use Of Time Into Utterance As A Predictive Feature For Language Modeling, Nigel Ward, Alejandro Vega

Departmental Technical Reports (CS)

In (Ward and Vega 2008) we examined how how word probabilities vary with time into utterance, and proposed a method for using this information to improve a language model. In this report we examine some ancillary issues in the modeling and exploitation of these regularities.


Functional Specification And Verification Of Object-Oriented Programs, Yoonsik Cheon Aug 2010

Functional Specification And Verification Of Object-Oriented Programs, Yoonsik Cheon

Departmental Technical Reports (CS)

One weakness of Hoare-style verification techniques based on first-order predicate logic is that reasoning is backward from postconditions to preconditions. A natural, forward reasoning is possible by viewing a program as a mathematical function that maps one program state to another. This functional program verification technique requires a minimal mathematical background as it uses equational reasoning based on sets and functions. Thus, it can be easily taught and used in practice. In this paper, we formalize a functional program specification and verification technique and extend it for object-oriented programs. Our approach allows one to formally specify and verify the behavior …


Why Ellipsoid Constraints, Ellipsoid Clusters, And Riemannian Space-Time: Dvoretzky's Theorem Revisited, Karen Villaverde, Olga Kosheleva, Martine Ceberio Aug 2010

Why Ellipsoid Constraints, Ellipsoid Clusters, And Riemannian Space-Time: Dvoretzky's Theorem Revisited, Karen Villaverde, Olga Kosheleva, Martine Ceberio

Departmental Technical Reports (CS)

In many practical applications, we encounter ellipsoid constraints, ellipsoid-shaped clusters, etc. A usual justification for this ellipsoid shape comes from the fact that many real-life quantities are normally distributed, and for a multi-variate normal distribution, a natural confidence set (containing the vast majority of the objects) is an ellipsoid. However, ellipsoid appear more frequently than normal distributions (which occur in about half of the cases). In this paper, we provide a new justification for ellipsoids based on a known mathematical result -- Dvoretzky's Theorem.


Towards An Efficient Bisection Of Ellipsoids, Paden Portillo, Martine Ceberio, Vladik Kreinovich Aug 2010

Towards An Efficient Bisection Of Ellipsoids, Paden Portillo, Martine Ceberio, Vladik Kreinovich

Departmental Technical Reports (CS)

Constraints are often represented as ellipsoids. One of the main advantages of such constrains is that, in contrast to boxes, over which optimization of even quadratic functions is NP-hard, optimization of a quadratic function over an ellipsoid is feasible. Sometimes, the area described by constrains is too large, so it is reasonable to bisect this area (one or several times) and solve the optimization problem for all the sub-areas. Bisecting a box, we still get a box, but bisecting an ellipsoid, we do not get an ellipsoid. Usually, this problem is solved by enclosing the half-ellipsoid in a larger ellipsoid, …


From Computing Sets Of Optima, Pareto Sets, And Sets Of Nash Equilibria To General Decision-Related Set Computations, Vladik Kreinovich Jul 2010

From Computing Sets Of Optima, Pareto Sets, And Sets Of Nash Equilibria To General Decision-Related Set Computations, Vladik Kreinovich

Departmental Technical Reports (CS)

Several algorithms have been proposed to compute sets of optima, Pareto sets, sets of Nash equilibria. In this paper, we present a general algorithm for decision-related set computations that includes all these algorithms as particular cases.

To make our algorithm understandable to people working in optimization and in game theory, we also provide motivations and explanations for our formalizations of the corresponding problems and for the related notions of computable mathematics.