Multisensor change detection with nonlinear canonical correlation

The analysis of multi-modal and multi-sensor images is nowadays of paramount importance for Earth Observation (EO) applications. There exist a variety of methods that aim at fusing the different sources of information to obtain a compact representation of such datasets. However, for change detection existing methods are often unable to deal with heterogeneous image sources and very few consider possible nonlinearities in the data. Additionally, the availability of labeled information is very limited in change detection applications. For these reasons, we present the use of a semi-supervised kernel-based feature extraction technique. It incorporates a manifold regularization accounting for the geometric distribution and jointly addressing the small sample problem. An exhaustive example using Landsat 5 data illustrates the potential of the method for multi-sensor change detection.

Assembling genes from predicted exons in linear time with dynamic programming

In a number of programs for gene structure prediction in higher eukaryotic genomic sequences, exon prediction is decoupled from gene assembly: a large pool of candidate exons is predicted and scored from features located in the query DNA sequence, and candidate genes are assembled from such a pool as sequences of nonoverlapping frame-compatible exons. Genes are scored as a function of the scores of the assembled exons, and the highest scoring candidate gene is assumed to be the most likely gene encoded by the query DNA sequence. Considering additive gene scoring functions, currently available algorithms to determine such a highest scoring candidate gene run in time proportional to the square of the number of predicted exons. Here, we present an algorithm whose running time grows only linearly with the size of the set of predicted exons. Polynomial algorithms rely on the fact that, while scanning the set of predicted exons, the highest scoring gene ending in a given exon can be obtained by appending the exon to the highest scoring among the highest scoring genes ending at each compatible preceding exon. The algorithm here relies on the simple fact that such highest scoring gene can be stored and updated. This requires scanning the set of predicted exons simultaneously by increasing acceptor and donor position. On the other hand, the algorithm described here does not assume an underlying gene structure model. Indeed, the definition of valid gene structures is externally defined in the so-called Gene Model. The Gene Model specifies simply which gene features are allowed immediately upstream which other gene features in valid gene structures. This allows for great flexibility in formulating the gene identification problem. In particular it allows for multiple-gene two-strand predictions and for considering gene features other than coding exons (such as promoter elements) in valid gene structures.

Mixed testicular germ cell tumor presenting as metastatic pure choriocarcinoma involving the maxillary gingiva.

Gingival metastases are infrequent and invariably associated with a widespread disease and a poor prognosis. Because of their unremarkable clinical appearance, they can be difficult to distinguish from more common gingival hyperplastic or reactive lesions, such as pyogenic granuloma, peripheral giant cell granuloma, and peripheral ossifying granuloma. We are reporting here an unusual case of a 36-year-old man with a mixed testicular germ cell tumor presenting as a metastatic pure choriocarcinoma involving the maxillary gingiva, extending from the first left premolar to the left second maxillary molar, mimicking a 'benign looking' gingival mass. Gingival metastases may be the first manifestation of a widespread metastatic disease and therefore particular attention must be paid to gingival lesions associated with atypical clinical symptoms and/or signs.

Population pharmacokinetics of ganciclovir in solid-organ transplant recipients receiving oral valganciclovir.

Valganciclovir (VGC) is an oral prodrug of ganciclovir (GCV) recently introduced for prophylaxis and treatment of cytomegalovirus infection. Optimal concentration exposure for effective and safe VGC therapy would require either reproducible VGC absorption and GCV disposition or dosage adjustment based on therapeutic drug monitoring (TDM). We examined GCV population pharmacokinetics in solid organ transplant recipients receiving oral VGC, including the influence of clinical factors, the magnitude of variability, and its impact on efficacy and tolerability. Nonlinear mixed effect model (NONMEM) analysis was performed on plasma samples from 65 transplant recipients under VGC prophylaxis or treatment. A two-compartment model with first-order absorption appropriately described the data. Systemic clearance was markedly influenced by the glomerular filtration rate (GFR), patient gender, and graft type (clearance/GFR = 1.7 in kidney, 0.9 in heart, and 1.2 in lung and liver recipients) with interpatient and interoccasion variabilities of 26 and 12%, respectively. Body weight and sex influenced central volume of distribution (V(1) = 0.34 liter/kg in males and 0.27 liter/kg in females [20% interpatient variability]). No significant drug interaction was detected. The good prophylactic efficacy and tolerability of VGC precluded the demonstration of any relationship with GCV concentrations. In conclusion, this analysis highlights the importance of thorough adjustment of VGC dosage to renal function and body weight. Considering the good predictability and reproducibility of the GCV profile after treatment with oral VGC, routine TDM does not appear to be clinically indicated in solid-organ transplant recipients. However, GCV plasma measurement may still be helpful in specific clinical situations.

Audit report on Highway Safety Projects administered by The Integer Group Midwest for the year ended September 30, 2006

Audit report on Highway Safety Projects administered by The Integer Group Midwest for the year ended September 30, 2006

An experiment on Nash implementation

We perform an experimental test of Maskin's canonical mechanism for Nashimplementation, using 3 subjects in non-repeated groups, as well as 3 outcomes, states of nature, and integer choices. We find that this mechanism succesfully implements the desired outcome a large majority of the time and an imbedded comprehension test indicates that subjects were generally able to comprehend their decision tasks. The performance can also be improved by imposing a fine on non designated dissidents. We offer some explanations for the imperfect implementation, including risk preferences, the possibilities that agents have for collusion, and the mixed strategy equilibria of the game.

A randomized concave programming method for choice network revenue management

Models incorporating more realistic models of customer behavior, as customers choosing froman offer set, have recently become popular in assortment optimization and revenue management.The dynamic program for these models is intractable and approximated by a deterministiclinear program called the CDLP which has an exponential number of columns. However, whenthe segment consideration sets overlap, the CDLP is difficult to solve. Column generationhas been proposed but finding an entering column has been shown to be NP-hard. In thispaper we propose a new approach called SDCP to solving CDLP based on segments and theirconsideration sets. SDCP is a relaxation of CDLP and hence forms a looser upper bound onthe dynamic program but coincides with CDLP for the case of non-overlapping segments. Ifthe number of elements in a consideration set for a segment is not very large (SDCP) can beapplied to any discrete-choice model of consumer behavior. We tighten the SDCP bound by(i) simulations, called the randomized concave programming (RCP) method, and (ii) by addingcuts to a recent compact formulation of the problem for a latent multinomial-choice model ofdemand (SBLP+). This latter approach turns out to be very effective, essentially obtainingCDLP value, and excellent revenue performance in simulations, even for overlapping segments.By formulating the problem as a separation problem, we give insight into why CDLP is easyfor the MNL with non-overlapping considerations sets and why generalizations of MNL posedifficulties. We perform numerical simulations to determine the revenue performance of all themethods on reference data sets in the literature.

A new compact linear programming formulation for choice network revenue management

The choice network revenue management model incorporates customer purchase behavioras a function of the offered products, and is the appropriate model for airline and hotel networkrevenue management, dynamic sales of bundles, and dynamic assortment optimization.The optimization problem is a stochastic dynamic program and is intractable. A certainty-equivalencerelaxation of the dynamic program, called the choice deterministic linear program(CDLP) is usually used to generate dyamic controls. Recently, a compact linear programmingformulation of this linear program was given for the multi-segment multinomial-logit (MNL)model of customer choice with non-overlapping consideration sets. Our objective is to obtaina tighter bound than this formulation while retaining the appealing properties of a compactlinear programming representation. To this end, it is natural to consider the affine relaxationof the dynamic program. We first show that the affine relaxation is NP-complete even for asingle-segment MNL model. Nevertheless, by analyzing the affine relaxation we derive a newcompact linear program that approximates the dynamic programming value function betterthan CDLP, provably between the CDLP value and the affine relaxation, and often comingclose to the latter in our numerical experiments. When the segment consideration sets overlap,we show that some strong equalities called product cuts developed for the CDLP remain validfor our new formulation. Finally we perform extensive numerical comparisons on the variousbounds to evaluate their performance.

On the throughput-WIP trade-off in queueing systems, diminishing returns and the threshold property: A linear programming approach

We present a new unifying framework for investigating throughput-WIP(Work-in-Process) optimal control problems in queueing systems,based on reformulating them as linear programming (LP) problems withspecial structure: We show that if a throughput-WIP performance pairin a stochastic system satisfies the Threshold Property we introducein this paper, then we can reformulate the problem of optimizing alinear objective of throughput-WIP performance as a (semi-infinite)LP problem over a polygon with special structure (a thresholdpolygon). The strong structural properties of such polygones explainthe optimality of threshold policies for optimizing linearperformance objectives: their vertices correspond to the performancepairs of threshold policies. We analyze in this framework theversatile input-output queueing intensity control model introduced byChen and Yao (1990), obtaining a variety of new results, including (a)an exact reformulation of the control problem as an LP problem over athreshold polygon; (b) an analytical characterization of the Min WIPfunction (giving the minimum WIP level required to attain a targetthroughput level); (c) an LP Value Decomposition Theorem that relatesthe objective value under an arbitrary policy with that of a giventhreshold policy (thus revealing the LP interpretation of Chen andYao's optimality conditions); (d) diminishing returns and invarianceproperties of throughput-WIP performance, which underlie thresholdoptimality; (e) a unified treatment of the time-discounted andtime-average cases.

Common cleaning products can be dangerous when mixed, 2008

Common household cleaning products can cause injury when mixed. Some combinations produce harmful fumes and other dangerous by-products.

Implementing TURF analysis through binary linear programming

This paper introduces the approach of using Total Unduplicated Reach and Frequency analysis (TURF) to design a product line through a binary linear programming model. This improves the efficiency of the search for the solution to the problem compared to the algorithms that have been used to date. The results obtained through our exact algorithm are presented, and this method shows to be extremely efficient both in obtaining optimal solutions and in computing time for very large instances of the problem at hand. Furthermore, the proposed technique enables the model to be improved in order to overcome the main drawbacks presented by TURF analysis in practice.

Nonlinear models and small sample performance of the generalized method of moments

In this paper I explore the issue of nonlinearity (both in the datageneration process and in the functional form that establishes therelationship between the parameters and the data) regarding the poorperformance of the Generalized Method of Moments (GMM) in small samples.To this purpose I build a sequence of models starting with a simple linearmodel and enlarging it progressively until I approximate a standard (nonlinear)neoclassical growth model. I then use simulation techniques to find the smallsample distribution of the GMM estimators in each of the models.

On p-beauty contest integer games

In this paper we provide a full characterization of the pure-strategyNash Equilibria for the p-Beauty Contest Game when we restrict player schoices to integer numbers. Opposed to the case of real number choices,equilibrium uniqueness may be lost depending on the value of p and thenumber of players: in particular, as p approaches 1 any symmetric profileconstitutes a Nash Equilibrium. We also show that any experimental p-BeautyContest Game can be associated to a game with the integer restriction andthus multiplicity of equilibria becomes an issue. Finally, we show thatin these games the iterated deletion of weakly dominated strategies maynot lead to a single outcome while the iterated best-reply process alwaysdoes (though the outcome obtained depends on the initial conditions).

Restless bandits, linear programming relaxations and a primal-dual index heuristic

We develop a mathematical programming approach for the classicalPSPACE - hard restless bandit problem in stochastic optimization.We introduce a hierarchy of n (where n is the number of bandits)increasingly stronger linear programming relaxations, the lastof which is exact and corresponds to the (exponential size)formulation of the problem as a Markov decision chain, while theother relaxations provide bounds and are efficiently computed. Wealso propose a priority-index heuristic scheduling policy fromthe solution to the first-order relaxation, where the indices aredefined in terms of optimal dual variables. In this way wepropose a policy and a suboptimality guarantee. We report resultsof computational experiments that suggest that the proposedheuristic policy is nearly optimal. Moreover, the second-orderrelaxation is found to provide strong bounds on the optimalvalue.