Genetic programming for the RoboCup Rescue Simulation System

The Robocup Rescue Simulation System (RCRSS) is a dynamic system of multi-agent interaction, simulating a large-scale urban disaster scenario. Teams of rescue agents are charged with the tasks of minimizing civilian casualties and infrastructure damage while competing against limitations on time, communication, and awareness. This thesis provides the first known attempt of applying Genetic Programming (GP) to the development of behaviours necessary to perform well in the RCRSS. Specifically, this thesis studies the suitability of GP to evolve the operational behaviours required of each type of rescue agent in the RCRSS. The system developed is evaluated in terms of the consistency with which expected solutions are the target of convergence as well as by comparison to previous competition results. The results indicate that GP is capable of converging to some forms of expected behaviour, but that additional evolution in strategizing behaviours must be performed in order to become competitive. An enhancement to the standard GP algorithm is proposed which is shown to simplify the initial search space allowing evolution to occur much quicker. In addition, two forms of population are employed and compared in terms of their apparent effects on the evolution of control structures for intelligent rescue agents. The first is a single population in which each individual is comprised of three distinct trees for the respective control of three types of agents, the second is a set of three co-evolving subpopulations one for each type of agent. Multiple populations of cooperating individuals appear to achieve higher proficiencies in training, but testing on unseen instances raises the issue of overfitting.

Elucidation of odour-potent compounds and sensory profiles of Vidal blanc and Riesling icewines from the Niagara Peninsula : effect of harvest date and crop level

I t was hypothesized that the freeze/thaw cycles endured by icewine grapes would change their chemical composition, resulting in unique chemical fingerprint and sensory properties, and would be affected by harvest date (HD) and crop level (CL). The objectives were: 1) to identify odour-active compounds using gas chromatographic and sensory analysis; 2) to determine the effect of CL and HD on these compounds; 3) to determine the icewine sensory profiles; 4) to correlate analytical and sensory results for an overall icewine profile. CharmAnalysis™ determined the Top 15 odour-potent compounds in Vidal and Riesling icewine and table wines; 24 and 23 compounds, respectively. The majority of the compounds had the highest concentrations in the icewines compared to table wines. These compounds were used as the foundation for assessing differences in icewine chemical profiles from different HD and CL. Vidal and Riesling icewine were made from grapes picked at different HD; HI : 19 December; H2: 29 December; H3: 18 January; H4: 11 February (Vidal only). HI wines differed from H3 and H4 wines in both Vidal and Riesling for aroma compounds and sensory profiles. - Three·CL [control (fully cropped), cluster thin at fruit set to one basal cluster/shoot (TFS), and cluster thin at veraison to one basal cluster/shoot (TV)] were evaluated for Riesling and Vidal cultivars over two seasons. Vidal icewines had the highest concentration of aroma compounds in the control and TV icewines in 2003 and in TFS icewines in 2004. In Riesling, most aroma compounds had the highest concentration in the TV icewines and the lowest concentration in the TFS wine for both years. The thinned treatments were associated with almost all of the sensory attributes in both cultivars, both years. HD and CL affected the chemical variables, aroma compounds and sensory properties of Vidal and Riesling icewines and freeze/thaw events changed their sensory profile. The most odour-potent compounds were p-damascenone, cis-rose oxide, 1- octen-3-ol, 4-vinylguaiacol, ethyl octanoate, and ethyl hexanoate. The role of Pdamascenone as a marker compound for icewine requires further investigation. This research provides a strong foundation for the understanding the odour-active volatiles and sensory profiles important to icewine.

IMPACT OF CROP LEVEL AND HANG TIME ON THE COMPOSITION OF FOUR WINE GRAPE CULTIVARS FROM THE NIAGARA REGION

This study analyzed the use of two viticultural practices: “crop level” (half crop; HC, and full crop; FC) and “hang times”, and their impact on the composition of four grape cultivars; Pinot gris, Riesling, Cabernet Franc and Cabernet Sauvignon from the Niagara Region and wine volatile composition by GC-MS. It was hypothesized that keeping a full crop with a longer hang time would have a greater impact on wine quality than reducing the crop level. In all cultivars, a reduction of crop level induced reductions in yield, clusters per vine and crop load, with increases in Brix. Extended hang time also increased Brix related to desiccation. The climatic conditions at harvest had an impact on hang time effects. The GC-MS analysis detected the presence of 30 volatile components in the wine, with different odour activity values. Harvest time had a positive impact than crop reduction in almost all compounds.

Statement of John O'Connor Damaged Wheat Crop

Statement (handwritten, 3 pages) in which John O’Connor states that his wheat crop of 1834 was damaged. A fence was also down which resulted in his wheat crop being destroyed by cattle and pigs. The defendants had to pay the plaintiff for damages. S. D. Woodruff was the arbitrator in this case, Aug. 1835.

Chart of station 2, crop sections of the old back ditch on the south side of the feeder, station 45, station 118 and the total length from the culvert to lot no. 5

Chart of station 2, crop sections of the old back ditch on the south side of the feeder, station 45, station 118 and the total length from the culvert to lot no. 5. This is signed by Fred Holmes, April 13, 1857.

Morphophysiological traits in crop improvement case study-sorghum [by] Ratikanta Maiti; ed. [by] Pedro A. Wesche-Ebeling, Manuel Rojas Garcidueñas [and] Paul R. Earl

UANL

Simulation-Based Finite and Large Sample Tests in Multivariate Regressions.

In the context of multivariate linear regression (MLR) models, it is well known that commonly employed asymptotic test criteria are seriously biased towards overrejection. In this paper, we propose a general method for constructing exact tests of possibly nonlinear hypotheses on the coefficients of MLR systems. For the case of uniform linear hypotheses, we present exact distributional invariance results concerning several standard test criteria. These include Wilks' likelihood ratio (LR) criterion as well as trace and maximum root criteria. The normality assumption is not necessary for most of the results to hold. Implications for inference are two-fold. First, invariance to nuisance parameters entails that the technique of Monte Carlo tests can be applied on all these statistics to obtain exact tests of uniform linear hypotheses. Second, the invariance property of the latter statistic is exploited to derive general nuisance-parameter-free bounds on the distribution of the LR statistic for arbitrary hypotheses. Even though it may be difficult to compute these bounds analytically, they can easily be simulated, hence yielding exact bounds Monte Carlo tests. Illustrative simulation experiments show that the bounds are sufficiently tight to provide conclusive results with a high probability. Our findings illustrate the value of the bounds as a tool to be used in conjunction with more traditional simulation-based test methods (e.g., the parametric bootstrap) which may be applied when the bounds are not conclusive.

Simulation-Based Finite-Sample Tests for Heteroskedasticity and ARCH Effects.

A wide range of tests for heteroskedasticity have been proposed in the econometric and statistics literature. Although a few exact homoskedasticity tests are available, the commonly employed procedures are quite generally based on asymptotic approximations which may not provide good size control in finite samples. There has been a number of recent studies that seek to improve the reliability of common heteroskedasticity tests using Edgeworth, Bartlett, jackknife and bootstrap methods. Yet the latter remain approximate. In this paper, we describe a solution to the problem of controlling the size of homoskedasticity tests in linear regression contexts. We study procedures based on the standard test statistics [e.g., the Goldfeld-Quandt, Glejser, Bartlett, Cochran, Hartley, Breusch-Pagan-Godfrey, White and Szroeter criteria] as well as tests for autoregressive conditional heteroskedasticity (ARCH-type models). We also suggest several extensions of the existing procedures (sup-type of combined test statistics) to allow for unknown breakpoints in the error variance. We exploit the technique of Monte Carlo tests to obtain provably exact p-values, for both the standard and the new tests suggested. We show that the MC test procedure conveniently solves the intractable null distribution problem, in particular those raised by the sup-type and combined test statistics as well as (when relevant) unidentified nuisance parameter problems under the null hypothesis. The method proposed works in exactly the same way with both Gaussian and non-Gaussian disturbance distributions [such as heavy-tailed or stable distributions]. The performance of the procedures is examined by simulation. The Monte Carlo experiments conducted focus on : (1) ARCH, GARCH, and ARCH-in-mean alternatives; (2) the case where the variance increases monotonically with : (i) one exogenous variable, and (ii) the mean of the dependent variable; (3) grouped heteroskedasticity; (4) breaks in variance at unknown points. We find that the proposed tests achieve perfect size control and have good power.

Simulation-Based Finite-Sample Normality Tests in Linear Regressions

In the literature on tests of normality, much concern has been expressed over the problems associated with residual-based procedures. Indeed, the specialized tables of critical points which are needed to perform the tests have been derived for the location-scale model; hence reliance on available significance points in the context of regression models may cause size distortions. We propose a general solution to the problem of controlling the size normality tests for the disturbances of standard linear regression, which is based on using the technique of Monte Carlo tests.

Simulation-Based Finite-and Large-sample Inference Methods in Multivariate Regressions and Seemingly Unrelated Regressions

In the context of multivariate regression (MLR) and seemingly unrelated regressions (SURE) models, it is well known that commonly employed asymptotic test criteria are seriously biased towards overrejection. in this paper, we propose finite-and large-sample likelihood-based test procedures for possibly non-linear hypotheses on the coefficients of MLR and SURE systems.

Testing Mean-Variance Efficiency in CAPM with Possibly Non-Gaussian Errors : An Exact Simulation-Based Approach

In this paper we propose exact likelihood-based mean-variance efficiency tests of the market portfolio in the context of Capital Asset Pricing Model (CAPM), allowing for a wide class of error distributions which include normality as a special case. These tests are developed in the frame-work of multivariate linear regressions (MLR). It is well known however that despite their simple statistical structure, standard asymptotically justified MLR-based tests are unreliable. In financial econometrics, exact tests have been proposed for a few specific hypotheses [Jobson and Korkie (Journal of Financial Economics, 1982), MacKinlay (Journal of Financial Economics, 1987), Gib-bons, Ross and Shanken (Econometrica, 1989), Zhou (Journal of Finance 1993)], most of which depend on normality. For the gaussian model, our tests correspond to Gibbons, Ross and Shanken’s mean-variance efficiency tests. In non-gaussian contexts, we reconsider mean-variance efficiency tests allowing for multivariate Student-t and gaussian mixture errors. Our framework allows to cast more evidence on whether the normality assumption is too restrictive when testing the CAPM. We also propose exact multivariate diagnostic checks (including tests for multivariate GARCH and mul-tivariate generalization of the well known variance ratio tests) and goodness of fit tests as well as a set estimate for the intervening nuisance parameters. Our results [over five-year subperiods] show the following: (i) multivariate normality is rejected in most subperiods, (ii) residual checks reveal no significant departures from the multivariate i.i.d. assumption, and (iii) mean-variance efficiency tests of the market portfolio is not rejected as frequently once it is allowed for the possibility of non-normal errors.

Finite-Sample Simulation-Based Inference in VAR Models with Applications to Order Selection and Causality Testing

Statistical tests in vector autoregressive (VAR) models are typically based on large-sample approximations, involving the use of asymptotic distributions or bootstrap techniques. After documenting that such methods can be very misleading even with fairly large samples, especially when the number of lags or the number of equations is not small, we propose a general simulation-based technique that allows one to control completely the level of tests in parametric VAR models. In particular, we show that maximized Monte Carlo tests [Dufour (2002)] can provide provably exact tests for such models, whether they are stationary or integrated. Applications to order selection and causality testing are considered as special cases. The technique developed is applied to quarterly and monthly VAR models of the U.S. economy, comprising income, money, interest rates and prices, over the period 1965-1996.

Éffectivité des subventions à la recherche et au développement, évaluation circonstancielle à l'aide d'une micro-simulation.

Rapport de recherche