Design and evaluation of learning algorithms for dynamic resource management in virtual networks

Network virtualisation is considerably gaining attentionas a solution to ossification of the Internet. However, thesuccess of network virtualisation will depend in part on how efficientlythe virtual networks utilise substrate network resources.In this paper, we propose a machine learning-based approachto virtual network resource management. We propose to modelthe substrate network as a decentralised system and introducea learning algorithm in each substrate node and substrate link,providing self-organization capabilities. We propose a multiagentlearning algorithm that carries out the substrate network resourcemanagement in a coordinated and decentralised way. The taskof these agents is to use evaluative feedback to learn an optimalpolicy so as to dynamically allocate network resources to virtualnodes and links. The agents ensure that while the virtual networkshave the resources they need at any given time, only the requiredresources are reserved for this purpose. Simulations show thatour dynamic approach significantly improves the virtual networkacceptance ratio and the maximum number of accepted virtualnetwork requests at any time while ensuring that virtual networkquality of service requirements such as packet drop rate andvirtual link delay are not affected.

Defining housing market areas using commuting and migration algorithms. Catalonia (Spain) as an applied case study

In the literature on housing market areas, different approaches can be found to defining them, for example, using travel-to-work areas and, more recently, making use of migration data. Here we propose a simple exercise to shed light on which approach performs better. Using regional data from Catalonia, Spain, we have computed housing market areas with both commuting data and migration data. In order to decide which procedure shows superior performance, we have looked at uniformity of prices within areas. The main finding is that commuting algorithms present more homogeneous areas in terms of housing prices.

MCMC Analysis Of Classical Time Series Algorithms

Identification of order of an Autoregressive Moving Average Model (ARMA) by the usual graphical method is subjective. Hence, there is a need of developing a technique to identify the order without employing the graphical investigation of series autocorrelations. To avoid subjectivity, this thesis focuses on determining the order of the Autoregressive Moving Average Model using Reversible Jump Markov Chain Monte Carlo (RJMCMC). The RJMCMC selects the model from a set of the models suggested by better fitting, standard deviation errors and the frequency of accepted data. Together with deep analysis of the classical Box-Jenkins modeling methodology the integration with MCMC algorithms has been focused through parameter estimation and model fitting of ARMA models. This helps to verify how well the MCMC algorithms can treat the ARMA models, by comparing the results with graphical method. It has been seen that the MCMC produced better results than the classical time series approach.

Chlorophyll fluorescence of tropical tree species in a semi-deciduous forest gap

The characterization of different ecological groups in a forest formation/succession is unclear. To better define the different successional classes, we have to consider ecophysiological aspects, such as the capacity to use or dissipate the light energy available. The main objective of this work was to assess the chlorophyll fluorescence emission of tropical tree species growing in a gap of a semi-deciduous forest. Three species of different ecological groups were selected: Croton floribundus Spreng. (pioneer, P), Astronium graveolens Jacq. (early secondary, Si), and Esenbeckia febrifuga A. Juss. (late secondary, St). The potential (Fv/Fm) and effective (deltaF/Fm') quantum efficiency of photosystem II, apparent electron transport rate (ETR), non-photochemical (qN) and photochemical (qP) quenching of fluorescence were evaluated, using a modulated fluorometer, between 7:30 and 11:00 h. Values of Fv/Fm remained constant in St, decreasing in P and Si after 9:30 h, indicating the occurrence of photoinhibition. Concerning the measurements taken under light conditions (deltaF/Fm', ETR, qP and qN), P and Si showed better photochemical performance, i.e., values of deltaF/Fm', ETR and qP were higher than St when light intensity was increased. Values of qN indicated that P and Si had an increasing tendency of dissipating the excess of energy absorbed by the leaf, whereas the opposite was found for St. The principal component analysis (PCA), considering all evaluated parameters, showed a clear distinction between St, P and Si, with P and Si being closer. The PCA results suggest that chlorophyll fluorescence may be a potential tool to differentiate tree species from distinct successional groups.

Chloroplastid pigment contents and chlorophyll a fluorescence in Amazonian tropical three species

Plants react to changes in light and hydrological conditions in terms of quantity and composition of chloroplastidic pigments, which affects the photosynthetic properties and consequently the accumulation of plant biomass. Thus, the chloroplastidic pigment concentration and chlorophyll a fluorescence of three Amazonian species (Bertholletia excelsa, Carapa guianensis e Dipteryx odorata) were investigated in sun and shade leaves form the tree crown collected during two distinct periods of precipitation (dry and rainy seasons). Pigment contents were determined by spectrophotometry and fluorescence variables were determined using a portable fluorometer. The results demonstrated that the species showed high concentrations of Chl a, Chl b e Chl total during the wet season in relation to the dry season, especially in shade leaves. A higher concentration of carotenoids was found in B. excelsa, when compared with leaves of C. guianensis and D. odorata. In leaves of B. excelsa and D. odorata no significant difference was found in relation to the photochemistry of photosystem II (Fv/Fm) between the wet and dry seasons. In conclusion, the three species react differently to variations in the light and precipitation conditions regarding light capture, aspects that might be considered in the management of forest plantations.

Efficiency of distinct data mining algorithms for classifying stress level in piglets from their vocalization

Among the challenges of pig farming in today's competitive market, there is factor of the product traceability that ensures, among many points, animal welfare. Vocalization is a valuable tool to identify situations of stress in pigs, and it can be used in welfare records for traceability. The objective of this work was to identify stress in piglets using vocalization, calling this stress on three levels: no stress, moderate stress, and acute stress. An experiment was conducted on a commercial farm in the municipality of Holambra, São Paulo State , where vocalizations of twenty piglets were recorded during the castration procedure, and separated into two groups: without anesthesia and local anesthesia with lidocaine base. For the recording of acoustic signals, a unidirectional microphone was connected to a digital recorder, in which signals were digitized at a frequency of 44,100 Hz. For evaluation of sound signals, Praat® software was used, and different data mining algorithms were applied using Weka® software. The selection of attributes improved model accuracy, and the best attribute selection was used by applying Wrapper method, while the best classification algorithms were the k-NN and Naive Bayes. According to the results, it was possible to classify the level of stress in pigs through their vocalization.

Scalable algorithms for height field illumination

Global illumination algorithms are at the center of realistic image synthesis and account for non-trivial light transport and occlusion within scenes, such as indirect illumination, ambient occlusion, and environment lighting. Their computationally most difficult part is determining light source visibility at each visible scene point. Height fields, on the other hand, constitute an important special case of geometry and are mainly used to describe certain types of objects such as terrains and to map detailed geometry onto object surfaces. The geometry of an entire scene can also be approximated by treating the distance values of its camera projection as a screen-space height field. In order to shadow height fields from environment lights a horizon map is usually used to occlude incident light. We reduce the per-receiver time complexity of generating the horizon map on N N height fields from O(N) of the previous work to O(1) by using an algorithm that incrementally traverses the height field and reuses the information already gathered along the path of traversal. We also propose an accurate method to integrate the incident light within the limits given by the horizon map. Indirect illumination in height fields requires information about which other points are visible to each height field point. We present an algorithm to determine this intervisibility in a time complexity that matches the space complexity of the produced visibility information, which is in contrast to previous methods which scale in the height field size. As a result the amount of computation is reduced by two orders of magnitude in common use cases. Screen-space ambient obscurance methods approximate ambient obscurance from the depth bu er geometry and have been widely adopted by contemporary real-time applications. They work by sampling the screen-space geometry around each receiver point but have been previously limited to near- field effects because sampling a large radius quickly exceeds the render time budget. We present an algorithm that reduces the quadratic per-pixel complexity of previous methods to a linear complexity by line sweeping over the depth bu er and maintaining an internal representation of the processed geometry from which occluders can be efficiently queried. Another algorithm is presented to determine ambient obscurance from the entire depth bu er at each screen pixel. The algorithm scans the depth bu er in a quick pre-pass and locates important features in it, which are then used to evaluate the ambient obscurance integral accurately. We also propose an evaluation of the integral such that results within a few percent of the ray traced screen-space reference are obtained at real-time render times.

Chlorophyll a fluorescence in rice plants exposed of herbicides of group imidazolinone

The objective of this work was to investigate the injuries caused to the photosynthetic apparatus of three types of rice exposed to application of imidazolinone group herbicides. Two experiments were conducted using herbicides Imazethapyr+imazapic and Imazapyr+imazapic, in a split-plot experimental design, and a 3 x 3 factorial, with six replications. The first factor (A) consisted of the herbicide rates 0, 100 e 200 g ha-1 of Imazethapyr+imazapic and 0, 140 e 280 g ha-1 of Imazapyr+imazapic; factor B consisted of type of rice (cv. Puitá Inta CL, sensitive red rice ecotype and red rice ecotype with suspected herbicide tolerance to Imidazolinone). Chlorophyll a fluorescence parameters were evaluated in plants at 30 days after herbicide application, using a portable fluorometer (HandyPEA, Hanstech). The photosynthetic metabolism of cv. Puitá Inta CL was found to tolerate commercial dosages of both herbicides. High sensitivity to the herbicides was observed for the sensitive red rice ecotype, while the photosynthetic apparatus of red rice ecotype with suspected herbicide tolerance showed high tolerance to both herbicides applied at rates higher than the commercial rate. The application of chemical herbicides of the imidazolinone group on rice plants causes changes in the photosynthetic metabolism of plants, detected by evaluating the emission of transient chlorophyll a fluorescence. This method can be useful in helping detect resistance and/or tolerance of red rice plants to herbicides of the imidazolinone group.

Chlorophyll fluorescence in guanandi tree (Calophyllum brasiliense) after herbicide application

Chlorophyll fluorescence is currently used as a rapid diagnostic and nondestructive method to detect and quantify damage on the photosynthetic apparatus of leaves on weeds, crops and ornamental/coniferous trees in response to both environmental stress and herbicides. This study aimed to evaluate chlorophyll fluorescence in guanandi plants (Calophyllum brasiliense) after application of different postemergence herbicides. The experiment was performed in a completely randomized design, with six treatments (control, bentazon, sulfentrazone, isoxaflutole, atrazine and glyphosate) and five replications. The herbicide treatments were applied with a stationary sprayer, and electron transport rate (ETR) was subsequently analyzed with OS5p Multi-Mode Chlorophyll Fluorometer. In the monitored period, guanandi plants subjected to atrazine showed higher sensitivity to chlorophyll fluorescence than the other treatments. Although bentazon is a photosystem II inhibitor, it showed no major changes in electron transport for the studied species and in the monitored period. In summary, ETR is a good parameter to evaluate the effect of some herbicides on Calophyllum brasiliense plants.

Chlorophyll fluorescence parameters in populations of two legume trees: Stryphnodendron adstringens (Mart.) Coville (Mimosoideae) and Cassia ferruginea (Schrad.) Schrad. ex DC. (Caesalpinoideae)

The aim of this study was to investigate the photosynthetic performance in populations of two legume tree species, Stryphnodendron adstringens (Mimosoideae), typical from Cerrado, and Cassia ferruginea (Caesalpinoideae) from the Atlantic Rain Forest. The photosynthetic traits were assessed by measures of chlorophyll fluorescence in progenies of naturally pollinated plants from three populations of S. adstringens and a population of C. ferruginea. Plants of S. adstringens growing under similar conditions of C. ferruginea plants demanded higher light values for photosynthesis saturation, 600 µmol.m-2.s-1 and 350 µmol.m-2.s-1 respectively, and showed higher intrinsic photosynthetic efficiency of photosystem II, Fv/Fm of 0.814 versus 0.783 in C. ferruginea. The highest values of Fv/Fm observed in S. adstringens can explain the highest electron transport rates (ETR) obtained for this species. No significant differences were found among progenies from different C. ferruginea trees nor among populations of S. adstringens, and only in few cases, variation among progenies within populations were found for S. adstringens plants. The fact that fluorescence parameters distinguished species but not populations or most of progenies may be related to low intraspecific genetic variation of these chlorophyll fluorescence traits or due to lack of expression on genetic differences in plants under no stressful conditions.

Efficient Optimization Algorithms for Nonlinear Data Analysis

Identification of low-dimensional structures and main sources of variation from multivariate data are fundamental tasks in data analysis. Many methods aimed at these tasks involve solution of an optimization problem. Thus, the objective of this thesis is to develop computationally efficient and theoretically justified methods for solving such problems. Most of the thesis is based on a statistical model, where ridges of the density estimated from the data are considered as relevant features. Finding ridges, that are generalized maxima, necessitates development of advanced optimization methods. An efficient and convergent trust region Newton method for projecting a point onto a ridge of the underlying density is developed for this purpose. The method is utilized in a differential equation-based approach for tracing ridges and computing projection coordinates along them. The density estimation is done nonparametrically by using Gaussian kernels. This allows application of ridge-based methods with only mild assumptions on the underlying structure of the data. The statistical model and the ridge finding methods are adapted to two different applications. The first one is extraction of curvilinear structures from noisy data mixed with background clutter. The second one is a novel nonlinear generalization of principal component analysis (PCA) and its extension to time series data. The methods have a wide range of potential applications, where most of the earlier approaches are inadequate. Examples include identification of faults from seismic data and identification of filaments from cosmological data. Applicability of the nonlinear PCA to climate analysis and reconstruction of periodic patterns from noisy time series data are also demonstrated. Other contributions of the thesis include development of an efficient semidefinite optimization method for embedding graphs into the Euclidean space. The method produces structure-preserving embeddings that maximize interpoint distances. It is primarily developed for dimensionality reduction, but has also potential applications in graph theory and various areas of physics, chemistry and engineering. Asymptotic behaviour of ridges and maxima of Gaussian kernel densities is also investigated when the kernel bandwidth approaches infinity. The results are applied to the nonlinear PCA and to finding significant maxima of such densities, which is a typical problem in visual object tracking.