Integrating personality research and animal contest theory: aggressiveness in the green swordtail <i>Xiphophorus helleri</i>

&lt;p&gt;Aggression occurs when individuals compete over limiting resources. While theoretical studies have long placed a strong emphasis on context-specificity of aggression, there is increasing recognition that consistent behavioural differences exist among individuals, and that aggressiveness may be an important component of individual personality. Though empirical studies tend to focus on one aspect or the other, we suggest there is merit in modelling both within-and among-individual variation in agonistic behaviour simultaneously. Here, we demonstrate how this can be achieved using multivariate linear mixed effect models. Using data from repeated mirror trials and dyadic interactions of male green swordtails, &lt;i&gt;Xiphophorus helleri&lt;/i&gt;, we show repeatable components of (co)variation in a suite of agonistic behaviour that is broadly consistent with a major axis of variation in aggressiveness. We also show that observed focal behaviour is dependent on opponent effects, which can themselves be repeatable but were more generally found to be context specific. In particular, our models show that within-individual variation in agonistic behaviour is explained, at least in part, by the relative size of a live opponent as predicted by contest theory. Finally, we suggest several additional applications of the multivariate models demonstrated here. These include testing the recently queried functional equivalence of alternative experimental approaches, (e. g., mirror trials, dyadic interaction tests) for assaying individual aggressiveness.&lt;/p&gt

Using numerical plant models and phenotypic correlation space to design achievable ideotypes

Numerical plant models can predict the outcome of plant traits modifications resulting from genetic variations, on plant performance, by simulating physiological processes and their interaction with the environment. Optimization methods complement those models to design ideotypes, i.e. ideal values of a set of plant traits resulting in optimal adaptation for given combinations of environment and management, mainly through the maximization of a performance criteria (e.g. yield, light interception). As use of simulation models gains momentum in plant breeding, numerical experiments must be carefully engineered to provide accurate and attainable results, rooting them in biological reality. Here, we propose a multi-objective optimization formulation that includes a metric of performance, returned by the numerical model, and a metric of feasibility, accounting for correlations between traits based on field observations. We applied this approach to two contrasting models: a process-based crop model of sunflower and a functional-structural plant model of apple trees. In both cases, the method successfully characterized key plant traits and identified a continuum of optimal solutions, ranging from the most feasible to the most efficient. The present study thus provides successful proof of concept for this enhanced modeling approach, which identified paths for desirable trait modification, including direction and intensity.Comment: 25 pages, 5 figures, 2017, Plant, Cell and Environmen

Structured light assisted real-time stereo photogrammetry for robotics and automation. Novel implementation of stereo matching

In this Master’s thesis project a novel implementation of a stereo matching based method is proposed. Moreover, an exhaustive analysis of the state-of-the-art algorithms in that field is outlined. Specifically, both standard and deep learning based methods have been extensively investigated, thus to provide useful insights for the designed implementation. Regarding the developed work, it is basically structured in the following manner. At first a research phase has been carried out, hence to simply and rapidly test the thought strategy. Subsequently, a first implementation of the algorithm has been designed and tested using data available from the Middlebury 2014 dataset, which is one of the most exploited dataset in the computer vision area. At this stage, numerous tests have been completed and consequently various changes to the algorithm pipeline have been made, in order to improve the final result. Finally, after that exhaustive researching phase the actual method has been designed and tested using real environment images obtained from the stereo device developed by the company, in which this work has been produced. Fundamental element of the project is indeed that stereo device. As a matter of fact, the designed algorithm in based on the data produced by the cameras that constitute it. Specifically, the main function of the system designed by LaDiMo is to make the built stereo matching based procedure simultaneously faster and accurate. As a matter of fact one of the main prerogative of the project was to create an algorithm that has to prove potential real-time results. This has been in fact, achieved by applying one of the two methods created. Specifically, it is a lightweight implementation, which strongly exploits the information coming from the LaDiMo device, thus to provide accurate results, keeping the computational time short. At the end of this Master’s thesis images showing the main outcomes obtained are proposed. Moreover, a discussion regarding the further improvements that are going to be added to the project is stated. In fact, the method implemented, being not optimized only demonstrate a potential real-time implementation, which would be certainly achieved through an efficient refactoring of the main pipeline

Persistent Homology in Sparse Regression and its Application to Brain Morphometry

Sparse systems are usually parameterized by a tuning parameter that determines the sparsity of the system. How to choose the right tuning parameter is a fundamental and difficult problem in learning the sparse system. In this paper, by treating the the tuning parameter as an additional dimension, persistent homological structures over the parameter space is introduced and explored. The structures are then further exploited in speeding up the computation using the proposed soft-thresholding technique. The topological structures are further used as multivariate features in the tensor-based morphometry (TBM) in characterizing white matter alterations in children who have experienced severe early life stress and maltreatment. These analyses reveal that stress-exposed children exhibit more diffuse anatomical organization across the whole white matter region.Comment: submitted to IEEE Transactions on Medical Imagin

Comparative Analyisis of Software Cost Estimation Project using Algorithmic Method

Software Cost Estimation has become an important factor to determine the efficiency of software development. There are many model of cost estimation like algorithmic model, top-down, and expert judgement. From all those models, Development in Algorithmic model is higher than the others. In this paper we present a comparative analysis of software cost project using algorithmic methods

Applying Absolute Residuals as Evaluation Criterion for Estimating the Development Time of Software Projects by Means of a Neuro-Fuzzy Approach

In the software development field, software practitioners expend between 30% and 40% more effort than is predicted. Accordingly, researchers have proposed new models for estimating the development effort such that the estimations of these models are close to actual ones. In this study, an application based on a new neuro-fuzzy system (NFS) is analyzed. The NFS accuracy was compared to that of a statistical multiple linear regression (MLR) model. The criterion for evaluating the accuracy of estimation models has mainly been the Magnitude of Relative Error (MRE), however, it was recently found that MRE is asymmetric, and the use of Absolute Residuals (AR) has been proposed, therefore, in this study, the accuracy results of the NFS and MLR were based on AR. After a statistical paired t-test was performed, results showed that accuracy of the New-NFS is statistically better than that of the MLR at the 99% confidence level. It can be concluded that a new-NFS could be used for predicting the effort of software development projects when they have been individually developed on a disciplined process.In the software development field, software practitioners expend between 30% and 40% more effort than is predicted. Accordingly, researchers have proposed new models for estimating the development effort such that the estimations of these models are close to actual ones. In this study, an application based on a new neuro-fuzzy system (NFS) is analyzed. The NFS accuracy was compared to that of a statistical multiple linear regression (MLR) model. The criterion for evaluating the accuracy of estimation models has mainly been the Magnitude of Relative Error (MRE), however, it was recently found that MRE is asymmetric, and the use of Absolute Residuals (AR) has been proposed, therefore, in this study, the accuracy results of the NFS and MLR were based on AR. After a statistical paired t-test was performed, results showed that accuracy of the New-NFS is statistically better than that of the MLR at the 99% confidence level. It can be concluded that a new-NFS could be used for predicting the effort of software development projects when they have been individually developed on a disciplined process

Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)

The implicit objective of the biennial "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) is to foster collaboration between international scientific teams by disseminating ideas through both specific oral/poster presentations and free discussions. For its second edition, the iTWIST workshop took place in the medieval and picturesque town of Namur in Belgium, from Wednesday August 27th till Friday August 29th, 2014. The workshop was conveniently located in "The Arsenal" building within walking distance of both hotels and town center. iTWIST'14 has gathered about 70 international participants and has featured 9 invited talks, 10 oral presentations, and 14 posters on the following themes, all related to the theory, application and generalization of the "sparsity paradigm": Sparsity-driven data sensing and processing; Union of low dimensional subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph sensing/processing; Blind inverse problems and dictionary learning; Sparsity and computational neuroscience; Information theory, geometry and randomness; Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?; Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website: http://sites.google.com/site/itwist1

An automated pattern recognition system for the quantification of inflammatory cells in hepatitis-C-infected liver biopsies

This paper presents an automated system for the quantification of inflammatory cells in hepatitis-C-infected liver biopsies. Initially, features are extracted from colour-corrected biopsy images at positions of interest identified by adaptive thresholding and clump decomposition. A sequential floating search method and principal component analysis are used to reduce dimensionality. Manually annotated training images allow supervised training. The performance of Gaussian parametric and mixture models is compared when used to classify regions as either inflammatory or healthy. The system is optimized using a response surface method that maximises the area under the receiver operating characteristic curve. This system is then tested on images previously ranked by a number of observers with varying levels of expertise. These results are compared to the automated system using Spearman rank correlation. Results show that this system can rank 15 test images, with varying degrees of inflammation, in strong agreement with five expert pathologists