A Phenomenological Analysis of the ΔEFT: Type-2 Seesaw Effective Field Theory

In this thesis a phenomenological analysis of the type-2 seesaw mechanism effective field theory expansion is performed. A complete non-redundant basis is obtained and compared to the Warsaw basis. Then, three different approaches are used to study and constrain the different operators of the effective field theory. Firstly, the model is studied assuming much lower energies than the scale of the type-2 seesaw mass by looking at its contribution to the Wilson coefficients of the Warsaw basis. This analysis is extended to include dimension-5 operators of the type-2 effective field theory. The second approach consists in probing the contribution of these operators to standard model couplings. We also study the possibility of constraining these operators along with the Warsaw basis. The last approach consists of studying the impact of some operators to LHC searches for new particles. Some new interesting interactions of this model are pointed out, and an analysis is carried out by using CMS data. Finally, a brief overlook into possible ultraviolet completions of this model is discussed at the end of this thesis

Hydrostatic Equilibrium of a Perfect Fluid Sphere with Exterior Higher-Dimensional Schwarzschild Spacetime

We discuss the question of how the number of dimensions of space and time can influence the equilibrium configurations of stars. We find that dimensionality does increase the effect of mass but not the contribution of the pressure, which is the same in any dimension. In the presence of a (positive) cosmological constant the condition of hydrostatic equilibrium imposes a lower limit on mass and matter density. We show how this limit depends on the number of dimensions and suggest that $\Lambda > 0$ is more effective in 4D than in higher dimensions. We obtain a general limit for the degree of compactification (gravitational potential on the boundary) of perfect fluid stars in $D$-dimensions. We argue that the effects of gravity are stronger in 4D than in any other number of dimensions. The generality of the results is also discussed

Abrupt shifts in phenology and vegetation productivity under climate extremes

©2015. American Geophysical Union. All Rights Reserved. Amplification of the hydrologic cycle as a consequence of global warming is predicted to increase climate variability and the frequency and severity of droughts. Recent large-scale drought and flooding over numerous continents provide unique opportunities to understand ecosystem responses to climatic extremes. In this study, we investigated the impacts of the early 21st century extreme hydroclimatic variations in southeastern Australia on phenology and vegetation productivity using Moderate Resolution Imaging Spectroradiometer Enhanced Vegetation Index and Standardized Precipitation-Evapotranspiration Index. Results revealed dramatic impacts of drought and wet extremes on vegetation dynamics, with abrupt between year changes in phenology. Drought resulted in widespread reductions or collapse in the normal patterns of seasonality such that in many cases there was no detectable phenological cycle during drought years. Across the full range of biomes examined, we found semiarid ecosystems to exhibit the largest sensitivity to hydroclimatic variations, exceeding that of arid and humid ecosystems. This result demonstrated the vulnerability of semiarid ecosystems to climatic extremes and potential loss of ecosystem resilience with future mega-drought events. A skewed distribution of hydroclimatic sensitivity with aridity is of global biogeochemical significance because it suggests that current drying trends in semiarid regions will reduce hydroclimatic sensitivity and suppress the large carbon sink that has been reported during recent wet periods (e.g., 2011 La Niña)

Probability-Based Dynamic Time Warping for Gesture Recognition on RGB-D Data

Dynamic Time Warping (DTW) is commonly used in gesture recognition tasks in order to tackle the temporal length variability of gestures. In the DTW framework, a set of gesture patterns are compared one by one to a maybe infinite test sequence, and a query gesture category is recognized if a warping cost below a certain threshold is found within the test sequence. Nevertheless, either taking one single sample per gesture category or a set of isolated samples may not encode the variability of such gesture category. In this paper, a probability-based DTW for gesture recognition is proposed. Different samples of the same gesture pattern obtained from RGB-Depth data are used to build a Gaussian-based probabilistic model of the gesture. Finally, the cost of DTW has been adapted accordingly to the new model. The proposed approach is tested in a challenging scenario, showing better performance of the probability-based DTW in comparison to state-of-the-art approaches for gesture recognition on RGB-D data

5-Dimensional Kaluza-Klein Theory with a Source

A free test particle in 5-dimensional Kaluza-Klein spacetime will show its electricity in the reduced 4-dimensional spacetime when it moves along the fifth dimension. In the light of this observation, we study the coupling of a 5-dimensional dust field with the Kaluza-Klein gravity. It turns out that the dust field can curve the 5-dimensional spacetime in such a way that it provides exactly the source of the electromagnetic field in the 4-dimensional spacetime after the dimensional reduction.Comment: 8 pages, three references adde

Accelerating Universe in a Big Bounce Model

Recent observations of Type Ia supernovae provide evidence for the acceleration of our universe, which leads to the possibility that the universe is entering an inflationary epoch. We simulate it under a ``big bounce'' model, which contains a time variable cosmological ``constant'' that is derived from a higher dimension and manifests itself in 4D spacetime as dark energy. By properly choosing the two arbitrary functions contained in the model, we obtain a simple exact solution in which the evolution of the universe is divided into several stages. Before the big bounce, the universe contracts from a $\Lambda$-dominated vacuum, and after the bounce, the universe expands. In the early time after the bounce, the expansion of the universe is decelerating. In the late time after the bounce, dark energy (i.e., the variable cosmological ``constant'') overtakes dark matter and baryons, and the expansion enters an accelerating stage. When time tends to infinity, the contribution of dark energy tends to two third of the total energy density of the universe, qualitatively in agreement with observations.Comment: Rextex4, 10 pages, 3 figures, revised and extended, accepted by Modern Physics Letter

Evolving weighting schemes for the Bag of Visual Words

The Bag of Visual Words (BoVW) is an established representation in computer vision. Taking inspiration from text mining, this representation has proved to be very effective in many domains. However, in most cases, standard term-weighting schemes are adopted (e.g., term-frequency or TF-IDF). It remains open the question of whether alternative weighting schemes could boost the performance of methods based on BoVW. More importantly, it is unknown whether it is possible to automatically learn and determine effective weighting schemes from scratch. This paper brings some light into both of these unknowns. On the one hand, we report an evaluation of the most common weighting schemes used in text mining, but rarely used in computer vision tasks. Besides, we propose an evolutionary algorithm capable of automatically learning weighting schemes for computer vision problems. We report empirical results of an extensive study in several computer vision problems. Results show the usefulness of the proposed method

ChaLearn LAP 2016: First Round Challenge on First Impressions - Dataset and Results

This paper summarizes the ChaLearn Looking at People 2016 First Impressions challenge data and results obtained by the teams in the first round of the competition. The goal of the competition was to automatically evaluate five “apparent” personality traits (the so-called “Big Five”) from videos of subjects speaking in front of a camera, by using human judgment. In this edition of the ChaLearn challenge, a novel data set consisting of 10,000 shorts clips from YouTube videos has been made publicly available. The ground truth for personality traits was obtained from workers of Amazon Mechanical Turk (AMT). To alleviate calibration problems between workers, we used pairwise comparisons between videos, and variable levels were reconstructed by fitting a Bradley-Terry-Luce model with maximum likelihood. The CodaLab open source platform was used for submission of predictions and scoring. The competition attracted, over a period of 2 months, 84 participants who are grouped in several teams. Nine teams entered the final phase. Despite the difficulty of the task, the teams made great advances in this round of the challenge

Deep Learning for Action and Gesture Recognition in Image Sequences: A Survey

Interest in automatic action and gesture recognition has grown considerably in the last few years. This is due in part to the large number of application domains for this type of technology. As in many other computer vision areas, deep learning based methods have quickly become a reference methodology for obtaining state-of-the-art performance in both tasks. This chapter is a survey of current deep learning based methodologies for action and gesture recognition in sequences of images. The survey reviews both fundamental and cutting edge methodologies reported in the last few years. We introduce a taxonomy that summarizes important aspects of deep learning for approaching both tasks. Details of the proposed architectures, fusion strategies, main datasets, and competitions are reviewed. Also, we summarize and discuss the main works proposed so far with particular interest on how they treat the temporal dimension of data, their highlighting features, and opportunities and challenges for future research. To the best of our knowledge this is the first survey in the topic. We foresee this survey will become a reference in this ever dynamic field of research