Turning noise into signal: learning from the scatter in the Hubble diagram

The supernova (SN) Hubble diagram residual contains valuable information on both the present matter power spectrum and its growth history. In this paper we show that this information can be retrieved with precision by combining both peculiar velocity and weak-lensing analysis on the data. To wit, peculiar velocity induces correlations on the nearby SN while lensing induces a non-Gaussian dispersion in faraway objects. We show that both effects have almost orthogonal degeneracies and discuss how they can be extracted simultaneously from the data. We analyze the JLA supernova catalog in a 14-dimensional parameter space, assuming a flexible growth-rate index $\gamma$. We arrive at the following marginalized constraints: $\sigma_8 = 0.65^{+0.23}_{-0.37}$ and $\gamma = 1.38^{+1.7}_{-0.65}$. Assuming instead GR as the correct gravitation theory (and thus $\gamma \equiv 0.55$), the constraints in $\sigma_8$ tighten further: $\sigma_8 = 0.40^{+0.21}_{-0.23}$. We show that these constraints complement well the ones obtained from other datasets and that they could improve substantially with more SNe.Comment: v2: Real data results corrected; forecast for future data added; discussion extended. v3: Improved discussion; matches published version. 8 figs 15 page

From MIDAS to deep learning: a comprehensive benchmark of big data economic forecasting models

Aquesta tesi proposa un nou punt de referència (benchmark) que inclou alguns dels models de pronòstic econòmic més populars. Entre altres característiques, avalua la capacitat de pronòstic d'un indicador basat en text, de models sofisticats de mostreig de dades mixtes (MIDAS) i de diverses arquitectures de xarxes neuronals. El benchmark mostra que, de tots els models considerats, un model de factor dinàmic simple d'un factor (DFM) que utilitza únicament variables econòmiques és el millor model per predir el creixement del producte interior brut. Per a la inflació, el millor model en el benchmark és el model ARIMA. No obstant això, aquest estudi troba que en períodes d'alta incertesa com 2020-2022, les previsions a llarg termini milloren en utilitzar DFMs més complexos per al producte interior brut i arquitectures de xarxes neuronals apropiades per a la inflació.Esta tesis propone un nuevo punto de referencia (benchmark) que incluye algunos de los modelos de pronóstico económico más populares. Entre otras características, evalúa la capacidad de pronóstico de un indicador basado en texto, de modelos sofisticados de muestreo de datos mixtos (MIDAS) y de varias arquitecturas de redes neuronales. El benchmark muestra que, de todos los modelos considerados, un modelo de factor dinámico simple de un factor (DFM) que utiliza únicamente variables económicas es el mejor modelo para predecir el crecimiento del producto interno bruto. Para la inflación, el mejor modelo en el benchmark es el modelo ARIMA. Sin embargo, este estudio encuentra que en períodos de alta incertidumbre como 2020-2022, las previsiones a largo plazo mejoran al usar DFMs más complejos para el producto interno bruto y arquitecturas de redes neuronales apropiadas para la inflación.This thesis proposes a new benchmark including some of the most popular economic forecasting models. Among other features, it tests the forecasting ability of a text-based indicator, of sophisticated mixed data sampling (MIDAS) models and of several neural network architectures. The benchmark shows that, of all the models considered, a simple one-factor dynamic factor model (DFM) using only economic variables is the best model to predict gross domestic product growth. For inflation, the best model in the benchmark is the ARIMA model. However, this study finds that in periods with high uncertainty like 2020-2022, long term forecasts improve when using more complex DFMs for gross domestic product, and appropriate neural network architectures for inflation

Evaluation and Attitude towards Homosexuality in the Irish Context: A Corpus-assisted Discourse Analysis of APPRAISAL Patterns in 2008 Newspaper Articles

The analysis of newspaper discourse offers valuable insights into how society represents or misrepresents certain social participants and their actions. In view of the bias claimed to exist in journalistic prose (Bednarek, 2006; White, 2006), it is not uncommon to find evidence of the mistreatment directed towards particular minorities (Baker et al., 2008; Fowler, 1991). In this paper, the ideological stance associated with a specific minority group (i.e. homosexuals) is brought to the forefront in 2008, when Ireland’s vibrant economy took a dramatic turn for the worse. Incidentally, this coincided with homosexuality taking centre stage in Ireland’s political agenda, as 2008 marked the final stage of the long drawn-out debate on the Civil Partnership Bill. This paper is designed to offer insights into how evaluative language may reflect the mentality of Irish society in relation to the LGBT community. Martin & White’s (2005) appraisal theory is highly relevant and applicable for this purpose, as it covers the idea of social esteem, social sanction, personal attitude and appreciation, which can be powerful indicators of a society’s take on current affairs. The methodology employed here is that of corpus-assisted discourse analysis (Stubbs, 1996). The dataset comprises over 200,000 words taken from three different newspapers: Two tabloids and one broadsheet. Our dataset is annotated on the basis of the categories in Martin & White’s (2005) subsystem of attitude (affect, judgement and appreciation). The application of this taxonomy uncovers a remarkably negative stance towards the Irish LGBT community in the sample analysed. This is particularly evident in the predominance of evaluative and emotive language associated with the categories of negative judgement and affect. Previous research on the same sample, looking at metaphor, transitivity and modality (e.g. Bartley & Hidalgo-Tenorio, 2015), has cast light on how homosexuals are repeatedly discriminated against and vilified in the Irish public arena. This study confirms the results so far obtained through the analysis of evaluative language

Modeling tumorspheres reveals cancer stem cell niche building and plasticity

Cancer stem cells have been shown to be critical to the development of a variety of solid cancers. The precise interplay mechanisms between cancer stem cells and the rest of a tissue are still not elucidated. To shed light on the interactions between stem and non-stem cancer cell populations we develop a two-population mathematical model, which is suitable to describe tumorsphere growth. Both interspecific and intraspecific interactions, mediated by the microenvironment, are included. We show that there is a tipping point, characterized by a transcritical bifurcation, where a purely non-stem cell attractor is replaced by a new attractor that contains both stem and differentiated cancer cells. The model is then applied to describe the outcome of a recent experiment. This description reveals that, while the intraspecific interactions are inhibitory, the interspecific interactions stimulate growth. This can be understood in terms of stem cells needing differentiated cells to reinforce their niches, and phenotypic plasticity favoring the de-differentiation of differentiated cells into cancer stem cells. We posit that this is a consequence of the deregulation of the quorum sensing that maintains homeostasis in healthy tissues.Fil: Benitez, Lucia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; ArgentinaFil: Barberis, Lucas Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; ArgentinaFil: Condat, Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentin

Art for Goodness Sake: A Chestertonian Critique of Art for Art’s Sake

Many Christian thinkers have embraced the notion “art for art’s sake.” Chesterton did not. To the contrary, he saw such an idea as deeply problematic for a Christian aesthetic. In the following article, I will explore some philosophical aspects of the “art for art’s sake” movement and then explain why Chesterton parted company with it

Event-Triggered Control for a Three DoF Manipulator Robot

In the classical approach of Time-Triggered Control (TTC),  the control signal is updated  at  each  sampling  time  as  well  as  the  system  states  to  be  controlled,  which could imply a redundancy in the computational calculation as well as in the transfer of information in the regulation objective. On the other hand, the Event-Triggered Control (ETC) approach performs the same task in an asynchronous way, i.e,, it only updates the control signal when a performance requirement is violated and the states are updated at each sampling time. This reduces the amount of computational calculation without affecting the performance of the closed loop system. For this reason, in the present work the ETC is developed for the stabilization of a manipulator robot with three Degree of Freedom (DoF) in the joint space where a Lyapunov Control Function (LCF) is proposed to formulate the event function (e¯), which indicates whether or not  is required  the  control  signal  updating.  Simulation results show the reduction of the updates compared with a TTC

Inferring origin–destination trip matrices from aggregate volumes on groups of links: a case study using volumes inferred from mobile phone data

The origin–destination matrix is an important source of information describing transport demand in a region. Most commonly used methods for matrix estimation use link volumes collected on a subset of links in order to update an existing matrix. Traditional volume data collection methods have significant shortcomings because of the high costs involved and the fact that detectors only provide status information at specified locations in the network. Better matrix estimates can be obtained when information is available about the overall distribution of traffic through time and space. Other existing technologies are not used in matrix estimation methods because they collect volume data aggregated on groups of links, rather than on single links. That is the case of mobile systems. Mobile phones sometimes cannot provide location accuracy for estimating flows on single links but do so on groups of links; in contrast, data can be acquired over a wider coverage without additional costs. This paper presents a methodology adapted to the concept of volume aggregated on groups of links in order to use any available volume data source in traditional matrix estimation methodologies. To calculate volume data, we have used a model that has had promising results in transforming phone call data into traffic movement data. The proposed methodology using vehicle volumes obtained by such a model is applied over a large real network as a case study. The experimental results reveal the efficiency and consistency of the solution proposed, making the alternative attractive for practical applications.Spanish Ministry of Science through R&D National Programmes (TRA2005-09138, ENE2008-05552)Vodafone Spain through the Minerva Project (1C-021