The Sun as a planet-host star : proxies from SDO images for HARPS radial-velocity variations

RDH gratefully acknowledges STFC studentship grant number ST/J500744/1, and a grant from the John Templeton Foundation. ACC and RF acknowledge support from STFC consolidated grants numbers ST/J001651/1 and ST/M001296/1. JL acknowledges support from NASA Origins of the Solar System grant No. NNX13AH79G and from STFC grant ST/M001296/1.The Sun is the only star whose surface can be directly resolved at high resolution, and therefore constitutes an excellent test case to explore the physical origin of stellar radial-velocity (RV) variability. We present HARPS observations of sunlight scattered off the bright asteroid 4/Vesta, from which we deduced the Sun's activity-driven RV variations. In parallel, the Helioseismic and Magnetic Imager instrument on board the Solar Dynamics Observatory provided us with simultaneous high spatial resolution magnetograms, Dopplergrams and continuum images of the Sun in the Fe i 6173 Å line. We determine the RV modulation arising from the suppression of granular blueshift in magnetized regions and the flux imbalance induced by dark spots and bright faculae. The rms velocity amplitudes of these contributions are 2.40 and 0.41 m s−1, respectively, which confirms that the inhibition of convection is the dominant source of activity-induced RV variations at play, in accordance with previous studies. We find the Doppler imbalances of spot and plage regions to be only weakly anticorrelated. Light curves can thus only give incomplete predictions of convective blueshift suppression. We must instead seek proxies that track the plage coverage on the visible stellar hemisphere directly. The chromospheric flux index R′HK derived from the HARPS spectra performs poorly in this respect, possibly because of the differences in limb brightening/darkening in the chromosphere and photosphere. We also find that the activity-driven RV variations of the Sun are strongly correlated with its full-disc magnetic flux density, which may become a useful proxy for activity-related RV noise.PostprintPeer reviewe

On deep generative modelling methods for protein-protein interaction

Proteins form the basis for almost all biological processes, identifying the interactions that proteins have with themselves, the environment, and each other are critical to understanding their biological function in an organism, and thus the impact of drugs designed to affect them. Consequently a significant body of research and development focuses on methods to analyse and predict protein structure and interactions. Due to the breadth of possible interactions and the complexity of structures, \textit{in sillico} methods are used to propose models of both interaction and structure that can then be verified experimentally. However the computational complexity of protein interaction means that full physical simulation of these processes requires exceptional computational resources and is often infeasible. Recent advances in deep generative modelling have shown promise in correctly capturing complex conditional distributions. These models derive their basic principles from statistical mechanics and thermodynamic modelling. While the learned functions of these methods are not guaranteed to be physically accurate, they result in a similar sampling process to that suggested by the thermodynamic principles of protein folding and interaction. However, limited research has been applied to extending these models to work over the space of 3D rotation, limiting their applicability to protein models. In this thesis we develop an accelerated sampling strategy for faster sampling of potential docking locations, we then address the rotational diffusion limitation by extending diffusion models to the space of $SO(3)$ and finally present a framework for the use of this rotational diffusion model to rigid docking of proteins

Numerical Non-Linear Modelling Algorithm Using Radial Kernels on Local Mesh Support

Estimation problems are frequent in several fields such as engineering, economics, and physics, etc. Linear and non-linear regression are powerful techniques based on optimizing an error defined over a dataset. Although they have a strong theoretical background, the need of supposing an analytical expression sometimes makes them impractical. Consequently, a group of other approaches and methodologies are available, from neural networks to random forest, etc. This work presents a new methodology to increase the number of available numerical techniques and corresponds to a natural evolution of the previous algorithms for regression based on finite elements developed by the authors improving the computational behavior and allowing the study of problems with a greater number of points. It possesses an interesting characteristic: Its direct and clear geometrical meaning. The modelling problem is presented from the point of view of the statistical analysis of the data noise considered as a random field. The goodness of fit of the generated models has been tested and compared with some other methodologies validating the results with some experimental campaigns obtained from bibliography in the engineering field, showing good approximation. In addition, a small variation on the data estimation algorithm allows studying overfitting in a model, that it is a problematic fact when numerical methods are used to model experimental values.This research has been partially funded by the Spanish Ministry of Science, Innovation and Universities, grant number RTI2018-101148-B-I00

Normalising least angle choice in Depthmap - and how it opens up new perspectives on the global and local analysis of city space

Depthmap embodies a theory of the city, as well as being a method for analysing the city. By solving outstanding problems of the normalisation of measures, most notably syntactic choice (mathematical betweenness), to permit comparison of cities of different sizes, we can gain new theoretical insights into their spatial structuring

Development of a smart grid for the proposed 33 KV ring main Distribution System in NIT Rourkela

The non-reliability of fossil fuels has forced the world to use energy efficiently. These days, it is being stressed to use the electrical power smartly so that energy does not go waste. And hence comes the concept of a Smart Grid. So it becomes necessary for reputed places of academics to develop the prototype of the same in their campus. National Institute of Technology (NIT) Rourkela intends to set up a 33KV Ring Main Distribution System including 33/0.433 KV substations in its campus. The present 11KV line will be discarded and replaced by the 33KV system. The main driving force behind this step by the management is to accommodate the stupendously increased power requirement of the institute. The above mentioned plan also includes, set up of Data Acquisition System (DAS) that intends to monitor the electrical equipment in the substations. This is being done not only to increase the accountability and reliability of the distribution system but also to encourage academic research in the distribution automation domain. All in all, an excellent step towards make the Grid, Smart. In this project work the focus is laid on getting load flow solution of the 33KV ring main system. Here the authors use a specialized algorithm for distribution network with high R/X value to obtain the load flow solution. Then using artificial neural networks computation, algorithms are implemented to do the load forecasting and dynamic tariff setting. At the end a Web Portal, the NITR e-Power Monitoring System is developed that will be an excellent interface to the public in general and will help the students of the institute to know their grid well. In short a conscious effort is put to make the grid more interactive

Graphical models for mixed data with categorical latent variables

Aquesta tesi pretén proporcionar una visió general dels models gràfics probabilístics en el context d'altres mètodes d'aprenentatge automàtic àmpliament utilitzats, i com aquests mètodes es poden formalitzar utilitzant models de independència condicionada i estadística algebraica. A base de comparar les Mixtures Gaussianes amb les Xarxes Neuronals interpretades com a models generatius, proposem un model gràfic per a dades mixtes (variables discretes i contítnues) que proporciona una base teòrica sòlida i una manera d'analitzar la Màquina de Boltzmann Restringida Gaussiana-Bernoulli. Això s'utilitza per modelar variables amb una distribució gaussiana condicionada, amb variables latents discretes. A més a més, aquesta tesi es centra en els procediments d'aprenentatge i mostreig, així com en l'ús de tècniques d'estadística algebraica per a descriure la expressivitat del model, fent servir models d'independència i de mixtura per a les varietats semi-algebraiques dels cumulants.Esta tesis pretende proporcionar una visión general de los modelos gráficos probabilísticos en el contexto de otros métodos de aprendizaje automático ampliamente utilizados, y cómo estos métodos pueden ser formalizados utilizando modelos de independencia condicional y estadística algebraica. Al comparar los populares modelos de Mezclas Gaussianas con las redes neuronales vistas como modelos generativos, proponemos un modelo gráfico para datos mixtos (variables discretas y continuas) que sirve de base teórica sólida a la vez que permite analizar la Máquina de Boltzmann Restringida Gaussiana-Bernoulli. Esto se utiliza para modelar variables con una distribución gaussiana condicionada, con variables latentes discretas. Además, esta tesis se explaya sobre los procedimientos de aprendizaje y muestreo del modelo, así como en el uso de técnicas de estadística algebraica para describir la expresividad del mismo, utilizando modelos de independencia y mezcla para las variedades semi-algebraicas de los cumulantes.This thesis aims to provide an overview of probabilistic graphical models in the context of other widely used machine learning methods, and how these methods can be formalised using conditional independence models and algebraic statistics. By comparing the extremely popular Gaussian Mixture Models and Neural Networks as generative graphical models, we are able to propose a graphical model for mixed data (discrete and continuous components) that provides a solid theoretical background and a way to analyse the Gaussian-Bernoulli Restricted Boltzmann Machine. This is used to model variables with a Gaussian conditional distribution, with discrete latent variables. On top of that, this thesis then goes into learning and sampling procedures as well as using techniques from algebraic statistics to further depict the expressibility of the model, making use of independence and mixture models for cumulant semi-algebraic varieties.Outgoin

Where creativity comes from: the social spaces of embodied minds

This paper explores creative design, social interaction and perception. It proposes that creativity at a social level is not a result of many individuals trying to be creative at a personal level, but occurs naturally in the social interaction between comparatively simple minds embodied in a complex world. Particle swarm algorithms can model group interaction in shared spaces, but design space is not necessarily one pre-defined space of set parameters on which everyone can agree, as individual minds are very different. A computational model is proposed that allows a similar swarm to occur between spaces of different description and even dimensionality. This paper explores creative design, social interaction and perception. It proposes that creativity at a social level is not a result of many individuals trying to be creative at a personal level, but occurs naturally in the social interaction between comparatively simple minds embodied in a complex world. Particle swarm algorithms can model group interaction in shared spaces, but design space is not necessarily one pre-defined space of set parameters on which everyone can agree, as individual minds are very different. A computational model is proposed that allows a similar swarm to occur between spaces of different description and even dimensionality