Seeking topological phase transition applying pressure to Ag3AuSe2 and Ag3Te2Au

[EN] For many years the Solid State Physics has contributed significantly to the modern society, giving a profound understanding of how semiconductors behave, which led to a revolution with the improvement of transistors. Furthermore, the striking advances in computation have assisted to solve numerically heavy calculus, such as the ones arisen from Quantum Theory, which includes calculations regarding crystals. Recently, a whole new field of Solid State Physics has arisen, the Topological Materials, but we will focus on the Topological Insulators (TI). A topological insulator is a material with non-trivial symmetry-protected topological order that behaves as an insulator in its interior but whose surface contains conducting states, meaning that electrons can only move along the surface of the material. However, having a conducting surface is not unique to topological insulators, since ordinary band insulators can also support conductive surface states. What makes TI special is that their surface states are symmetry-protected by particle number conservation and time-reversal symmetry. Topological insulators are characterized by an index (known as Z2 topological invariants) similar to the genus1 in topology. As long as time-reversal symmetry is preserved, in other words, as long as there is no magnetism, the Z2 index cannot change by small perturbations and the conducting states at the surface are symmetry-protected. A brand new way to study TI is presented in the literature [1], where they use Group Theory in order to determine the topology of crystals. One important property of these topological invariants is that they are robust against perturbations. In a few years, different phases displaying topological properties have been found: topological insulators, Weyl semimetals and non symmorphic materials whose electric properties are protected by time reversal symmetry or some crystalline symmetry. A Weyl node is basically a band crossing close to the Fermi level, where the dispersion is linear and is protected by time reversal or inversion symmetry. Consequently, the charge carriers, responsible for electrical conduction, can be considered as massless fermions, supported theoretically by the Dirac equation. The main objective of this project is to seek topological materials, for this purpose we will study two crystals, Ag3AuSe2 and Ag3Te2Au. These materials are trivial insulators under zero pressure, therefore, we will apply pressure to each material and calculate their band structure, with the information obtained from those calculations we will be able to determine if the material is topological or not, as we will explain in section B. In this dossier we will start introducing topological matter, then we will explain some basics about the Density Functional Theory (DFT), and we will define some important concepts about topology, which are related with the topic of this project, such as representations and irreducible representations. Next we will expose some general properties of the materials we are studying (symmetry group, lattice parameters, band structure...). After that we will apply pressure to the materials and observe how the band structure changes, yielding to new topological properties. Finally, we will present some conclusions about the results we obtain

Estudio de diferentes modelos de redes neuronales para el desarrollo de un clasificador de frases

[ES] La Inteligencia Artificial (IA) está en auge, gracias al avance de los ordenadores, capaces de realizar un mayor número de cálculos de forma más rápida, así como también gracias a los nuevos métodos y modelos para desarrollar redes neuronales. Más aún, dentro de la IA existen también diversos subgrupos, ya que su versatilidad permite realizar diferentes tareas: análisis de imágenes, reconocimiento de voz, sistemas de diálogo. . . En el caso del análisis de imágenes, fácilmente nos viene a la cabeza el reconocimiento facial que realizan los teléfonos móviles. Aunque poco a poco se van desarrollando nuevas IA-es capaces de localizar y reconocer diferentes objetos en una imagen. Como curiosidad, señalamos el software Google Deep Dream [1], el cual podemos entrenar para que busque cosas concretas en una imagen, es decir, podríamos entrenarlo para que buscara formas de ojos en una imagen, para que después situara ojos en los lugares de la imagen donde las haya ’visualizado’. Los reconocedores de voz también están muy arraigados en los teléfonos móviles, entre otras aplicaciones. Los primeros sistemas, creados en 1952, solo eran capaces de detectar la voz de una sola persona y solamente reconocían 10 palabras individualmente. A principios de los 70, la Agencia de Proyectos de Investigación Avanzada del Departamento de Defensa (DARPA), junto con la que participaron empresas como IBM, desarrollo un sistema que permitía reconocer hasta 1.000 palabras distintas. A partir de los años 80, se crearon sistemas que podían reconocer hasta 20.000 palabras, aunque solamente podían distinguir las palabras de forma individual. Hoy en día, los reconocedores de voz son capaces de identificar una gran variedad de palabras, de voces distintas y reconocerlas en conjuntos. Por último, están los sistemas de diálogo, en los cuales nos centraremos en este trabajo. Más concretamente, nuestro trabajo se basará en desarrollar una red neuronal que analice semánticamente las frases de entrada (2.1), es decir, hará el proceso de NLU (Natural Language Understanding). Para ello, dispondremos del corpus proporcionado por el Proyecto Europeo EMPATHIC [2], el cual analizaremos en el capítulo 5. Este proyecto tiene como objetivo desarrollar un avatar para asistir a personas mayores, y hacer coaching en el ámbito de la salud física y psíquica de la persona, buscando siempre una conversación que exponga los problemas o malestares del usuario y poder motivarlo a mejorar su situación. Para mantener una conversación con el usuario se deben llevar a cabo estos procesos: transcripción del mensaje de voz, comprensión del texto (NLU), obtención de una representación del texto de salida1 y por ´ultimo creación y devolución la respuesta optima

Structural Restricted Boltzmann Machine for image denoising and classification

Restricted Boltzmann Machines are generative models that consist of a layer of hidden variables connected to another layer of visible units, and they are used to model the distribution over visible variables. In order to gain a higher representability power, many hidden units are commonly used, which, in combination with a large number of visible units, leads to a high number of trainable parameters. In this work we introduce the Structural Restricted Boltzmann Machine model, which taking advantage of the structure of the data in hand, constrains connections of hidden units to subsets of visible units in order to reduce significantly the number of trainable parameters, without compromising performance. As a possible area of application, we focus on image modelling. Based on the nature of the images, the structure of the connections is given in terms of spatial neighbourhoods over the pixels of the image that constitute the visible variables of the model. We conduct extensive experiments on various image domains. Image denoising is evaluated with corrupted images from the MNIST dataset. The generative power of our models is compared to vanilla RBMs, as well as their classification performance, which is assessed with five different image domains. Results show that our proposed model has a faster and more stable training, while also obtaining better results compared to an RBM with no constrained connections between its visible and hidden units

Spectral and optical properties of Ag3Au(Se2,Te2) and dark matterdetection

Paper • The following article is Open access Spectral and optical properties of Ag3Au(Se2,Te2) and dark matter detection M-Á Sánchez-Martínez6,1 , I Robredo6,2,3, A Bidaurrazaga3, A Bergara2,3,4, F de Juan2,5, A G Grushin1 and M G Vergniory7,2,5 Published 29 October 2019 • © 2019 The Author(s). Published by IOP Publishing Ltd Journal of Physics: Materials, Volume 3, Number 1 Focus on Topological Matter Citation M-Á Sánchez-Martínez et al 2020 J. Phys. Mater. 3 014001 Download Article PDF Figures References 692 Total downloads 4 4 total citations on Dimensions. Turn on MathJax Share this article Share this content via email Share on Facebook Share on Twitter Share on Google+ Share on Mendeley Article information Abstract In this work we study the electronic structure of ${\mathrm{Ag}}_{3}{\mathrm{AuSe}}_{2}$ and ${\mathrm{Ag}}_{3}{\mathrm{AuTe}}_{2}$, two chiral insulators whose gap can be tuned through small changes in the lattice parameter by applying hydrostatic pressure or choosing different growth protocols. Based on first principles calculations we compute their band structure for different values of the lattice parameters and show that while ${\mathrm{Ag}}_{3}{\mathrm{AuSe}}_{2}$ retains its direct narrow gap at the Γ point, ${\mathrm{Ag}}_{3}{\mathrm{AuTe}}_{2}$ can turn into a metal. Focusing on ${\mathrm{Ag}}_{3}{\mathrm{AuSe}}_{2}$ we derive a low energy model around Γ using group theory, which we use to calculate the optical conductivity for different values of the lattice constant. We discuss our results in the context of detection of light dark matter particles, which have masses of the order of a keV, and conclude that ${\mathrm{Ag}}_{3}{\mathrm{AuSe}}_{2}$ satisfies three important requirements for a suitable detector: small Fermi velocities, meV band gap, and low photon screening. Our work motivates the growth of high-quality and large samples of ${\mathrm{Ag}}_{3}{\mathrm{AuSe}}_{2}$ to be used as target materials in dark matter detectors.We acknowledge support from the European Union's Horizon 2020 research and innovation programme under the Marie-Sklodowska-Curie grant agreement No. 754303 and the GreQuE Cofund programme (MASM). AGG is also supported by the ANR under the grant ANR-18-CE30-0001-01 and the European FET-OPEN SCHINES project No. 829044. MGV acknowledges the IS2016-75862-P national project of the Spanish MINECO. AB acknowledges financial support from the Spanish Ministry of Economy and Competitiveness (FIS2016-76617-P) and the Department of Education, Universities and Research of the Basque Government and the University of the Basque Country (IT756-13)

Seeking topological phase transition applying pressure to Ag3AuSe2 and Ag3Te2Au

[EN] For many years the Solid State Physics has contributed significantly to the modern society, giving a profound understanding of how semiconductors behave, which led to a revolution with the improvement of transistors. Furthermore, the striking advances in computation have assisted to solve numerically heavy calculus, such as the ones arisen from Quantum Theory, which includes calculations regarding crystals. Recently, a whole new field of Solid State Physics has arisen, the Topological Materials, but we will focus on the Topological Insulators (TI). A topological insulator is a material with non-trivial symmetry-protected topological order that behaves as an insulator in its interior but whose surface contains conducting states, meaning that electrons can only move along the surface of the material. However, having a conducting surface is not unique to topological insulators, since ordinary band insulators can also support conductive surface states. What makes TI special is that their surface states are symmetry-protected by particle number conservation and time-reversal symmetry. Topological insulators are characterized by an index (known as Z2 topological invariants) similar to the genus1 in topology. As long as time-reversal symmetry is preserved, in other words, as long as there is no magnetism, the Z2 index cannot change by small perturbations and the conducting states at the surface are symmetry-protected. A brand new way to study TI is presented in the literature [1], where they use Group Theory in order to determine the topology of crystals. One important property of these topological invariants is that they are robust against perturbations. In a few years, different phases displaying topological properties have been found: topological insulators, Weyl semimetals and non symmorphic materials whose electric properties are protected by time reversal symmetry or some crystalline symmetry. A Weyl node is basically a band crossing close to the Fermi level, where the dispersion is linear and is protected by time reversal or inversion symmetry. Consequently, the charge carriers, responsible for electrical conduction, can be considered as massless fermions, supported theoretically by the Dirac equation. The main objective of this project is to seek topological materials, for this purpose we will study two crystals, Ag3AuSe2 and Ag3Te2Au. These materials are trivial insulators under zero pressure, therefore, we will apply pressure to each material and calculate their band structure, with the information obtained from those calculations we will be able to determine if the material is topological or not, as we will explain in section B. In this dossier we will start introducing topological matter, then we will explain some basics about the Density Functional Theory (DFT), and we will define some important concepts about topology, which are related with the topic of this project, such as representations and irreducible representations. Next we will expose some general properties of the materials we are studying (symmetry group, lattice parameters, band structure...). After that we will apply pressure to the materials and observe how the band structure changes, yielding to new topological properties. Finally, we will present some conclusions about the results we obtain

Estudio de diferentes modelos de redes neuronales para el desarrollo de un clasificador de frases

[ES] La Inteligencia Artificial (IA) está en auge, gracias al avance de los ordenadores, capaces de realizar un mayor número de cálculos de forma más rápida, así como también gracias a los nuevos métodos y modelos para desarrollar redes neuronales. Más aún, dentro de la IA existen también diversos subgrupos, ya que su versatilidad permite realizar diferentes tareas: análisis de imágenes, reconocimiento de voz, sistemas de diálogo. . . En el caso del análisis de imágenes, fácilmente nos viene a la cabeza el reconocimiento facial que realizan los teléfonos móviles. Aunque poco a poco se van desarrollando nuevas IA-es capaces de localizar y reconocer diferentes objetos en una imagen. Como curiosidad, señalamos el software Google Deep Dream [1], el cual podemos entrenar para que busque cosas concretas en una imagen, es decir, podríamos entrenarlo para que buscara formas de ojos en una imagen, para que después situara ojos en los lugares de la imagen donde las haya ’visualizado’. Los reconocedores de voz también están muy arraigados en los teléfonos móviles, entre otras aplicaciones. Los primeros sistemas, creados en 1952, solo eran capaces de detectar la voz de una sola persona y solamente reconocían 10 palabras individualmente. A principios de los 70, la Agencia de Proyectos de Investigación Avanzada del Departamento de Defensa (DARPA), junto con la que participaron empresas como IBM, desarrollo un sistema que permitía reconocer hasta 1.000 palabras distintas. A partir de los años 80, se crearon sistemas que podían reconocer hasta 20.000 palabras, aunque solamente podían distinguir las palabras de forma individual. Hoy en día, los reconocedores de voz son capaces de identificar una gran variedad de palabras, de voces distintas y reconocerlas en conjuntos. Por último, están los sistemas de diálogo, en los cuales nos centraremos en este trabajo. Más concretamente, nuestro trabajo se basará en desarrollar una red neuronal que analice semánticamente las frases de entrada (2.1), es decir, hará el proceso de NLU (Natural Language Understanding). Para ello, dispondremos del corpus proporcionado por el Proyecto Europeo EMPATHIC [2], el cual analizaremos en el capítulo 5. Este proyecto tiene como objetivo desarrollar un avatar para asistir a personas mayores, y hacer coaching en el ámbito de la salud física y psíquica de la persona, buscando siempre una conversación que exponga los problemas o malestares del usuario y poder motivarlo a mejorar su situación. Para mantener una conversación con el usuario se deben llevar a cabo estos procesos: transcripción del mensaje de voz, comprensión del texto (NLU), obtención de una representación del texto de salida1 y por ´ultimo creación y devolución la respuesta optima