Dirac electrons and domain walls: a realization in junctions of ferromagnets and topological insulators

We study a system of Dirac electrons with finite density of charge carriers coupled to an external electromagnetic field in two spatial dimensions, with a domain wall (DW) mass term. The interface between a thin-film ferromagnet and a three-dimensional topological insulator provides a condensed-matter realization of this model, when an out-of-plane domain wall magnetization is coupled to the TI surface states. We show how, for films with very weak intrinsic in-plane anisotropies, the torque generated by the edge electronic current flowing along the DW competes with an effective in-plane anisotropy energy, induced by quantum fluctuations of the chiral electrons bound to the wall, in a mission to drive the internal angle of the DW from a Bloch configuration towards a N\'eel configuration. Both the edge current and the induced anisotropy contribute to stabilize the internal angle, so that for weak intrinsic in-plane anisotropies DW motion is still possible without suffering from an extremely early Walker breakdown.Comment: 18 pages, 3 figure

Chirality-dependent transmission of spin waves through domain walls

Spin-wave technology (magnonics) has the potential to further reduce the size and energy consumption of information processing devices. In the submicrometer regime (exchange spin waves), topological defects such as domain walls may constitute active elements to manipulate spin waves and perform logic operations. We predict that spin waves that pass through a domain wall in an ultrathin perpendicular-anisotropy film experience a phase shift that depends on the orientation of the domain wall (chirality). The effect, which is absent in bulk materials, originates from the interfacial Dzyaloshinskii-Moriya interaction and can be interpreted as a geometric phase. We demonstrate analytically and by means of micromagnetic simulations that the phase shift is strong enough to switch between constructive and destructive interference. The two chirality states of the domain wall may serve as a memory bit or spin-wave switch in magnonic devices.Comment: 11 pages, 10 figures (incl. supp. mat.); Phys. Rev. Lett. (accepted

Influencia de los parámetros nutricionalesen cirugía de cadera

Bajo la hipótesis de que los parámetros nutricionales influyen en el desarrollo perioperatorio de la cirugía de cadera realizamos un estudio prospectivo observacional de tipo clínico sobre 100 pacientes con una edad media de 76 años. Realizamos una evaluación clínica al ingreso: talla, peso, pliegue cutáneo, circunferencia braquial y muscular. Medición de proteínas plasmáticas y linfocitos totales al ingreso, en el postoperatorio y cada 7 días. Definimos los pacientes según los valores nutricionales analíticos y clínicos. El 51% de nuestros pacientes presentaban desnutrición caloricoproteica. La media transfusional en malnutridos fue 600 c.c. frente a 350 c.c. del grupo nutrido (p < 0,05). El 73% de las complicaciones se presentaron en pacientes malnutridos. No hallamos diferencias en los períodos de sedestación y deambulación, aunque sí en la estancia hospitalaria, 15 días frente a 18,5 días (p < 0,01). La prealbúmina mostró variaciones de forma más precoz ante el acto quirúrgico.A prospective observation was conducted with the hypothesis that the nutritional situation has influence in postoperative development of hip surgery [n = 100; age 76 years (55-95)]. A nutritional evaluation was performed before surgery through the evolution of height, body weight, skin fold and braquial and muscular circunference. Serie proteins and lymphocytes levels were measured during hospital admission, in postoperative and each seven days. A 51% of patients showed protein-energy malnutrition (PEM). Transfusional haematic requirements were 600 c c . in malnutrition patients and 350 c.c. in the rest (p < 0.05). Complications appeared in PEM patients in 73% cases. The hospital stay was longer in those patients who presented PEM upon admittance (p < 0.01). Postoperatively, the prealbumin was the protein whose serie level had faster changes

ASLP-MULAN: Audio speech and language processing for multimedia analytics

Our intention is generating the right mixture of audio, speech and language technologies with big data ones. Some audio, speech and language automatic technologies are available or gaining enough degree of maturity as to be able to help to this objective: automatic speech transcription, query by spoken example, spoken information retrieval, natural language processing, unstructured multimedia contents transcription and description, multimedia files summarization, spoken emotion detection and sentiment analysis, speech and text understanding, etc. They seem to be worthwhile to be joined and put at work on automatically captured data streams coming from several sources of information like YouTube, Facebook, Twitter, online newspapers, web search engines, etc. to automatically generate reports that include both scientific based scores and subjective but relevant summarized statements on the tendency analysis and the perceived satisfaction of a product, a company or another entity by the general population

AMIC: Affective multimedia analytics with inclusive and natural communication

Traditionally, textual content has been the main source of information extraction and indexing, and other technologies that are capable of extracting information from the audio and video of multimedia documents have joined later. Other major axis of analysis is the emotional and affective aspect intrinsic in human communication. This information of emotions, stances, preferences, figurative language, irony, sarcasm, etc. is fundamental and irreplaceable for a complete understanding of the content in conversations, speeches, debates, discussions, etc. The objective of this project is focused on advancing, developing and improving speech and language technologies as well as image and video technologies in the analysis of multimedia content adding to this analysis the extraction of affective-emotional information. As additional steps forward, we will advance in the methodologies and ways for presenting the information to the user, working on technologies for language simplification, automatic reports and summary generation, emotional speech synthesis and natural and inclusive interaction

Towards an unsupervised speaking style voice building framework: multi-style speaker diarization

Current text-to-speech systems are developed using studio-recorded speech in a neutral style or based on acted emotions. However, the proliferation of media sharing sites would allow developing a new generation of speech-based systems which could cope with spontaneous and styled speech. This paper proposes an architecture to deal with realistic recordings and carries out some experiments on unsupervised speaker diarization. In order to maximize the speaker purity of the clusters while keeping a high speaker coverage, the paper evaluates the F-measure of a diarization module, achieving high scores (>85%) especially when the clusters are longer than 30 seconds, even for the more spontaneous and expressive styles (such as talk shows or sports)

Proposing a speech to gesture translation architecture for Spanish deaf people.

This article describes an architecture for translating speech into Spanish Sign Language (SSL). The architecture proposed is made up of four modules: speech recognizer, semantic analysis, gesture sequence generation and gesture playing. For the speech recognizer and the semantic analysis modules, we use software developed by IBM and CSLR (Center for Spoken Language Research at University of Colorado), respectively. Gesture sequence generation and gesture animation are the modules on which we have focused our main effort. Gesture sequence generation uses semantic concepts (obtained from the semantic analysis) associating them with several SSL gestures. This association is carried out based on a number of generation rules. For gesture animation, we have developed an animated agent (virtual representation of a human person) and a strategy for reducing the effort in gesture animation. This strategy consists of making the system automatically generate all agent positions necessary for the gesture animation. In this process, the system uses a few main agent positions (two or three per second) and some interpolation strategies, both issues previously generated by the service developer (the person who adapts the architecture proposed in this paper to a specific domain). Related to this module, we propose a distance between agent positions and a measure of gesture complexity. This measure can be used to analyze the gesture perception versus its complexity. With the architecture proposed, we are not trying to build a domain independent translator but a system able to translate speech utterances into gesture sequences in a restricted domain: railway, flights or weather information

On the dynamic adaptation of language models based on dialogue information

We present an approach to adapt dynamically the language models (LMs) used by a speech recognizer that is part of a spoken dialogue system. We have developed a grammar generation strategy that automatically adapts the LMs using the semantic information that the user provides (represented as dialogue concepts), together with the information regarding the intentions of the speaker (inferred by the dialogue manager, and represented as dialogue goals). We carry out the adaptation as a linear interpolation between a background LM, and one or more of the LMs associated to the dialogue elements (concepts or goals) addressed by the user. The interpolation weights between those models are automatically estimated on each dialogue turn, using measures such as the posterior probabilities of concepts and goals, estimated as part of the inference procedure to determine the actions to be carried out. We propose two approaches to handle the LMs related to concepts and goals. Whereas in the first one we estimate a LM for each one of them, in the second one we apply several clustering strategies to group together those elements that share some common properties, and estimate a LM for each cluster. Our evaluation shows how the system can estimate a dynamic model adapted to each dialogue turn, which helps to improve the performance of the speech recognition (up to a 14.82% of relative improvement), which leads to an improvement in both the language understanding and the dialogue management tasks