215,961 research outputs found

    GOLDSTONE THEOREM AND JAHN-TELLER EFFECT

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    The Goldstone theorem requires that a many-body system with broken symmetry has an excitation branch, whose frequency tends to zero in the limit of infinite wavelength. We treat a system where the broken symmetry comes from the terms which give rise to the Jahn-Teller effect. Both the excitation branches we discuss in detail have finite frequencies at infinite wavelength when there is no Jahn-Teller term; the introduction of this term forces one branch to have zero frequency at infinite wavelength, in agreement with the Goldstone theorem The main point of this paper is this striking illustration of Goldstone's conjecture. Some of the simpler features of the excitation branches are discussed, they do not appear to have been treated in detail in the literature. Systems of ions in twofold degenerate E ground states may exhibit such excitations, which will have a characteristic velocity considerably less than that of sound

    Sex differences in the relationship between sensation seeking, trait emotional intelligence and delinquent behaviour

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    Levels of sensation seeking peak in adolescence and there is a well-documented association with delinquency and other risk taking behaviours. The present study investigated the potential moderating effect of trait Emotional Intelligence (EI) on this relationship. Trait EI encompasses high levels of empathy and emotion regulation and is associated with positive outcomes and wellbeing. 96 young adults (48 female; overall Mage = 19.76) completed measures of sensation seeking, trait EI and self-reported delinquent behaviours since age 12. Results indicated that sensation seeking and frequency of delinquent behaviours were positively associated, but this effect was moderated by trait EI for male participants - those with lower trait EI showed a greater increase in delinquency in line with a rise in sensation seeking. No moderation effect was observed for females, and females with higher levels of trait EI reported more delinquent behaviours. The results are discussed in terms of the protective role of trait EI in supporting self-regulation and whether, for some females, high levels of EI might predispose to antisocial relational behaviours

    The Cone Dysfunction Syndromes

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    The cone dysfunction syndromes are a heterogeneous group of inherited, predominantly stationary retinal disorders characterised by reduced central vision, and varying degrees of colour vision abnormalities, nystagmus and photophobia. This review details the following conditions: complete and incomplete achromatopsia, blue-cone monochromatism, oligocone trichromacy, bradyopsia, and Bornholm eye disease. We describe the clinical, psychophysical, electrophysiological and imaging findings that are characteristic to each condition, in order to aid their accurate diagnosis, as well as highlight some classically held notions about these diseases that have come to be challenged over recent years. The latest data regarding the genetic aetiology and pathological changes observed in the cone dysfunction syndromes are discussed, and, where relevant, translational avenues of research, including completed and anticipated interventional clinical trials, for some of the diseases described herein will be presented. Finally, we briefly review the current management of these disorders

    Transition Property for α\alpha-Power Free Languages with α2\alpha\geq 2 and k3k\geq 3 Letters

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    In 1985, Restivo and Salemi presented a list of five problems concerning power free languages. Problem 44 states: Given α\alpha-power-free words uu and vv, decide whether there is a transition from uu to vv. Problem 55 states: Given α\alpha-power-free words uu and vv, find a transition word ww, if it exists. Let Σk\Sigma_k denote an alphabet with kk letters. Let Lk,αL_{k,\alpha} denote the α\alpha-power free language over the alphabet Σk\Sigma_k, where α\alpha is a rational number or a rational "number with ++". If α\alpha is a "number with ++" then suppose k3k\geq 3 and α2\alpha\geq 2. If α\alpha is "only" a number then suppose k=3k=3 and α>2\alpha>2 or k>3k>3 and α2\alpha\geq 2. We show that: If uLk,αu\in L_{k,\alpha} is a right extendable word in Lk,αL_{k,\alpha} and vLk,αv\in L_{k,\alpha} is a left extendable word in Lk,αL_{k,\alpha} then there is a (transition) word ww such that uwvLk,αuwv\in L_{k,\alpha}. We also show a construction of the word ww

    THE SPECTROSCOPY OF CRYSTAL DEFECTS - A COMPENDIUM OF DEFECT NOMENCLATURE

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    The authors bring together tables of current defect nomenclature and a summary of the rules actually practised (rather than idealised schemes) in choosing such labels for signals obtained with a range of spectroscopies. As well as providing a source of reference for the user lost in a maze of labels, the compilation also indicates parallels between similar defect species in very different systems (e.g. ice and quartz), even though the relationships may be far from obvious from the labels. The systems considered are all non-metals, namely ionic crystals (including oxides), silica, semiconductors (e.g. III-V and tetrahedrally coordinated II-VI), valence crystals (e.g. diamond, c-Si, a-Si) and other special hosts like ice and conducting polymers

    Data-efficient learning of feedback policies from image pixels using deep dynamical models

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    Data-efficient reinforcement learning (RL) in continuous state-action spaces using very high-dimensional observations remains a key challenge in developing fully autonomous systems. We consider a particularly important instance of this challenge, the pixels-to-torques problem, where an RL agent learns a closed-loop control policy ( torques ) from pixel information only. We introduce a data-efficient, model-based reinforcement learning algorithm that learns such a closed-loop policy directly from pixel information. The key ingredient is a deep dynamical model for learning a low-dimensional feature embedding of images jointly with a predictive model in this low-dimensional feature space. Joint learning is crucial for long-term predictions, which lie at the core of the adaptive nonlinear model predictive control strategy that we use for closed-loop control. Compared to state-of-the-art RL methods for continuous states and actions, our approach learns quickly, scales to high-dimensional state spaces, is lightweight and an important step toward fully autonomous end-to-end learning from pixels to torques
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