No Arabic abstract
The Coulomb repulsion, impeding electrons motion, has an important impact on the charge dynamics. It mainly causes a reduction of the effective metallic Drude weight (proportional to the so-called optical kinetic energy), encountered in the optical conductivity, with respect to the expectation within the nearly-free electron limit (defining the so-called band kinetic energy), as evinced from band-structure theory. In principle, the ratio between the optical and band kinetic energy allows defining the degree of electronic correlations. Through spectral weight arguments based on the excitation spectrum, we provide an experimental tool, free from any theoretical or band-structure based assumptions, in order to estimate the degree of electronic correlations in several systems. We first address the novel iron-pnictide superconductors, which serve to set the stage for our approach. We then revisit a large variety of materials, ranging from superconductors, to Kondo-like systems as well as materials close to the Mott-insulating state. As comparison we also tackle materials, where the electron-phonon coupling dominates. We establish a direct relationship between the strength of interaction and the resulting reduction of the optical kinetic energy of the itinerant charge carriers.
In correlated metals derived from Mott insulators, the motion of an electron is impeded by Coulomb repulsion due to other electrons. This phenomenon causes a substantial reduction in the electrons kinetic energy leading to remarkable experimental manifestations in optical spectroscopy. The high-Tc superconducting cuprates are perhaps the most studied examples of such correlated metals. The occurrence of high-Tc superconductivity in the iron pnictides puts a spotlight on the relevance of correlation effects in these materials. Here we present an infrared and optical study on single crystals of the iron pnictide superconductor LaFePO. We find clear evidence of electronic correlations in metallic LaFePO with the kinetic energy of the electrons reduced to half of that predicted by band theory of nearly free electrons. Hallmarks of strong electronic many-body effects reported here are important because the iron pnictides expose a new pathway towards a correlated electron state that does not explicitly involve the Mott transition.
We investigate the effects of multi-task learning using the recently introduced task of semantic tagging. We employ semantic tagging as an auxiliary task for three different NLP tasks: part-of-speech tagging, Universal Dependency parsing, and Natural Language Inference. We compare full neural network sharing, partial neural network sharing, and what we term the learning what to share setting where negative transfer between tasks is less likely. Our findings show considerable improvements for all tasks, particularly in the learning what to share setting, which shows consistent gains across all tasks.
We present an approach that combines the local density approximation (LDA) and the dynamical mean-field theory (DMFT) in the framework of the full-potential linear augmented plane waves (FLAPW) method. Wannier-like functions for the correlated shell are constructed by projecting local orbitals onto a set of Bloch eigenstates located within a certain energy window. The screened Coulomb interaction and Hunds coupling are calculated from a first-principle constrained RPA scheme. We apply this LDA+DMFT implementation, in conjunction with continuous-time quantum Monte-Carlo, to study the electronic correlations in LaFeAsO. Our findings support the physical picture of a metal with intermediate correlations. The average value of the mass renormalization of the Fe 3d bands is about 1.6, in reasonable agreement with the picture inferred from photoemission experiments. The discrepancies between different LDA+DMFT calculations (all technically correct) which have been reported in the literature are shown to have two causes: i) the specific value of the interaction parameters used in these calculations and ii) the degree of localization of the Wannier orbitals chosen to represent the Fe 3d states, to which many-body terms are applied. The latter is a fundamental issue in the application of many-body calculations, such as DMFT, in a realistic setting. We provide strong evidence that the DMFT approximation is more accurate and more straightforward to implement when well-localized orbitals are constructed from a large energy window encompassing Fe-3d, As-4p and O-2p, and point out several difficulties associated with the use of extended Wannier functions associated with the low-energy iron bands. Some of these issues have important physical consequences, regarding in particular the sensitivity to the Hunds coupling.
Learning problems form an important category of computational tasks that generalizes many of the computations researchers apply to large real-life data sets. We ask: what concept classes can be learned privately, namely, by an algorithm whose output does not depend too heavily on any one input or specific training example? More precisely, we investigate learning algorithms that satisfy differential privacy, a notion that provides strong confidentiality guarantees in contexts where aggregate information is released about a database containing sensitive information about individuals. We demonstrate that, ignoring computational constraints, it is possible to privately agnostically learn any concept class using a sample size approximately logarithmic in the cardinality of the concept class. Therefore, almost anything learnable is learnable privately: specifically, if a concept class is learnable by a (non-private) algorithm with polynomial sample complexity and output size, then it can be learned privately using a polynomial number of samples. We also present a computationally efficient private PAC learner for the class of parity functions. Local (or randomized response) algorithms are a practical class of private algorithms that have received extensive investigation. We provide a precise characterization of local private learning algorithms. We show that a concept class is learnable by a local algorithm if and only if it is learnable in the statistical query (SQ) model. Finally, we present a separation between the power of interactive and noninteractive local learning algorithms.
We discuss the features of instabilities in binary systems, in particular, for asymmetric nuclear matter. We show its relevance for the interpretation of results obtained in experiments and in ab initio simulations of the reaction between $^{124}Sn+^{124}Sn$ at 50AMeV.}