Owing to the unusual geometry of kagome lattices-lattices made of corner-sharing triangles-their electrons are useful for studying the physics of frustrated, correlated and topological quantum electronic states. In the presence of strong spin-orbit coupling, the magnetic and electronic structures of kagome lattices are further entangled, which can lead to hitherto unknown spin-orbit phenomena. Here we use a combination of vector-magnetic-field capability and scanning tunnelling microscopy to elucidate the spin-orbit nature of the kagome ferromagnet FeSn and explore the associated exotic correlated phenomena. We discover that a many-body electronic state from the kagome lattice couples strongly to the vector field with three-dimensional anisotropy, exhibiting a magnetization-driven giant nematic (two-fold-symmetric) energy shift. Probing the fermionic quasi-particle interference reveals consistent spontaneous nematicity-a clear indication of electron correlation-and vector magnetization is capable of altering this state, thus controlling the many-body electronic symmetry. These spin-driven giant electronic responses go well beyond Zeeman physics and point to the realization of an underlying correlated magnetic topological phase. The tunability of this kagome magnet reveals a strong interplay between an externally applied field, electronic excitations and nematicity, providing new ways of controlling spin-orbit properties and exploring emergent phenomena in topological or quantum materials.
By the first-principles electronic structure calculations, we have systematically studied the electronic structures of recently discovered extremely large magnetoresistance (XMR) materials LaSb and LaBi. We find that both LaSb and LaBi are semimetals with the electron and hole carriers in perfect balance. The calculated carrier densities in the order of 10 20 cm −3 are in good agreement with the experimental values, implying long mean free time of carriers and thus high carrier mobilities. With a semiclassical two-band model, the perfect charge compensation and high carrier mobilities naturally explain (i) the XMR observed in LaSb and LaBi; (ii) the non-saturating quadratic dependence of XMR on external magnetic field; and (iii) the resistivity plateau in the turn-on temperature behavior at very low temperatures. The explanation of these features without resorting to the topological effect indicates that they should be the common characteristics of all perfectly electron-hole compensated semimetals.
BackgroundClouds and MapReduce have shown themselves to be a broadly useful approach to scientific computing especially for parallel data intensive applications. However they have limited applicability to some areas such as data mining because MapReduce has poor performance on problems with an iterative structure present in the linear algebra that underlies much data analysis. Such problems can be run efficiently on clusters using MPI leading to a hybrid cloud and cluster environment. This motivates the design and implementation of an open source Iterative MapReduce system Twister.ResultsComparisons of Amazon, Azure, and traditional Linux and Windows environments on common applications have shown encouraging performance and usability comparisons in several important non iterative cases. These are linked to MPI applications for final stages of the data analysis. Further we have released the open source Twister Iterative MapReduce and benchmarked it against basic MapReduce (Hadoop) and MPI in information retrieval and life sciences applications.ConclusionsThe hybrid cloud (MapReduce) and cluster (MPI) approach offers an attractive production environment while Twister promises a uniform programming environment for many Life Sciences applications.MethodsWe used commercial clouds Amazon and Azure and the NSF resource FutureGrid to perform detailed comparisons and evaluations of different approaches to data intensive computing. Several applications were developed in MPI, MapReduce and Twister in these different environments.
The effects of electron doping and phonon vibrations on the magnetic properties of monolayer and bilayer FeSe epitaxial films on SrTiO3 have been studied, respectively, using first-principles calculations with van der Waals correction. For monolayer FeSe epitaxial film, the combined effect of electron doping and phonon vibrations readily leads to magnetic frustration between the collinear antiferromagnetic state and the checkerboard antiferromagnetic Néel state. For bilayer FeSe epitaxial film, such magnetic frustration is much more easily induced by electron doping in its bottom layer than its top layer. The underlying physics is that the doped electrons are accumulated at the interface between the FeSe layers and the substrate. These results are consistent with existing experimental studies.
Abstract-Many scientific applications suffer from the lack of a unified approach to support the management and efficient processing of large-scale data. The Twister MapReduce Framework, which not only supports the traditional MapReduce programming model but also extends it by allowing iterations, addresses these problems. This paper describes how Twister is applied to several kinds of scientific applications such as BLAST, MDS Interpolation and GTM Interpolation in a non-iterative style and to MDS without interpolation in an iterative style. The results show the applicability of Twister to data parallel and EM algorithms with small overhead and increased efficiency. Keywords-Twister; Iterative MapReduce; Cloud; Scientific Applications I.INTRODUCTIONScientific applications are required to process large amounts of data. In recent years, typical input data sets have grown in size from gigabytes to terabytes, and even petabytescale input data is becoming more common. These large data sets already far exceed the computing capability of one computer, and while the computing tasks can be parallelized on several computers, the execution may still take days or weeks to complete.This situation demands better parallel algorithms and distributed computing technologies which can manage scientific applications efficiently. The MapReduce Framework [1] is one such kind technology which has become popular in recent years. KeyValue pairs make the input be distributed and processed in parallel at a fine level of granularity. The combination of Map tasks and Reduce tasks satisfies the task flow of most kinds of applications, and these tasks are also well managed under the runtime platform.This paper introduces the Twister MapReduce Framework [2], an expansion of the traditional MapReduce Framework. The main characteristic of Twister is that it supports not only non-iterative MapReduce applications but also an iterative MapReduce programming model to efficiently support Expectation-maximization (EM) algorithms that suffer from communication complications. These algorithms are common in scientific applications but are not well handled by previous MapReduce implementations such as Hadoop [3].Twister uses a publish/subscribe messaging middleware system for command communication and data transfers. It supports MapReduce in the manner of "configure once, and run many time" [2]. Data can be easily scattered from the client node to compute nodes and combined back into client node through Twister's API. With these features, Twister supports iterative MapReduce computations efficiently when compared to other MapReduce runtimes. Twister can be applied to Cloud architecture, having been successfully deployed on the Amazon EC2 platform [4].The main focus of this paper is on the applicability of Twister to scientific problems, as demonstrated through the implementation of several scientific applications. In the following sections, an overview of Twister is first presented, introducing its programming model and architecture. Then, four scientific...
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