7,080 research outputs found
Quasi-Whittaker modules for the Schr\"odinger algebra
In this paper, we construct a new class of modules for the Schr\"{o}dinger
algebra \mS, called quasi-Whittaker module. Different from \cite{[ZC]}, the
quasi-Whittaker module is not induced by the Borel subalgebra of the
Schr\"{o}dinger algebra related with the triangular decomposition, but its
Heisenberg subalgebra \mH. We prove that, for a simple \mS-module ,
is a quasi-Whittaker module if and only if is a locally finite
\mH-module; Furthermore, we classify the simple quasi-Whittaker modules by
the elements with the action similar to the center elements in U(\mS) and
their quasi-Whittaker vectors. Finally, we characterize arbitrary
quasi-Whittaker modules.Comment: 17 page
How has TV dramas legitimised China's rural neoliberal transformation agenda?
The Chinese state is leading a neoliberal transformation in China's rural area. A growing number of rural topic TV dramas choose to follow its agenda. However, it is not clear why the TV drama industry gets involved in this rural transformation process, and how much these dramas can help the state to carry out its policies. This study aims to address these issues. By conducting in-depth interviews with government officials, drama professionals and peasants in two villages, supplemented by analyses of relevant literature and archives, this research reveals how China's rural neoliberal transformation process looks like when it intersects with China's media marketisation process. It concludes that the Chinese state is increasingly collaborating with the market for the interpenetration of political-economic interests, and thereby joins the global discussion on how neoliberalism, as a way of governing, works in different socio-political contexts
Theoretical Exploration on the Magnetic Properties of Ferromagnetic Metallic Glass: An Ising Model on Random Recursive Lattice
The ferromagnetic Ising spins are modeled on a recursive lattice constructed
from random-angled rhombus units with stochastic configurations, to study the
magnetic properties of the bulk Fe-based metallic glass. The integration of
spins on the structural glass model well represents the magnetic moments in the
glassy metal. The model is exactly solved by the recursive calculation
technique. The magnetization of the amorphous Ising spins, i.e. the glassy
metallic magnet is investigated by our modeling and calculation on a
theoretical base. The results show that the glassy metallic magnets has a lower
Curie temperature, weaker magnetization, and higher entropy comparing to the
regular ferromagnet in crystal form. These findings can be understood with the
randomness of the amorphous system, and agrees well with others' experimental
observations.Comment: 11 pages, 5 figure
Direct reconstruction of dynamical dark energy from observational Hubble parameter data
Reconstructing the evolution history of the dark energy equation of state
parameter directly from observational data is highly valuable in
cosmology, since it contains substantial clues in understanding the nature of
the accelerated expansion of the Universe. Many works have focused on
reconstructing using Type Ia supernova data, however, only a few studies
pay attention to Hubble parameter data. In the present work, we explore the
merit of Hubble parameter data and make an attempt to reconstruct from
them through the principle component analysis approach. We find that current
Hubble parameter data perform well in reconstructing ; though, when
compared to supernova data, the data are scant and their quality is worse. Both
CDM and evolving models can be constrained within at
redshifts
and even at redshifts 0.1 z 1 by
using simulated data of observational quality.Comment: 25 pages, 11 figure
Distilling Word Embeddings: An Encoding Approach
Distilling knowledge from a well-trained cumbersome network to a small one
has recently become a new research topic, as lightweight neural networks with
high performance are particularly in need in various resource-restricted
systems. This paper addresses the problem of distilling word embeddings for NLP
tasks. We propose an encoding approach to distill task-specific knowledge from
a set of high-dimensional embeddings, which can reduce model complexity by a
large margin as well as retain high accuracy, showing a good compromise between
efficiency and performance. Experiments in two tasks reveal the phenomenon that
distilling knowledge from cumbersome embeddings is better than directly
training neural networks with small embeddings.Comment: Accepted by CIKM-16 as a short paper, and by the Representation
Learning for Natural Language Processing (RL4NLP) Workshop @ACL-16 for
presentatio
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