96,421 research outputs found
Spin-bias driven magnetization reversal and nondestructive detection in a single molecular magnet
The magnetization reversal in a single molecular magnet (SMM) weakly coupled
to an electrode with spin-dependent splitting of chemical potentials (spin
bias) is theoretically investigated by means of the rate equation. A
microscopic mechanism for the reversal is demonstrated by the avalanche
dynamics at the reversal point. The magnetization as a function of the spin
bias shows hysteresis loops tunable by the gate voltage and varying with
temperature. The nondestructive measurement to the orientation of giant spin in
SMM is presented by measuring the fully polarized electric current in the
response to a small spin bias. For Mnac molecule, its small transverse
anisotropy only slightly violates the results above. The situation when there
is an angle between the easy axis of the SMM and the spin quantization
direction of the electrode is also studied.Comment: 17 pages, 12 figure
Hardcore bosons on checkerboard lattices near half filling: geometric frustration, vanishing charge order and fractional phase
We study a spinless hardcore boson model on checkerboard lattices by Green
function Monte Carlo method. At half filling, the ground state energy is
obtained up to lattice and extrapolated to infinite size, the
staggered pseudospin magnetization is found to vanish in the thermodynamic
limit. Thus the charge order is absent in this system. Away from
half filling, two defects induced by each hole (particle) may carry fractional
charge (). For one hole case, we study how the defect-defect
correlation changes with , which is the ratio between the hopping integral
and cyclic exchange, equals to when . Moreover, we argue that
these fractional defects may propagate independently when the concentration of
holes (or defects) is large enough
Calculation of renormalized viscosity and resistivity in magnetohydrodynamic turbulence
A self-consistent renormalization (RG) scheme has been applied to nonhelical
magnetohydrodynamic turbulence with normalized cross helicity and
. Kolmogorov's 5/3 powerlaw is assumed in order to compute the
renormalized parameters. It has been shown that the RG fixed point is stable
for . The renormalized viscosity and resistivity
have been calculated, and they are found to be positive for all
parameter regimes. For and large Alfv\'{e}n ratio (ratio of
kinetic and magnetic energies) , and . As
is decreased, increases and decreases, untill where both and are approximately zero. For large ,
both and vary as . The renormalized parameters for
the case are also reported.Comment: 19 pages REVTEX, 3 ps files (Phys. Plasmas, v8, 3945, 2001
On Horizontal and Vertical Separation in Hierarchical Text Classification
Hierarchy is a common and effective way of organizing data and representing
their relationships at different levels of abstraction. However, hierarchical
data dependencies cause difficulties in the estimation of "separable" models
that can distinguish between the entities in the hierarchy. Extracting
separable models of hierarchical entities requires us to take their relative
position into account and to consider the different types of dependencies in
the hierarchy. In this paper, we present an investigation of the effect of
separability in text-based entity classification and argue that in hierarchical
classification, a separation property should be established between entities
not only in the same layer, but also in different layers. Our main findings are
the followings. First, we analyse the importance of separability on the data
representation in the task of classification and based on that, we introduce a
"Strong Separation Principle" for optimizing expected effectiveness of
classifiers decision based on separation property. Second, we present
Hierarchical Significant Words Language Models (HSWLM) which capture all, and
only, the essential features of hierarchical entities according to their
relative position in the hierarchy resulting in horizontally and vertically
separable models. Third, we validate our claims on real-world data and
demonstrate that how HSWLM improves the accuracy of classification and how it
provides transferable models over time. Although discussions in this paper
focus on the classification problem, the models are applicable to any
information access tasks on data that has, or can be mapped to, a hierarchical
structure.Comment: Full paper (10 pages) accepted for publication in proceedings of ACM
SIGIR International Conference on the Theory of Information Retrieval
(ICTIR'16
Active Semi-Supervised Learning Using Sampling Theory for Graph Signals
We consider the problem of offline, pool-based active semi-supervised
learning on graphs. This problem is important when the labeled data is scarce
and expensive whereas unlabeled data is easily available. The data points are
represented by the vertices of an undirected graph with the similarity between
them captured by the edge weights. Given a target number of nodes to label, the
goal is to choose those nodes that are most informative and then predict the
unknown labels. We propose a novel framework for this problem based on our
recent results on sampling theory for graph signals. A graph signal is a
real-valued function defined on each node of the graph. A notion of frequency
for such signals can be defined using the spectrum of the graph Laplacian
matrix. The sampling theory for graph signals aims to extend the traditional
Nyquist-Shannon sampling theory by allowing us to identify the class of graph
signals that can be reconstructed from their values on a subset of vertices.
This approach allows us to define a criterion for active learning based on
sampling set selection which aims at maximizing the frequency of the signals
that can be reconstructed from their samples on the set. Experiments show the
effectiveness of our method.Comment: 10 pages, 6 figures, To appear in KDD'1
Melt-growth dynamics in CdTe crystals
We use a new, quantum-mechanics-based bond-order potential (BOP) to reveal
melt-growth dynamics and fine-scale defect formation mechanisms in CdTe
crystals. Previous molecular dynamics simulations of semiconductors have shown
qualitatively incorrect behavior due to the lack of an interatomic potential
capable of predicting both crystalline growth and property trends of many
transitional structures encountered during the melt crystal
transformation. Here we demonstrate successful molecular dynamics simulations
of melt-growth in CdTe using a BOP that significantly improves over other
potentials on property trends of different phases. Our simulations result in a
detailed understanding of defect formation during the melt-growth process.
Equally important, we show that the new BOP enables defect formation mechanisms
to be studied at a scale level comparable to empirical molecular dynamics
simulation methods with a fidelity level approaching quantum-mechanical method
Distribution of equilibrium free energies in a thermodynamic system with broken ergodicity
At low temperatures the configurational phase space of a macroscopic complex
system (e.g., a spin-glass) of interacting particles may split
into an exponential number of
ergodic sub-spaces (thermodynamic states). Previous theoretical studies assumed
that the equilibrium collective behavior of such a system is determined by its
ground thermodynamic states of the minimal free-energy density, and that the
equilibrium free energies follow the distribution of exponential decay. Here we
show that these assumptions are not necessarily valid. For some complex
systems, the equilibrium free-energy values may follow a Gaussian distribution
within an intermediate temperature range, and consequently their equilibrium
properties are contributed by {\em excited} thermodynamic states. This work
will help improving our understanding of the equilibrium statistical mechanics
of spin-glasses and other complex systems.Comment: 7 pages, 2 figure
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