18,563 research outputs found
New Results for Diffusion in Lorentz Lattice Gas Cellular Automata
New calculations to over ten million time steps have revealed a more complex
diffusive behavior than previously reported, of a point particle on a square
and triangular lattice randomly occupied by mirror or rotator scatterers. For
the square lattice fully occupied by mirrors where extended closed particle
orbits occur, anomalous diffusion was still found. However, for a not fully
occupied lattice the super diffusion, first noticed by Owczarek and Prellberg
for a particular concentration, obtains for all concentrations. For the square
lattice occupied by rotators and the triangular lattice occupied by mirrors or
rotators, an absence of diffusion (trapping) was found for all concentrations,
except on critical lines, where anomalous diffusion (extended closed orbits)
occurs and hyperscaling holds for all closed orbits with {\em universal}
exponents and . Only one point on these critical lines can be related to a
corresponding percolation problem. The questions arise therefore whether the
other critical points can be mapped onto a new percolation-like problem, and of
the dynamical significance of hyperscaling.Comment: 52 pages, including 18 figures on the last 22 pages, email:
[email protected]
Single grain heating due to inelastic cotunneling
We study heating effects of a single metallic quantum dot weakly coupled to
two leads. The dominant mechanism for heating at low temperatures is due to
inelastic electron cotunneling processes. We calculate the grain temperature
profile as a function of grain parameters, bias voltage, and time and show that
for nanoscale size grains the heating effects are pronounced and easily
measurable in experiments.Comment: 4 pages, 3 figures, revtex4, extended and corrected versio
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
Anisotropy Reversal of the Upper Critical Field at Low Temperatures and Spin-Locked Superconductivity in K2Cr3As3
We report the first measurements of the anisotropic upper critical field
for KCrAs single crystals up to 60 T and K. Our results show that the upper critical field parallel to the Cr
chains, , exhibits a paramagnetically-limited behavior,
whereas the shape of the curve (perpendicular to the Cr
chains) has no evidence of paramagnetic effects. As a result, the curves
and cross at K, so that
the anisotropy parameter
increases from near to at 0.6 K. This behavior of is inconsistent with triplet
superconductivity but suggests a form of singlet superconductivity with the
electron spins locked onto the direction of Cr chains
Growth of High-Mobility Bi2Te2Se Nanoplatelets on hBN Sheets by van der Waals Epitaxy
The electrical detection of the surface states of topological insulators is
strongly impeded by the interference of bulk conduction, which commonly arises
due to pronounced doping associated with the formation of lattice defects. As
exemplified by the topological insulator Bi2Te2Se, we show that via van der
Waals epitaxial growth on thin hBN substrates the structural quality of such
nanoplatelets can be substantially improved. The surface state carrier mobility
of nanoplatelets on hBN is increased by a factor of about 3 compared to
platelets on conventional Si/SiOx substrates, which enables the observation of
well-developed Shubnikov-de Haas oscillations. We furthermore demonstrate the
possibility to effectively tune the Fermi level position in the films with the
aid of a back gate
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