5,022 research outputs found
Text Categorization for Authorship based on the Features of Lingual Conceptual Expression
PACLIC 21 / Seoul National University, Seoul, Korea / November 1-3, 200
Magnetoresistance from Fermi Surface Topology
Extremely large non-saturating magnetoresistance has recently been reported
for a large number of both topologically trivial and non-trivial materials.
Different mechanisms have been proposed to explain the observed
magnetotransport properties, yet without arriving to definitive conclusions or
portraying a global picture. In this work, we investigate the transverse
magnetoresistance of materials by combining the Fermi surfaces calculated from
first principles with the Boltzmann transport theory approach relying on the
semiclassical model and the relaxation time approximation. We first consider a
series of simple model Fermi surfaces to provide a didactic introduction into
the charge-carrier compensation and open-orbit mechanisms leading to
non-saturating magnetoresistance. We then address in detail magnetotransport in
three representative materials: (i) copper, a prototypical nearly free-electron
metal characterized by the open Fermi surface that results in an intricate
angular magnetoresistance, (ii) bismuth, a topologically trivial semimetal in
which very large magnetoresistance is known to result from charge-carrier
compensation, and (iii) tungsten diphosphide WP2, a recently discovered type-II
Weyl semimetal that holds the record of magnetoresistance in compounds. In all
three cases our calculations show excellent agreement with both the field
dependence of magnetoresistance and its anisotropy measured at low
temperatures. Furthermore, the calculations allow for a full interpretation of
the observed features in terms of the Fermi surface topology. These results
will help addressing a number of outstanding questions, such as the role of the
topological phase in the pronounced large non-saturating magnetoresistance
observed in topological materials.Comment: 13 pages, 9 figure
Three-dimensional numerical study of flow characteristic and membrane fouling evolution in an enzymatic membrane reactor
In order to enhance the understanding of membrane fouling mechanism, the
hydrodynamics of granular flow in a stirred enzymatic membrane reactor was
numerically investigated in the present study. A three-dimensional Euler-Euler
model, coupled with k-e mixture turbulence model and drag function for
interphase momentum exchange, was applied to simulate the two-phase
(fluid-solid) turbulent flow. Numerical simulations of single- or two-phase
turbulent flow under various stirring speed were implemented. The numerical
results coincide very well with some published experimental data. Results for
the distributions of velocity, shear stress and turbulent kinetic energy were
provided. Our results show that the increase of stirring speed could not only
enlarge the circulation loops in the reactor, but it can also increase the
shear stress on the membrane surface and accelerate the mixing process of
granular materials. The time evolution of volumetric function of granular
materials on the membrane surface has qualitatively explained the evolution of
membrane fouling.Comment: 10 panges, 8 figure
Random Entity Quantization for Parameter-Efficient Compositional Knowledge Graph Representation
Representation Learning on Knowledge Graphs (KGs) is essential for downstream
tasks. The dominant approach, KG Embedding (KGE), represents entities with
independent vectors and faces the scalability challenge. Recent studies propose
an alternative way for parameter efficiency, which represents entities by
composing entity-corresponding codewords matched from predefined small-scale
codebooks. We refer to the process of obtaining corresponding codewords of each
entity as entity quantization, for which previous works have designed
complicated strategies. Surprisingly, this paper shows that simple random
entity quantization can achieve similar results to current strategies. We
analyze this phenomenon and reveal that entity codes, the quantization outcomes
for expressing entities, have higher entropy at the code level and Jaccard
distance at the codeword level under random entity quantization. Therefore,
different entities become more easily distinguished, facilitating effective KG
representation. The above results show that current quantization strategies are
not critical for KG representation, and there is still room for improvement in
entity distinguishability beyond current strategies. The code to reproduce our
results is available at https://github.com/JiaangL/RandomQuantization.Comment: Accepted to EMNLP 202
Visualization of all two-qubit states via partial-transpose-moments
Efficiently detecting entanglement based on measurable quantities is a basic
problem for quantum information processing. Recently, the measurable quantities
called partial-transpose (PT)-moments have been proposed to detect and
characterize entanglement. In the recently published paper [L. Zhang \emph{et
al.}, \href{https://doi.org/10.1002/andp.202200289}{Ann. Phys.(Berlin)
\textbf{534}, 2200289 (2022)}], we have already identified the 2-dimensional
(2D) region, comprised of the second and third PT-moments, corresponding to
two-qubit entangled states, and described the whole region for all two-qubit
states. In the present paper, we visualize the 3D region corresponding to all
two-qubit states by further involving the fourth PT-moment (the last one for
two-qubit states). The characterization of this 3D region can finally be
achieved by optimizing some polynomials. Furthermore, we identify the dividing
surface which separates the two parts of the whole 3D region corresponding to
entangled and separable states respectively. Due to the measurability of
PT-moments, we obtain a complete and operational criterion for the detection of
two-qubit entanglement.Comment: 29 pages, LaTeX, 8 figures, 2 table
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