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Intent classification and slot filling are two critical tasks for natural
language understanding. Traditionally the two tasks have been deemed to proceed
independently. However, more recently, joint models for intent classification
and slot filling have achieved state-of-the-art performance, and have proved
that there exists a strong relationship between the two tasks. This article is
a compilation of past work in natural language understanding, especially joint
intent classification and slot filling. We observe three milestones in this
research so far: Intent detection to identify the speaker's intention, slot
filling to label each word token in the speech/text, and finally, joint intent
classification and slot filling tasks. In this article, we describe trends,
approaches, issues, data sets, evaluation metrics in intent classification and
slot filling. We also discuss representative performance values, describe
shared tasks, and provide pointers to future work, as given in prior works. To
interpret the state-of-the-art trends, we provide multiple tables that describe
and summarise past research along different dimensions, including the types of
features, base approaches, and dataset domain used.
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This is the user manual for CosmoLattice, a modern package for lattice
simulations of the dynamics of interacting scalar and gauge fields in an
expanding universe. CosmoLattice incorporates a series of features that makes
it very versatile and powerful: $i)$ it is written in C++ fully exploiting the
object oriented programming paradigm, with a modular structure and a clear
separation between the physics and the technical details, $ii)$ it is MPI-based
and uses a discrete Fourier transform parallelized in multiple spatial
dimensions, which makes it specially appropriate for probing scenarios with
well-separated scales, running very high resolution simulations, or simply very
long ones, $iii)$ it introduces its own symbolic language, defining field
variables and operations over them, so that one can introduce differential
equations and operators in a manner as close as possible to the continuum,
$iv)$ it includes a library of numerical algorithms, ranging from $O(\delta
t^2)$ to $O(\delta t^{10})$ methods, suitable for simulating global and gauge
theories in an expanding grid, including the case of `self-consistent'
expansion sourced by the fields themselves. Relevant observables are provided
for each algorithm (e.g.~energy densities, field spectra, lattice snapshots)
and we note that remarkably all our algorithms for gauge theories always
respect the Gauss constraint to machine precision. In this manual we explain
how to obtain and run CosmoLattice in a computer (let it be your laptop,
desktop or a cluster). We introduce the general structure of the code and
describe in detail the basic files that any user needs to handle. We explain
how to implement any model characterized by a scalar potential and a set of
scalar fields, either singlets or interacting with $U(1)$ and/or $SU(2)$ gauge
fields. CosmoLattice is publicly available at www.cosmolattice.net.
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A spin-1/2 Heisenberg model on honeycomb lattice is investigated by doing
triplon analysis and quantum Monte Carlo calculations. This model, inspired by
Cu$_2$(pymca)$_3$(ClO$_4$), has three different antiferromagnetic exchange
interactions ($J_A$, $J_B$, $J_C$) on three different sets of nearest-neighbour
bonds which form a kagome superlattice. While the model is bipartite and
unfrustrated, its quantum phase diagram is found to be dominated by a quantum
paramagnetic phase that is best described as a spin-gapped hexagonal-singlet
state. The N\'eel antiferromagnetic order survives only in a small region
around $J_A=J_B=J_C$. The magnetization produced by external magnetic field is
found to exhibit plateaus at 1/3 and 2/3 of the saturation value, or at 1/3
alone, or no plateaus. Notably, the plateaus exist only inside a bounded region
within the hexagonal-singlet phase. This study provides a clear understanding
of the spin-gapped behaviour and magnetization plateaus observed in
Cu$_2$(pymca)$_3$(ClO$_4$), and also predicts the possible disappearance of 2/3
plateau under pressure.
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Atomic carbon (CI) has been proposed to be a global tracer of the molecular
gas as a substitute for CO, however, its utility remains unproven. To evaluate
the suitability of CI as the tracer, we performed [CI]$(^3P_1-^3P_0)$
(hereinafter [CI](1-0)) mapping observations of the northern part of the nearby
spiral galaxy M83 with the ASTE telescope and compared the distributions of
[CI](1-0) with CO lines (CO(1-0), CO(3-2), and $^{13}$CO(1-0)), HI, and
infrared (IR) emission (70, 160, and 250$ \mu$m). The [CI](1-0) distribution in
the central region is similar to that of the CO lines, whereas [CI](1-0) in the
arm region is distributed outside the CO. We examined the dust temperature,
$T_{\rm dust}$, and dust mass surface density, $\Sigma_{\rm dust}$, by fitting
the IR continuum-spectrum distribution with a single-temperature modified
blackbody. The distribution of $\Sigma_{\rm dust}$ shows a much better
consistency with the integrated intensity of CO(1-0) than with that of
[CI](1-0), indicating that CO(1-0) is a good tracer of the cold molecular gas.
The spatial distribution of the [CI] excitation temperature, $T_{\rm ex}$, was
examined using the intensity ratio of the two [CI] transitions. An appropriate
$T_{\rm ex}$ at the central, bar, arm, and inter-arm regions yields a constant
[C]/[H$_2$] abundance ratio of $\sim7 \times 10^{-5}$ within a range of 0.1 dex
in all regions. We successfully detected weak [CI](1-0) emission, even in the
inter-arm region, in addition to the central, arm, and bar regions, using
spectral stacking analysis. The stacked intensity of [CI](1-0) is found to be
strongly correlated with $T_{\rm dust}$. Our results indicate that the atomic
carbon is a photodissociation product of CO, and consequently, compared to
CO(1-0), [CI](1-0) is less reliable in tracing the bulk of "cold" molecular gas
in the galactic disk.
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Using Galois theory of functional equations, we give a new proof of the main
result of the paper "Transcendental transcendency of certain functions of
Poincar\'e" by J.F. Ritt, on the differential transcendence of the solutions of
the functional equation R(y(t))=y(qt), where R is a rational function with
complex coefficients which verifies R(0)=0, R'(0)=q, where q is a complex
number with |q|>1. We also give a partial result in the case of an algebraic
function R.
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We construct an injection from the set of permutations of length $n$ that
contain exactly one copy of the decreasing pattern of length $k$ to the set of
permutations of length $n+2$ that avoid that pattern. We then prove that the
generating function counting the former is not rational, and in the case when
$k$ is even and $k\geq 4$, it is not even algebraic. We extend our injection
and our nonrationality result to a larger class of patterns.
|
This paper proposes a non-autoregressive extension of our previously proposed
sequence-to-sequence (S2S) model-based voice conversion (VC) methods. S2S
model-based VC methods have attracted particular attention in recent years for
their flexibility in converting not only the voice identity but also the pitch
contour and local duration of input speech, thanks to the ability of the
encoder-decoder architecture with the attention mechanism. However, one of the
obstacles to making these methods work in real-time is the autoregressive (AR)
structure. To overcome this obstacle, we develop a method to obtain a model
that is free from an AR structure and behaves similarly to the original S2S
models, based on a teacher-student learning framework. In our method, called
"FastS2S-VC", the student model consists of encoder, decoder, and attention
predictor. The attention predictor learns to predict attention distributions
solely from source speech along with a target class index with the guidance of
those predicted by the teacher model from both source and target speech. Thanks
to this structure, the model is freed from an AR structure and allows for
parallelization. Furthermore, we show that FastS2S-VC is suitable for real-time
implementation based on a sliding-window approach, and describe how to make it
run in real-time. Through speaker-identity and emotional-expression conversion
experiments, we confirmed that FastS2S-VC was able to speed up the conversion
process by 70 to 100 times compared to the original AR-type S2S-VC methods,
without significantly degrading the audio quality and similarity to target
speech. We also confirmed that the real-time version of FastS2S-VC can be run
with a latency of 32 ms when run on a GPU.
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A nested coordinate system is a reassigning of independent variables to take
advantage of geometric or symmetry properties of a particular application.
Polar, cylindrical and spherical coordinate systems are primary examples of
such a regrouping that have proved their importance in the separation of
variables method for solving partial differential equations. Geometric algebra
offers powerful complimentary algebraic tools that are unavailable in other
treatments.
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The study of the mapping class group of the plane minus a Cantor set uses a
graph of loops, which is an analogous of the curve graph in the study of
mapping class groups of compact surfaces. The Gromov boundary of this loop
graph can be described in terms of "cliques of high-filling rays": high-filling
rays are simple geodesics of the surface which are complicated enough to be
infinitely far away from any loop in the graph. Moreover, these rays are
arranged in cliques: any two high-filling rays which are both disjoint from a
third one are necessarily mutually disjoint. Every such clique is a point of
the Gromov-boundary of the loop graph. Some examples of cliques with any finite
number of high-filling rays are already known.
In this paper, we construct an infinite clique of high-filling rays.
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We consider the asymptotic expansion of the functional series
\[S_{\mu,\gamma}(a;\lambda)=\sum_{n=1}^\infty \frac{n^\gamma e^{-\lambda
n^2/a^2}}{(n^2+a^2)^\mu}\] for real values of the parameters $\gamma$,
$\lambda>0$ and $\mu\geq0$ as $|a|\to \infty$ in the sector $|\arg\,a|<\pi/4$.
For general values of $\gamma$ the expansion is of algebraic type with terms
involving the Riemann zeta function and a terminating confluent hypergeometric
function. Of principal interest in this study is the case corresponding to even
integer values of $\gamma$, where the algebraic-type expansion consists of a
finite number of terms together with a contribution comprising an infinite
sequence of increasingly subdominant exponentially small expansions. This
situation is analogous to the well-known Poisson-Jacobi formula corresponding
to the case $\mu=\gamma=0$. Numerical examples are provided to illustrate the
accuracy of these expansions.
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We present a novel method for predicting accurate depths from monocular
images with high efficiency. This optimal efficiency is achieved by exploiting
wavelet decomposition, which is integrated in a fully differentiable
encoder-decoder architecture. We demonstrate that we can reconstruct
high-fidelity depth maps by predicting sparse wavelet coefficients. In contrast
with previous works, we show that wavelet coefficients can be learned without
direct supervision on coefficients. Instead we supervise only the final depth
image that is reconstructed through the inverse wavelet transform. We
additionally show that wavelet coefficients can be learned in fully
self-supervised scenarios, without access to ground-truth depth. Finally, we
apply our method to different state-of-the-art monocular depth estimation
models, in each case giving similar or better results compared to the original
model, while requiring less than half the multiply-adds in the decoder network.
Code at https://github.com/nianticlabs/wavelet-monodepth
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This work presents a novel target-free extrinsic calibration algorithm for a
3D Lidar and an IMU pair using an Extended Kalman Filter (EKF) which exploits
the \textit{motion based calibration constraint} for state update. The steps
include, data collection by motion excitation of the Lidar Inertial Sensor
suite along all degrees of freedom, determination of the inter sensor rotation
by using rotational component of the aforementioned \textit{motion based
calibration constraint} in a least squares optimization framework, and finally,
the determination of inter sensor translation using the \textit{motion based
calibration constraint} for state update in an Extended Kalman Filter (EKF)
framework. We experimentally validate our method using data collected in our
lab and open-source (https://github.com/unmannedlab/imu_lidar_calibration) our
contribution for the robotics research community.
|
The field of quantum simulations in ultra-cold atomic gases has been
remarkably successful. In principle it allows for an exact treatment of a
variety of highly relevant lattice models and their emergent phases of matter.
But so far there is a lack in the theoretical literature concerning the
systematic study of the effects of the trap potential as well as the finite
size of the systems, as numerical studies of such non periodic, correlated
fermionic lattices models are numerically demanding beyond one dimension. We
use the recently introduced real-space truncated unity functional
renormalization group to study these boundary and trap effects with a focus on
their impact on the superconducting phase of the $2$D Hubbard model. We find
that in the experiments not only lower temperatures need to be reached compared
to current capabilities, but also system size and trap potential shape play a
crucial role to simulate emergent phases of matter.
|
The vast majority of semantic segmentation approaches rely on pixel-level
annotations that are tedious and time consuming to obtain and suffer from
significant inter and intra-expert variability. To address these issues, recent
approaches have leveraged categorical annotations at the slide-level, that in
general suffer from robustness and generalization. In this paper, we propose a
novel weakly supervised multi-instance learning approach that deciphers
quantitative slide-level annotations which are fast to obtain and regularly
present in clinical routine. The extreme potentials of the proposed approach
are demonstrated for tumor segmentation of solid cancer subtypes. The proposed
approach achieves superior performance in out-of-distribution, out-of-location,
and out-of-domain testing sets.
|
The purpose of this report is to look at the measures of importance of
components in systems in terms of reliability. In the first work of Birnbaum
(1968) on this subject, many interesting studies were created and important
indicators were constructed that allowed to organize the components of complex
systems. They are helpful in analyzing the reliability of the designed systems,
establishing the principles of operation and maintenance. The significance
measures presented here are collected and discussed regarding the motivation
behind their creation. They concern an approach in which both elements and
systems are binary, and the possibility of generalization to multistate systems
is only mentioned. Among the discussed is one new proposal using the methods of
game theory, combining the sensitivity to the structure of the system and the
operational effects on the system performance. The presented severity measures
use a knowledge of the system structure as well as reliability and wear and
tear, and whether the components can be repaired and maintained.
|
Driven by increased complexity of dynamical systems, the solution of system
of differential equations through numerical simulation in optimization problems
has become computationally expensive. This paper provides a smart data driven
mechanism to construct low dimensional surrogate models. These surrogate models
reduce the computational time for solution of the complex optimization problems
by using training instances derived from the evaluations of the true objective
functions. The surrogate models are constructed using combination of proper
orthogonal decomposition and radial basis functions and provides system
responses by simple matrix multiplication. Using relative maximum absolute
error as the measure of accuracy of approximation, it is shown surrogate models
with latin hypercube sampling and spline radial basis functions dominate
variable order methods in computational time of optimization, while preserving
the accuracy. These surrogate models also show robustness in presence of model
non-linearities. Therefore, these computational efficient predictive surrogate
models are applicable in various fields, specifically to solve inverse problems
and optimal control problems, some examples of which are demonstrated in this
paper.
|
First-order nonadiabatic coupling matrix elements (fo-NACMEs) are the basic
quantities in theoretical descriptions of electronically nonadiabatic processes
that are ubiquitous in molecular physics and chemistry. Given the large size of
systems of chemical interests, time-dependent density functional theory (TDDFT)
is usually the first choice. However, the lack of wave functions in TDDFT
renders the formulation of NAC-TDDFT for fo-NACMEs conceptually difficult. The
present account aims to analyze the available variants of NAC-TDDFT in a
critical but concise manner and meanwhile point out the proper ways for
implementation. It can be concluded, from both theoretical and numerical points
of view, that the equation of motion-based variant of NAC-TDDFT is the right
choice. Possible future developments of this variant are also highlighted.
|
Immense field enhancement and nanoscale confinement of light are possible
within nanoparticle-on-mirror (NPoM) plasmonic resonators, which enable novel
optically-activated physical and chemical phenomena, and render these
nanocavities greatly sensitive to minute structural changes, down to the atomic
scale. Although a few of these structural parameters, primarily linked to the
nanoparticle and the mirror morphology, have been identified, the impact of
molecular assembly and organization of the spacer layer between them has often
been left uncharacterized. Here, we experimentally investigate how the complex
and reconfigurable nature of a thiol-based self-assembled monolayer (SAM)
adsorbed on the mirror surface impacts the optical properties of the NPoMs. We
fabricate NPoMs with distinct molecular organizations by controlling the
incubation time of the mirror in the thiol solution. Afterwards, we investigate
the structural changes that occur under laser irradiation by tracking the
bonding dipole plasmon mode, while also monitoring Stokes and anti-Stokes Raman
scattering from the molecules as a probe of their integrity. First, we find an
effective decrease in the SAM height as the laser power increases, compatible
with an irreversible change of molecule orientation caused by heating. Second,
we observe that the nanocavities prepared with a densely packed and more
ordered monolayer of molecules are more prone to changes in their resonance
compared to samples with sparser and more disordered SAMs. Our measurements
indicate that molecular orientation and packing on the mirror surface play a
key role in determining the stability of NPoM structures and hence highlight
the under-recognized significance of SAM characterization in the development of
NPoM-based applications.
|
Recent literature has demonstrated that the use of per-channel energy
normalization (PCEN), has significant performance improvements over traditional
log-scaled mel-frequency spectrograms in acoustic sound event detection (SED)
in a multi-class setting with overlapping events. However, the configuration of
PCEN's parameters is sensitive to the recording environment, the
characteristics of the class of events of interest, and the presence of
multiple overlapping events. This leads to improvements on a class-by-class
basis, but poor cross-class performance. In this article, we experiment using
PCEN spectrograms as an alternative method for SED in urban audio using the
UrbanSED dataset, demonstrating per-class improvements based on parameter
configuration. Furthermore, we address cross-class performance with PCEN using
a novel method, Multi-Rate PCEN (MRPCEN). We demonstrate cross-class SED
performance with MRPCEN, demonstrating improvements to cross-class performance
compared to traditional single-rate PCEN.
|
We study the Bose polaron problem in a nonequilibrium setting, by considering
an impurity embedded in a quantum fluid of light realized by exciton-polaritons
in a microcavity, subject to a coherent drive and dissipation on account of
pump and cavity losses. We obtain the polaron effective mass, the drag force
acting on the impurity, and determine polaron trajectories at a semiclassical
level. We find different dynamical regimes, originating from the unique
features of the excitation spectrum of driven-dissipative polariton fluids, in
particular a non-trivial regime of acceleration against the flow. Our work
promotes the study of impurity dynamics as an alternative testbed for probing
superfluidity in quantum fluids of light.
|
In this paper, we focus on the performance of vehicle-to-vehicle (V2V)
communication adopting the Dedicated Short Range Communication (DSRC)
application in periodic broadcast mode. An analytical model is studied and a
fixed point method is used to analyze the packet delivery ratio (PDR) and mean
delay based on the IEEE 802.11p standard in a fully connected network under the
assumption of perfect PHY performance. With the characteristics of V2V
communication, we develop the Semi-persistent Contention Density Control
(SpCDC) scheme to improve the DSRC performance. We use Monte Carlo simulation
to verify the results obtained by the analytical model. The simulation results
show that the packet delivery ratio in SpCDC scheme increases more than 10%
compared with IEEE 802.11p in heavy vehicle load scenarios. Meanwhile, the mean
reception delay decreases more than 50%, which provides more reliable road
safety.
|
Spectral factorization is a prominent tool with several important
applications in various areas of applied science. Wiener and Masani proved the
existence of matrix spectral factorization. Their theorem has been extended to
the multivariable case by Helson and Lowdenslager. Solving the problem
numerically is challenging in both situations, and also important due to its
practical applications. Therefore, several authors have developed algorithms
for factorization. The Janashia-Lagvilava algorithm is a relatively new method
for matrix spectral factorization which has proved to be useful in several
applications. In this paper, we extend this method to the multivariable case.
Consequently, a new numerical algorithm for multivariable matrix spectral
factorization is constructed.
|
We investigate the torque field and skyrmion movement at an interface between
a ferromagnet hosting a skyrmion and a material with strong spin-orbit
interaction. We analyze both semiconductor materials and topological insulators
using a Hamiltonian model that includes a linear term. The spin torque inducing
current is considered to flow in the single band limit therefore a quantum
model of current is used. Skyrmion movement due spin transfer torque proves to
be more difficult in presence of spin orbit interaction in the case where only
interface in-plane currents are present. However, edge effects in narrow
nanowires can be used to drive the skyrmion movement and to exert a limited
control on its movement direction. We also show the differences and
similarities between torque fields due to electric current in the many and in
the single band limits.
|
For any second-order scalar PDE $\mathcal{E}$ in one unknown function, that
we interpret as a hypersurface of a second-order jet space $J^2$, we construct,
by means of the characteristics of $\mathcal{E}$, a sub-bundle of the contact
distribution of the underlying contact manifold $J^1$, consisting of conic
varieties. We call it the contact cone structure associated with $\mathcal{E}$.
We then focus on symplectic Monge-Amp\`ere equations in 3 independent
variables, that are naturally parametrized by a 13-dimensional real projective
space. If we pass to the field of complex numbers $\mathbb{C}$, this projective
space turns out to be the projectivization of the 14-dimensional irreducible
representation of the simple Lie group $\mathsf{Sp}(6,\mathbb{C})$: the
associated moment map allows to define a rational map $\varpi$ from the space
of symplectic 3D Monge-Amp\`ere equations to the projectivization of the space
of quadratic forms on a $6$-dimensional symplectic vector space. We study in
details the relationship between the zero locus of the image of $\varpi$,
herewith called the cocharacteristic variety, and the contact cone structure of
a 3D Monge-Amp\`ere equation $\mathcal{E}$: under the hypothesis of
non-degenerate symbol, we prove that these two constructions coincide. A key
tool in achieving such a result will be a complete list of mutually
non-equivalent quadratic forms on a $6$-dimensional symplectic space, which has
an interest on its own.
|
What do word vector representations reveal about the emotions associated with
words? In this study, we consider the task of estimating word-level emotion
intensity scores for specific emotions, exploring unsupervised, supervised, and
finally a self-supervised method of extracting emotional associations from word
vector representations. Overall, we find that word vectors carry substantial
potential for inducing fine-grained emotion intensity scores, showing a far
higher correlation with human ground truth ratings than achieved by
state-of-the-art emotion lexicons.
|
Landau suggested that the low-temperature properties of metals can be
understood in terms of long-lived quasiparticles with all complex interactions
included in Fermi-liquid parameters, such as the effective mass $m^{\star}$.
Despite its wide applicability, electronic transport in bad or strange metals
and unconventional superconductors is controversially discussed towards a
possible collapse of the quasiparticle concept. Here we explore the
electrodynamic response of correlated metals at half filling for varying
correlation strength upon approaching a Mott insulator. We reveal persistent
Fermi-liquid behavior with pronounced quadratic dependences of the optical
scattering rate on temperature and frequency, along with a puzzling elastic
contribution to relaxation. The strong increase of the resistivity beyond the
Ioffe-Regel-Mott limit is accompanied by a `displaced Drude peak' in the
optical conductivity. Our results, supported by a theoretical model for the
optical response, demonstrate the emergence of a bad metal from resilient
quasiparticles that are subject to dynamical localization and dissolve near the
Mott transition.
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In his 1935 Gedankenexperiment, Erwin Schr\"{o}dinger imagined a poisonous
substance which has a 50% probability of being released, based on the decay of
a radioactive atom. As such, the life of the cat and the state of the poison
become entangled, and the fate of the cat is determined upon opening the box.
We present an experimental technique that keeps the cat alive on any account.
This method relies on the time-resolved Hong-Ou-Mandel effect: two long,
identical photons impinging on a beam splitter always bunch in either of the
outputs. Interpreting the first photon detection as the state of the poison,
the second photon is identified as the state of the cat. Even after the
collapse of the first photon's state, we show their fates are intertwined
through quantum interference. We demonstrate this by a sudden phase change
between the inputs, administered conditionally on the outcome of the first
detection, which steers the second photon to a pre-defined output and ensures
that the cat is always observed alive.
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The exsitance of three-dimensional Hall effect (3DQHE) due to spontaneous
Fermi surface instabilities in strong magnetic field was proposed decades ago,
and has stimulated recent progress in experiments. The reports in recent
experiments show that the Hall plateaus and vanishing transverse
magneto-resistivities (TMRs) (which are two main signatures of 3DQHE) are not
easy to be observed in natural materials. And two main different explanations
of the slow varying slope like Hall plateaus and non-vanishing
TMRs (which can be called as quasi-quantized Hall effect (QQHE)) have been
proposed. By studying the magneto-transport with a simple effective periodic 3D
system, we show how 3DQHE can be achieved in certain parameter regimes at
first. We find two new mechanisms that may give rise to QQHE. One mechanism is
the "low" Fermi energy effect, and the other is the "strong" impurity effect.
Our studies also proved that the artificial superlattice is an ideal platform
for realizing 3DQHE with high layer barrier periodic potential.
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Presence often is considered the most important quale describing the
subjective feeling of being in a computer-generated (virtual) or
computer-mediated environment. The identification and separation of two
orthogonal presence components, i.e., the place illusion and the plausibility
illusion, has been an accepted theoretical model describing Virtual Reality
(VR) experiences for some time. In this model, immersion is a proposed
contributing factor to the place illusion. Lately, copresence and social
presence illusions have extended this model, and coherence was proposed as a
contributing factor to the plausibility illusion. Such factors strive to
identify (objectively) measurable characteristics of an experience, e.g.,
systems properties that allow controlled manipulations of VR experiences. This
perspective article challenges this presence-oriented VR theory. First, we
argue that a place illusion cannot be the major construct to describe the much
wider scope of Virtual, Augmented, and Mixed Reality (VR, AR, MR: or XR for
short). Second, we argue that there is no plausibility illusion but merely
plausibility, and we derive the place illusion as a consequence of a plausible
generation of spatial cues, and similarly for all of the current model's
so-defined illusions. Finally, we propose coherence and plausibility to become
the central essential conditions in a novel theoretical model describing XR
experiences and effects.
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Many teleoperation tasks require three or more tools working together, which
need the cooperation of multiple operators. The effectiveness of such schemes
may be limited by communication. Trimanipulation by a single operator using an
artificial third arm controlled together with their natural arms is a promising
solution to this issue. Foot-controlled interfaces have previously shown the
capability to be used for the continuous control of robot arms. However, the
use of such interfaces for controlling a supernumerary robotic limb (SRLs) in
coordination with the natural limbs, is not well understood. In this paper, a
teleoperation task imitating physically coupled hands in a virtual reality
scene was conducted with 14 subjects to evaluate human performance during
tri-manipulation. The participants were required to move three limbs together
in a coordinated way mimicking three arms holding a shared physical object. It
was found that after a short practice session, the three-hand tri-manipulation
using a single subject's hands and foot was still slower than dyad operation,
however, they displayed similar performance in success rate and higher motion
efficiency than two person's cooperation.
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The linear frequency modulated (LFM) frequency agile radar (FAR) can
synthesize a wide signal bandwidth through coherent processing while keeping
the bandwidth of each pulse narrow. In this way, high range resolution profiles
(HRRP) can be obtained without increasing the hardware system cost.
Furthermore, the agility provides improved both robustness to jamming and
spectrum efficiency. Motivated by the Newtonalized orthogonal matching pursuit
(NOMP) for line spectral estimation problem, the NOMP for the FAR radar termed
as NOMP-FAR is designed to process each coarse range bin to extract the HRRP
and velocities of multiple targets, including the guide for determining the
oversampling factor and the stopping criterion. In addition, it is shown that
the target will cause false alarm in the nearby coarse range bins, a
postprocessing algorithm is then proposed to suppress the ghost targets.
Numerical simulations are conducted to demonstrate the effectiveness of
NOMP-FAR.
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We present the task description and discussion on the results of the DCASE
2021 Challenge Task 2. In 2020, we organized an unsupervised anomalous sound
detection (ASD) task, identifying whether a given sound was normal or anomalous
without anomalous training data. In 2021, we organized an advanced unsupervised
ASD task under domain-shift conditions, which focuses on the inevitable problem
of the practical use of ASD systems. The main challenge of this task is to
detect unknown anomalous sounds where the acoustic characteristics of the
training and testing samples are different, i.e., domain-shifted. This problem
frequently occurs due to changes in seasons, manufactured products, and/or
environmental noise. We received 75 submissions from 26 teams, and several
novel approaches have been developed in this challenge. On the basis of the
analysis of the evaluation results, we found that there are two types of
remarkable approaches that TOP-5 winning teams adopted: 1) ensemble approaches
of ``outlier exposure'' (OE)-based detectors and ``inlier modeling'' (IM)-based
detectors and 2) approaches based on IM-based detection for features learned in
a machine-identification task.
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Quantitative information on tumor heterogeneity and cell load could assist in
designing effective and refined personalized treatment strategies. It was
recently shown by us that such information can be inferred from the diffusion
parameter D derived from the diffusion-weighted MRI (DWI) if a relation between
D and cell density can be established. However, such relation cannot a priori
be assumed to be constant for all patients and tumor types. Hence to assist in
clinical decisions in palliative settings, the relation needs to be established
without tumor resection. It is here demonstrated that biopsies may contain
sufficient information for this purpose if the localization of biopsies is
chosen as systematically elaborated in this paper. A superpixel-based method
for automated optimal localization of biopsies from the DWI D-map is proposed.
The performance of the DWI-guided procedure is evaluated by extensive
simulations of biopsies. Needle biopsies yield sufficient histological
information to establish a quantitative relationship between D-value and cell
density, provided they are taken from regions with high, intermediate, and low
D-value in DWI. The automated localization of the biopsy regions is
demonstrated from a NSCLC patient tumor. In this case, even two or three
biopsies give a reasonable estimate. Simulations of needle biopsies under
different conditions indicate that the DWI-guidance highly improves the
estimation results. Tumor cellularity and heterogeneity in solid tumors may be
reliably investigated from DWI and a few needle biopsies that are sampled in
regions of well-separated D-values, excluding adipose tissue. This procedure
could provide a way of embedding in the clinical workflow assistance in cancer
diagnosis and treatment based on personalized information.
|
In this paper we give a classification of the asymptotic expansion of the
$q$-expansion of reciprocals of Eisenstein series $E_k$ of weight $k$ for the
modular group $\func{SL}_2(\mathbb{Z})$. For $k \geq 12$ even, this extends
results of Hardy and Ramanujan, and Berndt, Bialek and Yee, utilizing the
Circle Method on the one hand, and results of Petersson, and Bringmann and
Kane, developing a theory of meromorphic Poincar{\'e} series on the other. We
follow a uniform approach, based on the zeros of the Eisenstein series with the
largest imaginary part. These special zeros provide information on the
singularities of the Fourier expansion of $1/E_k(z)$ with respect to $q = e^{2
\pi i z}$.
|
Interactive single-image segmentation is ubiquitous in the scientific and
commercial imaging software. In this work, we focus on the single-image
segmentation problem only with some seeds such as scribbles. Inspired by the
dynamic receptive field in the human being's visual system, we propose the
Gaussian dynamic convolution (GDC) to fast and efficiently aggregate the
contextual information for neural networks. The core idea is randomly selecting
the spatial sampling area according to the Gaussian distribution offsets. Our
GDC can be easily used as a module to build lightweight or complex segmentation
networks. We adopt the proposed GDC to address the typical single-image
segmentation tasks. Furthermore, we also build a Gaussian dynamic pyramid
Pooling to show its potential and generality in common semantic segmentation.
Experiments demonstrate that the GDC outperforms other existing convolutions on
three benchmark segmentation datasets including Pascal-Context, Pascal-VOC
2012, and Cityscapes. Additional experiments are also conducted to illustrate
that the GDC can produce richer and more vivid features compared with other
convolutions. In general, our GDC is conducive to the convolutional neural
networks to form an overall impression of the image.
|
For any positive integer $r$, we construct a smooth complex projective
rational surface which has at least $r$ real forms not isomorphic over
$\mathbb{R}$.
|
We present a very simple form of the supercharges and the Hamiltonian of
${\cal N} {=}\,2$ supersymmetric extension of $n$-particle
Ruijsenaars--Schneider models for three cases of the interaction:
$1/(x_i-x_j)$, $1/tan(x_i-x_j)$, $1/tanh(x_i-x_j)$. The long "fermionic tails"
of the supercharges and Hamiltonian rolled up in the simple rational functions
depending on fermionic bilinears.
|
We present a study of the structure and differential capacitance of electric
double layers of aqueous electrolytes. We consider Electric Double Layer
Capacitors (EDLC) composed of spherical cations and anions in a dielectric
continuum confined between a planar cathode and anode. The model system
includes steric as well as Coulombic ion-ion and ion-electrode interactions. We
compare results of computationally expensive, but "exact", Brownian Dynamics
(BD) simulations with approximate, but cheap, calculations based on classical
Density Functional Theory (DFT). Excellent overall agreement is found for a
large set of system parameters $-$ including variations in concentrations,
ionic size- and valency-asymmetries, applied voltages, and electrode separation
$-$ provided the differences between the canonical ensemble of the BD
simulations and the grand-canonical ensemble of DFT are properly taken into
account. In particular a careful distinction is made between the differential
capacitance $C_N$ at fixed number of ions and $C_\mu$ at fixed ionic chemical
potential. Furthermore, we derive and exploit their thermodynamic relations. In
the future these relations are also useful for comparing and contrasting.
|
We analyse an extremal question on the degrees of the link graphs of a finite
regular graph, that is, the subgraphs induced by non-trivial spheres. We show
that if $G$ is $d$-regular and connected but not complete then some link graph
of $G$ has minimum degree at most $\lfloor 2d/3\rfloor-1$, and if $G$ is
sufficiently large in terms of $d$ then some link graph has minimum degree at
most $\lfloor d/2\rfloor-1$; both bounds are best possible. We also give the
corresponding best-possible result for the corresponding problem where
subgraphs induced by balls, rather than spheres, are considered.
We motivate these questions by posing a conjecture concerning expansion of
link graphs in large bounded-degree graphs, together with a heuristic
justification thereof.
|
#P-hardness of computing matrix immanants are proved for each member of a
broad class of shapes and restricted sets of matrices. The class is
characterized in the following way. If a shape of size $n$ in it is in form
$(w,\mathbf{1}+\lambda)$ or its conjugate is in that form, where $\mathbf{1}$
is the all-$1$ vector, then $|\lambda|$ is $n^{\varepsilon}$ for some
$0<\varepsilon$, $\lambda$ can be tiled with $1\times 2$ dominos and
$(3w+3h(\lambda)+1)|\lambda| \le n$, where $h(\lambda)$ is the height of
$\lambda$. The problem remains \#P-hard if the immanants are evaluated on
$0$-$1$ matrices. We also give hardness proofs of some immanants whose shape
$\lambda = (\mathbf{1}+\lambda_d)$ has size $n$ such that $|\lambda_d| =
n^{\varepsilon}$ for some $0<\varepsilon<\frac{1}{2}$, and for some $w$, the
shape $\lambda_d/(w)$ is tilable with $1\times 2$ dominos. The \#P-hardness
result holds when these immanants are evaluated on adjacency matrices of
planar, directed graphs, however, in these cases the edges have small positive
integer weights.
|
The chromaticity diagram associated with the CIE 1931 color matching
functions is shown to be slightly non-convex. While having no impact on
practical colorimetric computations, the non-convexity does have a significant
impact on the shape of some optimal object color reflectance distributions
associated with the outer surface of the object color solid. Instead of the
usual two-transition Schrodinger form, many optimal colors exhibit higher
transition counts. A linear programming formulation is developed and is used to
locate where these higher-transition optimal object colors reside on the object
color solid surface. The regions of higher transition count appear to have a
point-symmetric complementary structure. The final peer-reviewed version (to
appear) contains additional material concerning convexification of the
color-matching functions and and additional analysis of modern
"physiologically-relevant" CMFs transformed from cone fundamentals.
|
Raman spectroscopy is an advantageous method for studying the local structure
of materials, but the interpretation of measured spectra is complicated by the
presence of oblique phonons in polycrystals of polar materials. Whilst group
theory considerations and standard ab initio calculations are helpful, they are
often valid only for single crystals. In this paper, we introduce a method for
computing Raman spectra of polycrystalline materials from first principles. We
start from the standard approach based on the (Placzek) rotation invariants of
the Raman tensors and extend it to include the effect of the coupling between
the lattice vibrations and the induced electric field, and the electro-optic
contribution, relevant for polar materials like ferroelectrics. As exemplified
by applying the method to rhombohedral BaTiO3, AlN, and LiNbO3, such an
extension brings the simulated Raman spectrum to a much better correspondence
with the experimental one. Additional advantages of the method are that it is
general, permits automation, and thus can be used in high-throughput fashion.
|
This paper is a continuation of our article (European J. Math.,
https://doi.org/10.1007/s40879-020-00419-8). The notion of a poor complex
compact manifold was introduced there and the group $Aut(X)$ for a $P^1$-bundle
over such a manifold was proven to be very Jordan. We call a group $G$ very
Jordan if it contains a normal abelian subgroup $G_0$ such that the orders of
finite subgroups of the quotient $G/G_0$ are bounded by a constant depending on
$G$ only.
In this paper we provide explicit examples of infinite families of poor
manifolds of any complex dimension, namely simple tori of algebraic dimension
zero. Then we consider a non-trivial holomorphic $P^1$-bundle $(X,p,Y)$ over a
non-uniruled complex compact Kaehler manifold $Y$. We prove that $Aut(X)$ is
very Jordan provided some additional conditions on the set of sections of $p$
are met. Applications to $P^1$-bundles over non-algebraic complex tori are
given.
|
In this work we obtain a geometric characterization of the measures $\mu$ in
$\mathbb{R}^{n+1}$ with polynomial upper growth of degree $n$ such that the
$n$-dimensional Riesz transform $\mathcal{R}\mu (x) = \int
\frac{x-y}{|x-y|^{n+1}}\,d\mu(y)$ belongs to $L^2(\mu)$, under the assumption
that $\mu$ satisfies the following Wolff energy estimate, for any ball
$B\subset\mathbb{R}^{n+1}$: $$\int_B \int_0^\infty
\left(\frac{\mu(B(x,r))}{r^{n-\frac38}}\right)^2\,\frac{dr}r\,d\mu(x)\leq
M\,\bigg(\frac{\mu(2B)}{r(B)^{n-\frac38}}\bigg)^2\,\mu(2B).$$ More precisely,
we show that $\mu$ satisfies the following estimate:
$$\|\mathcal{R}\mu\|_{L^2(\mu)}^2 + \|\mu\|\approx \int\!\!\int_0^\infty
\beta_{\mu,2}(x,r)^2\,\frac{\mu(B(x,r))}{r^n}\,\frac{dr}r\,d\mu(x) + \|\mu\|,$$
where $\beta_{\mu,2}(x,r)^2 = \inf_L \frac1{r^n}\int_{B(x,r)}
\left(\frac{\mathrm{dist}(y,L)}r\right)^2\,d\mu(y),$ with the infimum taken
over all affine $n$-planes $L\subset\mathbb{R}^{n+1}$. In a companion paper
which relies on the results obtained in this work it is shown that the same
result holds without the above assumption regarding the Wolff energy of $\mu$.
This result has important consequences for the Painlev\'e problem for Lipschitz
harmonic functions.
|
We propose a new approach for trading VIX futures. We assume that the term
structure of VIX futures follows a Markov model. Our trading strategy selects a
position in VIX futures by maximizing the expected utility for a day-ahead
horizon given the current shape and level of the term structure.
Computationally, we model the functional dependence between the VIX futures
curve, the VIX futures positions, and the expected utility as a deep neural
network with five hidden layers. Out-of-sample backtests of the VIX futures
trading strategy suggest that this approach gives rise to reasonable portfolio
performance, and to positions in which the investor will be either long or
short VIX futures contracts depending on the market environment.
|
In this paper we study the connectivity of Fatou components for maps in a
large family of singular perturbations. We prove that, for some parameters
inside the family, the dynamical planes for the corresponding maps present
Fatou components of arbitrarily large connectivity and we determine precisely
these connectivities. In particular, these results extend the ones obtained in
[Can17, Can18].
|
Cross features play an important role in click-through rate (CTR) prediction.
Most of the existing methods adopt a DNN-based model to capture the cross
features in an implicit manner. These implicit methods may lead to a
sub-optimized performance due to the limitation in explicit semantic modeling.
Although traditional statistical explicit semantic cross features can address
the problem in these implicit methods, it still suffers from some challenges,
including lack of generalization and expensive memory cost. Few works focus on
tackling these challenges. In this paper, we take the first step in learning
the explicit semantic cross features and propose Pre-trained Cross Feature
learning Graph Neural Networks (PCF-GNN), a GNN based pre-trained model aiming
at generating cross features in an explicit fashion. Extensive experiments are
conducted on both public and industrial datasets, where PCF-GNN shows
competence in both performance and memory-efficiency in various tasks.
|
The number of photographs taken worldwide is growing rapidly and steadily.
While a small subset of these images is annotated and shared by users through
social media platforms, due to the sheer number of images in personal photo
repositories (shared or not shared), finding specific images remains
challenging. This survey explores existing image retrieval techniques as well
as photo-organizer applications to highlight their relative strengths in
addressing this challenge.
|
Partitioning graphs into blocks of roughly equal size is widely used when
processing large graphs. Currently there is a gap in the space of available
partitioning algorithms. On the one hand, there are streaming algorithms that
have been adopted to partition massive graph data on small machines. In the
streaming model, vertices arrive one at a time including their neighborhood and
then have to be assigned directly to a block. These algorithms can partition
huge graphs quickly with little memory, but they produce partitions with low
solution quality. On the other hand, there are offline (shared-memory)
multilevel algorithms that produce partitions with high quality but also need a
machine with enough memory. We make a first step to close this gap by
presenting an algorithm that computes significantly improved partitions of huge
graphs using a single machine with little memory in streaming setting. First,
we adopt the buffered streaming model which is a more reasonable approach in
practice. In this model, a processing element can store a buffer, or batch, of
nodes before making assignment decisions. When our algorithm receives a batch
of nodes, we build a model graph that represents the nodes of the batch and the
already present partition structure. This model enables us to apply multilevel
algorithms and in turn compute much higher quality solutions of huge graphs on
cheap machines than previously possible. To partition the model, we develop a
multilevel algorithm that optimizes an objective function that has previously
shown to be effective for the streaming setting. This also removes the
dependency on the number of blocks k from the running time compared to the
previous state-of-the-art. Overall, our algorithm computes, on average, 75.9%
better solutions than Fennel using a very small buffer size. In addition, for
large values of k our algorithm becomes faster than Fennel.
|
Let $X$ be a nonempty set and let $T(X)$ be the full transformation semigroup
on $X$. The main objective of this paper is to study the subsemigroup
$\overline{\Omega}(X, Y)$ of $T(X)$ defined by \[\overline{\Omega}(X, Y) =
\{f\in T(X)\colon Yf = Y\},\] where $Y$ is a fixed nonempty subset of $X$. We
describe regular elements in $\overline{\Omega}(X, Y)$ and show that
$\overline{\Omega}(X, Y)$ is regular if and only if $Y$ is finite. We
characterize unit-regular elements in $\overline{\Omega}(X, Y)$ and prove that
$\overline{\Omega}(X, Y)$ is unit-regular if and only if $X$ is finite. We
characterize Green's relations on $\overline{\Omega}(X, Y)$ and prove that
$\mathcal{D} =\mathcal{J}$ on $\overline{\Omega}(X, Y)$ if and only if $Y$ is
finite. We also determine ideals of $\overline{\Omega}(X, Y)$ and investigate
its kernel. This paper extends several results appeared in the literature.
|
We elaborate on the correspondence between the canonical partition function
in asymptotically AdS universes and the no-boundary proposal for positive
vacuum energy. For the case of a pure cosmological constant, the analytic
continuation of the AdS partition function is seen to define the no-boundary
wave function (in dS) uniquely in the simplest minisuperspace model. A
consideration of the AdS gravitational path integral implies that on the dS
side, saddle points with Hawking-Moss/Coleman-De Luccia-type tunnelling
geometries are irrelevant. This implies that simple topology changing
geometries do not contribute to the nucleation of the universe. The analytic
AdS/dS equivalence holds up once tensor fluctuations are added. It also works,
at the level of the saddle point approximation, when a scalar field with a mass
term is included, though in the latter case, it is the mass that must be
analytically continued. Our results illustrate the emergence of time from space
by means of a Stokes phenomenon, in the case of positive vacuum energy.
Furthermore, we arrive at a new characterisation of the no-boundary condition,
namely that there should be no momentum flux at the nucleation of the universe.
|
The ultimate detection limit of optical biosensors is often limited by
various noise sources, including those introduced by the optical measurement
setup. While sophisticated modifications to instrumentation may reduce noise, a
simpler approach that can benefit all sensor platforms is the application of
signal processing to minimize the deleterious effects of noise. In this work,
we show that applying complex Morlet wavelet convolution to Fabry-P\'erot
interference fringes characteristic of thin film reflectometric biosensors
effectively filters out white noise and low frequency reflectance variations.
Subsequent calculation of an average difference in phase between the filtered
analyte and reference signals enables a significant reduction in the limit of
detection (LOD) enabling closer competition with current state-of-the-art
techniques. This method is applied on experimental data sets of thin film
porous silicon sensors (PSi) in buffered solution and complex media obtained
from two different laboratories. The demonstrated improvement in LOD achieved
using wavelet convolution and average phase difference paves the way for PSi
optical biosensors to operate with clinically relevant detection limits for
medical diagnostics, environmental monitoring, and food safety.
|
In addition to the well-known gas phase mass-metallicity relation (MZR),
recent spatially-resolved observations have shown that local galaxies also obey
a mass-metallicity gradient relation (MZGR) whereby metallicity gradients can
vary systematically with galaxy mass. In this work, we use our
recently-developed analytic model for metallicity distributions in galactic
discs, which includes a wide range of physical processes -- radial advection,
metal diffusion, cosmological accretion, and metal-enriched outflows -- to
simultaneously analyse the MZR and MZGR. We show that the same physical
principles govern the shape of both: centrally-peaked metal production favours
steeper gradients, and this steepening is diluted by the addition of metal-poor
gas, which is supplied by inward advection for low-mass galaxies and by
cosmological accretion for massive galaxies. The MZR and the MZGR both bend at
galaxy stellar mass $\sim 10^{10} - 10^{10.5}\,\rm{M_{\odot}}$, and we show
that this feature corresponds to the transition of galaxies from the
advection-dominated to the accretion-dominated regime. We also find that both
the MZR and MZGR strongly suggest that low-mass galaxies preferentially lose
metals entrained in their galactic winds. While this metal-enrichment of the
galactic outflows is crucial for reproducing both the MZR and the MZGR at the
low-mass end, we show that the flattening of gradients in massive galaxies is
expected regardless of the nature of their winds.
|
Direct Volume Rendering (DVR) using Volumetric Path Tracing (VPT) is a
scientific visualization technique that simulates light transport with objects'
matter using physically-based lighting models. Monte Carlo (MC) path tracing is
often used with surface models, yet its application for volumetric models is
difficult due to the complexity of integrating MC light-paths in volumetric
media with none or smooth material boundaries. Moreover, auxiliary
geometry-buffers (G-buffers) produced for volumes are typically very noisy,
failing to guide image denoisers relying on that information to preserve image
details. This makes existing real-time denoisers, which take noise-free
G-buffers as their input, less effective when denoising VPT images. We propose
the necessary modifications to an image-based denoiser previously used when
rendering surface models, and demonstrate effective denoising of VPT images. In
particular, our denoising exploits temporal coherence between frames, without
relying on noise-free G-buffers, which has been a common assumption of existing
denoisers for surface-models. Our technique preserves high-frequency details
through a weighted recursive least squares that handles heterogeneous noise for
volumetric models. We show for various real data sets that our method improves
the visual fidelity and temporal stability of VPT during classic DVR operations
such as camera movements, modifications of the light sources, and editions to
the volume transfer function.
|
In the last decade, substantial progress has been made towards standardizing
the syntax of graph query languages, and towards understanding their semantics
and complexity of evaluation. In this paper, we consider temporal property
graphs (TPGs) and propose temporal regular path queries (TRPQs) that
incorporate time into TPG navigation. Starting with design principles, we
propose a natural syntactic extension of the MATCH clause of popular graph
query languages. We then formally present the semantics of TRPQs, and study the
complexity of their evaluation. We show that TRPQs can be evaluated in
polynomial time if TPGs are time-stamped with time points, and identify
fragments of the TRPQ language that admit efficient evaluation over a more
succinct interval-annotated representation. Finally, we implement a fragment of
the language in a state-of-the-art dataflow framework, and experimentally
demonstrate that TRPQ can be evaluated efficiently.
|
Let $G$ be a simple undirected graph with vertex set $V(G)=\{v_1, v_2,
\ldots, v_n\}$ and edge set $E(G)$. The Sombor matrix $\mathcal{S}(G)$ of a
graph $G$ is defined so that its $(i,j)$-entry is equal to $\sqrt{d_i^2+d_j^2}$
if the vertices $v_i$ and $v_j$ are adjacent, and zero otherwise, where $d_i$
denotes the degree of vertex $v_i$ in $G$. In this paper, lower and upper
bounds on the spectral radius, energy and Estrada index of the Sombor matrix of
graphs are obtained, and the respective extremal graphs are characterized.
|
COVID19 has impacted Indian engineering institutions (EIs) enormously. It has
tightened its knot around EIs that forced their previous half shut shades
completely down to prevent the risk of spreading COVID19. In such a situation,
fetching new enrollments on EI campuses is a difficult and challenging task, as
students behavior and family preferences have changed drastically due to mental
stress and emotions attached to them. Consequently, it becomes a prerequisite
to examine the choice characteristics influencing the selection of EI during
the COVID-19 pandemic to make it normal for new enrollments.
The purpose of this study is to critically examine choice characteristics
that affect students choice for EI and consequently to explore relationships
between institutional characteristics and the suitability of EI during the
COVID19 pandemic across students characteristics. The findings of this study
revealed dissimilarities across students characteristics regarding the
suitability of EIs under pandemic conditions. Regression analysis revealed that
EI characteristics such as proximity, image and reputation, quality education
and curriculum delivery have significantly contributed to suitability under
COVID19. At the micro level, multiple relationships were noted between EI
characteristics and the suitability of EI under the pandemic across students
characteristics. The study has successfully demonstrated how choice
characteristics can be executed to regulate the suitability of EI under the
COVID19 pandemic for the inclusion of diversity. It is useful for policy makers
and academicians to reposition EIs that fetch diversity during the pandemic.
This study is the first to provide insights into the performance of choice
characteristics and their relationship with the suitability of EIs under a
pandemic and can be a yardstick in administering new enrollments.
|
We introduce Reflective Hamiltonian Monte Carlo (ReHMC), an HMC-based
algorithm, to sample from a log-concave distribution restricted to a convex
body. We prove that, starting from a warm start, the walk mixes to a
log-concave target distribution $\pi(x) \propto e^{-f(x)}$, where $f$ is
$L$-smooth and $m$-strongly-convex, within accuracy $\varepsilon$ after
$\widetilde O(\kappa d^2 \ell^2 \log (1 / \varepsilon))$ steps for a
well-rounded convex body where $\kappa = L / m$ is the condition number of the
negative log-density, $d$ is the dimension, $\ell$ is an upper bound on the
number of reflections, and $\varepsilon$ is the accuracy parameter. We also
developed an efficient open source implementation of ReHMC and we performed an
experimental study on various high-dimensional data-sets. The experiments
suggest that ReHMC outperfroms Hit-and-Run and Coordinate-Hit-and-Run regarding
the time it needs to produce an independent sample and introduces practical
truncated sampling in thousands of dimensions.
|
When flipping a fair coin, let $W = L_1L_2...L_N$ with $L_i\in\{H,T\}$ be a
binary word of length $N=2$ or $N=3$. In this paper, we establish second- and
third-order linear recurrence relations and their generating functions to
discuss the probabilities $p_{W}(n)$ that binary words $W$ appear for the first
time after $n$ coin tosses.
|
Climate change and global warming are the significant challenges of the new
century. A viable solution to mitigate greenhouse gas emissions is via a
globally incentivized market mechanism proposed in the Kyoto protocol. In this
view, the carbon dioxide (or other greenhouse gases) emission is considered a
commodity, forming a carbon trading system. There have been attempts in
developing this idea in the past decade with limited success. The main
challenges of current systems are fragmented implementations, lack of
transparency leading to over-crediting and double-spending, and substantial
transaction costs that transfer wealth to brokers and agents. We aim to create
a Carbon Credit Ecosystem using smart contracts that operate in conjunction
with blockchain technology in order to bring more transparency, accessibility,
liquidity, and standardization to carbon markets. This ecosystem includes a
tokenization mechanism to securely digitize carbon credits with clear minting
and burning protocols, a transparent mechanism for distribution of tokens, a
free automated market maker for trading the carbon tokens, and mechanisms to
engage all stakeholders, including the energy industry, project verifiers,
liquidity providers, NGOs, concerned citizens, and governments. This approach
could be used in a variety of other credit/trading systems.
|
We demonstrate a successful navigation and docking control system for the
John Deere Tango autonomous mower, using only a single camera as the input.
This vision-only system is of interest because it is inexpensive, simple for
production, and requires no external sensing. This is in contrast to existing
systems that rely on integrated position sensors and global positioning system
(GPS) technologies. To produce our system we combined a state-of-the-art object
detection architecture, You Only Look Once (YOLO), with a reinforcement
learning (RL) architecture, Double Deep QNetworks (Double DQN). The object
detection network identifies features on the mower and passes its output to the
RL network, providing it with a low-dimensional representation that enables
rapid and robust training. Finally, the RL network learns how to navigate the
machine to the desired spot in a custom simulation environment. When tested on
mower hardware, the system is able to dock with centimeter-level accuracy from
arbitrary initial locations and orientations.
|
Few researches have been proposed specifically for real-time semantic
segmentation in rainy environments. However, the demand in this area is huge
and it is challenging for lightweight networks. Therefore, this paper proposes
a lightweight network which is specially designed for the foreground
segmentation in rainy environments, named De-raining Semantic Segmentation
Network (DRSNet). By analyzing the characteristics of raindrops, the
MultiScaleSE Block is targetedly designed to encode the input image, it uses
multi-scale dilated convolutions to increase the receptive field, and SE
attention mechanism to learn the weights of each channels. In order to combine
semantic information between different encoder and decoder layers, it is
proposed to use Asymmetric Skip, that is, the higher semantic layer of encoder
employs bilinear interpolation and the output passes through pointwise
convolution, then added element-wise to the lower semantic layer of decoder.
According to the control experiments, the performances of MultiScaleSE Block
and Asymmetric Skip compared with SEResNet18 and Symmetric Skip respectively
are improved to a certain degree on the Foreground Accuracy index. The
parameters and the floating point of operations (FLOPs) of DRSNet is only 0.54M
and 0.20GFLOPs separately. The state-of-the-art results and real-time
performances are achieved on both the UESTC all-day Scenery add rain
(UAS-add-rain) and the Baidu People Segmentation add rain (BPS-add-rain)
benchmarks with the input sizes of 192*128, 384*256 and 768*512. The speed of
DRSNet exceeds all the networks within 1GFLOPs, and Foreground Accuracy index
is also the best among the similar magnitude networks on both benchmarks.
|
Understanding electrical energy demand at the consumer level plays an
important role in planning the distribution of electrical networks and offering
of off-peak tariffs, but observing individual consumption patterns is still
expensive. On the other hand, aggregated load curves are normally available at
the substation level. The proposed methodology separates substation aggregated
loads into estimated mean consumption curves, called typical curves, including
information given by explanatory variables. In addition, a model-based
clustering approach for substations is proposed based on the similarity of
their consumers typical curves and covariance structures. The methodology is
applied to a real substation load monitoring dataset from the United Kingdom
and tested in eight simulated scenarios.
|
The measurements of $V_{us}$ in leptonic $(K_{\mu 2})$ and semileptonic
$(K_{l3})$ kaon decays exhibit a $3\sigma$ disagreement, which could originate
either from physics beyond the Standard Model or some large unidentified
Standard Model systematic effects. Clarifying this issue requires a careful
examination of all existing Standard Model inputs. Making use of a
newly-proposed computational framework and the most recent lattice QCD results,
we perform a comprehensive re-analysis of the electroweak radiative corrections
to the $K_{e3}$ decay rates that achieves an unprecedented level of precision
of $10^{-4}$, which improves the current best results by almost an order of
magnitude. No large systematic effects are found, which suggests that the
electroweak radiative corrections should be removed from the ``list of
culprits'' responsible for the $K_{\mu 2}$--$K_{l3}$ discrepancy.
|
We present a new method to capture detailed human motion, sampling more than
1000 unique points on the body. Our method outputs highly accurate 4D
(spatio-temporal) point coordinates and, crucially, automatically assigns a
unique label to each of the points. The locations and unique labels of the
points are inferred from individual 2D input images only, without relying on
temporal tracking or any human body shape or skeletal kinematics models.
Therefore, our captured point trajectories contain all of the details from the
input images, including motion due to breathing, muscle contractions and flesh
deformation, and are well suited to be used as training data to fit advanced
models of the human body and its motion. The key idea behind our system is a
new type of motion capture suit which contains a special pattern with
checkerboard-like corners and two-letter codes. The images from our
multi-camera system are processed by a sequence of neural networks which are
trained to localize the corners and recognize the codes, while being robust to
suit stretching and self-occlusions of the body. Our system relies only on
standard RGB or monochrome sensors and fully passive lighting and the passive
suit, making our method easy to replicate, deploy and use. Our experiments
demonstrate highly accurate captures of a wide variety of human poses,
including challenging motions such as yoga, gymnastics, or rolling on the
ground.
|
The application of remaining useful life (RUL) prediction has taken great
importance in terms of energy optimization, cost-effectiveness, and risk
mitigation. The existing RUL prediction algorithms mostly constitute deep
learning frameworks. In this paper, we implement LSTM and GRU models and
compare the obtained results with a proposed genetically trained neural
network. The current models solely depend on Adam and SGD for optimization and
learning. Although the models have worked well with these optimizers, even
little uncertainties in prognostics prediction can result in huge losses. We
hope to improve the consistency of the predictions by adding another layer of
optimization using Genetic Algorithms. The hyper-parameters - learning rate and
batch size are optimized beyond manual capacity. These models and the proposed
architecture are tested on the NASA Turbofan Jet Engine dataset. The optimized
architecture can predict the given hyper-parameters autonomously and provide
superior results.
|
In this paper, we study the optimal transmission of a multi-quality tiled 360
virtual reality (VR) video from a multi-antenna server (e.g., access point or
base station) to multiple single-antenna users in a multiple-input
multiple-output (MIMO)-orthogonal frequency division multiple access (OFDMA)
system. We minimize the total transmission power with respect to the subcarrier
allocation constraints, rate allocation constraints, and successful
transmission constraints, by optimizing the beamforming vector and subcarrier,
transmission power and rate allocation. The formulated resource allocation
problem is a challenging mixed discrete-continuous optimization problem. We
obtain an asymptotically optimal solution in the case of a large antenna array,
and a suboptimal solution in the general case. As far as we know, this is the
first work providing optimization-based design for 360 VR video transmission in
MIMO-OFDMA systems. Finally, by numerical results, we show that the proposed
solutions achieve significant improvement in performance compared to the
existing solutions.
|
We discover that deep ReLU neural network classifiers can see a
low-dimensional Riemannian manifold structure on data. Such structure comes via
the local data matrix, a variation of the Fisher information matrix, where the
role of the model parameters is taken by the data variables. We obtain a
foliation of the data domain and we show that the dataset on which the model is
trained lies on a leaf, the data leaf, whose dimension is bounded by the number
of classification labels. We validate our results with some experiments with
the MNIST dataset: paths on the data leaf connect valid images, while other
leaves cover noisy images.
|
Backhauling services through satellite systems have doubled between 2012 and
2018. There is an increasing demand for this service for which satellite
systems typically allocate a fixed resource. This solution may not help in
optimizing the usage of the scarce satellite resource.
This study measures the relevance of using dynamic resource allocation
mechanisms for backhaul services through satellite systems. The satellite
system is emulated with OpenSAND, the LTE system with Amarisoft and the
experiments are orchestrated by OpenBACH. We compare the relevance of applying
TCP PEP mechanisms and dynamic resource allocations for different traffic
services by measuring the QoE for web browsing, data transfer and VoIP
applications.
The main conclusions are the following. When the system is congested, PEP and
layer-2 access mechanisms do not provide significant improvements. When the
system is not congested, data transfer can be greatly improved through
protocols and channel access mechanism optimization. Tuning the Constant Rate
Assignment can help in reducing the cost of the resource and provide QoE
improvements when the network is not loaded.
|
Grain boundaries (GBs) are planar lattice defects that govern the properties
of many types of polycrystalline materials. Hence, their structures have been
investigated in great detail. However, much less is known about their chemical
features, owing to the experimental difficulties to probe these features at the
atomic length scale inside bulk material specimens. Atom probe tomography (APT)
is a tool capable of accomplishing this task, with an ability to quantify
chemical characteristics at near-atomic scale. Using APT data sets, we present
here a machine-learning-based approach for the automated quantification of
chemical features of GBs. We trained a convolutional neural network (CNN) using
twenty thousand synthesized images of grain interiors, GBs, or triple
junctions. Such a trained CNN automatically detects the locations of GBs from
APT data. Those GBs are then subjected to compositional mapping and analysis,
including revealing their in-plane chemical decoration patterns. We applied
this approach to experimentally obtained APT data sets pertaining to three case
studies, namely, Ni-P, Pt-Au, and Al-Zn-Mg-Cu alloys. In the first case, we
extracted GB-specific segregation features as a function of misorientation and
coincidence site lattice character. Secondly, we revealed interfacial excesses
and in-plane chemical features that could not have been found by standard
compositional analyses. Lastly, we tracked the temporal evolution of chemical
decoration from early-stage solute GB segregation in the dilute limit to
interfacial phase separation, characterized by the evolution of complex
composition patterns. This machine-learning-based approach provides
quantitative, unbiased, and automated access to GB chemical analyses, serving
as an enabling tool for new discoveries related to interface thermodynamics,
kinetics, and the associated chemistry-structure-property relations.
|
The Fock space $\mathcal{F}(\mathbb{C}^n)$ is the space of holomorphic
functions on $\mathbb{C}^n$ that are square-integrable with respect to the
Gaussian measure on $\mathbb{C}^n$. This space plays an important role in
several subfields of analysis and representation theory. In particular, it has
for a long time been a model to study Toeplitz operators. Esmeral and Maximenko
showed in 2016 that radial Toeplitz operators on $\mathcal{F}(\mathbb{C})$
generate a commutative $C^*$-algebra which is isometrically isomorphic to the
$C^*$-algebra $C_{b,u}(\mathbb{N}_0,\rho_1)$. In this article, we extend the
result to $k$-quasi-radial symbols acting on the Fock space
$\mathcal{F}(\mathbb{C}^n)$. We calculate the spectra of the said Toeplitz
operators and show that the set of all eigenvalue functions is dense in the
$C^*$-algebra $C_{b,u}(\mathbb{N}_0^k,\rho_k)$ of bounded functions on
$\mathbb{N}_0^k$ which are uniformly continuous with respect to the square-root
metric. In fact, the $C^*$-algebra generated by Toeplitz operators with
quasi-radial symbols is $C_{b,u}(\mathbb{N}_0^k,\rho_k)$.
|
We present optical follow-up imaging obtained with the Katzman Automatic
Imaging Telescope, Las Cumbres Observatory Global Telescope Network, Nickel
Telescope, Swope Telescope, and Thacher Telescope of the LIGO/Virgo
gravitational wave (GW) signal from the neutron star-black hole (NSBH) merger
GW190814. We searched the GW190814 localization region (19 deg$^{2}$ for the
90th percentile best localization), covering a total of 51 deg$^{2}$ and 94.6%
of the two-dimensional localization region. Analyzing the properties of 189
transients that we consider as candidate counterparts to the NSBH merger,
including their localizations, discovery times from merger, optical spectra,
likely host-galaxy redshifts, and photometric evolution, we conclude that none
of these objects are likely to be associated with GW190814. Based on this
finding, we consider the likely optical properties of an electromagnetic
counterpart to GW190814, including possible kilonovae and short gamma-ray burst
afterglows. Using the joint limits from our follow-up imaging, we conclude that
a counterpart with an $r$-band decline rate of 0.68 mag day$^{-1}$, similar to
the kilonova AT 2017gfo, could peak at an absolute magnitude of at most $-17.8$
mag (50% confidence). Our data are not constraining for ''red'' kilonovae and
rule out ''blue'' kilonovae with $M>0.5 M_{\odot}$ (30% confidence). We
strongly rule out all known types of short gamma-ray burst afterglows with
viewing angles $<$17$^{\circ}$ assuming an initial jet opening angle of
$\sim$$5.2^{\circ}$ and explosion energies and circumburst densities similar to
afterglows explored in the literature. Finally, we explore the possibility that
GW190814 merged in the disk of an active galactic nucleus, of which we find
four in the localization region, but we do not find any candidate counterparts
among these sources.
|
Ultrafast lasers are ideal tools to process transparent materials because
they spatially confine the deposition of laser energy within the material's
bulk via nonlinear photoionization processes. Nonlinear propagation and
filamentation were initially regarded as deleterious effects. But in the last
decade, they turned out to be benefits to control energy deposition over long
distances. These effects create very high aspect ratio structures which have
found a number of important applications, particularly for glass separation
with non-ablative techniques. This chapter reviews the developments of
in-volume ultrafast laser processing of transparent materials. We discuss the
basic physics of the processes, characterization means, filamentation of
Gaussian and Bessel beams and provide an overview of present applications.
|
Hyperspectral imaging at cryogenic temperatures is used to investigate
exciton and trion propagation in MoSe$_2$ monolayers encapsulated with
hexagonal boron nitride (hBN). Under a tightly focused, continuous-wave laser
excitation, the spatial distribution of neutral excitons and charged trions
strongly differ at high excitation densities. Remarkably, in this regime the
trion distribution develops a halo shape, similar to that previously observed
in WS2 monolayers at room temperature and under pulsed excitation. In contrast,
the exciton distribution only presents a moderate broadening without the
appereance of a halo. Spatially and spectrally resolved luminescence spectra
reveal the buildup of a significant temperature gradient at high excitation
power, that is attributed to the energy relaxation of photoinduced hot
carriers. We show, via a numerical resolution of the transport equations for
excitons and trions, that the halo can be interpreted as thermal drift of
trions due to a Seebeck term in the particle current. The model shows that the
difference between trion and exciton profiles is simply understood in terms of
the very different lifetimes of these two quasiparticles.
|
The conformational states of a semiflexible polymer enclosed in a volume
$V:=\ell^{3}$ are studied as stochastic realizations of paths using the
stochastic curvature approach developed in [Rev. E 100, 012503 (2019)], in the
regime whenever $3\ell/\ell_ {p}> 1$, where $\ell_{p}$ is the persistence
length. The cases of a semiflexible polymer enclosed in a cube and sphere are
considered. In these cases, we explore the Spakowitz-Wang type polymer shape
transition, where the critical persistence length distinguishes between an
oscillating and a monotonic phase at the level of the mean-square end-to-end
distance. This shape transition provides evidence of a universal signature of
the behavior of a semiflexible polymer confined in a compact domain.
|
We construct a cosmological model from the inception of the
Friedmann-Lem\^aitre-Robertson-Walker metric into the field equations of the
$f(R,L_m)$ gravity theory, with $R$ being the Ricci scalar and $L_m$ being the
matter lagrangian density. The formalism is developed for a particular
$f(R,L_m)$ function, namely $R/16\pi +(1+\sigma R)L_{m}$, with $\sigma$ being a
constant that carries the geometry-matter coupling. Our solutions are
remarkably capable of evading the Big-Bang singularity as well as predict the
cosmic acceleration with no need for the cosmological constant, but simply as a
consequence of the geometry-matter coupling terms in the Friedmann-like
equations.
|
Molecular science is governed by the dynamics of electrons, atomic nuclei,
and their interaction with electromagnetic fields. A reliable physicochemical
understanding of these processes is crucial for the design and synthesis of
chemicals and materials of economic value. Although some problems in this field
are adequately addressed by classical mechanics, many require an explicit
quantum mechanical description. Such quantum problems represented by
exponentially large wave function should naturally benefit from quantum
computation on a number of logical qubits that scales only linearly with system
size. In this perspective, we focus on the potential of quantum computing for
solving relevant problems in the molecular sciences -- molecular physics,
chemistry, biochemistry, and materials science.
|
This research discusses multi-criteria decision making (MCDM) using Fuzzy-AHP
methods of tourism. The fuzzy-AHP process will rank tourism trends based on
data from social media. Social media is one of the channels with the largest
source of data input in determining tourism development. The development uses
social media interactions based on the facilities visited, including reviews,
stories, likes, forums, blogs, and feedback. This experiment aims to prioritize
facilities that are the trend of tourism. The priority ranking uses weight
criteria and the ranking process. The highest rank is in the attractions of the
Park/Picnic Area, with the final weight calculation value of 0.6361. Fuzzy-AHP
can rank optimally with an MSE value of \approx 0.0002.
|
Despite their recent success on image denoising, the need for deep and
complex architectures still hinders the practical usage of CNNs. Older but
computationally more efficient methods such as BM3D remain a popular choice,
especially in resource-constrained scenarios. In this study, we aim to find out
whether compact neural networks can learn to produce competitive results as
compared to BM3D for AWGN image denoising. To this end, we configure networks
with only two hidden layers and employ different neuron models and layer widths
for comparing the performance with BM3D across different AWGN noise levels. Our
results conclusively show that the recently proposed self-organized variant of
operational neural networks based on a generative neuron model (Self-ONNs) is
not only a better choice as compared to CNNs, but also provide competitive
results as compared to BM3D and even significantly surpass it for high noise
levels.
|
This work investigates the feasibility of using input-output data-driven
control techniques for building control and their susceptibility to
data-poisoning techniques. The analysis is performed on a digital replica of
the KTH Livein Lab, a non-linear validated model representing one of the KTH
Live-in Lab building testbeds. This work is motivated by recent trends showing
a surge of interest in using data-based techniques to control cyber-physical
systems. We also analyze the susceptibility of these controllers to
data-poisoning methods, a particular type of machine learning threat geared
towards finding imperceptible attacks that can undermine the performance of the
system under consideration. We consider the Virtual Reference Feedback Tuning
(VRFT), a popular data-driven control technique, and show its performance on
the KTH Live-In Lab digital replica. We then demonstrate how poisoning attacks
can be crafted and illustrate the impact of such attacks. Numerical experiments
reveal the feasibility of using data-driven control methods for finding
efficient control laws. However, a subtle change in the datasets can
significantly deteriorate the performance of VRFT.
|
Scanning real-life scenes with modern registration devices typically give
incomplete point cloud representations, mostly due to the limitations of the
scanning process and 3D occlusions. Therefore, completing such partial
representations remains a fundamental challenge of many computer vision
applications. Most of the existing approaches aim to solve this problem by
learning to reconstruct individual 3D objects in a synthetic setup of an
uncluttered environment, which is far from a real-life scenario. In this work,
we reformulate the problem of point cloud completion into an object
hallucination task. Thus, we introduce a novel autoencoder-based architecture
called HyperPocket that disentangles latent representations and, as a result,
enables the generation of multiple variants of the completed 3D point clouds.
We split point cloud processing into two disjoint data streams and leverage a
hypernetwork paradigm to fill the spaces, dubbed pockets, that are left by the
missing object parts. As a result, the generated point clouds are not only
smooth but also plausible and geometrically consistent with the scene. Our
method offers competitive performances to the other state-of-the-art models,
and it enables a~plethora of novel applications.
|
Using five year monitoring observations, we did a blind search for pulses for
rotating radio transient (RRAT) J0139+33 and PSR B0320+39. At the interval \pm
1.5m of the time corresponding to the source passing through the meridian, we
detected 39377 individual pulses for the pulsar B0320+39 and 1013 pulses for
RRAT J0139+33. The share of registered pulses from the total number of observed
periods for the pulsar B0320+39 is 74%, and for the transient J0139+33 it is
0.42%. Signal-to-noise ratio (S/N) for the strongest registered pulses is
approximately equal to: S/N = 262 (for B0320+39) and S/N = 154 (for J0139+33).
Distributions of the number of detected pulses in S/N units for the pulsar
and for the rotating transient are obtained. The distributions could be
approximated with a lognormal and power dependencies. For B0320+39 pulsar, the
dependence is lognormal, it turns into a power dependence at high values of
S/N, and for RRAT J0139+33, the distribution of pulses by energy is described
by a broken (bimodal) power dependence with an exponent of about 0.4 and 1.8
(S/N < 19 and S/N > 19).
We have not detected regular (pulsar) emission of J0139+33. Analysis of the
obtained data suggests that RRAT J0139+33 is a pulsar with giant pulses.
|
A Banach space X has the SHAI (surjective homomorphisms are injective)
property provided that for every Banach space Y, every continuous surjective
algebra homomorphism from the bounded linear operators on X onto the bounded
linear operators on Y is injective. The main result gives a sufficient
condition for X to have the SHAI property. The condition is satisfied for L^p
(0, 1) for 1 < p < \infty, spaces with symmetric bases that have finite cotype,
and the Schatten p-spaces for 1 < p < \infty.
|
This paper introduces our systems for all three subtasks of SemEval-2021 Task
4: Reading Comprehension of Abstract Meaning. To help our model better
represent and understand abstract concepts in natural language, we well-design
many simple and effective approaches adapted to the backbone model (RoBERTa).
Specifically, we formalize the subtasks into the multiple-choice question
answering format and add special tokens to abstract concepts, then, the final
prediction of question answering is considered as the result of subtasks.
Additionally, we employ many finetuning tricks to improve the performance.
Experimental results show that our approaches achieve significant performance
compared with the baseline systems. Our approaches achieve eighth rank on
subtask-1 and tenth rank on subtask-2.
|
How should we understand the social and political effects of the datafication
of human life? This paper argues that the effects of data should be understood
as a constitutive shift in social and political relations. We explore how
datafication, or quantification of human and non-human factors into binary
code, affects the identity of individuals and groups. This fundamental shift
goes beyond economic and ethical concerns, which has been the focus of other
efforts to explore the effects of datafication and AI. We highlight that
technologies such as datafication and AI (and previously, the printing press)
both disrupted extant power arrangements, leading to decentralization, and
triggered a recentralization of power by new actors better adapted to
leveraging the new technology. We use the analogy of the printing press to
provide a framework for understanding constitutive change. The printing press
example gives us more clarity on 1) what can happen when the medium of
communication drastically alters how information is communicated and stored; 2)
the shift in power from state to private actors; and 3) the tension of
simultaneously connecting individuals while driving them towards narrower
communities through algorithmic analyses of data.
|
The concept of an angle is one that often causes difficulties in metrology.
These are partly caused by a confusing mixture of several mathematical terms,
partly by real mathematical difficulties and finally by imprecise terminology.
The purpose of this publication is to clarify misunderstandings and to explain
why strict terminology is important. It will also be shown that most
misunderstandings regarding the `radian' can be avoided if some simple rules
are obeyed.
|
We explore the parameter space of a U(1) extension of the standard model --
also called the super-weak model -- from the point of view of explaining the
observed dark matter energy density in the Universe. The new particle spectrum
contains a complex scalar singlet and three right-handed neutrinos, among which
the lightest one is the dark matter candidate. We explore both freeze-in and
freeze-out mechanisms of dark matter production. In both cases, we find regions
in the plane of the super-weak coupling vs. the mass of the new gauge boson
that are not excluded by current experimental constraints. These regions are
distinct and the one for freeze-out will be explored in searches for neutral
gauge boson in the near future.
|
This paper focuses on a core task in computational sustainability and
statistical ecology: species distribution modeling (SDM). In SDM, the
occurrence pattern of a species on a landscape is predicted by environmental
features based on observations at a set of locations. At first, SDM may appear
to be a binary classification problem, and one might be inclined to employ
classic tools (e.g., logistic regression, support vector machines, neural
networks) to tackle it. However, wildlife surveys introduce structured noise
(especially under-counting) in the species observations. If unaccounted for,
these observation errors systematically bias SDMs. To address the unique
challenges of SDM, this paper proposes a framework called StatEcoNet.
Specifically, this work employs a graphical generative model in statistical
ecology to serve as the skeleton of the proposed computational framework and
carefully integrates neural networks under the framework. The advantages of
StatEcoNet over related approaches are demonstrated on simulated datasets as
well as bird species data. Since SDMs are critical tools for ecological science
and natural resource management, StatEcoNet may offer boosted computational and
analytical powers to a wide range of applications that have significant social
impacts, e.g., the study and conservation of threatened species.
|
We study phase contributions of wave functions that occur in the evolution of
Gaussian surface gravity water wave packets with nonzero initial momenta
propagating in the presence and absence of an effective external linear
potential. Our approach takes advantage of the fact that in contrast to matter
waves, water waves allow us to measure both their amplitudes and phases.
|
Non-Hermitian systems show a non-Hermitian skin effect, where the bulk states
are localized at a boundary of the systems with open boundary conditions. In
this paper, we study dependence of the localization length of the eigenstates
on a system size in a specific non-Hermitian model with a critical
non-Hermitian skin effect, where the energy spectrum undergoes discontinuous
transition in the thermodynamic limit. We analytically show that the
eigenstates exhibit remarkable localization, known as scale-free localization,
where the localization length is proportional to a system size. Our result
gives a theoretical support for the scale-free localization, which has been
proposed only numerically in previous works.
|
Recently, [8] has proposed that heterogeneity of infectiousness (and
susceptibility) across individuals in infectious diseases, plays a major role
in affecting the Herd Immunity Threshold (HIT). Such heterogeneity has been
observed in COVID-19 and is recognized as overdispersion (or
"super-spreading"). The model of [8] suggests that super-spreaders contribute
significantly to the effective reproduction factor, R, and that they are likely
to get infected and immune early in the process. Consequently, under R_0 = 3
(attributed to COVID-19), the Herd Immunity Threshold (HIT) is as low as 5%, in
contrast to 67% according to the traditional models [1, 2, 4, 10]. This work
follows up on [8] and proposes that heterogeneity of infectiousness
(susceptibility) has two "faces" whose mix affects dramatically the HIT: (1)
Personal-Trait-, and (2) Event-Based- Infectiousness (Susceptibility). The
former is a personal trait of specific individuals (super-spreaders) and is
nullified once those individuals are immune (as in [8]). The latter is
event-based (e.g cultural super-spreading events) and remains effective
throughout the process, even after the super-spreaders immune. We extend [8]'s
model to account for these two factors, analyze it and conclude that the HIT is
very sensitive to the mix between (1) and (2), and under R_0 = 3 it can vary
between 5% and 67%. Preliminary data from COVID-19 suggests that herd immunity
is not reached at 5%. We address operational aspects and analyze the effects of
lockdown strategies on the spread of a disease. We find that herd immunity (and
HIT) is very sensitive to the lockdown type. While some lockdowns affect
positively the disease blocking and increase herd immunity, others have adverse
effects and reduce the herd immunity.
|
The quantum operator $\hat{T}_3$, corresponding to the projection of the
toroidal moment on the $z$ axis, admits several self-adjoint extensions, when
defined on the whole $\mathbb{R}^3$ space. $\hat{T}_3$ commutes with
$\hat{L}_3$ (the projection of the angular momentum operator on the $z$ axis)
and they have a \textit{natural set of coordinates} $(k,u,\phi)$ where $\phi$
is the azimuthal angle. The second set of \textit{natural coordinates} is
$(k_1,k_2,u)$, where $k_1 = k\cos\phi$, $k_2 = k\sin\phi$. In both sets,
$\hat{T}_3 = -i\hbar\partial/\partial u$, so any operator that is a function of
$k$ and the partial derivatives with respect to the \textit{natural variables}
$(k, u, \phi)$ commute with $\hat{T}_3$ and $\hat{L}_3$. Similarly, operators
that are functions of $k_1$, $k_2$, and the partial derivatives with respect to
$k_1$, $k_2$, and $u$ commute with $\hat{T}_3$. Therefore, we introduce here
the operators $\hat{p}_{k} \equiv -i \hbar \partial/\partial k$,
$\hat{p}^{(k1)} \equiv -i \hbar \partial/\partial k_1$, and $\hat{p}^{(k2)}
\equiv -i \hbar \partial/\partial k_2$ and express them in the $(x,y,z)$
coordinates. One may also invert the relations and write the typical operators,
like the momentum $\hat{\bf p} \equiv -i\hbar {\bf \nabla}$ or the kinetic
energy $\hat{H}_0 \equiv -\hbar^2\Delta/(2m)$ in terms of the "toroidal"
operators $\hat{T}_3$, $\hat{p}^{(k)}$, $\hat{p}^{(k1)}$, $\hat{p}^{(k2)}$,
and, eventually, $\hat{L}_3$. The formalism may be applied to specific physical
systems, like nuclei, condensed matter systems, or metamaterials. We exemplify
it by calculating the momentum operator and the free particle Hamiltonian in
terms of \textit{natural coordinates} in a thin torus, where the general
relations get considerably simplified.
|
Electronic states in the gap of a superconductor inherit intriguing many-body
properties from the superconductor. Here, we create these in-gap states by
manipulating Cr atomic chains on the $\beta$-Bi$_2$Pd superconductor. We find
that the topological properties of the in-gap states can greatly vary depending
on the crafted spin chain. These systems make an ideal platform for non-trivial
topological phases because of the large atom-superconductor interactions and
the existence of a large Rashba coupling at the Bi-terminated surface. We study
two spin chains, one with atoms two-lattice-parameter apart and one with
square-root-of-two lattice parameters. Of these, only the second one is in a
topologically non-trivial phase, in correspondence with the spin interactions
for this geometry.
|
Using density functional theory combined with nonequilibrium Green's function
method, the transport properties of borophene-based nano gas sensors with gold
electrodes are calculated, and comprehensive understandings regarding the
effects of gas molecules, MoS$_2$ substrate and gold electrodes to the
transport properties of borophene are made. Results show that borophene-based
sensors can be used to detect and distinguish CO, NO, NO$_2$ and NH$_3$ gas
molecules, MoS$_2$ substrate leads to a non-linear behavior on the
current-voltage characteristic, and gold electrodes provide charges to
borophene and form a potential barrier, which reduced the current values
compared to the current of the systems without gold electrodes. Our studies not
only provide useful information on the computationally design of
borophene-based gas sensors, but also help understand the transport behaviors
and underlying physics of 2D metallic materials with metal electrodes.
|
RX J0123.4-7321 is a well-established Be star X-ray binary system (BeXRB) in
the Small Magellanic Cloud (SMC). Like many such systems the variable X-ray
emission is driven by the underlying behaviour of the mass donor Be star.
Previous work has shown that the optical and X-ray were characterised by
regular outbursts at the proposed binary period of 119 d. However around
February 2008 the optical behaviour changed substantially, with the previously
regular optical outbursts ending. Reported here are new optical (OGLE) and
X-ray (Swift) observations covering the period after 2008 which suggest an
almost total circumstellar disc loss followed by a gradual recovery. This
indicates the probable transition of a Be star to a B star, and back again.
However, at the time of the most recent OGLE data (March 2020) the
characteristic periodic outbursts had yet to return to their early state,
indicating that the disk still had some re-building yet to complete.
|
Convection has been discussed in the field of accretion discs for several
decades, both as a means of angular momentum transport and also because of its
role in controlling discs' vertical structure via heat transport. If the gas is
sufficiently ionized and threaded by a weak magnetic field, convection might
interact in non-trivial ways with the magnetorotational instability (MRI).
Recently, vertically stratified local simulations of the MRI have reported
considerable variation in the angular momentum transport, as measured by the
stress to thermal pressure ratio $\alpha$, when convection is thought to be
present. Although MRI turbulence can act as a heat source for convection, it is
not clear how the instabilities will interact dynamically. Here we aim to
investigate the interplay between the two instabilities in controlled numerical
experiments, and thus isolate the generic features of their interaction. We
perform vertically stratified, 3D MHD shearing box simulations with a perfect
gas equation of state with the conservative, finite-volume code PLUTO. We find
two characteristic outcomes of the interaction between the two instabilities:
straight MRI and MRI/convective cycles, with the latter exhibiting alternating
phases of convection-dominated flow (during which the turbulent transport is
weak) and MRI-dominated flow. During the latter phase we find that $\alpha$ is
enhanced by nearly an order of magnitude, reaching peak values of $\sim 0.08$.
In addition, we find that convection in the non-linear phase takes the form of
large-scale and oscillatory convective cells. Convection can also help the MRI
persist to lower Rm than it would otherwise do. Finally we discuss how our
results help interpret simulations of Dwarf Novae.
|
In this paper we study Chow motives whose identity map is killed by a natural
number. Examples of such objects were constructed by Gorchinskiy-Orlov. We
introduce various invariants of torsion motives, in particular, the $p$-level.
We show that this invariant bounds from below the dimension of the variety a
torsion motive $M$ is a direct summand of and imposes restrictions on motivic
and singular cohomology of $M$. We study in more details the $p$-torsion
motives of surfaces, in particular, the Godeaux torsion motive. We show that
such motives are in 1-to-1 correspondence with certain Rost cycle submodules of
free modules over $H^*_{et}$. This description is parallel to that of mod-$p$
reduced motives of curves.
|
A very useful identity for Parseval frames for Hilbert spaces was obtained by
Balan, Casazza, Edidin, and Kutyniok. In this paper, we obtain a similar
identity for Parseval p-approximate Schauder frames for Banach spaces which
admits a homogeneous semi-inner product in the sense of Lumer-Giles.
|
We construct the hydrodynamic theory of coherent collective motion
("flocking") at a solid-liquid interface. The polar order parameter and
concentration of a collection of "active" (self-propelled) particles at a
planar interface between a passive, isotropic bulk fluid and a solid surface
are dynamically coupled to the bulk fluid. We find that such systems are
stable, and have long-range orientational order, over a wide range of
parameters. When stable, these systems exhibit "giant number fluctuations",
i.e., large fluctuations of the number of active particles in a fixed large
area. Specifically, these number fluctuations grow as the $3/4$th power of the
mean number within the area. Stable systems also exhibit anomalously rapid
diffusion of tagged particles suspended in the passive fluid along any
directions in a plane parallel to the solid-liquid interface, whereas the
diffusivity along the direction perpendicular to the plane is non-anomalous. In
other parameter regimes, the system becomes unstable.
|
As the fundamental physical process with many astrophysical implications, the
diffusion of cosmic rays (CRs) is determined by their interaction with
magnetohydrodynamic (MHD) turbulence. We consider the magnetic mirroring effect
arising from MHD turbulence on the diffusion of CRs. Due to the intrinsic
superdiffusion of turbulent magnetic fields, CRs with large pitch angles that
undergo mirror reflection, i.e., bouncing CRs, are not trapped between magnetic
mirrors, but move diffusively along the turbulent magnetic field, leading to a
new type of parallel diffusion, i.e., mirror diffusion. This mirror diffusion
is in general slower than the diffusion of non-bouncing CRs with small pitch
angles that undergo gyroresonant scattering. The critical pitch angle at the
balance between magnetic mirroring and pitch-angle scattering is important for
determining the diffusion coefficients of both bouncing and non-bouncing CRs
and their scalings with the CR energy. We find non-universal energy scalings of
diffusion coefficients, depending on the properties of MHD turbulence.
|
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