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Topological Dirac semimetals are a class of semimetals that host
symmetry-protected Dirac points near the Fermi level, which arise due to a band
inversion of the conduction and valence bands. In this work, we study the less
explored class of \emph{noncentrosymmetric} topological Dirac semimetals in
three dimensions. We identify the noncentrosymmetric crystallographic point
groups required to stabilize fourfold degenerate band crossings and derive
model Hamiltonians for all distinct types of band inversions allowed by
symmetry. Using these model Hamiltonians, which emphasize the physical nature
of the allowed couplings, we establish the generic electronic phase diagram
noncentrosymmetric Dirac semimetals and show that it generically includes
phases with coexistent Weyl point nodes or Weyl line nodes. In particular, for
one specific type of band inversion in sixfold symmetric systems we show that
Weyl line nodes are always present. Based on first-principles calculations, we
predict that BiPd$_2$O$_4$ is a noncentrosymmetric Dirac semimetal under 20 Gpa
pressure and hosts topological type-II Dirac points on the fourfold rotation
axis. Furthermore, we propose that the hexagonal polar alloy
LiZnSb$_{x}$Bi$_{1-x}$ realizes a Dirac semimetal with coexistent Weyl points.
Interestingly, the emergence and location of the Weyl points is highly tunable
and can be controlled by the alloy concentration $x$. More generally, our
results not only establish band-inverted noncentrosymmetric systems as a broad
and versatile class of topological semimetals, but also provide a framework for
studying the quantum nonlinear Hall effect and nonlinear optical properties in
the Dirac semimetals.
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The ability to engineer the properties of quantum optical states is essential
for quantum information processing applications. Here, we demonstrate tunable
control of spatial correlations between photon pairs produced by spontaneous
parametric down-conversion. By shaping the spatial pump beam profile in a
type-I collinear configuration, we tailor the spatial structure of coincidences
between photon pairs entangled in high dimensions, without effect on intensity.
The results highlight fundamental aspects of spatial coherence and hold
potential for the development of quantum technologies based on high-dimensional
spatial entanglement.
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The arithmetic of N, Z, Q, R can be extended to a graph arithmetic where N is
the semiring of finite simple graphs and where Z and Q are integral domains,
culminating in a Banach algebra R. A single network completes to the Wiener
algebra. We illustrate the compatibility with topology and spectral theory.
Multiplicative linear functionals like Euler characteristic, the Poincare
polynomial or the zeta functions can be extended naturally. These functionals
can also help with number theoretical questions. The story of primes is a bit
different as the integers are not a unique factorization domain, because there
are many additive primes. Most graphs are multiplicative primes.
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An ultrafast laser based on coherent beam combination of four ytterbium-doped
step-index fiber amplifiers is presented. The system delivers an average power
of 3.5 kW and a pulse duration of 430 fs at 80 MHz repetition rate. The beam
quality is excellent (M2<1.24x1.10) and the relative intensity noise is as low
as 1% in the frequency span from 1 Hz to 1 MHz. The system is turn-key operable
as it features an automated spatial and temporal alignment of the
interferometric amplification channels.
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The recently described pushframe imager, a parallelized single pixel camera
capturing with a pushbroom-like motion, is intrinsically suited to both
remote-sensing and compressive sampling. It optically applies a 2D mask to the
imaged scene, before performing light integration along a single spatial axis,
but previous work has not made use of the architecture's potential for taking
measurements sparsely. In this paper we develop a strongly performing static
binarized noiselet compressive sampling mask design, tailored to pushframe
hardware, allowing both a single exposure per motion time-step, and retention
of 2D correlations in the scene. Results from simulated and real-world captures
are presented, with performance shown to be similar to that of immobile -- and
hence inappropriate for satellite use -- whole-scene imagers. A particular
feature of our sampling approach is that the degree of compression can be
varied without altering the pattern, and we demonstrate the utility of this for
efficiently storing and transmitting multi-spectral images.
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When network products and services become more valuable as their userbase
grows (network effects), this tendency can become a major determinant of how
they compete with each other in the market and how the market is structured.
Network effects are traditionally linked to high market concentration,
early-mover advantages, and entry barriers, and in the cryptoasset market they
have been used as a valuation tool too. The recent resurgence of Bitcoin has
been partly attributed to network effects too. We study the existence of
network effects in six cryptoassets from their inception to obtain a high-level
overview of the application of network effects in the cryptoasset market. We
show that contrary to the usual implications of network effects, they do not
serve to concentrate the cryptoasset market, nor do they accord any one
cryptoasset a definitive competitive advantage, nor are they consistent enough
to be reliable valuation tools. Therefore, while network effects do occur in
cryptoasset networks, they are not a defining feature of the cryptoasset market
as a whole.
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We consider electronic and magnetic properties of chromium, a well-known
itinerant antiferromagnet, by a combination of density functional theory (DFT)
and dynamical mean-field theory (DMFT). We find that electronic correlation
effects in chromium, in contrast to its neighbours in the periodic table, are
weak, leading to the quasiparticle mass enhancement factor ${m^*/m \approx
1.2}$. Our results for local spin-spin correlation functions and distribution
of weigths of atomic configurations indicate that the local magnetic moments
are not formed. Similarly to previous results of DFT at ambient pressure, the
non-uniform magnetic susceptibility as a function of momentum possesses close
to the wave vector ${{\mathbf Q}_{\rm H}=(0,0,2\pi/a)}$ ($a$ is the lattice
constant) sharp maxima, corresponding to Kohn anomalies. We find that these
maxima are preserved by the interaction and are not destroyed by pressure. Our
calculations qualitatively capture a decrease of the N\'eel temperature with
pressure and a breakdown of itinerant antiferomagnetism at pressure of $\sim$9
GPa in agreement with experimental data, although the N\'eel temperature is
significantly overestimated because of the mean-field nature of DMFT.
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While most existing segmentation methods usually combined the powerful
feature extraction capabilities of CNNs with Conditional Random Fields (CRFs)
post-processing, the result always limited by the fault of CRFs . Due to the
notoriously slow calculation speeds and poor efficiency of CRFs, in recent
years, CRFs post-processing has been gradually eliminated. In this paper, an
improved Generative Adversarial Networks (GANs) for image semantic segmentation
task (semantic segmentation by GANs, Seg-GAN) is proposed to facilitate further
segmentation research. In addition, we introduce Convolutional CRFs (ConvCRFs)
as an effective improvement solution for the image semantic segmentation task.
Towards the goal of differentiating the segmentation results from the ground
truth distribution and improving the details of the output images, the proposed
discriminator network is specially designed in a full convolutional manner
combined with cascaded ConvCRFs. Besides, the adversarial loss aggressively
encourages the output image to be close to the distribution of the ground
truth. Our method not only learns an end-to-end mapping from input image to
corresponding output image, but also learns a loss function to train this
mapping. The experiments show that our method achieves better performance than
state-of-the-art methods.
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We propose a relativistic quantum Otto cycle between an entangled state of
two qubits and their composite excited (or ground) state whose efficiency can
be greater than the usual single qubit quantum Otto engine. The hot and cold
reservoirs are constructed by providing uniform accelerations to these qubits
along with the interaction between the background field and individual qubits.
The efficiency, as measured from one of the qubits' frame, not only depends on
the energy gap of the states but also the relative acceleration between them.
For lower acceleration of our observer's qubit compared to the other one, the
cycle is more efficient than the single qubit quantum Otto engine. Furthermore,
a complete protocol to construct such a cycle is being provided.
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In recent years, trends towards studying simulated games have gained momentum
in the fields of artificial intelligence, cognitive science, psychology, and
neuroscience. The intersections of these fields have also grown recently, as
researchers increasing study such games using both artificial agents and human
or animal subjects. However, implementing games can be a time-consuming
endeavor and may require a researcher to grapple with complex codebases that
are not easily customized. Furthermore, interdisciplinary researchers studying
some combination of artificial intelligence, human psychology, and animal
neurophysiology face additional challenges, because existing platforms are
designed for only one of these domains. Here we introduce Modular
Object-Oriented Games, a Python task framework that is lightweight, flexible,
customizable, and designed for use by machine learning, psychology, and
neurophysiology researchers.
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We use a hybrid k dot p theory - tight binding (HkpTB) model to describe
interlayer coupling simultaneously in both Bernal and twisted graphene
structures. For Bernal-aligned interfaces, HkpTB is parametrized using the full
Slonczewski-Weiss-McClure (SWMcC) Hamiltonian of graphite, which is then used
to refine the commonly used minimal model for twisted interfaces, by deriving
additional terms that reflect all details of the full SWMcC model of graphite.
We find that these terms introduce some electron-hole asymmetry in the band
structure of twisted bilayers, but in twistronic multilayer graphene, they
produce only a subtle change of moire miniband spectra, confirming the broad
applicability of the minimal model for implementing the twisted interface
coupling in such systems.
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What will the future of UAV cellular communications be? In this tutorial
article, we address such a compelling yet difficult question by embarking on a
journey from 5G to 6G and sharing a large number of realistic case studies
supported by original results. We start by overviewing the status quo on UAV
communications from an industrial standpoint, providing fresh updates from the
3GPP and detailing new 5G NR features in support of aerial devices. We then
show the potential and the limitations of such features. In particular, we
demonstrate how sub-6 GHz massive MIMO can successfully tackle cell selection
and interference challenges, we showcase encouraging mmWave coverage
evaluations in both urban and suburban/rural settings, and we examine the
peculiarities of direct device-to-device communications in the sky. Moving on,
we sneak a peek at next-generation UAV communications, listing some of the use
cases envisioned for the 2030s. We identify the most promising 6G enablers for
UAV communication, those expected to take the performance and reliability to
the next level. For each of these disruptive new paradigms (non-terrestrial
networks, cell-free architectures, artificial intelligence, reconfigurable
intelligent surfaces, and THz communications), we gauge the prospective
benefits for UAVs and discuss the main technological hurdles that stand in the
way. All along, we distil our numerous findings into essential takeaways, and
we identify key open problems worthy of further study.
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Composite minimization is a powerful framework in large-scale convex
optimization, based on decoupling of the objective function into terms with
structurally different properties and allowing for more flexible algorithmic
design. In this work, we introduce a new algorithmic framework for
complementary composite minimization, where the objective function decouples
into a (weakly) smooth and a uniformly convex term. This particular form of
decoupling is pervasive in statistics and machine learning, due to its link to
regularization.
The main contributions of our work are summarized as follows. First, we
introduce the problem of complementary composite minimization in general normed
spaces; second, we provide a unified accelerated algorithmic framework to
address broad classes of complementary composite minimization problems; and
third, we prove that the algorithms resulting from our framework are
near-optimal in most of the standard optimization settings. Additionally, we
show that our algorithmic framework can be used to address the problem of
making the gradients small in general normed spaces. As a concrete example, we
obtain a nearly-optimal method for the standard $\ell_1$ setup (small gradients
in the $\ell_\infty$ norm), essentially matching the bound of Nesterov (2012)
that was previously known only for the Euclidean setup. Finally, we show that
our composite methods are broadly applicable to a number of regression
problems, leading to complexity bounds that are either new or match the best
existing ones.
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The formation of the most massive quasars observed at high redshifts requires
extreme inflows of gas down to the length scales of the central compact object.
Here, we estimate the maximum inflow rate allowed by gravity down to the
surface of supermassive stars, the possible progenitors of these supermassive
black holes. We use the continuity equation and the assumption of free-fall to
derive maximum allowed inflow rates for various density profiles. We apply our
approach to the mass-radius relation of rapidly accreting supermassive stars to
estimate an upper limit to the accretion rates allowed during the formation of
these objects. We find that the maximum allowed rate $\dot M_{\rm max}$ is
given uniquely by the compactness of the accretor. For the compactness of
rapidly accreting supermassive stars, $\dot M_{\rm max}$ is related to the
stellar mass $M$ by a power-law $\dot M_{\rm max}\propto M^{3/4}$. The rates of
atomically cooled halos (0.1 -- 10 M$_\odot$ yr$^{-1}$) are allowed as soon as
$M\gtrsim1$ M$_\odot$. The largest rates expected in galaxy mergers
($10^4-10^5$ M$_\odot$ yr$^{-1}$) become accessible once the accretor is
supermassive ($M\gtrsim10^4$ M$_\odot$). These results suggest that
supermassive stars can accrete up to masses $>10^6$ M$_\odot$ before they
collapse via the general-relativistic instability. At such masses, the collapse
is expected to lead to the direct formation of a supermassive black hole even
within metal-rich gas, resulting in a black hole seed that is significantly
heavier than in conventional direct collapse models for atomic cooling halos.
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We give a new proof of the uniformization theorem of the leaves of a
lamination by surfaces of hyperbolic conformal type. We use a laminated version
of the Ricci flow to prove the existence of a laminated Riemannian metric
(smooth on the leaves, transversaly continuous) with leaves of constant
Gaussian curvature equal to -1, which is conformally equivalent to the original
metric.
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How to handle gender with machine learning is a controversial topic. A
growing critical body of research brought attention to the numerous issues
transgender communities face with the adoption of current automatic gender
recognition (AGR) systems. In contrast, we explore how such technologies could
potentially be appropriated to support transgender practices and needs,
especially in non-Western contexts like Japan. We designed a virtual makeup
probe to assist transgender individuals with passing, that is to be perceived
as the gender they identify as. To understand how such an application might
support expressing transgender individuals gender identity or not, we
interviewed 15 individuals in Tokyo and found that in the right context and
under strict conditions, AGR based systems could assist transgender passing.
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The background magnetic-field formalism of Lattice QCD has been used recently
to calculate the magnetic polarizability of the charged pion. These $n_f = 2 +
1$ numerical simulations are electro-quenched, such that the virtual sea-quarks
of the QCD vacuum do not interact with the background field. To understand the
impact of this, we draw on partially quenched chiral perturbation theory. In
this case, the leading term proportional to $1/M_\pi$ arises at tree level from
$\mathcal{L}_4$. To describe the results from lattice QCD, while maintaining
the exact leading terms of chiral perturbation theory, we introduce a Pad\'e
approximant designed to reproduce the slow variation observed in the lattice
QCD results. Two-loop contributions are introduced to assess the systematic
uncertainty associated with higher-order terms of the expansion. Upon
extrapolation, the magnetic polarizability of the charged pion at the physical
pion mass is found to be $\beta_{\pi^\pm}=-1.70\,(14)_{\rm stat}(25)_{\rm
syst}\times 10^{-4}$ fm$^3$, in good agreement with the recent experimental
measurement.
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In this paper, we focus on the fairness issues regarding unsupervised outlier
detection. Traditional algorithms, without a specific design for algorithmic
fairness, could implicitly encode and propagate statistical bias in data and
raise societal concerns. To correct such unfairness and deliver a fair set of
potential outlier candidates, we propose Deep Clustering based Fair Outlier
Detection (DCFOD) that learns a good representation for utility maximization
while enforcing the learnable representation to be subgroup-invariant on the
sensitive attribute. Considering the coupled and reciprocal nature between
clustering and outlier detection, we leverage deep clustering to discover the
intrinsic cluster structure and out-of-structure instances. Meanwhile, an
adversarial training erases the sensitive pattern for instances for fairness
adaptation. Technically, we propose an instance-level weighted representation
learning strategy to enhance the joint deep clustering and outlier detection,
where the dynamic weight module re-emphasizes contributions of likely-inliers
while mitigating the negative impact from outliers. Demonstrated by experiments
on eight datasets comparing to 17 outlier detection algorithms, our DCFOD
method consistently achieves superior performance on both the outlier detection
validity and two types of fairness notions in outlier detection.
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Presence of large-scale surface magnetic field in early-type stars leads to
several unique electromagnetic phenomena producing radiation over X-ray to
radio bands. Among them, the rarest type of emission is electron cyclotron
maser emission (ECME) observed as periodic, circularly polarized radio pulses.
The phenomenon was first discovered in the hot magnetic star CU Vir. Past
observations of this star led to the consensus that the star produces only
right circularly polarized ECME, suggesting that only one magnetic hemisphere
takes part in the phenomenon. Here we present the first ultra-wideband (0.4$-$4
GHz) study of this star using the upgraded Giant Metrewave Radio telescope and
the Karl G. Jansky Very Large Array, which led to the surprising discovery of
ECME of both circular polarizations up to around 1.5 GHz. The GHz observations
also allowed us to infer that the upper ECME cut-off frequency is at $\gtrsim
5\,\mathrm{GHz}$. The sub-GHz observation led to the unexpected observation of
more than two pairs of ECME pulses per rotation cycle. In addition, we report
the discovery of a `giant pulse', and transient enhancements, which are
potentially the first observational evidence of `centrifugal breakout' of
plasma from the innermost part of the stellar magnetosphere. The stark contrast
between the star's behavior at GHz and sub-GHz frequencies could either be due
to propagation effects, a manifestation of varying magnetic field topology as a
function of height, or a signature of an additional `ECME engine'.
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Sub-Terahertz frequencies (frequencies above 100 GHz) have the potential to
satisfy the unprecedented demand on data rate on the order of hundreds of Gbps
for sixth-generation (6G) wireless communications and beyond. Accurate beam
tracking and rapid beam selection are increasingly important since antenna
arrays with more elements generate narrower beams to compensate for additional
path loss within the first meter of propagation distance at sub-THz
frequencies. Realistic channel models for above 100 GHz are needed, and should
include spatial consistency to model the spatial and temporal channel evolution
along the user trajectory. This paper introduces recent outdoor urban microcell
(UMi) propagation measurements at 142 GHz along a 39 m $\times$ 12 m
rectangular route (102 m long), where each consecutive and adjacent receiver
location is 3 m apart from each other. The measured power delay profiles and
angular power spectrum at each receiver location are used to study spatial
autocorrelation properties of various channel parameters such as shadow fading,
delay spread, and angular spread along the track. Compared to the correlation
distances reported in the 3GPP TR 38.901 for frequencies below 100 GHz, the
measured correlation distance of shadow fading at 142 GHz (3.8 m) is much
shorter than the 10-13 m as specified in 3GPP; the measured correlation
distances of delay spread and angular spread at 142 GHz (both 12 m) are
comparable to the 7-10 m as specified in 3GPP. This result may guide the
development of a statistical spatially consistent channel model for frequencies
above 100 GHz in the UMi street canyon environment.
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We derive a general expression for the absorptive part of the one-loop photon
polarization tensor in a strongly magnetized quark-gluon plasma at nonzero
baryon chemical potential. To demonstrate the application of the main result in
the context of heavy-ion collisions, we study the effect of a nonzero baryon
chemical potential on the photon emission rate. The rate and the ellipticity of
photon emission are studied numerically as a function the transverse momentum
(energy) for several values of temperature and chemical potential. When the
chemical potential is small compared to the temperature, the rates of the quark
and antiquark splitting processes (i.e., $q\rightarrow q +\gamma$ and
$\bar{q}\rightarrow \bar{q} +\gamma$, respectively) are approximately the same.
However, the quark splitting gradually becomes the dominant process with
increasing the chemical potential. We also find that increasing the chemical
potential leads to a growing total photon production rate but has only a small
effect on the ellipticity of photon emission. The quark-antiquark annihilation
($q+\bar{q}\rightarrow \gamma$) also contributes to the photon production, but
its contribution remains relatively small for a wide range of temperatures and
chemical potentials investigated.
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Topic trajectory information provides crucial insight into the dynamics of
topics and their evolutionary relationships over a given time. Also, this
information can help to improve our understanding on how new topics have
emerged or formed through a sequential or interrelated events of emergence,
modification and integration of prior topics. Nevertheless, the implementation
of the existing methods for topic trajectory identification is rarely available
as usable software. In this paper, we present TopicTracker, a platform for
topic trajectory identification and visualisation. The key of Topic Tracker is
that it can represent the three facets of information together, given two kinds
of input: a time-stamped topic profile consisting of the set of the underlying
topics over time, and the evolution strength matrix among them: evolutionary
pathways of dynamic topics, evolution states of the topics, and topic
importance. TopicTracker is a publicly available software implemented using the
R software.
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Mechanical strength of amyloid beta fibrils has been known to be correlated
with neuronal cell death. Here, we resorted to steered molecular dynamics (SMD)
simulations to mechanically stretch a single S-shape amyloid beta Abeta11-42
dodecamer fibril in vacuum. It was found that the weakest sites at which the
fibril was ruptured due to mechanical extension were exclusively at the
interfaces of alanine and glutamic acid distributed throughout the fibril. It
was also revealed that the free energy required to unfold the fibril to form a
long linear conformation is equivalent to ~ 210 eV, being several thousand
times larger than thermal voltage at room temperature. As a consequence, within
solution a larger free energy is needed for such a maximal stretching based on
the fact that amyloid beta fibrils are structurally more stable in solution due
to the interplay between their hydrophobic cores and solution's entropy.
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Multivariate functional data can be intrinsically multivariate like movement
trajectories in 2D or complementary like precipitation, temperature, and wind
speeds over time at a given weather station. We propose a multivariate
functional additive mixed model (multiFAMM) and show its application to both
data situations using examples from sports science (movement trajectories of
snooker players) and phonetic science (acoustic signals and articulation of
consonants). The approach includes linear and nonlinear covariate effects and
models the dependency structure between the dimensions of the responses using
multivariate functional principal component analysis. Multivariate functional
random intercepts capture both the auto-correlation within a given function and
cross-correlations between the multivariate functional dimensions. They also
allow us to model between-function correlations as induced by e.g.\ repeated
measurements or crossed study designs. Modeling the dependency structure
between the dimensions can generate additional insight into the properties of
the multivariate functional process, improves the estimation of random effects,
and yields corrected confidence bands for covariate effects. Extensive
simulation studies indicate that a multivariate modeling approach is more
parsimonious than fitting independent univariate models to the data while
maintaining or improving model fit.
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In this paper we prove the existence and uniqueness of strong solution of the
incompressible Navier-Stokes equations with damping $\alpha
(e^{\beta|u|^2}-1)u$.
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The hypothesis that strange quark matter is the true ground state of matter
has been investigated for almost four decades, but only a few works have
explored the dynamics of binary systems of quark stars. This is partly due to
the numerical challenges that need to be faced when modelling the large
discontinuities at the surface of these stars. We here present a novel
technique in which the EOS of a quark star is suitably rescaled to produce a
smooth change of the specific enthalpy across a very thin crust. The
introduction of the crust has been carefully tested by considering the
oscillation properties of isolated quark stars, showing that the response of
the simulated quark stars matches accurately the perturbative predictions.
Using this technique, we have carried out the first fully general-relativistic
simulations of the merger of quark-star binaries finding several important
differences between quark-star binaries and hadronic-star binaries with the
same mass and comparable tidal deformability. In particular, we find that
dynamical mass loss is significantly suppressed in quark-star binaries. In
addition, quark-star binaries have merger and post-merger frequencies that obey
the same quasi-universal relations derived from hadron stars if expressed in
terms of the tidal deformability, but not when expressed in terms of the
average stellar compactness. Hence, it may be difficult to distinguish the two
classes of stars if no information on the stellar radius is available. Finally,
differences are found in the distributions in velocity and entropy of the
ejected matter, for which quark-stars have much smaller tails. Whether these
differences in the ejected matter will leave an imprint in the electromagnetic
counterpart and nucleosynthetic yields remains unclear, calling for the
construction of an accurate model for the evaporation of the ejected quarks
into nucleons.
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We introduce a new benchmark dataset, namely VinDr-RibCXR, for automatic
segmentation and labeling of individual ribs from chest X-ray (CXR) scans. The
VinDr-RibCXR contains 245 CXRs with corresponding ground truth annotations
provided by human experts. A set of state-of-the-art segmentation models are
trained on 196 images from the VinDr-RibCXR to segment and label 20 individual
ribs. Our best performing model obtains a Dice score of 0.834 (95% CI,
0.810--0.853) on an independent test set of 49 images. Our study, therefore,
serves as a proof of concept and baseline performance for future research.
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Incentive mechanism design is crucial for enabling federated learning. We
deal with clustering problem of agents contributing to federated learning
setting. Assuming agents behave selfishly, we model their interaction as a
stable coalition partition problem using hedonic games where agents and
clusters are the players and coalitions, respectively. We address the following
question: is there a family of hedonic games ensuring a Nash-stable coalition
partition? We propose the Nash-stable set which determines the family of
hedonic games possessing at least one Nash-stable partition, and analyze the
conditions of non-emptiness of the Nash-stable set. Besides, we deal with the
decentralized clustering. We formulate the problem as a non-cooperative game
and prove the existence of a potential game.
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Magnetic resonance imaging (MRI) acquisition, reconstruction, and
segmentation are usually processed independently in the conventional practice
of MRI workflow. It is easy to notice that there are significant relevances
among these tasks and this procedure artificially cuts off these potential
connections, which may lead to losing clinically important information for the
final diagnosis. To involve these potential relations for further performance
improvement, a sequential multi-task joint learning network model is proposed
to train a combined end-to-end pipeline in a differentiable way, aiming at
exploring the mutual influence among those tasks simultaneously. Our design
consists of three cascaded modules: 1) deep sampling pattern learning module
optimizes the $k$-space sampling pattern with predetermined sampling rate; 2)
deep reconstruction module is dedicated to reconstructing MR images from the
undersampled data using the learned sampling pattern; 3) deep segmentation
module encodes MR images reconstructed from the previous module to segment the
interested tissues. The proposed model retrieves the latently interactive and
cyclic relations among those tasks, from which each task will be mutually
beneficial. The proposed framework is verified on MRB dataset, which achieves
superior performance on other SOTA methods in terms of both reconstruction and
segmentation.
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Clustering is one of the most fundamental tasks in machine learning.
Recently, deep clustering has become a major trend in clustering techniques.
Representation learning often plays an important role in the effectiveness of
deep clustering, and thus can be a principal cause of performance degradation.
In this paper, we propose a clustering-friendly representation learning method
using instance discrimination and feature decorrelation. Our
deep-learning-based representation learning method is motivated by the
properties of classical spectral clustering. Instance discrimination learns
similarities among data and feature decorrelation removes redundant correlation
among features. We utilize an instance discrimination method in which learning
individual instance classes leads to learning similarity among instances.
Through detailed experiments and examination, we show that the approach can be
adapted to learning a latent space for clustering. We design novel
softmax-formulated decorrelation constraints for learning. In evaluations of
image clustering using CIFAR-10 and ImageNet-10, our method achieves accuracy
of 81.5% and 95.4%, respectively. We also show that the softmax-formulated
constraints are compatible with various neural networks.
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The phase stability and equilibria of carbon dioxide is investigated from 125
-- 325K and 1 -- 10,000 atm using extensive molecular dynamics (MD) simulations
and the Two-Phase Thermodynamics (2PT) method. We devise a direct approach for
calculating phase diagrams in general, by considering the separate chemical
potentials of the isolated phase at specific points on the P-T diagram. The
unique ability of 2PT to accurately and efficiently approximate the entropy and
Gibbs energy of liquids thus allows for assignment of phase boundaries from
relatively short ($\mathrm{\sim}$ 100ps) MD simulations. We validate our
approach by calculating the critical properties of the flexible Elementary
Physical Model 2 (FEPM2), showing good agreement with previous results. We
show, however, that the incorrect description of the short-range Pauli force
and the lack of molecular charge polarization leads to deviations from
experiments at high pressures. We thus develop a many-body, fluctuating charge
model for CO${}_{2}$, termed CO${}_{2}$-Fq, from high level quantum mechanics
(QM) calculations, that accurately captures the condensed phase vibrational
properties of the solid (including the Fermi resonance at 1378 cm${}^{-1}$) as
well as the diffusional properties of the liquid, leading to overall excellent
agreement with experiments over the entire phase diagram. This work provides an
efficient computational approach for determining phase diagrams of arbitrary
systems and underscore the critical role of QM charge reorganization physics in
molecular phase stability.
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K\"ahler's geometric approach in which relativistic fermion fields are
treated as differential forms is applied in three spacetime dimensions. It is
shown that the resulting continuum theory is invariant under global
U($N)\otimes$U($N)$ field transformations, and has a parity-invariant mass
term, both symmetries shared in common with staggered lattice fermions. The
formalism is used to construct a version of the Thirring model with contact
interactions between conserved Noether currents. Under reasonable assumptions
about field rescaling after quantum corrections, a more general interaction
term is derived, sharing the same symmetries but now including terms which
entangle spin and taste degrees of freedom, which exactly coincides with the
leading terms in the staggered lattice Thirring model in the long-wavelength
limit. Finally truncated versions of the theory are explored; it is found that
excluding scalar and pseudoscalar components leads to a theory of six-component
fermion fields describing particles with spin 1, with fermion and antifermion
corresponding to states with definite circular polarisation. In the UV limit
only transverse states with just four non-vanishing components propagate.
Implications for the description of dynamics at a strongly interacting
renormalisation-group fixed point are discussed.
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Chiral optical effects are generally quantified along some specific incident
directions of exciting waves (especially for extrinsic chiralities of achiral
structures) or defined as direction-independent properties by averaging the
responses among all structure orientations. Though of great significance for
various applications, chirality extremization (maximized or minimized) with
respect to incident directions or structure orientations have not been
explored, especially in a systematic manner. In this study we examine the
chiral responses of open photonic structures from perspectives of quasi-normal
modes and polarization singularities of their far-field radiations. The
nontrivial topology of the momentum sphere secures the existence of singularity
directions along which mode radiations are either circularly or linearly
polarized. When plane waves are incident along those directions, the
reciprocity ensures ideal maximization and minimization of optical chiralities,
for corresponding mode radiations of circular and linear polarizations
respectively. For directions of general elliptical polarizations, we have
unveiled the subtle equality of a Stokes parameter and the circular dichroism,
showing that an intrinsically chiral structure can unexpectedly exhibit no
chirality at all or even chiralities of opposite handedness for different
incident directions. The framework we establish can be applied to not only
finite scattering bodies but also infinite periodic structures, encompassing
both intrinsic and extrinsic optical chiralities. We have effectively merged
two vibrant disciplines of chiral and singular optics, which can potentially
trigger more optical chirality-singularity related interdisciplinary studies.
|
The semantic matching capabilities of neural information retrieval can
ameliorate synonymy and polysemy problems of symbolic approaches. However,
neural models' dense representations are more suitable for re-ranking, due to
their inefficiency. Sparse representations, either in symbolic or latent form,
are more efficient with an inverted index. Taking the merits of the sparse and
dense representations, we propose an ultra-high dimensional (UHD)
representation scheme equipped with directly controllable sparsity. UHD's large
capacity and minimal noise and interference among the dimensions allow for
binarized representations, which are highly efficient for storage and search.
Also proposed is a bucketing method, where the embeddings from multiple layers
of BERT are selected/merged to represent diverse linguistic aspects. We test
our models with MS MARCO and TREC CAR, showing that our models outperforms
other sparse models
|
We explore the physics of topological lattice models in c-QED architectures
for arbitrary coupling strength, and the possibility of using the cavity
transmission as a topological marker. For this, we develop an approach
combining the input-output formalism with Mean-Field theory, which includes
self-consistency and quantum fluctuations to first order, and allows to go
beyond the small-coupling regime. We apply our formalism to the case of a
fermionic Su-Schrieffer-Heeger (SSH) chain. Our findings confirm that the
cavity can indeed act as a quantum sensor for topological phases, where the
initial state preparation plays a crutial role. Additionally, we discuss the
persistence of topological features when the coupling strength increases, in
terms of an effective Hamiltonian, and calculate the entanglement entropy. Our
approach can be applied to other fermionic systems, opening a route to the
characterization of their topological properties in terms of experimental
observables.
|
Hyperactive comets have high water production rates, with inferred
sublimation areas of order the surface area of the nucleus. Comets 46P/Wirtanen
and 103P/Hartley 2 are two examples of this cometary class. Based on
observations of comet Hartley 2 by the Deep Impact spacecraft, hyperactivity
appears to be caused by the ejection of water-ice grains and/or water-ice rich
chunks of nucleus into the coma. These materials increase the sublimating
surface area, and yield high water production rates. The historic close
approach of comet Wirtanen to Earth in 2018 afforded an opportunity to test
Hartley 2 style hyperactivity in a second Jupiter-family comet. We present high
spatial resolution, near-infrared spectroscopy of the inner coma of Wirtanen.
No evidence for the 1.5- or 2.0-$\mu$m water-ice absorption bands is found in
six 0.8-2.5 $\mu$m spectra taken around perihelion and closest approach to
Earth. In addition, the strong 3.0-$\mu$m water-ice absorption band is absent
in a 2.0-5.3 $\mu$m spectrum taken near perihelion. Using spectroscopic and
sublimation lifetime models we set constraints on the physical properties of
the ice grains in the coma, assuming they are responsible for the comet's
hyperactivity. We rule out pure water-ice grains of any size, given their long
lifetime. Instead, the hyperactivity of the nucleus and lack of water-ice
absorption features in our spectra can be explained either by icy grains on the
order of 1 $\mu$m in size with a small amount of low albedo dust (greater than
0.5% by volume), or large chunks containing significant amounts of water ice.
|
Human movement disorders or paralysis lead to the loss of control of muscle
activation and thus motor control. Functional Electrical Stimulation (FES) is
an established and safe technique for contracting muscles by stimulating the
skin above a muscle to induce its contraction. However, an open challenge
remains on how to restore motor abilities to human limbs through FES, as the
problem of controlling the stimulation is unclear. We are taking a robotics
perspective on this problem, by developing robot learning algorithms that
control the ultimate humanoid robot, the human body, through electrical muscle
stimulation. Human muscles are not trivial to control as actuators due to their
force production being non-stationary as a result of fatigue and other internal
state changes, in contrast to robot actuators which are well-understood and
stationary over broad operation ranges. We present our Deep Reinforcement
Learning approach to the control of human muscles with FES, using a recurrent
neural network for dynamic state representation, to overcome the unobserved
elements of the behaviour of human muscles under external stimulation. We
demonstrate our technique both in neuromuscular simulations but also
experimentally on a human. Our results show that our controller can learn to
manipulate human muscles, applying appropriate levels of stimulation to achieve
the given tasks while compensating for advancing muscle fatigue which arises
throughout the tasks. Additionally, our technique can learn quickly enough to
be implemented in real-world human-in-the-loop settings.
|
Let $(\kappa_n(a))_{n\geq 1}$ denote the sequence of free cumulants of a
random variable $a$ in a non-commutative probability space
$(\mathcal{A},\varphi)$. Based on some considerations on bipartite graphs, we
provide a formula to compute the cumulants $(\kappa_n(ab+ba))_{n\geq 1}$ in
terms of $(\kappa_n(a))_{n\geq 1}$ and $(\kappa_n(b))_{n\geq 1}$, where $a$ and
$b$ are freely independent. Our formula expresses the $n$-th free cumulant of
$ab+ba$ as a sum indexed by partitions in the set $\mathcal{Y}_{2n}$ of
non-crossing partitions of the form
\[ \sigma=\{B_1,B_3,\dots, B_{2n-1},E_1,\dots,E_r\}, \quad \text{with }r\geq
0, \]
such that $i\in B_{i}$ for $i=1,3,\dots,2n-1$ and $|E_j|$ even for $j\leq r$.
Therefore, by studying the sets $\mathcal{Y}_{2n}$ we obtain new results
regarding the distribution of $ab+ba$. For instance, the size
$|\mathcal{Y}_{2n}|$ is closely related to the case when $a,b$ are free Poisson
random variables of parameter 1. Our formula can also be expressed in terms of
cacti graphs. This graph theoretic approach suggests a natural generalization
that allows us to study quadratic forms in $k$ free random variables.
|
Deep reinforcement learning (DRL) is applied in safety-critical domains such
as robotics and autonomous driving. It achieves superhuman abilities in many
tasks, however whether DRL agents can be shown to act safely is an open
problem. Atari games are a simple yet challenging exemplar for evaluating the
safety of DRL agents and feature a diverse portfolio of game mechanics. The
safety of neural agents has been studied before using methods that either
require a model of the system dynamics or an abstraction; unfortunately, these
are unsuitable to Atari games because their low-level dynamics are complex and
hidden inside their emulator. We present the first exact method for analysing
and ensuring the safety of DRL agents for Atari games. Our method only requires
access to the emulator. First, we give a set of 43 properties that characterise
"safe behaviour" for 30 games. Second, we develop a method for exploring all
traces induced by an agent and a game and consider a variety of sources of game
non-determinism. We observe that the best available DRL agents reliably satisfy
only very few properties; several critical properties are violated by all
agents. Finally, we propose a countermeasure that combines a bounded
explicit-state exploration with shielding. We demonstrate that our method
improves the safety of all agents over multiple properties.
|
The phase structure of baryonic matter is investigated with focus on the role
of fluctuations beyond the mean-field approximation. The prototype test case
studied is the chiral nucleon-meson model, with added comments on the chiral
quark-meson model. Applications to the liquid-gas phase transition in nuclear
matter and extensions to dense matter are performed. The role of vacuum
fluctuations and thermal excitations is systematically explored. It is pointed
out that such fluctuations tend to stabilise the hadronic phase characterised
by spontaneously broken chiral symmetry, shifting the chiral restoration
transition to very high densities. This stabilisation effect is shown to be
further enhanced by additional dynamical fluctuations treated with functional
renormalisation group methods.
|
Deep learning (DL) has recently attracted increasing interest to improve
object type classification for automotive radar.In addition to high accuracy,
it is crucial for decision making in autonomous vehicles to evaluate the
reliability of the predictions; however, decisions of DL networks are
non-transparent. Current DL research has investigated how uncertainties of
predictions can be quantified, and in this article, we evaluate the potential
of these methods for safe, automotive radar perception. In particular we
evaluate how uncertainty quantification can support radar perception under (1)
domain shift, (2) corruptions of input signals, and (3) in the presence of
unknown objects. We find that in agreement with phenomena observed in the
literature,deep radar classifiers are overly confident, even in their wrong
predictions. This raises concerns about the use of the confidence values for
decision making under uncertainty, as the model fails to notify when it cannot
handle an unknown situation. Accurate confidence values would allow optimal
integration of multiple information sources, e.g. via sensor fusion. We show
that by applying state-of-the-art post-hoc uncertainty calibration, the quality
of confidence measures can be significantly improved,thereby partially
resolving the over-confidence problem. Our investigation shows that further
research into training and calibrating DL networks is necessary and offers
great potential for safe automotive object classification with radar sensors.
|
ESA's INTEGRAL space mission has achieved unique results for solar and
terrestrial physics, although spacecraft operations nominally excluded the
possibility to point at the Sun or the Earth. The Earth avoidance was, however,
exceptionally relaxed for special occultation observations of the Cosmic X-ray
Background (CXB), which on some occasions allowed the detection of strong X-ray
auroral emission. In addition, the most intense solar flares can be bright
enough to be detectable from outside the field of view of the main instruments.
This article presents for the first time the auroral observations by INTEGRAL
and reviews earlier studies of the most intense solar flares. We end by briefly
summarising the studies of the Earth's radiation belts, which can be considered
as another topic of serendipitous science with INTEGRAL.
|
Non-invasive therapeutic ultrasound methods, such as high-intensity focused
ultrasound (HIFU), have limited access to tissue targets shadowed by bones or
presence of gas. This study demonstrates that an ultrasonically actuated
medical needle can be used to translate nanoparticles and fluids under the
action of nonlinear phenomena, potentially overcoming some limitations of HIFU.
A simulation study was first conducted to study the delivery of a tracer with
an ultrasonically actuated needle (33 kHz) inside a porous medium acting as a
model for soft tissue. The model was then validated experimentally in different
concentrations of agarose gel showing a close match with the experimental
results, when diluted soot nanoparticles (diameter < 150 nm) were employed as
delivered entity. An additional simulation study demonstrated a threefold
increase of the volume covered by the delivered agent in liver under a constant
injection rate, when compared to without ultrasound. This method, if developed
to its full potential, could serve as a cost effective way to improve safety
and efficacy of drug therapies by maximizing the concentration of delivered
entities within e.g. a small lesion, while minimizing exposure outside the
lesion.
|
Cone Beam Computed Tomography(CBCT) is a now known method to conduct CT
imaging. Especially, The Low Dose CT imaging is one of possible options to
protect organs of patients when conducting CT imaging. Therefore Low Dose CT
imaging can be an alternative instead of Standard dose CT imaging. However Low
Dose CT imaging has a fundamental issue with noises within results compared to
Standard Dose CT imaging. Currently, there are lots of attempts to erase the
noises. Most of methods with artificial intelligence have many parameters and
unexplained layers or a kind of black-box methods. Therefore, our research has
purposes related to these issues. Our approach has less parameters than usual
methods by having Iterative learn-able bilateral filtering approach with Deep
reinforcement learning. And we applied The Iterative learn-able filtering
approach with deep reinforcement learning to sinograms and reconstructed volume
domains. The method and the results of the method can be much more explainable
than The other black box AI approaches. And we applied the method to Helical
Cone Beam Computed Tomography(CBCT), which is the recent CBCT trend. We tested
this method with on 2 abdominal scans(L004, L014) from Mayo Clinic TCIA
dataset. The results and the performances of our approach overtake the results
of the other previous methods.
|
In this paper, we consider the energy conservation and regularity of the weak
solution $u$ to the Navier-Stokes equations in the endpoint case. We first
construct a divergence-free field $u(t,x)$ which satisfies $\lim_{t\to
T}\sqrt{T-t}||u(t)||_{BMO}<\infty$ and $\lim_{t\to
T}\sqrt{T-t}||u(t)||_{L^\infty}=\infty$ to demonstrate that the Type II
singularity is admissible in the endpoint case $u\in L^{2,\infty}(BMO)$.
Secondly, we prove that if a suitable weak solution $u(t,x)$ satisfying
$||u||_{L^{2,\infty}([0,T];BMO(\Omega))}<\infty$ for arbitrary
$\Omega\subseteq\mathbb{R}^3$ then the local energy equality is valid on
$[0,T]\times\Omega$. As a corollary, we also prove
$||u||_{L^{2,\infty}([0,T];BMO(\mathbb{R}^3))}<\infty$ implies the global
energy equality on $[0,T]$. Thirdly, we show that as the solution $u$
approaches a finite blowup time $T$, the norm $||u(t)||_{BMO}$ must blow up at
a rate faster than $\frac{c}{\sqrt{T-t}}$ with some absolute constant $c>0$.
Furthermore, we prove that if
$||u_3||_{L^{2,\infty}([0,T];BMO(\mathbb{R}^3))}=M<\infty$ then there exists a
small constant $c_M$ depended on $M$ such that if
$||u_h||_{L^{2,\infty}([0,T];BMO(\mathbb{R}^3))}\leq c_M$ then $u$ is regular
on $(0,T]\times\mathbb{R}^3$.
|
We propose a series-based nonparametric specification test for a regression
function when data are spatially dependent, the `space' being of a general
economic or social nature. Dependence can be parametric, parametric with
increasing dimension, semiparametric or any combination thereof, thus covering
a vast variety of settings. These include spatial error models of varying types
and levels of complexity. Under a new smooth spatial dependence condition, our
test statistic is asymptotically standard normal. To prove the latter property,
we establish a central limit theorem for quadratic forms in linear processes in
an increasing dimension setting. Finite sample performance is investigated in a
simulation study, with a bootstrap method also justified and illustrated, and
empirical examples illustrate the test with real-world data.
|
Inelastic neutron scattering (INS) is a key method for studying magnetic
excitations in spin systems, including molecular spin clusters. The method has
significantly advanced in recent years and now permits to probe the scattering
intensity as a function of the energy transfer and the momentum-transfer vector
Q. It was recently shown that high molecular symmetry facilitates the analysis
of spectra. Point-group symmetry imposes selection rules in isotropic as well
as anisotropic spin models. Furthermore, the Q-dependence of the INS intensity
may be completely determined by the point-group symmetry of the states involved
in a transition, thereby affording a clear separation of dynamics (energies,
transition strengths) and geometrical features (Q-dependencies). The present
work addresses this issue for anisotropic spin models. We identify a number of
cases where the Q-dependence is completely fixed by the point-group symmetry.
For six- and eight-membered planar spin rings and two polyhedra (the cube and
the icosahedron) we tabulate and plot the corresponding powder-averaged
universal intensity functions. The outlined formalism straightforwardly applies
to other highly-symmetric systems and should be useful for future analyses of
INS spectra by focusing on those features that contain information on either
spin dynamics or the point-group symmetry of states.
|
We report the detection of CH$_3$OH emission in comet 46P/Wirtanen on UT 2018
December 8 and 9 using the Atacama Compact Array (ACA), part of the Atacama
Large Millimeter/Submillimeter Array (ALMA). These interferometric measurements
of CH$_3$OH along with continuum emission from dust probed the inner coma
($<$2000 km from the nucleus) of 46P/Wirtanen approximately one week before its
closest approach to Earth ($\Delta$ = 0.089 -- 0.092 au), revealing rapidly
varying and anisotropic CH$_3$OH outgassing during five separate ACA executions
between UT 23:57 December 7 and UT 04:55 December 9, with a clear progression
in the spectral line profiles over a timescale of minutes. We present
spectrally integrated flux maps, production rates, rotational temperatures, and
spectral line profiles of CH$_3$OH during each ACA execution. The variations in
CH$_3$OH outgassing are consistent with Wirtanen's 9 hr nucleus rotational
period derived from optical and millimeter wavelength measurements and thus are
likely coupled to the changing illumination of active sites on the nucleus. The
consistent blue offset of the line center indicates enhanced CH$_3$OH
sublimation from the sunward hemisphere of the comet, perhaps from icy grains.
These results demonstrate the exceptional capabilities of the ACA for
time-resolved measurements of comets such as 46P/Wirtanen.
|
Direct numerical simulations have been performed for heat and momentum
transfer in internally heated turbulent shear flow with constant bulk mean
velocity and temperature, $u_{b}$ and $\theta_{b}$, between parallel,
isothermal, no-slip and permeable walls. The wall-normal transpiration velocity
on the walls $y=\pm h$ is assumed to be proportional to the local pressure
fluctuations, i.e. $v=\pm \beta p/\rho$ (Jim\'enez et al., J. Fluid Mech., vol.
442, 2001, pp.89-117). The temperature is supposed to be a passive scalar, and
the Prandtl number is set to unity. Turbulent heat and momentum transfer in
permeable-channel flow for $\beta u_{b}=0.5$ has been found to exhibit distinct
states depending on the Reynolds number $Re_b=2h u_b/\nu$. At $Re_{b}\lesssim
10^4$, the classical Blasius law of the friction coefficient and its similarity
to the Stanton number, $St\approx c_{f}\sim Re_{b}^{-1/4}$, are observed,
whereas at $Re_{b}\gtrsim 10^4$, the so-called ultimate scaling, $St\sim
Re_b^0$ and $c_{f}\sim Re_b^0$, is found. The ultimate state is attributed to
the appearance of large-scale intense spanwise rolls with the length scale of
$O(h)$ arising from the Kelvin-Helmholtz type of shear-layer instability over
the permeable walls. The large-scale rolls can induce large-amplitude velocity
fluctuations of $O(u_b)$ as in free shear layers, so that the Taylor
dissipation law $\epsilon\sim u_{b}^{3}/h$ (or equivalently $c_{f}\sim Re_b^0$)
holds. In spite of strong turbulence promotion there is no flow separation, and
thus large-amplitude temperature fluctuations of $O(\theta_b)$ can also be
induced similarly. As a consequence, the ultimate heat transfer is achieved,
i.e., a wall heat flux scales with $u_{b}\theta_{b}$ (or equivalently $St\sim
Re_b^0$) independent of thermal diffusivity, although the heat transfer on the
walls is dominated by thermal conduction.
|
This paper considers the problem of nonstationary process monitoring under
frequently varying operating conditions. Traditional approaches generally
misidentify the normal dynamic deviations as faults and thus lead to high false
alarms. Besides, they generally consider single relatively steady operating
condition and suffer from the catastrophic forgetting issue when learning
successive operating conditions. In this paper, recursive cointegration
analysis (RCA) is first proposed to distinguish the real faults from normal
systems changes, where the model is updated once a new normal sample arrives
and can adapt to slow change of cointegration relationship. Based on the
long-term equilibrium information extracted by RCA, the remaining short-term
dynamic information is monitored by recursive principal component analysis
(RPCA). Thus a comprehensive monitoring framework is built. When the system
enters a new operating condition, the RCA-RPCA model is rebuilt to deal with
the new condition. Meanwhile, elastic weight consolidation (EWC) is employed to
settle the `catastrophic forgetting' issue inherent in RPCA, where significant
information of influential parameters is enhanced to avoid the abrupt
performance degradation for similar modes. The effectiveness of the proposed
method is illustrated by a practical industrial system.
|
In this paper, we prove that a Sasakian pseudo-metric manifold which admits
an $\eta-$Ricci soliton is an $\eta-$Einstein manifold, and if the potential
vector field of the $\eta-$Ricci soliton is not a Killing vector field then the
manifold is $\mathcal{D}-$homothetically fixed, and the vector field leaves the
structure tensor field invariant. Next, we prove that a $K-$contact
pseudo-metric manifold with a gradient $\eta-$Ricci soliton metric is
$\eta-$Einstein. Moreover, we study contact pseudo-metric manifolds admitting
an $\eta-$Ricci soliton with a potential vector field point-wise colinear with
the Reeb vector field. Finally, we study gradient $\eta-$Ricci solitons on
$(\kappa, \mu)$-contact pseudo-metric manifolds.
|
Self-supervised learning for depth estimation possesses several advantages
over supervised learning. The benefits of no need for ground-truth depth,
online fine-tuning, and better generalization with unlimited data attract
researchers to seek self-supervised solutions. In this work, we propose a new
self-supervised framework for stereo matching utilizing multiple images
captured at aligned camera positions. A cross photometric loss, an
uncertainty-aware mutual-supervision loss, and a new smoothness loss are
introduced to optimize the network in learning disparity maps end-to-end
without ground-truth depth information. To train this framework, we build a new
multiscopic dataset consisting of synthetic images rendered by 3D engines and
real images captured by real cameras. After being trained with only the
synthetic images, our network can perform well in unseen outdoor scenes. Our
experiment shows that our model obtains better disparity maps than previous
unsupervised methods on the KITTI dataset and is comparable to supervised
methods when generalized to unseen data. Our source code and dataset are
available at https://sites.google.com/view/multiscopic.
|
Resource Public Key Infrastructure (RPKI) is vital to the security of
inter-domain routing. However, RPKI enables Regional Internet Registries (RIRs)
to unilaterally takedown IP prefixes - indeed, such attacks have been launched
by nation-state adversaries. The threat of IP prefix takedowns is one of the
factors hindering RPKI adoption.
In this work, we propose the first distributed RPKI system, based on
threshold signatures, that requires the coordination of a number of RIRs to
make changes to RPKI objects; hence, preventing unilateral prefix takedown. We
perform extensive evaluations using our implementation demonstrating the
practicality of our solution. Furthermore, we show that our system is scalable
and remains efficient even when RPKI is widely deployed.
|
Dujmovi\'{c}, Joret, Micek, Morin, Ueckerdt and Wood recently in [Planar
graphs have bounded queue-number, Journal of the ACM, Volume 67, Issue 4,
Article No.: 22, August 2020] showed some attractive graph product structure
theorems for planar graphs. By using the product structure, they proved that
planar graphs have bounded queue-number $48$; in [Planar graphs have bounded
nonrepetitive chromatic number, Advances in Combinatorics, 5, 11 pp, 2020], the
authors proved that planar graphs have bounded nonrepetitive chromatic number
$768$.
In this paper, still by using some product structure theorem, we improve the
upper bound of queue-number of planar graphs to $27$ and the non-repetitive
chromatic number to $320$. We also study powers of trees. We show a graph
product structure theorem of the $k$-th power $T^k$ of tree $T$, then use it
giving an upper bound of the nonrepetitive~chromatic~number of $T^k$. We also
give an asymptotically tight upper bound of the queue-number of $T^k$.
|
We study sets of recurrence, in both measurable and topological settings, for
actions of $(\mathbb{N},\times)$ and $(\mathbb{Q}^{>0},\times)$. In particular,
we show that autocorrelation sequences of positive functions arising from
multiplicative systems have positive additive averages. We also give criteria
for when sets of the form $\{(an+b)^{\ell}/(cn+d)^{\ell}: n \in \mathbb{N}\}$
are sets of multiplicative recurrence, and consequently we recover two recent
results in number theory regarding completely multiplicative functions and the
Omega function.
|
The interaction between magnetic and acoustic excitations have recently
inspired many interdisciplinary studies ranging from fundamental physics to
circuit implementation. Specifically, the exploration of their coherent
interconversion enabled via the magnetoelastic coupling opens a new playground
combining straintronics and spintronics, and provides a unique platform for
building up on-chip coherent information processing networks with miniaturized
magnonic and acoustic devices. In this Perspective, we will focus on the recent
progress of magnon-phonon coupled dynamic systems, including materials,
circuits, imaging and new physics. In particular, we highlight the unique
features such as nonreciprocal acoustic wave propagation and strong coupling
between magnons and phonons in magnetic thin-film systems, which provides a
unique platform for their coherent manipulation and transduction. We will also
review the frontier of surface acoustic wave resonators in coherent quantum
transduction and discuss how the novel acoustic circuit design can be applied
in microwave spintronics.
|
We conjecture and verify a set of universal relations between global
parameters of hot and fast-rotating compact stars, including a relation
connecting the masses of the mass-shedding (Kepler) and static configurations.
We apply these relations to the GW170817 event by adopting the scenario in
which a hypermassive compact star remnant formed in a merger evolves into a
supramassive compact star that collapses into a black hole once the stability
line for such stars is crossed. We deduce an upper limit on the maximum mass of
static, cold neutron stars $ 2.15^{+0.10}_{-0.07}\le M^\star_{\mathrm{TOV}} \le
2.24^{+0.12}_{-0.10} $ for the typical range of entropy per baryon $2 \le S/A
\le 3$ and electron fraction $Y_e = 0.1$ characterizing the hot hypermassive
star. Our result implies that accounting for the finite temperature of the
merger remnant relaxes previously derived constraints on the value of the
maximum mass of a cold, static compact star.
|
For distant iris recognition, a long focal length lens is generally used to
ensure the resolution ofiris images, which reduces the depth of field and leads
to potential defocus blur. To accommodate users at different distances, it is
necessary to control focus quickly and accurately. While for users in motion,
it is expected to maintain the correct focus on the iris area continuously. In
this paper, we introduced a novel rapid autofocus camera for active refocusing
ofthe iris area ofthe moving objects using a focus-tunable lens. Our end-to-end
computational algorithm can predict the best focus position from one single
blurred image and generate a lens diopter control signal automatically. This
scene-based active manipulation method enables real-time focus tracking of the
iris area ofa moving object. We built a testing bench to collect real-world
focal stacks for evaluation of the autofocus methods. Our camera has reached an
autofocus speed ofover 50 fps. The results demonstrate the advantages of our
proposed camera for biometric perception in static and dynamic scenes. The code
is available at https://github.com/Debatrix/AquulaCam.
|
We minimize the stray electric field in a linear Paul trap quickly and
accurately, by applying interferometry pulse sequences to a trapped ion optical
qubit. The interferometry sequences are sensitive to the change of ion
equilibrium position when the trap stiffness is changed, and we use this to
determine the stray electric field. The simplest pulse sequence is a two-pulse
Ramsey sequence, and longer sequences with multiple pulses offer a higher
precision. The methods allow the stray field strength to be minimized beyond
state-of-the-art levels, with only modest experimental requirements. Using a
sequence of nine pulses we reduce the 2D stray field strength to
$(10.5\pm0.8)\,\mathrm{mV\,m^{-1}}$ in $11\,\mathrm{s}$ measurement time. The
pulse sequences are easy to implement and automate, and they are robust against
laser detuning and pulse area errors.
We use interferometry sequences with different lengths and precisions to
measure the stray field with an uncertainty below the standard quantum limit.
This marks a real-world case in which quantum metrology offers a significant
enhancement. Also, we minimize micromotion in 2D using a single probe laser, by
using an interferometry method together with the resolved sideband method; this
is useful for experiments with restricted optical access.
Furthermore, a technique presented in this work is related to quantum
protocols for synchronising clocks; we demonstrate these protocols here.
|
We learn an interactive vision-based driving policy from pre-recorded driving
logs via a model-based approach. A forward model of the world supervises a
driving policy that predicts the outcome of any potential driving trajectory.
To support learning from pre-recorded logs, we assume that the world is on
rails, meaning neither the agent nor its actions influence the environment.
This assumption greatly simplifies the learning problem, factorizing the
dynamics into a nonreactive world model and a low-dimensional and compact
forward model of the ego-vehicle. Our approach computes action-values for each
training trajectory using a tabular dynamic-programming evaluation of the
Bellman equations; these action-values in turn supervise the final vision-based
driving policy. Despite the world-on-rails assumption, the final driving policy
acts well in a dynamic and reactive world. At the time of writing, our method
ranks first on the CARLA leaderboard, attaining a 25% higher driving score
while using 40 times less data. Our method is also an order of magnitude more
sample-efficient than state-of-the-art model-free reinforcement learning
techniques on navigational tasks in the ProcGen benchmark.
|
We present new radio observations of the binary neutron star merger GW170817
carried out with the Karl G. Jansky Very large Array (VLA) more than 3\,yrs
after the merger. Our combined dataset is derived by co-adding more than
$\approx32$\,hours of VLA time on-source, and as such provides the deepest
combined observation (rms sensitivity $\approx 0.99\,\mu$Jy) of the GW170817
field obtained to date at 3\,GHz. We find no evidence for a late-time radio
re-brightening at a mean epoch of $t\approx 1200$\,d since merger, in contrast
to a $\approx 2.1\,\sigma$ excess observed at X-ray wavelengths at the same
mean epoch. Our measurements agree with expectations from the post-peak decay
of the radio afterglow of the GW170817 structured jet. Using these results, we
constrain the parameter space of models that predict a late-time radio
re-brightening possibly arising from the high-velocity tail of the GW170817
kilonova ejecta, which would dominate the radio and X-ray emission years after
the merger (once the structured jet afterglow fades below detection level). Our
results point to a steep energy-speed distribution of the kilonova ejecta (with
energy-velocity power law index $\alpha \gtrsim 5$). We suggest possible
implications of our radio analysis, when combined with the recent tentative
evidence for a late-time re-brightening in the X-rays, and highlight the need
for continued radio-to-X-ray monitoring to test different scenarios.
|
We have obtained new observations of the absorption system at
$z_\mathrm{abs}=0.48$ toward QSO Q0454-220, which we use to constrain its
chemical and physical conditions. The system features metal-enriched gas and
previously unknown low-metallicity gas detected $\sim 200 \, \mathrm{km \,
s^{-1}}$ blueward of the metal-enriched gas. The low-metallicity gas is
detected in multiple Lyman series lines but is not detected in any metal lines.
Our analysis includes low-ionization (e.g., Fe II, Mg II) metal lines,
high-ionization (e.g., C IV, O VI, N V) metal lines, and several Lyman series
lines. We use new UV spectra taken with HST/COS along with data taken from
HST/STIS, Keck/HIRES, and VLT/UVES. We find that the absorption system can be
explained with a photoionized low-ionization phase with $\mathrm{[Fe/H]} \sim
-0.5$ and $n_\mathrm{H} \sim 10^{-2.3} \, \mathrm{cm}^{-3}$, a photoionized
high-ionization phase with a conservative lower limit of $-3.3 <
\mathrm{[Fe/H]}$ and $n_\mathrm{H} \sim 10^{-3.8} \, \mathrm{cm}^{-3}$, and a
low-metallicity component with a conservative upper limit of $\mathrm{[Fe/H]} <
-2.5$ that may be photoionized or collisionally ionized. We suggest that the
low-ionization phase may be due to cold-flow accretion via large-scale
filamentary structure or due to recycled accretion while the high-ionization
phase is the result of ancient outflowing material from a nearby galaxy. The
low-metallicity component may come from pristine accretion. The velocity spread
and disparate conditions among the absorption system's components suggest a
combination of gas arising near galaxies along with gas arising from intergroup
material.
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Minimally invasive surgery mainly consists of a series of sub-tasks, which
can be decomposed into basic gestures or contexts. As a prerequisite of
autonomic operation, surgical gesture recognition can assist motion planning
and decision-making, and build up context-aware knowledge to improve the
surgical robot control quality. In this work, we aim to develop an effective
surgical gesture recognition approach with an explainable feature extraction
process. A Bidirectional Multi-Layer independently RNN (BML-indRNN) model is
proposed in this paper, while spatial feature extraction is implemented via
fine-tuning of a Deep Convolutional Neural Network(DCNN) model constructed
based on the VGG architecture. To eliminate the black-box effects of DCNN,
Gradient-weighted Class Activation Mapping (Grad-CAM) is employed. It can
provide explainable results by showing the regions of the surgical images that
have a strong relationship with the surgical gesture classification results.
The proposed method was evaluated based on the suturing task with data obtained
from the public available JIGSAWS database. Comparative studies were conducted
to verify the proposed framework. Results indicated that the testing accuracy
for the suturing task based on our proposed method is 87.13%, which outperforms
most of the state-of-the-art algorithms.
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R Coronae Borealis (RCB) stars are rare hydrogen-deficient carbon-rich
variable supergiants thought to be the result of dynamically unstable white
dwarf mergers. We attempt to model RCBs through all the relevant timescales by
simulating a merger event in Octo-tiger, a 3D adaptive mesh refinement (AMR)
hydrodynamics code and mapping the post-merger object into MESA, a 1D stellar
evolution code. We then post-process the nucleosynthesis on a much larger
nuclear reaction network to study the enhancement of s-process elements. We
present models that match observations or previous studies in most surface
abundances, isotopic ratios, early evolution and lifetimes. We also observe
similar mixing behavior as previous modeling attempts which result in the
partial He-burning products visible on the surface in observations. However, we
do note that our sub-solar models lack any enhancement in s-process elements,
which we attribute to a lack of hydrogen in the envelope. We also find that the
Oxygen-16/Oxygen-18 isotopic ratio is very sensitive to initial hydrogen
abundance and increases outside of the acceptable range with a hydrogen mass
fraction greater than $10^{-4}$.
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We report a detailed theoretical study of a coherent macroscopic
quantum-mechanical phenomenon - quantum beats of a single magnetic fluxon
trapped in a two-cell SQUID of high kinetic inductance. We calculate
numerically and analytically the low-lying energy levels of the fluxon, and
explore their dependence on externally applied magnetic fields. The quantum
dynamics of the fluxon shows quantum beats originating from its coherent
quantum tunneling between the SQUID cells. We analyze the experimental setup
based on a three-cell SQUID, allowing for time-resolved measurements of quantum
beats of the fluxon.
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We present an approach for continual learning (CL) that is based on fully
probabilistic (or generative) models of machine learning. In contrast to, e.g.,
GANs that are "generative" in the sense that they can generate samples, fully
probabilistic models aim at modeling the data distribution directly.
Consequently, they provide functionalities that are highly relevant for
continual learning, such as density estimation (outlier detection) and sample
generation. As a concrete realization of generative continual learning, we
propose Gaussian Mixture Replay (GMR). GMR is a pseudo-rehearsal approach using
a Gaussian Mixture Model (GMM) instance for both generator and classifier
functionalities. Relying on the MNIST, FashionMNIST and Devanagari benchmarks,
we first demonstrate unsupervised task boundary detection by GMM density
estimation, which we also use to reject untypical generated samples. In
addition, we show that GMR is capable of class-conditional sampling in the way
of a cGAN. Lastly, we verify that GMR, despite its simple structure, achieves
state-of-the-art performance on common class-incremental learning problems at
very competitive time and memory complexity.
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We report measurements of the parity-conserving beam-normal single-spin
elastic scattering asymmetries $B_n$ on $^{12}$C and $^{27}$Al, obtained with
an electron beam polarized transverse to its momentum direction. These
measurements add an additional kinematic point to a series of previous
measurements of $B_n$ on $^{12}$C and provide a first measurement on $^{27}$Al.
The experiment utilized the Qweak apparatus at Jefferson Lab with a beam energy
of 1.158 GeV. The average lab scattering angle for both targets was 7.7
degrees, and the average $Q^2$ for both targets was 0.02437 GeV$^2$ (Q=0.1561
GeV). The asymmetries are $B_n$ = -10.68 $\pm$ 0.90 stat) $\pm$ 0.57 (syst) ppm
for $^{12}$C and $B_n$ = -12.16 $\pm$ 0.58 (stat) $\pm$ 0.62 (syst) ppm for
$^{27}$Al. The results are consistent with theoretical predictions, and are
compared to existing data. When scaled by Z/A, the Q-dependence of all the
far-forward angle (theta < 10 degrees) data from $^{1}$H to $^{27}$Al can be
described by the same slope out to $Q \approx 0.35$ GeV. Larger-angle data from
other experiments in the same Q range are consistent with a slope about twice
as steep.
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Multi Layer Capacitors MLCs are considered as the most promising refrigerant
elements to design and develop electrocaloric cooling devices. Recently, the
heat transfer of these MLCs has been considered. However, the heat exchange
with the surrounding environment has been poorly, if not, addressed. In this
work, we measure by infrared thermography the temperature change versus time in
four different heat exchange configurations. Depending on the configurations,
Newtonian and non-Newtonian regimes with their corresponding Biot number are
determined allowing to provide useful thermal characteristics. Indeed, in case
of large area thermal pad contacts, heat transfer coefficients up to 3400 W m-2
K-1 are obtained showing that the standard MLCs already reach the needs for
designing efficient prototypes. We also determine the ideal Brayton cooling
power in case of thick wires contact which varies between 3.4 mW and 9.8 mW for
operating frequencies varying from 0.25 Hz to 1 Hz. While only heat conduction
is considered here, our work provides some design rules for improving heat
exchanges in future devices.
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Complex plasmas consist of microparticles embedded in a low-temperature
plasma and allow investigating various effects by tracing the motion of these
microparticles. Dust density waves appear in complex plasmas as self-excited
acoustic waves in the microparticle fluid at low neutral gas pressures. Here we
show that various properties of these waves depend on the position of the
microparticle cloud with respect to the plasma sheath and explain this finding
in terms of the underlying ion-drift instability. These results may be helpful
in better understanding the propagation of dust density waves in complex
plasmas and beyond, for instance, in astrophysical dusty plasmas.
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In the second of two papers on the peculiar interacting transient AT 2016jbu,
we present the bolometric lightcurve, identification and analysis of the
progenitor candidate, as well as preliminary modelling to help elucidate the
nature of this event. We identify the progenitor candidate for AT 2016jbu in
quiescence, and find it to be consistent with a $\sim$20 M$_{\odot}$ yellow
hypergiant surrounded by a dusty circumstellar shell. We see evidence for
significant photometric variability in the progenitor, as well as strong
H$\alpha$ emission consistent with pre-existing circumstellar material. The age
of the resolved stellar population surrounding AT 2016jbu, as well as
integral-field unit spectra of the region support a progenitor age of >16 Myr,
again consistent with a progenitor mass of $\sim$20 M$_{\odot}$. Through a
joint analysis of the velocity evolution of AT 2016jbu, and the photospheric
radius inferred from the bolometric lightcurve, we find that the transient is
consistent with two successive outbursts or explosions. The first outburst
ejected a shell of material with velocity 650 km $s^{-1}$, while the second
more energetic event ejected material at 4500 km $s^{-1}$. Whether the latter
is the core-collapse of the progenitor remains uncertain, as the required
ejecta mass is relatively low (few tenths of M$_{\odot}$). We also place a
restrictive upper limit on the ejected $^{56}$Ni mass of <0.016 M$_{\odot}$.
Using the BPASS code, we explore a wide range of possible progenitor systems,
and find that the majority of these are in binaries, some of which are
undergoing mass transfer or common envelope evolution immediately prior to
explosion. Finally, we use the SNEC code to demonstrate that the low-energy
explosion of some of these systems together with sufficient CSM can reproduce
the overall morphology of the lightcurve of AT 2016jbu.
|
In the present paper, we introduce a concept of Ricci curvature on
hypergraphs for a nonlinear Laplacian. We prove that our definition of the
Ricci curvature is a generalization of Lin-Lu-Yau coarse Ricci curvature for
graphs to hypergraphs. We also show a lower bound of nonzero eigenvalues of
Laplacian, gradient estimate of heat flow, and diameter bound of Bonnet-Myers
type for our curvature notion. This research leads to understanding how
nonlinearity of Laplacian causes complexity of curvatures.
|
The Jahn-Teller theorem constitutes one of the most popular and stringent
concepts, applicable to all fields of chemistry. In open shell transition
elements chemistry and physics, 3d4, 3d9, and 3d7(low-spin) configurations in
octahedral complexes serve as particular illustrative and firm examples, where
a striking change (distortion) in local geometry is associated to a substantial
reduction of electronic energy. However, there has been a lasting debate, about
the fact that the octahedra are found to exclusively elongate, (at least for eg
electrons). Against this background, the title compound displays two marked
features, (1) the octahedron of oxygen atoms around Os6+ (d2) is drastically
compressed, in contrast to the standard JT expectations, and (2) the splitting
of the t2g set induced by this compression is extreme, such that a diamagnetic
ground state results. What we see is obviously a Jahn-Teller distortion
resulting in a compression of the respective octahedron and acting on the t2g
set of orbitals. Both these issues are unprecedented. Noteworthy, the splitting
into a lower dxy (hosting two d electrons with opposite spin) and two higher
dxz and dyz orbitals is so large that for the first time ever the Hund's
coupling for t2g electrons is overcome. We show that these effects are not
forced by structural frustration, the structure offers sufficient space for Os
to shift the apical oxygen atoms to a standard distance. Local electronic
effects appear to be responsible, instead. The relevance of these findings is
far reaching, since they provide insights in the hierarchy of perturbations
defining ground states of open shell electronic systems. The system studied
here, offers substantially more structural and compositional degrees of
freedom, such that a configuration could form that enables Os6+ to adopt its
apparently genuine diamagnetic ground state.
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There are several methods for model selection in cosmology which have at
least two major goals, that of finding the correct model or predicting well. In
this work we discuss through a study of well-known model selection methods like
Akaike information criterion (AIC), Bayesian information criterion (BIC),
deviance information criterion (DIC) and Bayesian evidence, how these different
goals are pursued in each paradigm. We also apply another method for model
selection which less seen in cosmological literature, the Cross-validation
method. Using these methods we will compare two different scenarios in
cosmology, $\Lambda$CDM model and dynamical dark energy. We show that each of
the methods tends to different results in model selection. While BIC and
Bayesian evidence overrule the dynamical dark energy scenarios with 2 or 3
extra degree of freedom, the DIC and cross-validation method prefer these
dynamical models to $\Lambda$CDM model. Assuming the numerical results of
different analysis and combining cosmological and statistical aspects of the
subject, we propose cross-validation as an interesting method for model
selection in cosmology that can lead to different results in comparison with
usual methods of model selection.
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Sound event detection is a core module for acoustic environmental analysis.
Semi-supervised learning technique allows to largely scale up the dataset
without increasing the annotation budget, and recently attracts lots of
research attention. In this work, we study on two advanced semi-supervised
learning techniques for sound event detection. Data augmentation is important
for the success of recent deep learning systems. This work studies the
audio-signal random augmentation method, which provides an augmentation
strategy that can handle a large number of different audio transformations. In
addition, consistency regularization is widely adopted in recent
state-of-the-art semi-supervised learning methods, which exploits the
unlabelled data by constraining the prediction of different transformations of
one sample to be identical to the prediction of this sample. This work finds
that, for semi-supervised sound event detection, consistency regularization is
an effective strategy, especially the best performance is achieved when it is
combined with the MeanTeacher model.
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We derive the non-relativistic limit of a massive vector field. We show that
the Cartesian spatial components of the vector behave as three identical,
non-interacting scalar fields. We find classes of spherical, cylindrical, and
planar self-gravitating vector solitons in the Newtonian limit. The
gravitational properties of the lowest-energy vector solitons$\mathrm{-}$the
gravitational potential and density field$\mathrm{-}$depend only on the net
mass of the soliton and the vector particle mass. In particular, these
self-gravitating, ground-state vector solitons are independent of the
distribution of energy across the vector field components, and are
indistinguishable from their scalar-field counterparts. Fuzzy Vector Dark
Matter models can therefore give rise to halo cores with identical
observational properties to the ones in scalar Fuzzy Dark Matter models. We
also provide novel hedgehog vector soliton solutions, which cannot be observed
in scalar-field theories. The gravitational binding of the lowest-energy
hedgehog halo is about three times weaker than the ground-state vector soliton.
Finally, we show that no spherically symmetric solitons exist with a
divergence-free vector field.
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Given two algebraic groups $G$, $H$ over a field $k$, we investigate the
representability of the functor of morphisms (of schemes) $\mathbf{Hom}(G,H)$
and the subfunctor of homomorphisms (of algebraic groups) $\mathbf{Hom}_{\rm
gp}(G,H)$. We show that $\mathbf{Hom}(G,H)$ is represented by a group scheme,
locally of finite type, if the $k$-vector space $\mathcal{O}(G)$ is
finite-dimensional; the converse holds if $H$ is not \'etale. When $G$ is
linearly reductive and $H$ is smooth, we show that $\mathbf{Hom}_{\rm gp}(G,H)$
is represented by a smooth scheme $M$; moreover, every orbit of $H$ acting by
conjugation on $M$ is open.
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Modern power systems face a grand challenge in grid management due to
increased electricity demand, imminent disturbances, and uncertainties
associated with renewable generation, which can compromise grid security. The
security assessment is directly connected to the robustness of the operating
condition and is evaluated by analyzing proximity to the power flow solution
space's boundary. Calculating location of such a boundary is a computationally
challenging task, linked to the power flow equations' non-linear nature,
presence of technological constraints, and complicated network topology. In
this paper we introduce a general framework to characterize points on the power
flow solution space boundary in terms of auxiliary variables subject to
algebraic constraints. Then we develop an adaptive continuation algorithm to
trace 1-dimensional sections of boundary curves which exhibits robust
performance and computational tractability. Implementation of the algorithm is
described in detail, and its performance is validated on different test
networks.
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We investigate optimal states of photon pairs to excite a target transition
in a multilevel quantum system. With the help of coherent control theory for
two-photon absorption with quantum light, we infer the maximal population
achievable by optimal entangled vs. separable states of light. Interference
between excitation pathways, as well as the presence of nearby states, may
hamper the selective excitation of a particular target state, but we show that
quantum correlations can help to overcome this problem, and enhance the
achievable "selectivity" between two energy levels, i.e. the relative
difference in population transferred into each of them. We find that the added
value of optimal entangled states of light increases with broadening linewidths
of the target states.
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In non-centrosymmetric metals, spin-orbit coupling (SOC) induces
momentum-dependent spin polarization at the Fermi surfaces. This is exemplified
by the valley-contrasting spin polarization in monolayer transition metal
dichalcogenides (TMDCs) with in-plane inversion asymmetry. However, the valley
configuration of massive Dirac fermions in TMDCs is fixed by the graphene-like
structure, which limits the variety of spin-valley coupling. Here, we show that
the layered polar metal BaMn$X_2$ ($X =$Bi, Sb) hosts tunable
spin-valley-coupled Dirac fermions, which originate from the distorted $X$
square net with in-plane lattice polarization. We found that in spite of the
larger SOC, BaMnBi$_2$ has approximately one-tenth the lattice distortion of
BaMnSb$_2$, from which a different configuration of spin-polarized Dirac
valleys is theoretically predicted. This was experimentally observed as a clear
difference in the Shubnikov-de Haas oscillation at high fields between the two
materials. The chemically tunable spin-valley coupling in BaMn$X_2$ makes it a
promising material for various spin-valleytronic devices.
|
Traditionally, for most machine learning settings, gaining some degree of
explainability that tries to give users more insights into how and why the
network arrives at its predictions, restricts the underlying model and hinders
performance to a certain degree. For example, decision trees are thought of as
being more explainable than deep neural networks but they lack performance on
visual tasks. In this work, we empirically demonstrate that applying methods
and architectures from the explainability literature can, in fact, achieve
state-of-the-art performance for the challenging task of domain generalization
while offering a framework for more insights into the prediction and training
process. For that, we develop a set of novel algorithms including DivCAM, an
approach where the network receives guidance during training via gradient based
class activation maps to focus on a diverse set of discriminative features, as
well as ProDrop and D-Transformers which apply prototypical networks to the
domain generalization task, either with self-challenging or attention
alignment. Since these methods offer competitive performance on top of
explainability, we argue that the proposed methods can be used as a tool to
improve the robustness of deep neural network architectures.
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Static analysis tools typically address the problem of excessive false
positives by requiring programmers to explicitly annotate their code. However,
when faced with incomplete annotations, many analysis tools are either too
conservative, yielding false positives, or too optimistic, resulting in unsound
analysis results. In order to flexibly and soundly deal with
partially-annotated programs, we propose to build upon and adapt the gradual
typing approach to abstract-interpretation-based program analyses.
Specifically, we focus on null-pointer analysis and demonstrate that a gradual
null-pointer analysis hits a sweet spot, by gracefully applying static analysis
where possible and relying on dynamic checks where necessary for soundness. In
addition to formalizing a gradual null-pointer analysis for a core imperative
language, we build a prototype using the Infer static analysis framework, and
present preliminary evidence that the gradual null-pointer analysis reduces
false positives compared to two existing null-pointer checkers for Infer.
Further, we discuss ways in which the gradualization approach used to derive
the gradual analysis from its static counterpart can be extended to support
more domains. This work thus provides a basis for future analysis tools that
can smoothly navigate the tradeoff between human effort and run-time overhead
to reduce the number of reported false positives.
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In the absence of dissipation a non-rotating magnetic nanoparticle can be
stably levitated in a static magnetic field as a consequence of the spin origin
of its magnetization. Here, we study the effects of dissipation on the
stability of the system, considering the interaction with the background gas
and the intrinsic Gilbert damping of magnetization dynamics. We find that
dissipation limits the time over which a particle can be stably levitated. At
large applied magnetic fields we identify magnetization switching induced by
Gilbert damping as the key limiting factor for stable levitation. At low
applied magnetic fields and for small particle dimensions magnetization
switching is prevented due to the strong coupling of rotation and magnetization
dynamics, and the stability is mainly limited by the gas-induced dissipation.
In this latter case, high vacuum should be sufficient to extend stable
levitation over experimentally relevant timescales. Our results demonstrate the
possibility to experimentally observe the phenomenon of quantum spin stabilized
magnetic levitation.
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Cryptocurrencies are increasingly popular. Even people who are not experts
have started to invest in these securities, and nowadays, cryptocurrency
exchanges process transactions for over 100 billion US dollars per month. In
spite of this, many cryptocurrencies have low liquidity, and therefore, they
are highly prone to market manipulation. This paper performs an in-depth
analysis of two market manipulations organized by communities over the
Internet: The pump and dump and the crowd pump. The pump and dump scheme is a
fraud as old as the stock market. Now, it got new vitality in the loosely
regulated market of cryptocurrencies. Groups of highly coordinated people
arrange this scam, usually on Telegram and Discord. We monitored these groups
for more than 3 years detecting around 900 individual events. We analyze how
these communities are organized and how they carry out the fraud. We report on
three case studies of pump and dump. Then, we leverage our unique dataset of
the verified pump and dumps to build a machine learning model able to detect a
pump and dump in 25 seconds from the moment it starts, achieving the results of
94.5% of F1-score. Then, we move on to the crowd pump, a new phenomenon that
hit the news in the first months of 2021, when a Reddit community inflates the
price of the GameStop stocks (GME) of over 1,900% on Wall Street, the world's
largest stock exchange. Later, other Reddit communities replicate the operation
on the cryptocurrency markets. The targets were Dogecoin (DOGE) and Ripple
(XRP). We reconstruct how these operations developed, and we discuss
differences and analogies with the standard pump and dump. Lastly, we
illustrate how it is possible to leverage our classifier to detect this kind of
operation too.
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Two-dimensional (2D) magnetic materials with strong magnetostriction are
interesting systems for strain-tuning the magnetization, enabling potential for
realizing spintronic and nanomagnetic devices. Realizing this potential
requires understanding of the magneto-mechanical coupling in the 2D limit. In
this work, we suspend thin Cr$_2$Ge$_2$Te$_6$ layers, creating nanomechanical
membrane resonators. We probe its mechanical and magnetic properties as a
function of temperature and strain. Pronounced signatures of magneto-elastic
coupling are observed in the temperature-dependent resonance frequency of these
membranes near $T_{\rm C}$. We further utilize Cr$_2$Ge$_2$Te$_6$ in
heterostructures with thin layers of WSe$_2$ and FePS$_3$, which have positive
thermal expansion coefficients, to compensate the negative thermal expansion
coefficient of Cr$_2$Ge$_2$Te$_6$ and quantitatively probe the corresponding
$T_{\rm C}$. Finally, we induce a strain of $0.016\%$ in a suspended
heterostructure via electrostatic force and demonstrate a resulting enhancement
of $T_{\rm C}$ by $2.5 \pm 0.6$ K in the absence of an external magnetic field.
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Progress on the study of synchronisation in quantum systems has been largely
driven by specific examples which resulted in several examples of frequency
entrainment as well as mutual synchronisation. Here we study quantum
synchronisation by utilising Liouville space perturbation theory. We begin by
clarifying the role of centers, symmetries and oscillating coherences in the
context of quantum synchronisation. We then analyse the eigenspectrum of the
Liouville superoperator generating the dynamics of the quantum system and
determine the conditions under which synchronisation arises. We apply our
framework to derive a powerful relationship between energy conservation,
degeneracies and synchronisation in quantum systems. Finally, we demonstrate
our approach by analysing two mutually coupled thermal machines and the close
relationship between synchronisation and thermodynamic quantities.
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Structural optimization (topology, shapes, sizing) is an important tool for
facilitating the emergence of new concepts in structural design. Normally,
topology optimization is carried out at the early stage of design and then
shape and sizing design are performed sequentially. Unlike traditional topology
optimization method, explicit methodologies have attracted a great deal of
attention because of the advantages of shortcuting the costly CAD/CAE processes
while dealing with low order number of design variables compared to implicit
method (such as SIMP). This paper aims at presenting an adaptation of a
flow-inspired approach so-called Moving Node Approach (MNA) in topology
optimization. In this approach, the discretization is decoupled from the
material distribution and the final objective is to recognize the best beam
assembly while minimizing compliance. The paradigm has here changed and new
design variables are used such as nodes location, elements length/orientation
and size providing a lower number of design variables than pixels-based. The
methodology is validated using 2 classical testcases in the field of Topology
Optimization: the Cantilever beam and the L-Shape problem.
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The increase in the sensitivity of gravitational wave interferometers will
bring additional detections of binary black hole and double neutron star
mergers. It will also very likely add many merger events of black hole -
neutron star binaries. Distinguishing mixed binaries from binary black holes
mergers for high mass ratios could be challenging because in this situation the
neutron star coalesces with the black hole without experiencing significant
disruption. To investigate the transition of mixed binary mergers into those
behaving more like binary black hole coalescences, we present results from
merger simulations for different mass ratios. We show how the degree of
deformation and disruption of the neutron star impacts the inspiral and merger
dynamics, the properties of the final black hole, the accretion disk formed
from the circularization of the tidal debris, the gravitational waves, and the
strain spectrum and mismatches. The results also show the effectiveness of the
initial data method that generalizes the Bowen-York initial data for black hole
punctures to the case of binaries with neutron star companions.
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Pre-trained contextualized language models (PrLMs) have led to strong
performance gains in downstream natural language understanding tasks. However,
PrLMs can still be easily fooled by adversarial word substitution, which is one
of the most challenging textual adversarial attack methods. Existing defence
approaches suffer from notable performance loss and complexities. Thus, this
paper presents a compact and performance-preserved framework, Anomaly Detection
with Frequency-Aware Randomization (ADFAR). In detail, we design an auxiliary
anomaly detection classifier and adopt a multi-task learning procedure, by
which PrLMs are able to distinguish adversarial input samples. Then, in order
to defend adversarial word substitution, a frequency-aware randomization
process is applied to those recognized adversarial input samples. Empirical
results show that ADFAR significantly outperforms those newly proposed defense
methods over various tasks with much higher inference speed. Remarkably, ADFAR
does not impair the overall performance of PrLMs. The code is available at
https://github.com/LilyNLP/ADFAR
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We consider the renormalization-group evolution of the matrix element of
$\langle 0| \bar{q}(z)_\beta [z,0]b(0)_\alpha| \bar{B}\rangle$, which can be
used to define the distribution amplitudes for $B$ meson and widely applied in
studies of $B$ meson decays. The contribution to the renormalization constant
of the non-local operator $\bar{q}(z)_\beta [z,0]b(0)_\alpha$ is considered up
to one-loop order in QCD. Since the quark fields in this operator are not
directly coupled fields, momentum can not flow freely through this non-local
operator. Momentum involved in this operator can be treated stringently in
coordinate space. We find that the ultraviolet divergences regulated by
dimensional parameter $\epsilon$ cancel with each other, and the evolution
effect vanishes. The matrix element $\langle 0| \bar{q}(z)_\beta
[z,0]b(0)_\alpha| \bar{B}\rangle$ escapes from the renormalization-group
evolution. We then apply the matrix element in calculating $B\to\pi$ transition
form factor, where the matrix element is obtained by using the $B$ meson wave
function calculated in QCD-inspired potential model. By comparing with
experimental data for the semileptonic decay of $B\to \pi \ell\nu$ and
light-cone sum rule calculation, we analyse the perturbative and
non-perturbative contributions to $B\to\pi$ transition form factor in the frame
work of perturbative QCD approach. We find that the effectiveness of the
suppression of Sudakov factor to soft contribution depends on the end-point
behavior of $B$ meson wave function, and with the $B$-meson wave function used
in this work, soft contribution can not be well suppressed. The hard
contribution to the $B\to\pi$ transition form factor is about 59\%, and soft
contribution can be as large as 41\% in the naive calculation. To make the
perturbative calculation reliable, a soft momentum cutoff in the calculation
and soft form factor have to be introduced.
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It has been shown that temperature cycles on airless bodies of our Solar
System can cause damaging of surface materials. Nevertheless, propagation
mechanisms in the case of space objects are still poorly understood. Present
work combines a thermoelasticity model together with linear elastic fracture
mechanics theory to predict fracture propagation in the presence of thermal
gradients generated by diurnal temperature cycling and under conditions similar
to those existing on the asteroid Bennu. The crack direction is computed using
the maximal strain energy release rate criterion, which is implemented using
finite elements and the so-called G$\theta$ method (Uribe-Su\'arez et al. 2020.
Eng. Fracture Mech. 227:106918). Using the implemented methodology, crack
propagation direction for an initial crack tip in different positions and for
different orientations is computed. It is found that cracks preferentially
propagate in the North to South (N-S), in the North-East to South-West (NE-SW)
and in the North-West to South-East (NW-SE) directions. Finally, thermal
fatigue analysis was performed in order to estimate the crack growth rate.
Computed value is in good agreement with available experimental evidence.
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We establish sharp dynamical implications of convexity on symmetric spheres
that do not follow from dynamical convexity. It allows us to show the existence
of elliptic and non-hyperbolic periodic orbits and to furnish new examples of
dynamically convex contact forms, in any dimension, that are not equivalent to
convex ones via contactomorphisms that preserve the symmetry. Moreover, these
examples are $C^1$-stable in the sense that they are actually not equivalent to
convex ones via contactomorphisms that are $C^1$-close to those preserving the
symmetry. We also show the multiplicity of symmetric non-hyperbolic and
symmetric (not necessarily non-hyperbolic) closed Reeb orbits under suitable
pinching conditions.
|
We introduce a geometric operation, which we call the relative Whitney trick,
that removes a single double point between properly immersed surfaces in a
$4$-manifold with boundary. Using the relative Whitney trick we prove that
every link in a homology sphere is homotopic to a link that is topologically
slice in a contractible topological $4$-manifold. We further prove that any
link in a homology sphere is order $k$ Whitney tower concordant to a link in
$S^3$ for all $k$. Finally, we explore the minimum Gordian distance from a link
in $S^3$ to a homotopically trivial link. Extending this notion to links in
homology spheres, we use the relative Whitney trick to make explicit
computations for 3-component links and establish bounds in general.
|
We use relative hyperbolicity of mapping tori and Dehn fillings of relatively
hyperbolic groups to solve the conjugacy problem between certain outer
automorphisms. We reduce this problem to algorithmic problems entirely
expressed in terms of the parabolic subgroups of the mapping tori. As an
immediate application, we solve the conjugacy problem for outer automorphisms
of free groups, whose polynomial part is piecewise inner. This proposes a path
toward a full solution to the conjugacy problem for $\mathrm{Out}(F_n)$.
|
We investigate how the choice of equation of state (EOS) and resolution
conspire to affect the outcomes of giant impact (GI) simulations. We focus on
the simple case of equal mass collisions of two Earth-like $0.5\,M_\oplus$
proto-planets showing that the choice of EOS has a profound impact on the
outcome of such collisions as well as on the numerical convergence with
resolution. In simulations where the Tillotson EOS is used, impacts generate an
excess amount of vapour due to the lack of a thermodynamically consistent
treatment of phase transitions and mixtures. In oblique collisions this
enhances the artificial angular momentum (AM) transport from the planet to the
circum-planetary disc reducing the planet's rotation period over time. Even at
a resolution of $1.3 \times 10^6$ particles the result is not converged. In
head-on collisions the lack of a proper treatment of the solid/liquid-vapour
phase transition allows the bound material to expand to very low densities
which in turn results in very slow numerical convergence of the critical
specific impact energy for catastrophic disruption $Q_{RD}^*$ with increasing
resolution as reported in prior work. The simulations where ANEOS is used for
oblique impacts are already converged at a modest resolution of $10^5$
particles, while head-on collisions converge when they evidence the post-shock
formation of a dense iron-rich ring, which promotes gravitational
re-accumulation of material. Once sufficient resolution is reached to resolve
the liquid-vapour phase transition of iron in the ANEOS case, and this ring is
resolved, the value of $Q_{RD}^*$ has then converged.
|
We studied theoretically the effect of a low concentration of adsorbed polar
molecules on the optical conductivity of graphene, within the Kubo linear
response approximation. Our analysis is based on a continuum model
approximation that includes up to next to nearest neighbors in the pristine
graphene effective Hamiltonian, thus extending the field-theoretical analysis
developed in Refs.[1,2]. Our results show that the conductivity can be
expressed in terms of renormalized quasiparticle parameters $\tilde{v}_F$,
$\tilde{M}$ and $\tilde{\mu}$ that include the effect of the molecular surface
concentration $n_{dip}$ and dipolar moment $\boldsymbol{\mathcal{P}}$, thus
providing an analytical model for a graphene-based chemical sensor.
|
Learning an empirically effective model with generalization using limited
data is a challenging task for deep neural networks. In this paper, we propose
a novel learning framework called PurifiedLearning to exploit task-irrelevant
features extracted from task-irrelevant labels when training models on
small-scale datasets. Particularly, we purify feature representations by using
the expression of task-irrelevant information, thus facilitating the learning
process of classification. Our work is built on solid theoretical analysis and
extensive experiments, which demonstrate the effectiveness of PurifiedLearning.
According to the theory we proved, PurifiedLearning is model-agnostic and
doesn't have any restrictions on the model needed, so it can be combined with
any existing deep neural networks with ease to achieve better performance. The
source code of this paper will be available in the future for reproducibility.
|
Virtual-reality (VR) and augmented-reality (AR) technology is increasingly
combined with eye-tracking. This combination broadens both fields and opens up
new areas of application, in which visual perception and related cognitive
processes can be studied in interactive but still well controlled settings.
However, performing a semantic gaze analysis of eye-tracking data from
interactive three-dimensional scenes is a resource-intense task, which so far
has been an obstacle to economic use. In this paper we present a novel approach
which minimizes time and information necessary to annotate volumes of interest
(VOIs) by using techniques from object recognition. To do so, we train
convolutional neural networks (CNNs) on synthetic data sets derived from
virtual models using image augmentation techniques. We evaluate our method in
real and virtual environments, showing that the method can compete with
state-of-the-art approaches, while not relying on additional markers or
preexisting databases but instead offering cross-platform use.
|
On a compact K\"ahler manifold $X$, Toeplitz operators determine a
deformation quantization $(\operatorname{C}^\infty(X, \mathbb{C})[[\hbar]],
\star)$ with separation of variables [10] with respect to transversal complex
polarizations $T^{1, 0}X, T^{0, 1}X$ as $\hbar \to 0^+$ [15]. The analogous
statement is proved for compact symplectic manifolds with transversal
non-singular real polarizations [13].
In this paper, we establish the analogous result for transversal singular
real polarizations on compact toric symplectic manifolds $X$. Due to toric
singularities, half-form correction and localization of our Toeplitz operators
are essential. Via norm estimations, we show that these Toeplitz operators
determine a star product on $X$ as $\hbar \to 0^+$.
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We construct analogues of the Hecke operators for the moduli space of
G-bundles on a curve X over a local field F with parabolic structures at
finitely many points. We conjecture that they define commuting compact normal
operators on the Hilbert space of half-densities on this moduli space. In the
case F=C, we also conjecture that their joint spectrum is in a natural
bijection with the set of opers on X for the Langlands dual group with real
monodromy. This may be viewed as an analytic version of the Langlands
correspondence for complex curves. Furthermore, we conjecture an explicit
formula relating the eigenvalues of the Hecke operators and the global
differential operators studied in our previous paper arXiv:1908.09677. Assuming
the compactness conjecture, this formula follows from a certain system of
differential equations satisfied by the Hecke operators, which we prove in this
paper for G=PGL(n).
|
The potential of using millimeter-wave (mmWave) to encounter the current
bandwidth shortage has motivated packing more antenna elements in the same
physical size which permits the advent of massive
multiple-input-multiple-output (MIMO) for mmWave communication. However, with
increasing number of antenna elements, the ability of allocating a single
RF-chain per antenna becomes infeasible and unaffordable. As a cost-effective
alternative, the design of hybrid precoding has been considered where the
limited-scattering signals are captured by a high-dimensional RF precoder
realized by an analog phase-shifter network followed by a low-dimensional
digital precoder at baseband. In this paper, the max-min fair problem is
considered to design a low-complexity hybrid precoder for multi-group
multicasting systems in mmWave channels. The problem is non-trivial due to two
main reasons: the original max-min problem for multi-group multicasting for a
fully-digital precoder is non-convex, and the analog precoder places constant
modules constraint which restricts the feasible set of the precoders in the
design problem. Therefore, we consider a low complexity hybrid precoder design
to tackle and benefit from the mmWave channel structure. Each analog beamformer
was designed to maximize the minimum matching component for users within a
given group. Once obtained, the digital precoder was attained by solving the
max-min problem of the equivalent channel.
|
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