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Acoustic side-channel attacks on keyboards can bypass security measures in
many systems that use keyboards as one of the input devices. These attacks aim
to reveal users' sensitive information by targeting the sounds made by their
keyboards as they type. Most existing approaches in this field ignore the
negative impacts of typing patterns and environmental noise in their results.
This paper seeks to address these shortcomings by proposing an applicable
method that takes into account the user's typing pattern in a realistic
environment. Our method achieved an average success rate of 43% across all our
case studies when considering real-world scenarios.
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Planets are born in protostellar disks, which are now observed with enough
resolution to address questions about internal gas flows. Candidates for
driving the flows include magnetic forces, but ionization state estimates
suggest much of the gas mass decouples from magnetic fields. Thus,
hydrodynamical instabilities could play a major role. We investigate disk
dynamics under conditions typical for a T Tauri system, using global 3D
radiation hydrodynamics simulations with embedded particles and a resolution of
70 cells per scale height. Stellar irradiation heating is included with
realistic dust opacities. The disk starts in joint radiative balance and
hydrostatic equilibrium. The vertical shear instability (VSI) develops into
turbulence that persists up to at least 1600 inner orbits (143 outer orbits).
Turbulent speeds are a few percent of the local sound speed at the midplane,
increasing to 20%, or 100 m/s, in the corona. These are consistent with recent
upper limits on turbulent speeds from optically thin and thick molecular line
observations of TW Hya and HD 163296. The predominantly vertical motions
induced by the VSI efficiently lift particles upwards. Grains 0.1 and 1 mm in
size achieve scale heights greater than expected in isotropic turbulence. We
conclude that while kinematic constraints from molecular line emission do not
directly discriminate between magnetic and nonmagnetic disk models, the small
dust scale heights measured in HL Tau and HD 163296 favor turbulent magnetic
models, which reach lower ratios of the vertical kinetic energy density to the
accretion stress.
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We discuss the axiomatic basis of quantum mechanics and show that it is
neither general nor consistent, since its axioms are incompatible with each
other and moreover it does not incorporate the magnetic quantization as in the
cyclotron motion. A general and consistent system of axioms is conjectured
which incorporates also the magnetic quantization.
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In this paper we exploit the ideas and formalisms of twistor theory, to show
how, on Minkowski space, given a null solution of the wave equation, there are
precisely two null directions in $\ker df$, at least one of which is a
shear-free ray congruence.
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Multi-frame high dynamic range (HDR) imaging aims to reconstruct ghost-free
images with photo-realistic details from content-complementary but spatially
misaligned low dynamic range (LDR) images. Existing HDR algorithms are prone to
producing ghosting artifacts as their methods fail to capture long-range
dependencies between LDR frames with large motion in dynamic scenes. To address
this issue, we propose a novel image fusion transformer, referred to as IFT,
which presents a fast global patch searching (FGPS) module followed by a
self-cross fusion module (SCF) for ghost-free HDR imaging. The FGPS searches
the patches from supporting frames that have the closest dependency to each
patch of the reference frame for long-range dependency modeling, while the SCF
conducts intra-frame and inter-frame feature fusion on the patches obtained by
the FGPS with linear complexity to input resolution. By matching similar
patches between frames, objects with large motion ranges in dynamic scenes can
be aligned, which can effectively alleviate the generation of artifacts. In
addition, the proposed FGPS and SCF can be integrated into various deep HDR
methods as efficient plug-in modules. Extensive experiments on multiple
benchmarks show that our method achieves state-of-the-art performance both
quantitatively and qualitatively.
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We consider a many-body generalization of the Kapitza pendulum: the
periodically-driven sine-Gordon model. We show that this interacting system is
dynamically stable to periodic drives with finite frequency and amplitude. This
finding is in contrast to the common belief that periodically-driven unbounded
interacting systems should always tend to an absorbing infinite-temperature
state. The transition to an unstable absorbing state is described by a change
in the sign of the kinetic term in the effective Floquet Hamiltonian and
controlled by the short-wavelength degrees of freedom. We investigate the
stability phase diagram through an analytic high-frequency expansion, a
self-consistent variational approach, and a numeric semiclassical calculations.
Classical and quantum experiments are proposed to verify the validity of our
results.
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The natural mortality (M) and purse-seine catchability and selectivity were
estimated for Trachurus novaezelandiae, Richardson, 1843 (yellowtail scad)-a
small inshore pelagic species harvested off south-eastern Australia. Hazard
functions were applied to two decades of data describing catches (mostly stable
at a mean +- SE of 315 +- 14 t p.a.) and effort (declining from a maximum of
2289 to 642 boat days between 1999/00 and 2015/16) and inter-dispersed (over
nine years) annual estimates of size-at-age (0+ to 18 years) to enable survival
analysis. The data were best described by a model with eight parameters,
including catchability (estimated at < 0.1 x 10-7 boat day-1), M (0.22 year-1)
and variable age-specific selection up to 6 years with a 50% retention among
5-year olds (larger than the estimated age at maturation). The low catchability
implied minimal fishing mortality by the purse-seine fleet. Ongoing monitoring
and applied gear-based studies are required to validate purse-seine
catchability and selectivity, but the data nevertheless imply T. novaezelandiae
could incur substantial additional fishing effort and, in doing, so alleviate
pressure on other regional small pelagics.
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The quality of today's research is often tightly limited to the available
computing power and scalability of codes to many processors. For example,
tackling the problem of heating the solar corona requires a most realistic
description of the plasma dynamics and the magnetic field. Numerically solving
such a magneto-hydrodynamical (MHD) description of a small active region (AR)
on the Sun requires millions of computation hours on current high-performance
computing (HPC) hardware. The aim of this work is to describe methods for an
efficient parallelization of boundary conditions and data input/output (IO)
strategies that allow for a better scaling towards thousands of processors
(CPUs). The Pencil Code is tested before and after optimization to compare the
performance and scalability of a coronal MHD model above an AR. We present a
novel boundary condition for non-vertical magnetic fields in the photosphere,
where we approach the realistic pressure increase below the photosphere. With
that, magnetic flux bundles become narrower with depth and the flux density
increases accordingly. The scalability is improved by more than one order of
magnitude through the HPC-friendly boundary conditions and IO strategies. This
work describes also the necessary nudging methods to drive the MHD model with
observed magnetic fields from the Sun's photosphere. In addition, we present
the upper and lower atmospheric boundary conditions (photospheric and towards
the outer corona), including swamp layers to diminish perturbations before they
reach the boundaries. Altogether, these methods enable more realistic 3D MHD
simulations than previous models regarding the coronal heating problem above an
AR -- simply because of the ability to use a large amount of CPUs efficiently
in parallel.
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Most data analytics systems that require low-latency execution and efficient
utilization of computing resources, increasingly adopt two computational
paradigms, namely, incremental and approximate computing. Incremental
computation updates the output incrementally instead of re-computing everything
from scratch for successive runs of a job with input changes. Approximate
computation returns an approximate output for a job instead of the exact
output.
Both paradigms rely on computing over a subset of data items instead of
computing over the entire dataset, but they differ in their means for skipping
parts of the computation. Incremental computing relies on the memoization of
intermediate results of sub-computations, and reusing these memoized results
across jobs for sub-computations that are unaffected by the changed input.
Approximate computing relies on representative sampling of the entire dataset
to compute over a subset of data items.
In this thesis, we make the observation that these two computing paradigms
are complementary, and can be married together! The high level idea is to:
design a sampling algorithm that biases the sample selection to the memoized
data items from previous runs. To concretize this idea, we designed an online
stratified sampling algorithm that uses self-adjusting computation to produce
an incrementally updated approximate output with bounded error. We implemented
our algorithm in a data analytics system called IncAppox based on Apache Spark
Streaming. Our evaluation of the system shows that IncApprox achieves the
benefits of both incremental and approximate computing.
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In this paper we study the effect of the anisotropic stress generated by
neutrinos on the propagation of primordial cosmological gravitational waves.
The presence of anisotropic stress, like the one generated by free-streaming
neutrinos, partially absorbs the gravitational waves (GWs) propagating across
the Universe. We find that in the standard case of three neutrino families, 22%
of the intensity of the wave is absorbed, in fair agreement with previous
studies. We have also calculated the maximum possible amount of damping,
corresponding to the case of a flat Universe completely dominated by
ultrarelativistic collisionless particles. In this case 43% of the intensity of
the wave is absorbed. Finally, we have taken into account the effect of
collisions, using a simple form for the collision term parameterized by the
mean time between interactions, that allows to go smoothly from the case of a
tigthly-coupled fluid to that of a collisionless gas. The dependence of the
absorption on the neutrino energy density and on the effectiveness of the
interactions opens the interesting possibility of observing spectral features
related to particular events in the thermal history of the Universe, like
neutrino decoupling and electron-positron annihilation, both occurring at T~1
MeV. GWs entering the horizon at that time will have today a frequency $\nu\sim
10^{-9} \Hz$, a region that is going to be probed by Pulsar Timing Arrays.
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We present the construction of several microstate geometries of the
supersymmetric D1-D5-P black hole in which, within six-dimensional
supergravity, the momentum charge is carried by a vector field. The fully
backreacted geometries are smooth and horizonless: They are asymptotically
AdS$_3 \times S^3$ with an AdS$_2$ throat that smoothly caps off. We propose a
holographic dual for these bulk solutions and discuss their extension to
asymptotically flat space. In addition, we present several uplifts of the full
six-dimensional supersymmetric ansatz to ten-dimensions. In particular, we show
that there exists a frame in which geometries based on vector field momentum
carriers are entirely in the NS-sector of supergravity, making them possible
starting points for the exploration of stringy black-hole microstates.
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The dense environment of a galaxy cluster can radically transform the content
of in-falling galaxies. Recent observations have found a significant population
of active galactic nuclei (AGN) within "jellyfish galaxies," galaxies with
trailing tails of gas and stars that indicate significant ram pressure
stripping. The relationship between AGN and ram pressure stripping is not well
understood. In this letter, we investigate the connection between AGN activity
and ram pressure in a fully cosmological setting for the first time using the
RomulusC simulation, one of the highest resolution simulations of a galaxy
cluster to date. We find unambiguous morphological evidence for ram pressure
stripping. For lower mass galaxies (with stellar masses < 10^9.5 solar masses)
both star formation and black hole accretion are suppressed by ram pressure
before they reach pericenter, whereas for more massive galaxies accretion onto
the black hole is enhanced during pericentric passage. Our analysis also
indicates that as long as the galaxy retains gas, AGN with higher Eddington
ratios are more likely to be the found in galaxies experiencing higher ram
pressure. We conclude that prior to quenching star formation, ram pressure
triggers enhanced accretion onto the black hole, which then produces heating
and outflows due to AGN feedback. AGN feedback may in turn serve to aid in the
quenching of star formation in tandem with ram pressure.
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Using multi-wavelength imaging from the Wide Field Camera 3 on the Hubble
Space Telescope we study the stellar cluster populations of two adjacent fields
in the nearby face-on spiral galaxy, M83. The observations cover the galactic
centre and reach out to ~6 kpc, thereby spanning a large range of environmental
conditions, ideal for testing empirical laws of cluster disruption. The
clusters are selected by visual inspection to be centrally concentrated,
symmetric, and resolved on the images. We find that a large fraction of objects
detected by automated algorithms (e.g. SExtractor or Daofind) are not clusters,
but rather are associations. These are likely to disperse into the field on
timescales of tens of Myr due to their lower stellar densities and not due to
gas expulsion (i.e. they were never gravitationally bound). We split the sample
into two discrete fields (inner and outer regions of the galaxy) and search for
evidence of environmentally dependent cluster disruption. Colour-colour
diagrams of the clusters, when compared to simple stellar population models,
already indicate that a much larger fraction of the clusters in the outer field
are older by tens of Myr than in the inner field. This impression is quantified
by estimating each cluster's properties (age, mass, and extinction) and
comparing the age/mass distributions between the two fields. Our results are
inconsistent with "universal" age and mass distributions of clusters, and
instead show that the ambient environment strongly affects the observed
populations.
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A wide range of mechanisms have been put forward to explain the quenching of
star formation in galaxies with cosmic time, however, the true balance of
responsible mechanisms remains unknown. The identification and study of
galaxies that have shut down their star formation on different timescales might
elucidate which mechanisms dominate at different epochs and masses. Here we
study the population of rapidly quenched galaxies (RQGs) in the SIMBA
cosmological hydrodynamic simulation at $0.5<z<2$, comparing directly to
observational post-starburst galaxies in the UKIDSS Ultra Deep Survey via their
colour distributions and mass functions. We find that the fraction of quiescent
galaxies that are rapidly quenched in SIMBA is 59% (or 48% in terms of stellar
mass), which is higher than observed. A similar "downsizing" of RQGs is
observed in both SIMBA and the UDS, with RQGs at higher redshift having a
higher average mass. However, SIMBA produces too many RQGs at $1<z_q<1.5$ and
too few low mass RQGs at $0.5<z_q<1$. The precise colour distribution of SIMBA
galaxies compared to the observations also indicates various inconsistencies in
star formation and chemical enrichment histories, including an absence of
short, intense starbursts. Our results will help inform the next generation of
galaxy evolution models, particularly with respect to the quenching mechanisms
employed.
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Let G_p denote the tail function of Student's distribution with p degrees of
freedom. It is shown that the ratio G_q(x)/G_p(x) is decreasing in x>0 for any
p and q such that 0<p<q\le\infty. Therefore, G_q(x)<G_p(x) for all such p and q
and all x>0. Corollaries on the monotonicity of (generalized) moments and
ratios thereof are also given.
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Canonical Correlation Analysis (CCA) is a widely used statistical tool with
both well established theory and favorable performance for a wide range of
machine learning problems. However, computing CCA for huge datasets can be very
slow since it involves implementing QR decomposition or singular value
decomposition of huge matrices. In this paper we introduce L-CCA, a iterative
algorithm which can compute CCA fast on huge sparse datasets. Theory on both
the asymptotic convergence and finite time accuracy of L-CCA are established.
The experiments also show that L-CCA outperform other fast CCA approximation
schemes on two real datasets.
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We present results of a study of the muon decay in orbit (DIO) contribution
to the signal region of muon - electron conversion. Electrons from DIO are the
dominant source of background for muon - electron conversion experiments
because the endpoint of DIO electrons is the same as the energy of electrons
from elastic muon - electron conversion.
The probability of DIO contribution to the signal region was considered for a
tracker with Gaussian resolution function and with a realistic resolution
function obtained in the application of pattern recognition and momentum
reconstruction Kalman filter based procedure to GEANT simulated DIO events. It
is found that the existence of non Gaussian tails in the realistic resolution
function does not lead to a significant increase in DIO contribution to the
signal region.
The probability of DIO contribution to the calorimeter signal was studied in
dependence on the resolution, assuming a Gaussian resolution function of
calorimeter. In this study the geometrical acceptance played an important role,
suppressing DIO contribution of the intermediate range electrons from muon
decay in orbit.
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It has been shown that a Hamiltonian with an unbroken $\cP\cT$ symmetry also
possesses a hidden symmetry that is represented by the linear operator $\cC$.
This symmetry operator $\cC$ guarantees that the Hamiltonian acts on a Hilbert
space with an inner product that is both positive definite and conserved in
time, thereby ensuring that the Hamiltonian can be used to define a unitary
theory of quantum mechanics. In this paper it is shown how to construct the
operator $\cC$ for the $\cP\cT$-symmetric square well using perturbative
techniques.
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The magnetic properties of Li_{1-x}Ni_{1+x}O_2 compounds with x ranging
between 0.02 and 0.2 are investigated. Magnetization and ac susceptibility
measured at temperatures between 2 K and 300 K reveal a high sensitivity to x,
the excess Nickel concentration. We introduce a percolation model describing
the formation of Ni clusters and use an Ising model to simulate their magnetic
properties. Numerical results, obtained by a Monte-Carlo technique, are
compared to the experimental data. We show the existence of a critical
concentration, x_c = 0.136, locating the Ni percolation threshold. The system
is superparamagnetic for x<x_c, while it is ferrimagnetic for x>x_c. The 180
Ni-O-Ni inter-plane super-exchange coupling J_\perp \simeq -110K is confirmed
to be the predominant magnetic interaction. From the low temperature behavior,
we find a clear indication of a 90 Ni-O-Ni intra-plane antiferromagnetic
interaction $J_\parallel \simeq -1.5K$ which implies magnetic frustration.
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In soft porous media, deformation drives solute transport via the intrinsic
coupling between flow of the fluid and rearrangement of the pore structure.
Solute transport driven by periodic loading, in particular, can be of great
relevance in applications ranging from the geomechanics of contaminants in the
subsurface to the biomechanics of nutrient transport in living tissues,
scaffolds for tissue engineering, and biomedically employed hydrogels. However,
the basic features of this process have not previously been systematically
investigated. Here, we fill this hole in the context of a 1D model problem. We
do so by expanding the results from a companion study, in which we explored the
poromechanics of periodic deformations, by introducing and analysing the impact
of the resulting fluid and solid motion on solute transport. We first
characterise the independent roles of the three main mechanisms of solute
transport in porous media - advection, molecular diffusion, and hydrodynamic
dispersion - by examining their impacts on the solute concentration profile
during one loading cycle. We next explore the impact of the transport
parameters, showing how these alter the relative importance of diffusion and
dispersion. We then explore the loading parameters by considering a range of
loading periods - from slow to fast, relative to the poroelastic timescale -
and amplitudes - from infinitesimal to large. We show that solute spreading
over several loading cycle increases monotonically with amplitude, but is
maximised for intermediate periods because of the increasing poromechanical
localisation of the flow and deformation near the permeable boundary as the
period decreases.
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We investigate the possible enhancement to the discovery of the heavy Higgs
boson through the possible fourth SM family heavy neutrino. Using the channel
h-> v4 v4->mu W mu W-> mu j j mu j j, it is found that for certain ranges of
Higgs boson and v4 masses LHC could discover both of them simultaneously with 1
fb^-1 integrated luminosity.
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Slow (logarithmic) relaxation from a highly excited state is studied in a
Hamiltonian system with many degrees of freedom. The relaxation time is shown
to increase as the exponential of the square root of the energy of excitation,
in agreement with the Boltzmann-Jeans conjecture, while it is found to be
inversely proportional to residual Kolmogorov-Sinai entropy, introduced in this
Letter. The increase of the thermodynamic entropy through this relaxation
process is found to be proportional to this quantity.
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The Facility for Antiproton and Ion Research (FAIR) in Darmstadt, Germany,
provides unique possibilities for a new generation of hadron-, nuclear- and
atomic physics experiments. The future antiProton ANnihilations at DArmstadt
(PANDA or $\overline{\rm P}$ANDA) experiment at FAIR will offer a broad physics
programme, covering different aspects of the strong interaction. Understanding
the latter in the non-perturbative regime remains one of the greatest
challenges in contemporary physics. The antiproton-nucleon interaction studied
with PANDA provides crucial tests in this area. Furthermore, the
high-intensity, low-energy domain of PANDA allows for searches for physics
beyond the Standard Model, e.g. through high precision symmetry tests. This
paper takes into account a staged approach for the detector setup and for the
delivered luminosity from the accelerator. The available detector setup at the
time of the delivery of the first antiproton beams in the HESR storage ring is
referred to as the \textit{Phase One} setup. The physics programme that is
achievable during Phase One is outlined in this paper.
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It is known that orbital angular momentum (OAM) couples the Goos-Hanchen and
Imbert-Fedorov shifts. Here, we present the first study of these shifts when
the OAM-endowed LG(l,p) beams have higher-order radial mode index (p>0). We
show theoretically and experimentally that the angular shifts are enhanced by p
while the positional shifts are not. Since LG(l,p) modes form a complete basis
set for paraxial beams, our results can be used to predict beam shifts of
arbitrary modes of light.
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We obtained K-band spectro-interferometric observations of the Miras R Cnc, X
Hya, W Vel, and RW Vel with a spectral resolution of 1500 using the VLTI/AMBER
instrument. We obtained concurrent JHKL photometry using the the Mk II
instrument at the SAAO. Our sources have wavelength-dependent visibility values
that are consistent with earlier low-resolution AMBER observations of S Ori and
with the predictions of dynamic model atmosphere series based on self-excited
pulsation models. The wavelength-dependent UD diameters show a minimum near the
near-continuum bandpass at 2.25 um. They increase by up to 30% toward the H2O
band at 2.0 um and by up to 70% at the CO bandheads. The dynamic model
atmosphere series show a consistent wavelength-dependence, and their parameters
such as the visual phase, effective temperature, and distances are consistent
with independent estimates. The closure phases have significantly
wavelength-dependent non-zero values indicating deviations from point symmetry.
For example, the R Cnc closure phase is 110 degr in the 2.0 um H2O band,
corresponding for instance to an additional unresolved spot contributing 3% of
the total flux at a separation of ~4 mas. Our observations are consistent with
the predictions of the latest dynamic model atmosphere series based on
self-excited pulsation models. The wavelength-dependent radius variations are
interpreted as the effect of molecular layers. The wavelength-dependent closure
phase values are indicative of deviations from point symmetry at all
wavelengths, thus a complex non-spherical stratification of the extended
atmosphere. In particular, the significant deviation from point symmetry in the
H2O band is interpreted as a signature on large scales of inhomogeneities or
clumps in the water vapor layer. The observed inhomogeneities might be caused
by pulsation- and shock-induced chaotic motion in the extended atmosphere.
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We study the fermion pair production from a strong electric field in
boost-invariant coordinates in (3+1) dimensions and exploit the cylindrical
symmetry of the problem. This problem has been used previously as a toy model
for populating the central-rapidity region of a heavy-ion collision (when we
can replace the electric by a chromoelectric field). We derive and solve the
renormalized equations for the dynamics of the mean electric field and current
of the produced particles, when the field is taken to be a function only of the
fluid proper time $\tau = \sqrt{t^2-z^2}$. We determine the proper-time
evolution of the comoving energy density and pressure of the ensuing plasma and
the time evolution of suitable interpolating number operators. We find that
unlike in (1+1) dimensions, the energy density closely follows the longitudinal
pressure. The transverse momentum distribution of fermion pairs at large
momentum is quite different and larger than that expected from the constant
field result.
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Multidimensional unfolding methods are widely used for visualizing item
response data. Such methods project respondents and items simultaneously onto a
low-dimensional Euclidian space, in which respondents and items are represented
by ideal points, with person-person, item-item, and person-item similarities
being captured by the Euclidian distances between the points. In this paper, we
study the visualization of multidimensional unfolding from a statistical
perspective. We cast multidimensional unfolding into an estimation problem,
where the respondent and item ideal points are treated as parameters to be
estimated. An estimator is then proposed for the simultaneous estimation of
these parameters. Asymptotic theory is provided for the recovery of the ideal
points, shedding lights on the validity of model-based visualization. An
alternating projected gradient descent algorithm is proposed for the parameter
estimation. We provide two illustrative examples, one on users' movie rating
and the other on senate roll call voting.
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Information avalanches in social media are typically studied in a similar
fashion as avalanches of neuronal activity in the brain. Whereas a large body
of literature reveals substantial agreement about the existence of a unique
process characterizing neuronal activity across organisms, the dynamics of
information in online social media is far less understood. Statistical laws of
information avalanches are found in previous studies to be not robust across
systems, and radically different processes are used to represent plausible
driving mechanisms for information propagation. Here, we analyze almost 1
billion time-stamped events collected from a multitude of online platforms --
including Telegram, Twitter and Weibo -- over observation windows longer than
10 years to show that the propagation of information in social media is a
universal and critical process. Universality arises from the observation of
identical macroscopic patterns across platforms, irrespective of the details of
the specific system at hand. Critical behavior is deduced from the power-law
distributions, and corresponding hyperscaling relations, characterizing size
and duration of avalanches of information. Neuronal activity may be modeled as
a simple contagion process, where only a single exposure to activity may be
sufficient for its diffusion. On the contrary, statistical testing on our data
indicates that a mixture of simple and complex contagion, where involvement of
an individual requires exposure from multiple acquaintances, characterizes the
propagation of information in social media. We show that the complexity of the
process is correlated with the semantic content of the information that is
propagated. Conversational topics about music, movies and TV shows tend to
propagate as simple contagion processes, whereas controversial discussions on
political/societal themes obey the rules of complex contagion.
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We have used 2D Fabry-Perot absorption-line spectroscopy of the SB0 galaxy
NGC 7079 to measure its bar pattern speed, $\om$. As in all previous cases of
bar pattern speed measurements, we find a fast bar. We estimate that NGC 7079
has been undisturbed for at least the past Gyr or roughly 8 bar rotations, long
enough for the bar to have slowed down significantly through dynamical friction
if the disk is sub-maximal.
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The results of the recent experiments on the reaction $\pi^-p\to\pi^0\pi^0n$
performed at KEK, BNL, IHEP, and CERN are analyzed in detail. For the I=0
$\pi\pi$ S wave phase shift $\delta^0_0$ and inelasticity $\eta^0_0$ a new set
of data is obtained. Difficulties emerging when using the physical solutions
for the $\pi^0\pi^0$ S and D wave amplitudes extracted with the partial wave
analyses are discussed. Attention is drawn to the fact that, for the
$\pi^0\pi^0$ invariant mass, m, above 1 GeV, the other solutions, in principle,
are found to be more preferred. For clarifying the situation and further
studying the $f_0(980)$ resonance thorough experimental investigations of the
reaction $\pi^-p\to\pi^0\pi^0n$ in the m region near the $K\bar K$ threshold
are required.
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We locate gaps in the spectrum of a Hamiltonian on a periodic cuboidal (and
generally hyperrectangular) lattice graph with $\delta$ couplings in the
vertices. We formulate sufficient conditions under which the number of gaps is
finite. As the main result, we find a connection between the arrangement of the
gaps and the coefficients in a continued fraction associated with the ratio of
edge lengths of the lattice. This knowledge enables a straightforward
construction of a periodic quantum graph with any required number of spectral
gaps and---to some degree---to control their positions; i.e., to partially
solve the inverse spectral problem.
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Modeling of matter bounce in $f(R,T)$ gravity has been presented with no
violation of the null energy condition. Only a closed universe with negative
pressure is allowed in good agreement with some recent observations which favor
a universe with positive curvature. Our results agree with some recent works in
which a combination of positive curvature and vacuum energy leads to
non-singular bounces with no violation of the null energy condition. The
stability of the model has been discussed. The cosmographic parameters are
developed for the derived model to explain the accelerated expansion of the
universe.
|
The energy charging of a quantum battery is analyzed in an open quantum
setting, where the interaction between the battery element and the external
power source is mediated by an ancilla system (the quantum charger) that acts
as a controllable switch. Different implementations are analyzed putting
emphasis on the interplay between coherent energy pumping mechanisms and
thermalization.
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The T-980 bent crystal collimation experiment at the Tevatron has recently
acquired substantial enhancements. First, two new crystals - a 16-strip one
manufactured and characterized by the INFN Ferrara group and a quasi-mosaic
crystal manufactured and characterized by the PNPI group. Second, a two plane
telescope with 3 high-resolution pixel detectors per plane along with
corresponding mechanics, electronics, control and software has been
manufactured, tested and installed in the E0 crystal region. The purpose of the
pixel telescope is to measure and image channeled (CH), volume-reflected (VR)
and multiple volume-reflected (MVR) beam profiles produced by bent crystals.
Third, an ORIGIN-based system has been developed for thorough analysis of
experimental and simulation data. Results of analysis are presented for
different types of crystals used from 2005 to present for channeling and volume
reflection including pioneering tests of two-plane crystal collimation at the
collider, all in comparison with detailed simulations.
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Many galaxies contain magnetic fields supported by galactic dynamo action.
However, nothing definitive is known about magnetic fields in ring galaxies.
Here we investigate large-scale magnetic fields in a previously unexplored
context, namely ring galaxies, and concentrate our efforts on the structures
that appear most promising for galactic dynamo action, i.e. outer star-forming
rings in visually unbarred galaxies. We use tested methods for modelling
$\alpha-\Omega$ galactic dynamos, taking into account the available
observational information concerning ionized interstellar matter in ring
galaxies. Our main result is that dynamo drivers in ring galaxies are strong
enough to excite large-scale magnetic fields in the ring galaxies studied. The
variety of dynamo driven magnetic configurations in ring galaxies obtained in
our modelling is much richer than that found in classical spiral galaxies. In
particular, various long-lived transients are possible. An especially
interesting case is that of NGC 4513 where the ring counter-rotates with
respect to the disc. Strong shear in the region between the disc and the ring
is associated with unusually strong dynamo drivers for the counter-rotators.
The effect of the strong drivers is found to be unexpectedly moderate. With
counter-rotation in the disc, a generic model shows that a steady mixed parity
magnetic configuration, unknown for classical spiral galaxies, may be excited,
although we do not specifically model NGC 4513. We deduce that ring galaxies
constitute a morphological class of galaxies in which identification of
large-scale magnetic fields from observations of polarized radio emission, as
well as dynamo modelling, may be possible. Such studies have the potential to
throw additional light on the physical nature of rings, their lifetimes and
evolution.
|
We describe a general method for expanding a truncated G-iterative
Hasse-Schmidt derivation, where G is an algebraic group. We give examples of
algebraic groups for which our method works.
|
The dilepton production in elementary ${pp\to e^{+}e^{-}X}$ reactions at
BEVALAC energies $T_{lab}=1\div 5$ GeV is investigated. The calculations
include direct ${e^{+}e^{-}}$ decays of the vector mesons $\rho ^{0}$, $\omega
$, and $\phi $, Dalitz decays of the $\pi ^{0}$-, $\eta $-, $% \rho $-, $\omega
$-, and $\phi $-mesons, and of the baryon resonances $% \Delta (1232),N(1520),$
$... $ . The subthreshold vector meson production cross sections in $pp$
collisions are treated in a way sufficient to avoid double counting with the
inclusive vector meson production. The vector meson dominance model for the
transition form factors of the resonance Dalitz decays $R\to e^{+}e^{-}N$ is
used in an extended form to ensure correct asymptotics which are in agreement
with the quark counting rules. Such a modification gives an unified and
consistent description of both $R\to N\gamma $ radiative decays and $R\to N\rho
(\omega)$ meson decays.
The effect of multiple pion production on the experimental efficiency for the
detection of the dilepton pairs is studied. We find the dilepton yield in
reasonable agreement with the experimental data for the set of intermediate
energies whereas at the highest energy $T_{lab}=4.88$ GeV the number of
dilepton pairs is likely to be overestimated experimentally in the mass range
$M=300\div 700$ MeV.
|
In periodically driven (Floquet) systems, evolution typically results in an
infinite-temperature thermal state due to continuous energy absorption over
time. However, before reaching thermal equilibrium, such systems may
transiently pass through a meta-stable state known as a prethermal state. This
prethermal state can exhibit phenomena not commonly observed in equilibrium,
such as discrete time crystals (DTCs), making it an intriguing platform for
exploring out-of-equilibrium dynamics. Here, we investigate the relaxation
dynamics of initially prepared product states under periodic driving in a
kicked Ising model using the IBM Quantum Heron processor, comprising 133
superconducting qubits arranged on a heavy-hexagonal lattice, over up to $100$
time steps. We identify the presence of a prethermal regime characterised by
magnetisation measurements oscillating at twice the period of the Floquet cycle
and demonstrate its robustness against perturbations to the transverse field.
Our results provide evidence supporting the realisation of a period-doubling
DTC in a two-dimensional system. Moreover, we discover that the longitudinal
field induces additional amplitude modulations in the magnetisation with a
period incommensurate with the driving period, leading to the emergence of
discrete time quasicrystals (DTQCs). These observations are further validated
through comparison with tensor-network and state-vector simulations. Our
findings not only enhance our understanding of clean DTCs in two dimensions but
also highlight the utility of digital quantum computers for simulating the
dynamics of quantum many-body systems, addressing challenges faced by
state-of-the-art classical simulations.
|
The intrinsic decay rate of orthopositronium formed in ${\rm SiO_2}$ powder
is measured using the direct $2\gamma$ correction method such that the time
dependence of the pick-off annihilation rate is precisely determined using high
energy-resolution germanium detectors. As a systematic test, two different
types of ${\rm SiO_2}$ powder are used with consistent findings. The intrinsic
decay rate of orthopositronium is found to be $7.0396\pm0.0012 (stat.)\pm0.0011
(sys.)\mu s^{-1}$, which is consistent with previous measurements using ${\rm
SiO_2}$ powder with about twice the accuracy. Results agree well with a recent
$O(\alpha^2)$ QED prediction, varying $3.8-5.6$ experimental standard
deviations from other measurements.
|
Self-supervised protein language models have proved their effectiveness in
learning the proteins representations. With the increasing computational power,
current protein language models pre-trained with millions of diverse sequences
can advance the parameter scale from million-level to billion-level and achieve
remarkable improvement. However, those prevailing approaches rarely consider
incorporating knowledge graphs (KGs), which can provide rich structured
knowledge facts for better protein representations. We argue that informative
biology knowledge in KGs can enhance protein representation with external
knowledge. In this work, we propose OntoProtein, the first general framework
that makes use of structure in GO (Gene Ontology) into protein pre-training
models. We construct a novel large-scale knowledge graph that consists of GO
and its related proteins, and gene annotation texts or protein sequences
describe all nodes in the graph. We propose novel contrastive learning with
knowledge-aware negative sampling to jointly optimize the knowledge graph and
protein embedding during pre-training. Experimental results show that
OntoProtein can surpass state-of-the-art methods with pre-trained protein
language models in TAPE benchmark and yield better performance compared with
baselines in protein-protein interaction and protein function prediction. Code
and datasets are available in https://github.com/zjunlp/OntoProtein.
|
With the rapid development of Large Language Models (LLMs), it is crucial to
have benchmarks which can evaluate the ability of LLMs on different domains.
One common use of LLMs is performing tasks on scientific topics, such as
writing algorithms, querying databases or giving mathematical proofs. Inspired
by the way university students are evaluated on such tasks, in this paper, we
propose SciEx - a benchmark consisting of university computer science exam
questions, to evaluate LLMs ability on solving scientific tasks. SciEx is (1)
multilingual, containing both English and German exams, and (2) multi-modal,
containing questions that involve images, and (3) contains various types of
freeform questions with different difficulty levels, due to the nature of
university exams. We evaluate the performance of various state-of-the-art LLMs
on our new benchmark. Since SciEx questions are freeform, it is not
straightforward to evaluate LLM performance. Therefore, we provide human expert
grading of the LLM outputs on SciEx. We show that the free-form exams in SciEx
remain challenging for the current LLMs, where the best LLM only achieves
59.4\% exam grade on average. We also provide detailed comparisons between LLM
performance and student performance on SciEx. To enable future evaluation of
new LLMs, we propose using LLM-as-a-judge to grade the LLM answers on SciEx.
Our experiments show that, although they do not perform perfectly on solving
the exams, LLMs are decent as graders, achieving 0.948 Pearson correlation with
expert grading.
|
A field theory is proposed where the regular fermionic matter and the dark
fermionic matter are different states of the same "primordial" fermion fields.
In regime of the fermion densities typical for normal particle physics, each of
the primordial fermions splits into three generations identified with regular
fermions. In a simple model, this fermion families birth effect is accompanied
with the right lepton numbers conservation laws. It is possible to fit the muon
to electron mass ratio without fine tuning of the Yukawa coupling constants.
When fermion energy density becomes comparable with dark energy density, the
theory allows new type of states - Cosmo-Low Energy Physics (CLEP) states.
Neutrinos in CLEP state can be both a good candidate for dark matter and
responsible for a new type of dark energy. In the latter case the total energy
density of the universe is less than it would be in the universe free of
fermionic matter at all. The (quintessence) scalar field is coupled to dark
matter but its coupling to regular fermionic matter appears to be extremely
suppressed.
|
In dynamic spacetimes in which asymmetric gravitational collapse/expansion is
taking place, the timelike geodesic equation appears to exhibit an interesting
property: Relative to the collapsing configuration, free test particles undergo
gravitational "acceleration" and form a double-jet configuration parallel to
the axis of collapse. We illustrate this aspect of peculiar motion in simple
spatially homogeneous cosmological models such as the Kasner spacetime. To
estimate the effect of spatial inhomogeneities on cosmic jets, timelike
geodesics in the Ricci-flat double-Kasner spacetime are studied in detail.
While spatial inhomogeneities can significantly modify the structure of cosmic
jets, we find that under favorable conditions the double-jet pattern can
initially persist over a finite period of time for sufficiently small
inhomogeneities.
|
We give a description of asymptotic quadratic growth rates for geodesic
segments on covers of Veech surfaces in terms of the modular fiber
parameterizing coverings of a fixed Veech surface. To make the paper self
contained we derive the necessary asymptotic formulas from the Gutkin-Judge
formula. As an application of the method we define and analyze d-symmetric
elliptic differentials and their modular fibers F^{sym}_d. For given genus g,
g-symmetric elliptic differentials (with fixed base lattice) provide a
2-dimensional family of translation surfaces. We calculate several asymptotic
constants, to establish their dependence on the translation geometry of
F^{sym}_d and their sensitivity as SL(2,Z)-orbit invariants.
|
We examine the island size distribution function and spatial correlation
function of a model for island growth in the submonolayer regime in both 1 and
2 dimensions. In our model the islands do not grow in shape, and a fixed number
of adatoms are added, nucleate, and are trapped at islands as they diffuse.
We study the cases of various critical island sizes $i$ for nucleation as a
function of initial coverage. We found anomalous scaling of the island size
distribution for large $i$ . Using scaling, random walk theory, a version of
mean-field theory we obtain a closed form for the spatial correlation function.
Our analytic results are verified by Monte Carlo simulations.
|
The simple gesture of pointing can greatly augment ones ability to comprehend
states of the world based on observations. It triggers additional inferences
relevant to ones task at hand. We model an agents update to its belief of the
world based on individual observations using a partially observable Markov
decision process (POMDP), a mainstream artificial intelligence (AI) model of
how to act rationally according to beliefs formed through observation. On top
of that, we model pointing as a communicative act between agents who have a
mutual understanding that the pointed observation must be relevant and
interpretable. Our model measures relevance by defining a Smithian Value of
Information (SVI) as the utility improvement of the POMDP agent before and
after receiving the pointing. We model that agents calculate SVI by using the
cognitive theory of Smithian helping as a principle of coordinating separate
beliefs for action prediction and action evaluation. We then import SVI into
rational speech act (RSA) as the utility function of an utterance. These lead
us to a pragmatic model of pointing allowing for contextually flexible
interpretations. We demonstrate the power of our Smithian pointing model by
extending the Wumpus world, a classic AI task where a hunter hunts a monster
with only partial observability of the world. We add another agent as a guide
who can only help by marking an observation already perceived by the hunter
with a pointing or not, without providing new observations or offering any
instrumental help. Our results show that this severely limited and overloaded
communication nevertheless significantly improves the hunters performance. The
advantage of pointing is indeed due to a computation of relevance based on
Smithian helping, as it disappears completely when the task is too difficult or
too easy for the guide to help.
|
Fully-automatic general-purpose high-quality machine translation systems
(FGH-MT) are extremely difficult to build. In fact, there is no system in the
world for any pair of languages which qualifies to be called FGH-MT. The
reasons are not far to seek. Translation is a creative process which involves
interpretation of the given text by the translator. Translation would also vary
depending on the audience and the purpose for which it is meant. This would
explain the difficulty of building a machine translation system. Since, the
machine is not capable of interpreting a general text with sufficient accuracy
automatically at present - let alone re-expressing it for a given audience, it
fails to perform as FGH-MT. FOOTNOTE{The major difficulty that the machine
faces in interpreting a given text is the lack of general world knowledge or
common sense knowledge.}
|
We investigate a family of SU(3)$\times$U(1)$\times$U(1)-invariant
holographic flows and Janus solutions obtained from gauged $\mathcal{N}=8$
supergravity in four dimensions. We give complete details of how to use the
uplift formulae to obtain the corresponding solutions in M theory. While the
flow solutions appear to be singular from the four-dimensional perspective, we
find that the eleven-dimensional solutions are much better behaved and give
rise to interesting new classes of compactification geometries that are smooth,
up to orbifolds, in the infra-red limit. Our solutions involve new phases in
which M2 branes polarize partially or even completely into M5 branes. We derive
the eleven-dimensional supersymmetries and show that the eleven-dimensional
equations of motion and BPS equations are indeed satisfied as a consequence of
their four-dimensional counterparts. Apart from elucidating a whole new class
of eleven-dimensional Janus and flow solutions, our work provides extensive and
highly non-trivial tests of the recently-derived uplift formulae.
|
Exaggeration or context changes can render maintainability experience into
prejudice. For example, JavaScript is often seen as least elegant language and
hence of lowest maintainability. Such prejudice should not guide decisions
without prior empirical validation. We formulated 10 hypotheses about
maintainability based on prejudices and test them in a large set of open-source
projects (6,897 GitHub repositories, 402 million lines, 5 programming
languages). We operationalize maintainability with five static analysis
metrics. We found that JavaScript code is not worse than other code, Java code
shows higher maintainability than C# code and C code has longer methods than
other code. The quality of interface documentation is better in Java code than
in other code. Code developed by teams is not of higher and large code bases
not of lower maintainability. Projects with high maintainability are not more
popular or more often forked. Overall, most hypotheses are not supported by
open-source data.
|
Let $M$ be a 3-connected binary matroid and let $Y(M)$ be the set of elements
of $M$ avoiding at least $r(M)+1$ non-separating cocircuits of $M$. Lemos
proved that $M$ is non-graphic if and only if $Y(M)\neq\emp$. We generalize
this result when by establishing that $Y(M)$ is very large when $M$ is
non-graphic and $M$ has no $M\s(K_{3,3}"')$-minor if $M$ is regular. More
precisely that $|E(M)-Y(M)|\le 1$ in this case. We conjecture that when $M$ is
a regular matroid with an $M\s(K_{3,3})$-minor, then $r\s_M(E(M)-Y(M))\le 2$.
The proof of such conjecture is reduced to a computational verification.
|
Patients with Type I Diabetes (T1D) must take insulin injections to prevent
the serious long term effects of hyperglycemia - high blood glucose (BG).
Patients must also be careful not to inject too much insulin because this could
induce hypoglycemia (low BG), which can potentially be fatal. Patients
therefore follow a "regimen" that determines how much insulin to inject at
certain times. Current methods for managing this disease require adjusting the
patient's regimen over time based on the disease's behavior (recorded in the
patient's diabetes diary). If we can accurately predict a patient's future BG
values from his/her current features (e.g., predicting today's lunch BG value
given today's diabetes diary entry for breakfast, including insulin injections,
and perhaps earlier entries), then it is relatively easy to produce an
effective regimen. This study explores the challenges of BG modeling by
applying several machine learning algorithms and various data preprocessing
variations (corresponding to 312 [learner, preprocessed-dataset] combinations),
to a new T1D dataset containing 29 601 entries from 47 different patients. Our
most accurate predictor is a weighted ensemble of two Gaussian Process
Regression models, which achieved an errL1 loss of 2.70 mmol/L (48.65 mg/dl).
This was an unexpectedly poor result given that one can obtain an errL1 of 2.91
mmol/L (52.43 mg/dl) using the naive approach of simply predicting the
patient's average BG. For each of data-variant/model combination we report
several evaluation metrics, including glucose-specific metrics, and find
similarly disappointing results (the best model was only incrementally better
than the simplest measure). These results suggest that the diabetes diary data
that is typically collected may not be sufficient to produce accurate BG
prediction models; additional data may be necessary to build accurate BG
prediction models.
|
We present a maximum likelihood (ML) algorithm that is fast enough to detect
gamma-ray transients in real time on low-performance processors often used for
space applications. We validate the routine with simulations and find that,
relative to algorithms based on excess counts, the ML method is nearly twice as
sensitive, allowing detection of 240-280% more short gamma-ray bursts. We
characterize a reference implementation of the code, estimating its
computational complexity and benchmarking it on a range of processors. We
exercise the reference implementation on archival data from the Fermi Gamma-ray
Burst Monitor (GBM), verifying the sensitivity improvements. In particular, we
show that the ML algorithm would have detected GRB 170817A even if it had been
nearly four times fainter. We present an ad hoc but effective scheme for
discriminating transients associated with background variations. We show that
the on-board localizations generated by ML are accurate, but that refined
off-line localizations require a detector response matrix with about ten times
finer resolution than is current practice. Increasing the resolution of the GBM
response matrix could substantially reduce the few-degree systematic
uncertainty observed in the localizations of bright bursts.
|
Estimating the parameters from $k$ independent Bin$(n,p)$ random variables,
when both parameters $n$ and $p$ are unknown, is relevant to a variety of
applications. It is particularly difficult if $n$ is large and $p$ is small.
Over the past decades, several articles have proposed Bayesian approaches to
estimate $n$ in this setting, but asymptotic results could only be established
recently in \cite{Schneider}. There, posterior contraction for $n$ is proven in
the problematic parameter regime where $n\rightarrow\infty$ and $p\rightarrow0$
at certain rates. In this article, we study numerically how far the theoretical
upper bound on $n$ can be relaxed in simulations without losing posterior
consistency.
|
A time dependent geometry outside a spherically symmetric mass is proposed.
The source has zero energy density but nonzero radial and tangential pressures.
The time variable is interpreted as the duration of measurement performed upon
the physical system. For very short time intervals, the effect of the mass
source is much reduced, going to zero when $t \rightarrow 0$. All physical
quantities are finite when $t \rightarrow 0$ and $r \rightarrow 0$ and also at
infinity. The total energy flux measured on a hypersurface of constant $r$ is
vanishing.
|
Due to the highly parallelizable architecture, Transformer is faster to train
than RNN-based models and popularly used in machine translation tasks. However,
at inference time, each output word requires all the hidden states of the
previously generated words, which limits the parallelization capability, and
makes it much slower than RNN-based ones. In this paper, we systematically
analyze the time cost of different components of both the Transformer and
RNN-based model. Based on it, we propose a hybrid network of self-attention and
RNN structures, in which, the highly parallelizable self-attention is utilized
as the encoder, and the simpler RNN structure is used as the decoder. Our
hybrid network can decode 4-times faster than the Transformer. In addition,
with the help of knowledge distillation, our hybrid network achieves comparable
translation quality to the original Transformer.
|
We introduce LAMP: the Linear Additive Markov Process. Transitions in LAMP
may be influenced by states visited in the distant history of the process, but
unlike higher-order Markov processes, LAMP retains an efficient
parametrization. LAMP also allows the specific dependence on history to be
learned efficiently from data. We characterize some theoretical properties of
LAMP, including its steady-state and mixing time. We then give an algorithm
based on alternating minimization to learn LAMP models from data. Finally, we
perform a series of real-world experiments to show that LAMP is more powerful
than first-order Markov processes, and even holds its own against deep
sequential models (LSTMs) with a negligible increase in parameter complexity.
|
Superlinear convergence has been an elusive goal for black-box nonsmooth
optimization. Even in the convex case, the subgradient method is very slow, and
while some cutting plane algorithms, including traditional bundle methods, are
popular in practice, local convergence is still sluggish. Faster variants
depend either on problem structure or on analyses that elide sequences of
"null" steps. Motivated by a semi-structured approach to optimization and the
sequential quadratic programming philosophy, we describe a new bundle Newton
method that incorporates second-order objective information with the usual
linear approximation oracle. One representative problem class consists of
maxima of several smooth functions, individually inaccessible to the oracle.
Given as additional input just the cardinality of the optimal active set, we
prove local quadratic convergence. A simple implementation shows promise on
more general functions, both convex and nonconvex, and suggests first-order
analogues.
|
We apply classical algorithms for approximately solving constraint
satisfaction problems to find bounds on extremal eigenvalues of local
Hamiltonians. We consider spin Hamiltonians for which we have an upper bound on
the number of terms in which each spin participates, and find extensive bounds
for the operator norm and ground-state energy of such Hamiltonians under this
constraint. In each case the bound is achieved by a product state which can be
found efficiently using a classical algorithm.
|
This article examines the Bouton-Lie group invariants of the Navier-Stokes
equation (NSE) for incompressible fluids. Bouton's theory is applied to the
general scaling transformation admitted by the NSE and is used to derive all
self-similar solutions. In light of these, the criticality of the standard NSE
system is examined and criticality criteria are derived. The theorem of
Beale-Kato-Majda is used to rule out blow-up for a subset of Bouton's
self-similar solutions. For a subset of Leray's self-similar solutions, the
cavitation number of the fluid is found to be a scale-invariant, conserved
quantity. By extending the analysis of Bouton to higher-dimensioned manifolds,
additional conserved quantities are found, which could further elucidate the
physics of fluid turbulence.
|
The main approach to defining equivalence among acyclic directed causal
graphical models is based on the conditional independence relationships in the
distributions that the causal models can generate, in terms of the Markov
equivalence. However, it is known that when cycles are allowed in the causal
structure, conditional independence may not be a suitable notion for
equivalence of two structures, as it does not reflect all the information in
the distribution that is useful for identification of the underlying structure.
In this paper, we present a general, unified notion of equivalence for linear
Gaussian causal directed graphical models, whether they are cyclic or acyclic.
In our proposed definition of equivalence, two structures are equivalent if
they can generate the same set of data distributions. We also propose a weaker
notion of equivalence called quasi-equivalence, which we show is the extent of
identifiability from observational data. We propose analytic as well as
graphical methods for characterizing the equivalence of two structures.
Additionally, we propose a score-based method for learning the structure from
observational data, which successfully deals with both acyclic and cyclic
structures.
|
In this paper characterizations of graphs satisfying heat kernel estimates
for a wide class of space-time scaling functions are given. The equivalence of
the two-sided heat kernel estimate and the parabolic Harnack inequality is also
shown via the equivalence of the upper (lower) heat kernel estimate to the
parabolic mean value (and super mean value) inequality.
|
We comment on the implications of the recently measured CP asymmetry in B -->
Phi K_S decay. The data disfavor the Standard Model at 2.7 sigma and -if the
trend persists in the future with higher statistics - require the existence of
CP violation beyond that in the CKM matrix. In particular, the b --> s bar{s} s
decay amplitude would require new contributions of comparable size to the
Standard Model ones with an order one phase. While not every model can deliver
such a large amount of CP and flavor violation, those with substantial FCNC
couplings to the Z can reproduce the experimental findings.
|
Major Depressive Disorder (MDD) is a pervasive mental health condition that
affects 300 million people worldwide. This work presents a novel, BiLSTM-based
tri-modal model-level fusion architecture for the binary classification of
depression from clinical interview recordings. The proposed architecture
incorporates Mel Frequency Cepstral Coefficients, Facial Action Units, and uses
a two-shot learning based GPT-4 model to process text data. This is the first
work to incorporate large language models into a multi-modal architecture for
this task. It achieves impressive results on the DAIC-WOZ AVEC 2016 Challenge
cross-validation split and Leave-One-Subject-Out cross-validation split,
surpassing all baseline models and multiple state-of-the-art models. In
Leave-One-Subject-Out testing, it achieves an accuracy of 91.01%, an F1-Score
of 85.95%, a precision of 80%, and a recall of 92.86%.
|
Eye movements provide insight into what parts of an image a viewer finds most
salient, interesting, or relevant to the task at hand. Unfortunately, eye
tracking data, a commonly-used proxy for attention, is cumbersome to collect.
Here we explore an alternative: a comprehensive web-based toolbox for
crowdsourcing visual attention. We draw from four main classes of
attention-capturing methodologies in the literature. ZoomMaps is a novel
"zoom-based" interface that captures viewing on a mobile phone. CodeCharts is a
"self-reporting" methodology that records points of interest at precise viewing
durations. ImportAnnots is an "annotation" tool for selecting important image
regions, and "cursor-based" BubbleView lets viewers click to deblur a small
area. We compare these methodologies using a common analysis framework in order
to develop appropriate use cases for each interface. This toolbox and our
analyses provide a blueprint for how to gather attention data at scale without
an eye tracker.
|
We introduce endomorphisms of special jacobians and show that they satisfy
polynomial equations with all integer roots which we compute. The eigen-abelian
varieties for these endomorphisms are generalizations of Prym-Tjurin varieties
and naturally contain special curves representing cohomology classes which are
not expected to be represented by curves in generic abelian varieties.
|
comma.ai presents comma2k19, a dataset of over 33 hours of commute in
California's 280 highway. This means 2019 segments, 1 minute long each, on a
20km section of highway driving between California's San Jose and San
Francisco. The dataset was collected using comma EONs that have sensors similar
to those of any modern smartphone including a road-facing camera, phone GPS,
thermometers and a 9-axis IMU. Additionally, the EON captures raw GNSS
measurements and all CAN data sent by the car with a comma grey panda. Laika,
an open-source GNSS processing library, is also introduced here. Laika produces
40% more accurate positions than the GNSS module used to collect the raw data.
This dataset includes pose (position + orientation) estimates in a global
reference frame of the recording camera. These poses were computed with a
tightly coupled INS/GNSS/Vision optimizer that relies on data processed by
Laika. comma2k19 is ideal for development and validation of tightly coupled
GNSS algorithms and mapping algorithms that work with commodity sensors.
|
The interplay between shear and bulk viscosities on the flow harmonics,
$v_n$'s, at RHIC is investigated using the newly developed relativistic 2+1
hydrodynamical code v-USPhydro that includes bulk and shear viscosity effects
both in the hydrodynamic evolution and also at freeze-out. While shear
viscosity is known to attenuate the flow harmonics, we find that the inclusion
of bulk viscosity decreases the shear viscosity-induced suppression of the flow
harmonics bringing them closer to their values in ideal hydrodynamical
calculations. Depending on the value of the bulk viscosity to entropy density
ratio, $\zeta/s$, in the quark-gluon plasma, the bulk viscosity-driven
suppression of shear viscosity effects on the flow harmonics may require a
re-evaluation of the previous estimates of the shear viscosity to entropy
density ratio, $\eta/s$, of the quark-gluon plasma previously extracted by
comparing hydrodynamic calculations to heavy ion data.
|
Arag\'on Artacho and Campoy recently proposed a new method for computing the
projection onto the intersection of two closed convex sets in Hilbert space;
moreover, they proposed in 2018 a generalization from normal cone operators to
maximally monotone operators. In this paper, we complete this analysis by
demonstrating that the underlying curve converges to the nearest zero of the
sum of the two operators. We also provide a new interpretation of the
underlying operators in terms of the resolvent and the proximal average.
|
We have developed a fast, accurate and generally applicable method for
inferring the power spectrum and its uncertainties from maps of the cosmic
microwave background (CMB) in the presence of inhomogeneous and correlated
noise. For maps with 10 to 100 thousand pixels, we apply an exact power
spectrum estimation algorithm to submaps of the data at various resolutions,
and then combine the results in an optimal manner. To analyze larger maps
efficiently one must resort to sub-optimal combinations in which cross-map
power spectrum error correlations are only calculated approximately. We expect
such approximations to work well in general, and in particular for the
megapixel maps to come from the next generation of satellite missions.
|
Parabolic equations with homogeneous Dirichlet conditions on the boundary are
studied in a setting where the solutions are required to have a prescribed
change of the profile in fixed time, instead of a Cauchy condition. It is shown
that this problem is well-posed in L_2-setting. Existence and regularity
results are established, as well as an analog of the maximum principle.
|
A total dominator coloring of a graph G is a proper coloring of G in which
each vertex of the graph is adjacent to every vertex of some color class. The
total dominator chromatic number of a graph is the minimum number of color
classes in a total dominator coloring. In this article, we study the total
dominator coloring on middle graphs by giving several bounds for the case of
general graphs and trees. Moreover, we calculate explicitely the total
dominator chromatic number of the middle graph of several known families of
graphs.
|
Session-based recommender systems have attracted much attention recently. To
capture the sequential dependencies, existing methods resort either to data
augmentation techniques or left-to-right style autoregressive training.Since
these methods are aimed to model the sequential nature of user behaviors, they
ignore the future data of a target interaction when constructing the prediction
model for it. However, we argue that the future interactions after a target
interaction, which are also available during training, provide valuable signal
on user preference and can be used to enhance the recommendation quality.
Properly integrating future data into model training, however, is non-trivial
to achieve, since it disobeys machine learning principles and can easily cause
data leakage. To this end, we propose a new encoder-decoder framework named
Gap-filling based Recommender (GRec), which trains the encoder and decoder by a
gap-filling mechanism. Specifically, the encoder takes a partially-complete
session sequence (where some items are masked by purpose) as input, and the
decoder predicts these masked items conditioned on the encoded representation.
We instantiate the general GRec framework using convolutional neural network
with sparse kernels, giving consideration to both accuracy and efficiency. We
conduct experiments on two real-world datasets covering short-, medium-, and
long-range user sessions, showing that GRec significantly outperforms the
state-of-the-art sequential recommendation methods. More empirical studies
verify the high utility of modeling future contexts under our GRec framework.
|
A reconfigurable intelligent surface (RIS) enhanced non-orthogonal multiple
access assisted backscatter communication (RIS-NOMABC) system is considered. A
joint optimization problem over power reflection coefficients and phase shifts
is formulated. To solve this non-convex problem, a low complexity algorithm is
proposed by invoking the alternative optimization, successive convex
approximation and manifold optimization algorithms. Numerical results
corroborate that the proposed
RIS-NOMABC system outperforms the conventional non-orthogonal multiple access
assisted backscatter communication (NOMABC) system without RIS, and demonstrate
the feasibility and effectiveness of the proposed algorithm.
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Temperature dependence S(T) of the thermoelectric power of metallic systems
with cerium and ytterbium ions exhibits some characteristic features, which can
be used to classify these systems into distinct categories. The experimental
data are explained by the Kondo scattering in the presence of the crystal field
splitting and various shapes of S(T) are related to different Kondo scales that
characterize Ce and Yb ions at different temperatures. The low- and
high-temperature behaviors are calculated for different fixed point models and
the overall shape of S(T) is obtained by interpolation.
At high temperatures, we use the Coqblin-Schrieffer model and calculate S(T)
by perturbation expansion with renormalized coupling constants. The
renormalization is performed by the 'poor man's scaling'.
At low temperatures, we describe the dilute Ce and Yb alloys by an effective
spin-degenerate single-impurity Anderson model, and the stoichiometric
compounds by an effective spin-degenerate periodic Anderson model. The
parameters of these low-temperature models are such that their effective Kondo
scale coincides with the lowest Kondo scale of the Coqblin-Schrieffer model.
The interpolation between the results obtained for the Anderson model and the
Coqblin-Schrieffer model explains the overall thermoelectric properties of most
Ce and Yb intermetallics.
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The aim of these notes is to give an accessible and self-contained
introduction to the theory of gravitational waves as the theory of a
relativistic symmetric tensor field in a Minkowski background spacetime. This
is the approach of a particle physicist: the graviton is identified with a
particular irreducible representation of the Poincar\'e group, corresponding to
vanishing mass and spin two. It is shown how to construct an action functional
giving the linear dynamics of gravitons, and how General Relativity can be
obtained from it. The Hamiltonian formulation of the linear theory is examined
in detail. We study the emission of gravitational waves and apply the results
to the simplest case of a binary Newtonian system.
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Using results from our companion article [arXiv:1112.4824v2] on a Schauder
approach to existence of solutions to a degenerate-parabolic partial
differential equation, we solve three intertwined problems, motivated by
probability theory and mathematical finance, concerning degenerate diffusion
processes. We show that the martingale problem associated with a
degenerate-elliptic differential operator with unbounded, locally Holder
continuous coefficients on a half-space is well-posed in the sense of Stroock
and Varadhan. Second, we prove existence, uniqueness, and the strong Markov
property for weak solutions to a stochastic differential equation with
degenerate diffusion and unbounded coefficients with suitable H\"older
continuity properties. Third, for an Ito process with degenerate diffusion and
unbounded but appropriately regular coefficients, we prove existence of a
strong Markov process, unique in the sense of probability law, whose
one-dimensional marginal probability distributions match those of the given Ito
process.
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We present a continuous-time contract whereby a top-level player can
incentivize a hierarchy of players below him to act in his best interest
despite only observing the output of his direct subordinate. This paper extends
Sannikov's approach from a situation of asymmetric information between a
principal and an agent to one of hierarchical information between several
players. We develop an iterative algorithm for constructing an incentive
compatible contract and define the correct notion of concavity which must be
preserved during iteration. We identify conditions under which a dynamic
programming construction of an optimal dynamic contract can be reduced to only
a one-dimensional state space and one-dimensional control set, independent of
the size of the hierarchy. In this sense, our results contribute to the
applicability of dynamic programming on dynamic contracts for a large-scale
principal-agent hierarchy.
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In the ultraintense laser-solid interaction, our recent work [Phys. Rev. E
109, 035204 (2024)] has revealed that the linear Breit-Wheeler (BW) process via
photon-photon collisions will become the dominant mechanism for
electron-positron pair production at the normalized laser amplitude
$a_0<400$--$500$. Here, we investigate the impact of photon polarization on
linear Breit-Wheeler pair production in the similar laser-solid setup, mainly
focusing on the difference of positron yields between polarized and unpolarized
situations. Two facts serve as the motivation for this work: (i) the emitted
photons via nonlinear Compton scattering are highly linearly polarized in the
strong-field QED regime; (ii) the linear BW cross section
$\sigma_{\gamma\gamma}$ is dependent on photon polarization. By using
two-dimensional QED particle-in-cell simulations, we find that the photon
polarization effect can suppress the positron yield by 5% to 10% in linear BW
pair production. This is because the polarization directions of colliding
photons are predominantly parallel, resulting in a reduced
$\sigma_{\gamma\gamma}$ compared to the unpolarized cross section. The
suppression degree is decreased with the enhancement of the nonlinear QED
strength at higher laser intensities. This work emphasizes the importance of
photon polarization in accurately predicting linear BW pair production in
laser-driven plasmas.
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A new method based on the combination of small-anglescattering, reverse Monte
Carlo simulations, and an aggregate recognition algorithm is proposed to
characterize the structure of nanoparticle suspensions in solvents and polymer
nanocomposites, allowing detailedstudies of the impact of different
nanoparticle surface modifications.Experimental small-angle scattering is
reproduced using simulated annealing of configurations of polydisperse
particles in a simulation box compatible with the lowest experimental q-vector.
Then, properties of interest likeaggregation states are extracted from these
configurations and averaged. This approach has been applied to silane
surface-modified silica nanoparticles with different grafting groups, in
solvents and after casting into polymer matrices.It is shown that the chemistry
of the silane function, in particular mono- or trifunctionality possibly
related to patch formation, affects the dispersion state in a given medium, in
spite of an unchanged alkylchain length. Our approach may be applied to study
any dispersion or aggregation state of nanoparticles. Concerningnanocomposites,
the method has potential impact on the design of new formulations allowing
controlled tuning of nanoparticle dispersion.
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We present an experimental demonstration of Additive Point Source
Localization (APSL), a sparse parametric imaging algorithm that reconstructs
the 3D positions and activities of multiple gamma-ray point sources. Using a
handheld gamma-ray detector array and up to four $8$ ${\mu}$Ci $^{137}$Cs
gamma-ray sources, we performed both source-search and source-separation
experiments in an indoor laboratory environment. In the majority of the
source-search measurements, APSL reconstructed the correct number of sources
with position accuracies of ${\sim}20$ cm and activity accuracies (unsigned) of
${\sim}20\%$, given measurement times of two to three minutes and distances of
closest approach (to any source) of ${\sim}20$ cm. In source-separation
measurements where the detector could be moved freely about the environment,
APSL was able to resolve two sources separated by $75$ cm or more given only
${\sim}60$ s of measurement time. In these source-separation measurements, APSL
produced larger total activity errors of ${\sim}40\%$, but obtained source
separation distances accurate to within $15$ cm. We also compare our APSL
results against traditional Maximum Likelihood-Expectation Maximization (ML-EM)
reconstructions, and demonstrate improved image accuracy and interpretability
using APSL over ML-EM. These results indicate that APSL is capable of
accurately reconstructing gamma-ray source positions and activities using
measurements from existing detector hardware.
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This is a short, elementary survey article about taut submanifolds. In order
to simplify the exposition, we restrict to the case of compact smooth
submanifolds of Euclidean or spherical spaces. Some new, partial results
concerning taut 4-manifolds are discussed at the end of the text.
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We consider several spin-unrestricted random-phase approximation (RPA)
variants for calculating correlation energies, with and without range
separation, and test them on datasets of atomization energies and reaction
barrier heights. We show that range separation greatly improves the accuracy of
all RPA variants for these properties. Moreover, we show that a RPA variant
with exchange, hereafter referred to as RPAx-SO2, first proposed by Sz-abo and
Ostlund [A. Szabo and N. S. Ostlund, J. Chem. Phys. 67, 4351 (1977)] in a
spin-restricted closed-shell formalism, and extended here to a
spin-unrestricted formalism , provides on average the most accurate
range-separated RPA variant for atomization energies and reaction barrier
heights. Since this range-separated RPAx-SO2 method had already been shown to
be among the most accurate range-separated RPA variants for weak intermolecular
interactions [J. Toulouse, W. Zhu, A. Savin, G. Jansen, and J. G.
{\'A}ngy{\'a}n, J. Chem. Phys. 135, 084119 (2011)], this works confirms
range-separated RPAx-SO2 as a promising method for general chemical
applications.
|
In this note, by the umbra calculus method, the Sun and Zagier's congruences
involving the Bell numbers and the derangement numbers are generalized to the
polynomial cases. Some special congruences are also provided.
|
In this letter, we demonstrate a strong dependence of the electrostatic
deformation of doubly-clamped single-walled carbon nanotubes on both the field
strength and the tube length, using molecular simulations. Metallic nanotubes
are found to be more sensitive to an electric field than semiconducting ones of
the same size. For a given electric field, the induced deformation increases
with tube length but decreases with tube radius. Furthermore, it is found that
nanotubes can be more efficiently bent in a center-oriented transverse electric
field.
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This paper studies pairs trading using a nonlinear and non-Gaussian
state-space model framework. We model the spread between the prices of two
assets as an unobservable state variable and assume that it follows a
mean-reverting process. This new model has two distinctive features: (1) The
innovations to the spread is non-Gaussianity and heteroskedastic. (2) The mean
reversion of the spread is nonlinear. We show how to use the filtered spread as
the trading indicator to carry out statistical arbitrage. We also propose a new
trading strategy and present a Monte Carlo based approach to select the optimal
trading rule. As the first empirical application, we apply the new model and
the new trading strategy to two examples: PEP vs KO and EWT vs EWH. The results
show that the new approach can achieve a 21.86% annualized return for the
PEP/KO pair and a 31.84% annualized return for the EWT/EWH pair. As the second
empirical application, we consider all the possible pairs among the largest and
the smallest five US banks listed on the NYSE. For these pairs, we compare the
performance of the proposed approach with that of the existing popular
approaches, both in-sample and out-of-sample. Interestingly, we find that our
approach can significantly improve the return and the Sharpe ratio in almost
all the cases considered.
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The receiver operating characteristic curve is widely applied in measuring
the performance of diagnostic tests. Many direct and indirect approaches have
been proposed for modelling the ROC curve, and because of its tractability, the
Gaussian distribution has typically been used to model both populations. We
propose using a Gaussian mixture model, leading to a more flexible approach
that better accounts for atypical data. Monte Carlo simulation is used to
circumvent the issue of absence of a closed-form. We show that our method
performs favourably when compared to the crude binormal curve and to the
semi-parametric frequentist binormal ROC using the famous LABROC procedure.
|
We present low-frequency electrical resistance fluctuations, or noise, in
graphene-based field-effect devices with varying number of layers. In
single-layer devices the noise magnitude decreases with increasing carrier
density, which behaved oppositely in the devices with two or larger number of
layers accompanied by a suppression in noise magnitude by more than two orders
in the latter case. This behavior can be explained from the influence of
external electric field on graphene band structure, and provides a simple
transport-based route to isolate single-layer graphene devices from those with
multiple layers.
|
In this paper we give an exact analytical expression for the number of
spanning trees of an infinite family of outerplanar, small-world and
self-similar graphs. This number is an important graph invariant related to
different topological and dynamic properties of the graph, such as its
reliability, synchronization capability and diffusion properties. The
calculation of the number of spanning trees is a demanding and difficult task,
in particular for large graphs, and thus there is much interest in obtaining
closed expressions for relevant infinite graph families. We have also
calculated the spanning tree entropy of the graphs which we have compared with
those for graphs with the same average degree.
|
Support and rank varieties of modules over a group algebra of an elementary
abelian p-group have been well studied. In particular, Avrunin and Scott showed
that in this setting, the rank and support varieties are equivalent. Avramov
and Buchweitz proved an analogous result for pairs of modules over arbitrary
commutative local complete intersection rings. In this paper we study support
and rank varieties in the triangulated category of totally acyclic chain
complexes over a complete intersection ring and show that these varieties are
also equivalent.
|
The main goal of this paper is to find analytical solutions of a system of
nonlinear ordinary differential equations arising in the virus propagation in
blockchain networks. The presented method reduces the problem to an Abel
differential equation of the first kind and solve it directly.
|
We present the Abnormal Netsukuku Domain Name Anarchy system. ANDNA is the
distributed, non hierarchical and decentralised system of hostname management
used in the Netsukuku network.
|
The dawn of the fourth industrial revolution, Industry 4.0 has created great
enthusiasm among companies and researchers by giving them an opportunity to
pave the path towards the vision of a connected smart factory ecosystem.
However, in context of automotive industry there is an evident gap between the
requirements supported by the current automotive manufacturing execution
systems (MES) and the requirements proposed by industrial standards from the
International Society of Automation (ISA) such as, ISA-95, ISA-88 over which
the Industry 4.0 is being built on. In this paper, we bridge this gap by
following a model-based requirements engineering approach along with a gap
analysis process. Our work is mainly divided into three phases, (i) automotive
MES tool selection phase, (ii) requirements modeling phase, (iii) and gap
analysis phase based on the modeled requirements. During the MES tool selection
phase, we used known reliable sources such as, MES product survey reports,
white papers that provide in-depth and comprehensive information about various
comparison criteria and tool vendors list for the current MES landscape. During
the requirement modeling phase, we specified requirements derived from the
needs of ISA-95 and ISA-88 industrial standards using the general purpose
Systems Modeling Language (SysML). During the gap analysis phase, we find the
misalignment between standard requirements and the compliance of the existing
software tools to those standards.
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Random walks by single-node agents have been systematically conducted on
various types of complex networks in order to investigate how their topologies
can affect the dynamics of the agents. However, by fitting any network node,
these agents do not engage in topological interactions with the network. In the
present work, we describe random walks on complex networks performed by agents
that are actually small graphs. These agents can only occupy admissible
portions of the network onto which they fit topologically, hence their name
being taken as topologically-specific agents. These agents are also allowed to
move to adjacent subgraphs in the network, which have each node adjacent to the
original respective node of the agent. Two types of random walks are considered
here: uniformly random and influenced by an external field. The performance of
the random walks performed by three types of topologically-specific agents is
studied respectively to the obtained coverage considering three types of
complex networks (geometrical, Erd\H{o}s-R\'enyi, and Barab\'asi-Albert). The
number of nodes displaced at each random walk step is also obtained and
analyzed. Several interesting results are reported and discussed, including the
fact that, despite its intrinsic node degree heterogeneity, Barab\'asi-Albert
networks tend to allow relatively smooth and effective coverage by all the
considered topologically-specific agents. Erd\H{o}s-R\'enyi networks were also
found to yield large dispersions of node coverage. In addition, the triangle
agent was found to allow more effective random walks respectively to any of the
three considered networks.
|
Grasping the themes of social media content is key to understanding the
narratives that influence public opinion and behavior. The thematic analysis
goes beyond traditional topic-level analysis, which often captures only the
broadest patterns, providing deeper insights into specific and actionable
themes such as "public sentiment towards vaccination", "political discourse
surrounding climate policies," etc. In this paper, we introduce a novel
approach to uncovering latent themes in social media messaging. Recognizing the
limitations of the traditional topic-level analysis, which tends to capture
only overarching patterns, this study emphasizes the need for a finer-grained,
theme-focused exploration. Traditional theme discovery methods typically
involve manual processes and a human-in-the-loop approach. While valuable,
these methods face challenges in scalability, consistency, and resource
intensity in terms of time and cost. To address these challenges, we propose a
machine-in-the-loop approach that leverages the advanced capabilities of Large
Language Models (LLMs). To demonstrate our approach, we apply our framework to
contentious topics, such as climate debate and vaccine debate. We use two
publicly available datasets: (1) the climate campaigns dataset of 21k Facebook
ads and (2) the COVID-19 vaccine campaigns dataset of 9k Facebook ads. Our
quantitative and qualitative analysis shows that our methodology yields more
accurate and interpretable results compared to the baselines. Our results not
only demonstrate the effectiveness of our approach in uncovering latent themes
but also illuminate how these themes are tailored for demographic targeting in
social media contexts. Additionally, our work sheds light on the dynamic nature
of social media, revealing the shifts in the thematic focus of messaging in
response to real-world events.
|
Deep neural networks (DNNs) have achieved state-of-the-art performances in
many important domains, including medical diagnosis, security, and autonomous
driving. In these domains where safety is highly critical, an erroneous
decision can result in serious consequences. While a perfect prediction
accuracy is not always achievable, recent work on Bayesian deep networks shows
that it is possible to know when DNNs are more likely to make mistakes. Knowing
what DNNs do not know is desirable to increase the safety of deep learning
technology in sensitive applications. Bayesian neural networks attempt to
address this challenge. However, traditional approaches are computationally
intractable and do not scale well to large, complex neural network
architectures. In this paper, we develop a theoretical framework to approximate
Bayesian inference for DNNs by imposing a Bernoulli distribution on the model
weights. This method, called MC-DropConnect, gives us a tool to represent the
model uncertainty with little change in the overall model structure or
computational cost. We extensively validate the proposed algorithm on multiple
network architectures and datasets for classification and semantic segmentation
tasks. We also propose new metrics to quantify the uncertainty estimates. This
enables an objective comparison between MC-DropConnect and prior approaches.
Our empirical results demonstrate that the proposed framework yields
significant improvement in both prediction accuracy and uncertainty estimation
quality compared to the state of the art.
|
The latest results from CMS on R-Parity violating Supersymmetry based on the
19.5/fb full dataset from the 8 TeV LHC run of 2012 are reviewed. The results
are interpreted in the context of simplified models with multilepton and
b-quark jets signatures that have low missing transverse energy arising from
light top-squark pair with R-parity-violating decays of the lightest
supersymmetric particle. In addition to simplified model, a new approach for
phenomenological MSSM interpretation is shown which demonstrates that the
obtained results from multilepton final states are valid for a wide range of
supersymmetry models.
|
For any positive integer $n$, $\mathcal{A}_n$ is the class of all groups $G$
such that, for $0\leq i\leq n$, $H^i(\hat{G},A)\cong H^i(G,A)$ for every finite
discrete $\hat{G}$-module $A$. We describe certain types of free products with
amalgam and HNN extensions that are in some of the classes $\mathcal{A}_n$. In
addition, we investigate the residually finite groups in the class
$\mathcal{A}_2$.
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In this paper we analyze a stochastic interpretation of the one-dimensional
parabolic-parabolic Keller-Segel system without cut-off. It involves an
original type of McKean-Vlasov interaction kernel. At the particle level, each
particle interacts with all the past of each other particle by means of a time
integrated functional involving a singular kernel. At the mean-field level
studied here, the McKean-Vlasov limit process interacts with all the past time
marginals of its probability distribution in a similarly singular way. We prove
that the parabolic-parabolic Keller-Segel system in the whole Euclidean space
and the corresponding McKean-Vlasov stochastic differential equation are
well-posed for any values of the parameters of the model.
|
As the specification of the new 5G NR standard proceeds inside 3GPP, the
availability of a versatile, full-stack, End-to-End (E2E), and open source
simulator becomes a necessity to extract insights from the recently approved
3GPP specifications. This paper presents an extension to ns-3, a well-known
discrete-event network simulator, to support the NR Radio Access Network. The
present work describes the design and implementation choices at the MAC and PHY
layers, and it discusses a technical solution for managing different bandwidth
parts. Finally, we present calibration results, according to 3GPP procedures,
and we show how to get E2E performance indicators in a realistic deployment
scenario, with special emphasis on the E2E latency.
|
We demonstrate theoretically the possibility of using nano mechanical systems
as single photon routers. We show how EIT in cavity optomechanical systems can
be used to produce a switch for a probe field in a single photon Fock state
using very low pumping powers of few microwatt. We present estimates of vacuum
and thermal noise and show the optimal performance of the single photon switch
is deteriorated by only few percent even at temperatures of the order of 20 mK.
|
Subsets and Splits
Filtered Text Samples
Retrieves 100 samples of text containing the specific phrase "You are a helpful assistant", providing limited insight into the dataset.
Helpful Assistant Text Samples
Returns a limited set of rows containing the phrase 'helpful assistant' in the text, providing basic filtering of relevant entries.