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In this paper we analyze the performance issues involved in the generation of
auto- mated traffic reports for large IT infrastructures. Such reports allows
the IT manager to proactively detect possible abnormal situations and roll out
the corresponding cor- rective actions. With the ever-increasing bandwidth of
current networks, the design of automated traffic report generation systems is
very challenging. In a first step, the huge volumes of collected traffic are
transformed into enriched flow records obtained from diverse collectors and
dissectors. Then, such flow records, along with time series obtained from the
raw traffic, are further processed to produce a usable report. As will be
shown, the data volume in flow records is very large as well and requires
careful selection of the Key Performance Indicators (KPIs) to be included in
the report. In this regard, we discuss the use of high-level languages versus
low- level approaches, in terms of speed and versatility. Furthermore, our
design approach is targeted for rapid development in commodity hardware, which
is essential to cost-effectively tackle demanding traffic analysis scenarios.
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We introduce quantum Markov categories as a structure that refines and
extends a synthetic approach to probability theory and information theory so
that it includes quantum probability and quantum information theory. In this
broader context, we analyze three successively more general notions of
reversibility and statistical inference: ordinary inverses, disintegrations,
and Bayesian inverses. We prove that each one is a strictly special instance of
the latter for certain subcategories, providing a categorical foundation for
Bayesian inversion as a generalization of reversing a process. We unify the
categorical and $C^*$-algebraic notions of almost everywhere (a.e.)
equivalence. As a consequence, we prove many results including a universal
no-broadcasting theorem for S-positive categories, a generalized Fisher--Neyman
factorization theorem for a.e. modular categories, a relationship between error
correcting codes and disintegrations, and the relationship between Bayesian
inversion and Umegaki's non-commutative sufficiency.
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We present results of targeted searches for signatures of non-radial
oscillation modes (such as r- and g-modes) in neutron stars using {\it RXTE}
data from several accreting millisecond X-ray pulsars (AMXPs). We search for
potentially coherent signals in the neutron star rest frame by first removing
the phase delays associated with the star's binary motion and computing FFT
power spectra of continuous light curves with up to $2^{30}$ time bins. We
search a range of frequencies in which both r- and g-modes are theoretically
expected to reside. Using data from the discovery outburst of the 435 Hz pulsar
XTE J1751$-$305 we find a single candidate, coherent oscillation with a
frequency of $0.5727597 \times \nu_{spin} = 249.332609$ Hz, and a fractional
Fourier amplitude of $7.46 \times 10^{-4}$. We estimate the significance of
this feature at the $1.6 \times 10^{-3}$ level, slightly better than a
$3\sigma$ detection. We argue that possible mode identifications include
rotationally-modified g-modes associated with either a helium-rich surface
layer or a density discontinuity due to electron captures on hydrogen in the
accreted ocean. Alternatively, the frequency could be identified with that of
an inertial mode or an r-mode modified by the presence of a solid crust,
however, the r-mode amplitude required to account for the observed modulation
amplitude would induce a large spin-down rate inconsistent with the observed
pulse timing measurements. For the AMXPs XTE J1814$-$338 and NGC 6440 X-2 we do
not find any candidate oscillation signals, and we place upper limits on the
fractional Fourier amplitude of any coherent oscillations in our frequency
search range of $7.8\times 10^{-4}$ and $5.6 \times 10^{-3}$, respectively. We
briefly discuss the prospects and sensitivity for similar searches with future,
larger X-ray collecting area missions.
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The power-balanced hybrid optical imaging system is a special design of a
diffractive computational camera, introduced in this paper, with image
formation by a refractive lens and Multilevel Phase Mask (MPM). This system
provides a long focal depth with low chromatic aberrations thanks to MPM and a
high energy light concentration due to the refractive lens. We introduce the
concept of optical power balance between the lens and MPM which controls the
contribution of each element to modulate the incoming light. Additional unique
features of our MPM design are the inclusion of quantization of the MPM's shape
on the number of levels and the Fresnel order (thickness) using a smoothing
function. To optimize optical power-balance as well as the MPM, we build a
fully-differentiable image formation model for joint optimization of optical
and imaging parameters for the proposed camera using Neural Network techniques.
Additionally, we optimize a single Wiener-like optical transfer function (OTF)
invariant to depth to reconstruct a sharp image. We numerically and
experimentally compare the designed system with its counterparts, lensless and
just-lens optical systems, for the visible wavelength interval (400-700)nm and
the depth-of-field range (0.5-$\infty$m for numerical and 0.5-2m for
experimental). The attained results demonstrate that the proposed system
equipped with the optimal OTF overcomes its counterparts (even when they are
used with optimized OTF) in terms of reconstruction quality for off-focus
distances. The simulation results also reveal that optimizing the optical
power-balance, Fresnel order, and the number of levels parameters are essential
for system performance attaining an improvement of up to 5dB of PSNR using the
optimized OTF compared with its counterpart lensless setup.
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Impurity nuclear spin relaxation is studied theoretically. A single impurity
generates a bound state localized around the impurity atom in unconventional
superconductors. With increasing impurity potential, the relaxation rate
$T_1^{-1}$ is reduced by the impurity potential. However, it has a peak at low
temperatures due to the impurity bound state. The peak disappears at
non-impurity sites. The impurity site NMR measurement detecting a local
electronic structure just on the impurity atom is very useful for identifying
the unconventional pairing states.
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Landmark localization in images and videos is a classic problem solved in
various ways. Nowadays, with deep networks prevailing throughout machine
learning, there are revamped interests in pushing facial landmark detection
technologies to handle more challenging data. Most efforts use network
objectives based on L1 or L2 norms, which have several disadvantages. First of
all, the locations of landmarks are determined from generated heatmaps (i.e.,
confidence maps) from which predicted landmark locations (i.e., the means) get
penalized without accounting for the spread: a high scatter corresponds to low
confidence and vice-versa. For this, we introduce a LaplaceKL objective that
penalizes for a low confidence. Another issue is a dependency on labeled data,
which are expensive to obtain and susceptible to error. To address both issues
we propose an adversarial training framework that leverages unlabeled data to
improve model performance. Our method claims state-of-the-art on all of the
300W benchmarks and ranks second-to-best on the Annotated Facial Landmarks in
the Wild (AFLW) dataset. Furthermore, our model is robust with a reduced size:
1/8 the number of channels (i.e., 0.0398MB) is comparable to state-of-that-art
in real-time on CPU. Thus, we show that our method is of high practical value
to real-life application.
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Unified dark matter cosmologies economically combine missing matter and
energy in a single fluid. Of these models, the standard Chaplygin gas is
theoretically motivated, but faces problems in explaining large scale structure
if linear perturbations are directly imposed on the homogeneous fluid. However,
early formation of a clustered component of small halos is sufficient (and
necessary) for hierarchical clustering to proceed in a CDM-like component as in
the standard scenario, with the remaining homogeneous component acting as dark
energy. We examine this possibility. A linear analysis shows that a critical
Press-Schecter threshold for collapse can generally only be reached for
generalized Chaplygin gas models that mimic $\Lambda$CDM, or ones where
superluminal sound speeds occur. But the standard Chaplygin gas case turns out
to be marginal, with overdensities reaching order one in the linear regime.
This motivates a nonlinear analysis. A simple infall model suggests that
collapse is indeed possible for perturbations of order 1~kpc and above; for, as
opposed to standard gases, pressure forces decrease with increasing densities,
allowing for the collapse of linearly stable systems. This suggests that a
cosmological scenario based on the standard Chaplygin gas may not be ruled out
from the viewpoint of structure formation, as often assumed. On the other hand,
a 'nonlinear Jeans scale', constricting growth to scales $R \gtrsim {\rm kpc}$,
which may be relevant to the small scale problems of CDM, is predicted.
Finally, the background dynamics of clustered Chaplygin gas cosmologies is
examined and confronted with observational datasets. It is found to be viable
(at 1-sigma), with a mildly larger $H_0$ than $\Lambda$CDM, if the clustered
fraction is larger than $90 \%$.
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Researchers have developed neural network verification algorithms motivated
by the need to characterize the robustness of deep neural networks. The
verifiers aspire to answer whether a neural network guarantees certain
properties with respect to all inputs in a space. However, many verifiers
inaccurately model floating point arithmetic but do not thoroughly discuss the
consequences.
We show that the negligence of floating point error leads to unsound
verification that can be systematically exploited in practice. For a pretrained
neural network, we present a method that efficiently searches inputs as
witnesses for the incorrectness of robustness claims made by a complete
verifier. We also present a method to construct neural network architectures
and weights that induce wrong results of an incomplete verifier. Our results
highlight that, to achieve practically reliable verification of neural
networks, any verification system must accurately (or conservatively) model the
effects of any floating point computations in the network inference or
verification system.
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A large fraction of the electronic health records consists of clinical
measurements collected over time, such as blood tests, which provide important
information about the health status of a patient. These sequences of clinical
measurements are naturally represented as time series, characterized by
multiple variables and the presence of missing data, which complicate analysis.
In this work, we propose a surgical site infection detection framework for
patients undergoing colorectal cancer surgery that is completely unsupervised,
hence alleviating the problem of getting access to labelled training data. The
framework is based on powerful kernels for multivariate time series that
account for missing data when computing similarities. Our approach show
superior performance compared to baselines that have to resort to imputation
techniques and performs comparable to a supervised classification baseline.
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Recently, label consistent k-svd (LC-KSVD) algorithm has been successfully
applied in image classification. The objective function of LC-KSVD is consisted
of reconstruction error, classification error and discriminative sparse codes
error with L0-norm sparse regularization term. The L0-norm, however, leads to
NP-hard problem. Despite some methods such as orthogonal matching pursuit can
help solve this problem to some extent, it is quite difficult to find the
optimum sparse solution. To overcome this limitation, we propose a label
embedded dictionary learning (LEDL) method to utilise the L1-norm as the sparse
regularization term so that we can avoid the hard-to-optimize problem by
solving the convex optimization problem. Alternating direction method of
multipliers and blockwise coordinate descent algorithm are then exploited to
optimize the corresponding objective function. Extensive experimental results
on six benchmark datasets illustrate that the proposed algorithm has achieved
superior performance compared to some conventional classification algorithms.
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Long-range spatiotemporal correlations may play important roles in
nonequilibrium surface growth process. In order to investigate the effects of
long-range temporal correlation on dynamic scaling of growing surfaces, we
perform extensive numerical simulations of the (1+1)- and (2+1)-dimensional
Kardar-Parisi-Zhang (KPZ) growth system in the presence of temporally
correlated noise, and compare our results with previous theoretical predictions
and numerical simulations. We find that surface morphologies are obviously
affected with long-range temporal correlations, and as the temporal correlation
exponent increases, the KPZ surfaces develop gradually faceted patterns in the
saturated growth regimes. Our results show that the temporal correlated KPZ
system displays evidently nontrivial dynamic properties when $0<\theta<0.5$,
the characteristic roughness exponents satisfy $\alpha<\alpha_s$, and
$\alpha_{loc}$ exhibiting non-universal scaling within local window sizes,
which differs with the existing dynamic scaling classifications, both in the
(1+1)- and (2+1)-dimensions.
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We study ground states of two-dimensional Bose-Einstein condensates with
attractive interactions in a trap $V(x)$ rotating at the velocity $\Omega $. It
is known that there exist a critical rotational velocity $0<\Omega
^*:=\Omega^*(V)\leq \infty$ and a critical number $0<a^*<\infty$ such that for
any rotational velocity $0\le \Omega <\Omega ^*$, ground states exist if and
only if the coupling constant $a$ satisfies $a<a^*$. For a general class of
traps $V(x)$, which may not be symmetric, we prove in this paper that up to a
constant phase, there exists a unique ground state as $a\nearrow a^*$, where
$\Omega\in(0,\Omega^*)$ is fixed. This result extends essentially our recent
uniqueness result, where only the radially symmetric traps $V(x)$ could be
handled with.
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Support vector machines (SVMs) appeared in the early nineties as optimal
margin classifiers in the context of Vapnik's statistical learning theory.
Since then SVMs have been successfully applied to real-world data analysis
problems, often providing improved results compared with other techniques. The
SVMs operate within the framework of regularization theory by minimizing an
empirical risk in a well-posed and consistent way. A clear advantage of the
support vector approach is that sparse solutions to classification and
regression problems are usually obtained: only a few samples are involved in
the determination of the classification or regression functions. This fact
facilitates the application of SVMs to problems that involve a large amount of
data, such as text processing and bioinformatics tasks. This paper is intended
as an introduction to SVMs and their applications, emphasizing their key
features. In addition, some algorithmic extensions and illustrative real-world
applications of SVMs are shown.
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We point out interesting effects of additional massless Dirac fermions with
N_F colors upon the critical behavior of the Ginzburg-Landau model. For
increasing N_F, the model is driven into the type II regime of
superconductivity. The critical exponents are given as a function of N_F.
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Hydrogenated amorphous silicon alloy films are generally deposited by radio
frequency plasma enhanced chemical vapor deposition (RF PECVD) technique on
various types of substrates. Generally it is assumed that film quality remains
unchanged when deposited on textured or non-textured substrates. Here we
analyzed the difference in growth of thin film silicon layers when deposited in
a textured and a non-textured surface. In this investigation characteristics of
two solar cells were compared, where one cell was prepared on a textured
surface ( Cell-A) while the other prepared on a non-textured surface (Cell-B).
Defect analysis of the devices were carried out by simulation and device
modeling. It shows that the intrinsic film deposited on a textured surface was
more defective ($2.4\times 10^{17}$ cm$^{-3}$) than that deposited on a flat
surface ($3.2\times 10^{16}$ cm$^{-3}$). Although the primary differences in
these two cells were thickness of the active layer and nature of surface
texturing, the simulation results show that thin film deposited on a textured
surface may acquire an increased defect density than that deposited on a flat
surface. Lower effective flux density of $SiH_{3}$ precursors on the textured
surface can be one of the reasons for higher defect density in the film
deposited on textured surface. An Improved light coupling can be achieved by
using a thinner doped window layer. By changing the thickness from 15 nm to 3
nm, the short circuit current density increased from 16.4 mA/cm$^{2}$ to 20.96
mA/cm$^{2}$ and efficiency increased from $9.4\%$ to $12.32\%$.
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This document introduces the background and the usage of the Hyperspectral
City Dataset and the benchmark. The documentation first starts with the
background and motivation of the dataset. Follow it, we briefly describe the
method of collecting the dataset and the processing method from raw dataset to
the final release dataset, specifically, the version 1.0. We also provide the
detailed usage of the dataset and the evaluation metric for submitted the
result for the 2019 Hyperspectral City Challenge.
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We study a non-linear convective-diffusive equation, local in space and time,
which has its background in the dynamics of the thickness of a wetting film.
The presence of a non-linear diffusion predicts the existence of fronts as well
as shock fronts. Despite the absence of memory effects, solutions in the case
of pure non-linear diffusion exhibit an anomalous sub-diffusive scaling. Due to
a balance between non-linear diffusion and convection we, in particular, show
that solitary waves appear. For large times they merge into a single solitary
wave exhibiting a topological stability. Even though our results concern a
specific equation, numerical simulations supports the view that anomalous
diffusion and the solitary waves disclosed will be general features in such
non-linear convective-diffusive dynamics.
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The magnetization relaxation rate of small gamma-Fe2O3 particles dispersed in
a silica matrix has been measured from 60 mK to 5 K. It shows a minimum around
150 mK, that can be discussed in terms of either thermal or quantum relaxation
regime.
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We derive semiclassical equations of motion for an accelerated wavepacket in
a two-band system. We show that these equations can be formulated in terms of
the static band geometry described by the quantum metric. We consider the
specific cases of the Rashba Hamiltonian with and without a Zeeman term. The
semiclassical trajectories are in full agreement with the ones found by solving
the Schr\"odinger equation. This formalism successfully describes the adiabatic
limit and the anomalous Hall effect traditionally attributed to Berry
curvature. It also describes the opposite limit of coherent band superposition
giving rise to a spatially oscillating Zitterbewegung motion. At $k=0$, such
wavepacket exhibits a circular trajectory in real space, with its radius given
by the square root of the quantum metric. This quantity appears as a universal
length scale, providing a geometrical origin of the Compton wavelength.
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Hybrid quantum systems (HQSs) have attracted several research interests in
the last years. In this Letter, we report on the design, fabrication, and
characterization of a novel diamond architecture for HQSs that consists of a
high quality thin circular diamond membrane with embedded near-surface
nitrogen-vacancy centers (NVCs). To demonstrate this architecture, we employed
the NVCs by means of their optical and spin interfaces as nanosensors of the
motion of the membrane under static pressure and in-resonance vibration, as
well as the residual stress of the membrane. Driving the membrane at its
fundamental resonance mode, we observed coupling of this vibrational mode to
the spin of the NVCs by Hahn echo signal. Our realization of this architecture
will enable futuristic HQS-based applications in diamond piezometry and
vibrometry, as well as spin-mechanical and mechanically mediated spin-spin
coupling in quantum information science.
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Abnormal event detection, which refers to mining unusual interactions among
involved entities, plays an important role in many real applications. Previous
works mostly over-simplify this task as detecting abnormal pair-wise
interactions. However, real-world events may contain multi-typed attributed
entities and complex interactions among them, which forms an Attributed
Heterogeneous Information Network (AHIN). With the boom of social networks,
abnormal event detection in AHIN has become an important, but seldom explored
task. In this paper, we firstly study the unsupervised abnormal event detection
problem in AHIN. The events are considered as star-schema instances of AHIN and
are further modeled by hypergraphs. A novel hypergraph contrastive learning
method, named AEHCL, is proposed to fully capture abnormal event patterns.
AEHCL designs the intra-event and inter-event contrastive modules to exploit
self-supervised AHIN information. The intra-event contrastive module captures
the pair-wise and multivariate interaction anomalies within an event, and the
inter-event module captures the contextual anomalies among events. These two
modules collaboratively boost the performance of each other and improve the
detection results. During the testing phase, a contrastive learning-based
abnormal event score function is further proposed to measure the abnormality
degree of events. Extensive experiments on three datasets in different
scenarios demonstrate the effectiveness of AEHCL, and the results improve
state-of-the-art baselines up to 12.0% in Average Precision (AP) and 4.6% in
Area Under Curve (AUC) respectively.
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We utilise the final catalogue from the Pan-Andromeda Archaeological Survey
to investigate the links between the globular cluster system and field halo in
M31 at projected radii $R_p=25-150$ kpc. In this region the cluster radial
density profile exhibits a power-law decline with index $\Gamma=-2.37\pm0.17$,
matching that for the stellar halo component with [Fe/H] $<-1.1$. Spatial
density maps reveal a striking correspondence between the most luminous
substructures in the metal-poor field halo and the positions of many globular
clusters. By comparing the density of metal-poor halo stars local to each
cluster with the azimuthal distribution at commensurate radius, we reject the
possibility of no correlation between clusters and field overdensities with
high confidence. We use our stellar density measurements and previous kinematic
data to demonstrate that $\approx35-60\%$ of clusters exhibit properties
consistent with having been accreted into the outskirts of M31 at late times
with their parent dwarfs. Conversely, at least $\sim40\%$ of remote clusters
show no evidence for a link with halo substructure. The radial density profile
for this subgroup is featureless and closely mirrors that observed for the
apparently smooth component of the metal-poor stellar halo. We speculate that
these clusters are associated with the smooth halo; if so, their properties
appear consistent with a scenario where the smooth halo was built up at early
times via the destruction of primitive satellites. In this picture the entire
M31 globular cluster system outside $R_p=25$ kpc comprises objects accumulated
from external galaxies over a Hubble time of growth.
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Despite its remarkable empirical success as a highly competitive branch of
artificial intelligence, deep learning is often blamed for its widely known low
interpretation and lack of firm and rigorous mathematical foundation. However,
most theoretical endeavor is devoted in discriminative deep learning case,
whose complementary part is generative deep learning. To the best of our
knowledge, we firstly highlight landscape of empirical error in generative case
to complete the full picture through exquisite design of image super resolution
under norm based capacity control. Our theoretical advance in interpretation of
the training dynamic is achieved from both mathematical and biological sides.
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We aim to investigate the bolometric $L_{\mathrm{X}}-T$ relation for galaxy
groups, and study the impact of gas cooling, feedback from supermassive black
holes, and selection effects on it. With a sample of 26 galaxy groups we
obtained the best fit $L_{\mathrm{X}}-T$ relation for five different cases
depending on the ICM core properties and central AGN radio emission, and
determined the slopes, normalisations, intrinsic and statistical scatters for
both temperature and luminosity. Simulations were undertaken to correct for
selection effects (e.g. Malmquist bias) and the bias corrected relations for
groups and clusters were compared. The slope of the bias corrected
$L_{\mathrm{X}}-T$ relation is marginally steeper but consistent with clusters
($\sim 3$). Groups with a central cooling time less than 1 Gyr (SCC groups)
show indications of having the steepest slope and the highest normalisation.
For the groups, the bias corrected intrinsic scatter in $L_{\mathrm{X}}$ is
larger than the observed scatter for most cases, which is reported here for the
first time. Lastly, we see indications that the groups with an extended central
radio source have a much steeper slope than those groups which have a CRS with
only core emission. Additionally, we also see indications that the more
powerful radio AGN are preferentially located in NSCC groups rather than SCC
groups.
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The "similarity" degree of a unital operator algebra $A$ was defined and
studied in two recent papers of ours, where in particular we showed that it
coincides with the "length" of an operator algebra. This paper brings several
complements: we give direct proofs (with slight improvements) of several known
facts on the length which were only known via the degree, and we show that the
length of a type $II_1$ factor with property $\Gamma$ is at most 5, improving
on a previous bound ($\le 44$) due to E. Christensen.
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When seeking information not covered in patient-friendly documents, like
medical pamphlets, healthcare consumers may turn to the research literature.
Reading medical papers, however, can be a challenging experience. To improve
access to medical papers, we introduce a novel interactive interface-Paper
Plain-with four features powered by natural language processing: definitions of
unfamiliar terms, in-situ plain language section summaries, a collection of key
questions that guide readers to answering passages, and plain language
summaries of the answering passages. We evaluate Paper Plain, finding that
participants who use Paper Plain have an easier time reading and understanding
research papers without a loss in paper comprehension compared to those who use
a typical PDF reader. Altogether, the study results suggest that guiding
readers to relevant passages and providing plain language summaries, or
"gists," alongside the original paper content can make reading medical papers
easier and give readers more confidence to approach these papers.
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High quality Quantitative Precipitation Estimation at high spatiotemporal
resolution is crucial to many hydrologic/hydro-meteorological designs. Optimal
Quantitative Precipitation Estimation of rainfall improves the accuracy of
river and flash flood forecasts. In this study, we aim to merge multiple
rainfall estimates including rain gauge, radar, Inverse Distance Weighting,
Ordinary Co-Kriging, and Adaptive Conditional Bias Penalized Co-Kriging through
two most common model selection techniques known as Least Absolute Shrinkage
and Selection Operator and Bayesian Model Averaging. The methods were applied
to the entire United States for a certain period. Statistical measures such as
RMSE, ME, NSE, and Correlation Coefficient are used to investigate the accuracy
and reliability of the estimation models. It is shown that both BMA and LASSO
improve the precipitation estimation considering all ranges of rainfall
observation included. However, OCK and CBPCK technique outperforms other
methods in rainfall more than 10 mm. The IDW estimates show small bias, which
results in a poor estimation, which is due to the limitation in using secondary
variable radar. However, OCK and CBPCK address this problem by adding radar
rainfall estimates as the second variable.
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We show that the quasi-adiabatic evolution of a system governed by the Dicke
Hamiltonian can be described in terms of a self-induced quantum many-body
metrological protocol. This effect relies on the sensitivity of the ground
state to a small symmetry-breaking perturbation at the quantum phase
transition, that leads to the collapse of the wavefunciton into one of two
possible ground states. The scaling of the final state properties with the
number of atoms and with the intensity of the symmetry breaking field, can be
interpreted in terms of the precession time of an effective quantum
metrological protocol. We show that our ideas can be tested with spin-phonon
interactions in trapped ion setups. Our work points to a classification of
quantum phase transitions in terms of the capability of many-body quantum
systems for parameter estimation.
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We have recently studied a simplified version of the path integral for a
particle on a sphere, and more generally on maximally symmetric spaces, and
proved that Riemann normal coordinates allow the use of a quadratic kinetic
term in the particle action. The emerging linear sigma model contains a scalar
effective potential that reproduces the effects of the curvature. We present
here further details on the construction, and extend its perturbative
evaluation to orders high enough to read off the type-A trace anomalies of a
conformal scalar in dimensions d = 14 and d = 16.
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A long standing question in the theory of orthogonal matrix polynomials is
the matrix Bochner problem, the classification of $N \times N$ weight matrices
$W(x)$ whose associated orthogonal polynomials are eigenfunctions of a second
order differential operator. Based on techniques from noncommutative algebra
(semiprime PI algebras of Gelfand-Kirillov dimension one), we construct a
framework for the systematic study of the structure of the algebra $\mathcal
D(W)$ of matrix differential operators for which the orthogonal polynomials of
the weight matrix $W(x)$ are eigenfunctions. The ingredients for this algebraic
setting are derived from the analytic properties of the orthogonal matrix
polynomials. We use the representation theory of the algebras $\mathcal D(W)$
to resolve the matrix Bochner problem under the two natural assumptions that
the sum of the sizes of the matrix algebras in the central localization of
$\mathcal D(W)$ equals $N$ (fullness of $\mathcal D(W)$) and the leading
coefficient of the second order differential operator multiplied by the weight
$W(x)$ is positive definite. In the case of $2\times 2$ weights, it is proved
that fullness is satisfied as long as $\mathcal D(W)$ is noncommutative. The
two conditions are natural in that without them the problem is equivalent to
much more general ones by artificially increasing the size of the matrix
$W(x)$.
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This is the second paper in a series of four in which we use space adiabatic
methods in order to incorporate backreactions among the homogeneous and between
the homogeneous and inhomogeneous degrees of freedom in quantum cosmological
perturbation theory.
The purpose of the present paper is twofold. On the one hand, it illustrates
the formalism of space adiabatic perturbation theory (SAPT) for two simple
quantum mechanical toy models. On the other, it proves the main point, namely
that backreactions lead to additional correction terms in effective
Hamiltonians that one would otherwise neglect in a crude Born-Oppenheimer
approximation.
The first model that we consider is a harmonic oscillator coupled to an
anharmonic oscillator. We chose it because it displays many similarities with
the more interesting second model describing the coupling between an inflaton
and gravity restricted to the purely homogeneous and isotropic sector. These
results have potential phenomenological consequences in particular for quantum
cosmological theories describing big bounces such as Loop Quantum Cosmology
(LQC).
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The locomotion of Caenorhabditis elegans exhibits complex patterns. In
particular, the worm combines mildly curved runs and sharp turns to steer its
course. Both runs and sharp turns of various types are important components of
taxis behavior. The statistics of sharp turns have been intensively studied.
However, there have been few studies on runs, except for those on klinotaxis
(also called weathervane mechanism), in which the worm gradually curves toward
the direction with a high concentration of chemicals; this phenomenon was
discovered recently. We analyzed the data of runs by excluding sharp turns. We
show that the curving rate obeys long-tail distributions, which implies that
large curving rates are relatively frequent. This result holds true for
locomotion in environments both with and without a gradient of NaCl
concentration; it is independent of klinotaxis. We propose a phenomenological
computational model on the basis of a random walk with multiplicative noise.
The assumption of multiplicative noise posits that the fluctuation of the force
is proportional to the force exerted. The model reproduces the long-tail
property present in the experimental data.
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Usually the first course in mathematics is calculus. Its a core course in the
curriculum of the Business, Engineering and the Sciences. However many students
face difficulties to learn calculus. These difficulties are often caused by the
prior fear of mathematics. The students today cant live without using computer
technology. The uses of computer for teaching and learning can transform the
boring traditional methodology of teach to more active and attractive method.
In this paper, we will show how we can use Excel in teaching calculus to
improve our students learning and understanding through different types of
applications ranging from Business to Engineering. The effectiveness of the
proposed methodology was tested on a random sample of 45 students from
different majors over a period of two semesters.
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We have obtained contemporaneous light, color, and radial velocity data for
three proto-planetary nebulae (PPNe) over the years 2007 to 2015. The light and
velocity curves of each show similar periods of pulsation, with photometric
periods of 42 and 50 days for IRAS 17436+5003, 102 days for IRAS 18095+2704,
and 35 days for IRAS 19475+3119. The light and velocity curves are complex with
multiple periods and small, variable amplitudes. Nevertheless, at least over
limited time intervals, we were able to identify dominant periods in the light,
color, and velocity curves and compare the phasing of each. The color curves
appear to peak with or slightly after the light curves while the radial
velocity curves peak about a quarter of a cycle before the light curves.
Similar results were found previously for two other PPNe, although for them the
light and color appeared to be in phase. Thus it appears that PPNe are
brightest when smallest and hottest. These phase results differ from those
found for classical Cepheid variables, where the light and velocity differ by
half a cycle, and are hottest at about average size and expanding. However,
they do appear to have similar phasing to the larger amplitude pulsations seen
in RV Tauri variables. Presently, few pulsation models exist for PPNe, and
these do not fit the observations well, especially the longer periods observed.
Model fits to these new light and velocity curves would allow masses to be
determined for these post-AGB objects, and thereby provide important
constraints to post-AGB stellar evolution models of low and intermediate-mass
stars.
|
We demonstrate the selective control of the magnetic response and
photoluminescence properties of Er3+ centers with light, by associating them
with a highly conjugated beta-diketonate (1,3-di(2-naphthyl)-1,3-propanedione)
ligand. We demonstrate this system to be an optically-pumped molecular compound
emittingin infra-red, which can be employed as a precise heat-driving and
detecting unit for low temperatures
|
The sufficiently scattered condition (SSC) is a key condition in the study of
identifiability of various matrix factorization problems, including
nonnegative, minimum-volume, symmetric, simplex-structured, and polytopic
matrix factorizations. The SSC allows one to guarantee that the computed matrix
factorization is unique/identifiable, up to trivial ambiguities. However, this
condition is NP-hard to check in general. In this paper, we show that it can
however be checked in a reasonable amount of time in realistic scenarios, when
the factorization rank is not too large. This is achieved by formulating the
problem as a non-convex quadratic optimization problem over a bounded set. We
use the global non-convex optimization software Gurobi, and showcase the
usefulness of this code on synthetic data sets and on real-world hyperspectral
images.
|
We prove that a "random" free group outer automorphism is an ageometric fully
irreducible outer automorphism whose ideal Whitehead graph is a union of
triangles. In particular, we show that its attracting (and repelling) tree is a
nongeometric $\mathbb R$-tree all of whose branch points are trivalent
|
We present two new methods for linear elasticity with simultaneously yield
stress and displacement approximations of optimal accuracy in both the mesh
size h and polynomial degree p. This is achieved within the recently developed
discontinuous Petrov-Galerkin (DPG) framework. In this framework, both the
stress and the displacement approximations are discontinuous across element
interfaces. We study locking-free convergence properties and the
interrelationships between the two DPG methods.
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In this paper we use a generalization of Oevel's theorem about master
symmetries to relate them with superintegrability and quadratic algebras.
|
SMS messaging is a popular media of communication. Because of its popularity
and privacy, it could be used for many illegal purposes. Additionally, since
they are part of the day to day life, SMSes can be used as evidence for many
legal disputes. Since a cellular phone might be accessible to people close to
the owner, it is important to establish the fact that the sender of the message
is indeed the owner of the phone. For this purpose, the straight forward
solutions seem to be the use of popular stylometric methods. However, in
comparison with the data used for stylometry in the literature, SMSes have
unusual characteristics making it hard or impossible to apply these methods in
a conventional way. Our target is to come up with a method of authorship
detection of SMS messages that could still give a usable accuracy. We argue
that, considering the methods of author attribution, the best method that could
be applied to SMS messages is an n-gram method. To prove our point, we checked
two different methods of distribution comparison with varying number of
training and testing data. We specifically try to compare how well our
algorithms work under less amount of testing data and large number of candidate
authors (which we believe to be the real world scenario) against controlled
tests with less number of authors and selected SMSes with large number of
words. To counter the lack of information in an SMS message, we propose the
method of stacking together few SMSes.
|
Both experiments and direct numerical simulations have been used to
demonstrate that riblets can reduce turbulent drag by as much as $10\%$, but
their systematic design remains an open challenge. In this paper, we develop a
model-based framework to quantify the effect of streamwise-aligned
spanwise-periodic riblets on kinetic energy and skin-friction drag in turbulent
channel flow. We model the effect of riblets as a volume penalization in the
Navier-Stokes equations and use the statistical response of the
eddy-viscosity-enhanced linearized equations to quantify the effect of
background turbulence on the mean velocity and skin-friction drag. For
triangular riblets, our simulation-free approach reliably predicts
drag-reducing trends as well as mechanisms that lead to performance
deterioration for large riblets. We investigate the effect of height and
spacing on drag reduction and demonstrate a correlation between energy
suppression and drag-reduction for appropriately sized riblets. We also analyze
the effect of riblets on drag reduction mechanisms and turbulent flow
structures including very large scale motions. Our results demonstrate the
utility of our approach in capturing the effect of riblets on turbulent flows
using models that are tractable for analysis and optimization.
|
Self-supervised representation learning has achieved impressive results in
recent years, with experiments primarily coming on ImageNet or other similarly
large internet imagery datasets. There has been little to no work with these
methods on other smaller domains, such as satellite, textural, or biological
imagery. We experiment with several popular methods on an unprecedented variety
of domains. We discover, among other findings, that Rotation is by far the most
semantically meaningful task, with much of the performance of Jigsaw and
Instance Discrimination being attributable to the nature of their induced
distribution rather than semantic understanding. Additionally, there are
several areas, such as fine-grain classification, where all tasks underperform.
We quantitatively and qualitatively diagnose the reasons for these failures and
successes via novel experiments studying pretext generalization, random
labelings, and implicit dimensionality. Code and models are available at
https://github.com/BramSW/Extending_SSRL_Across_Domains/.
|
We determine the density of monic integer polynomials of given degree $n>1$
that have squarefree discriminant; in particular, we prove for the first time
that the lower density of such polynomials is positive. Similarly, we prove
that the density of monic integer polynomials $f(x)$, such that $f(x)$ is
irreducible and $\mathbb Z[x]/(f(x))$ is the ring of integers in its fraction
field, is positive, and is in fact given by $\zeta(2)^{-1}$.
It also follows from our methods that there are $\gg X^{1/2+1/n}$ monogenic
number fields of degree $n$ having associated Galois group $S_n$ and absolute
discriminant less than $X$, and we conjecture that the exponent in this lower
bound is optimal.
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Quadratic functions have applications in cryptography. In this paper, we
investigate the modular quadratic equation $$ ax^2+bx+c=0 \quad (mod \,\, 2^n),
$$ and provide a complete analysis of it. More precisely, we determine when
this equation has a solution and in the case that it has a solution, we not
only determine the number of solutions, but also give the set of solutions in
$O(n)$ time. One of the interesting results of our research is that, when this
equation has a solution, then the number of solutions is a power of two.
|
This paper is a study of power series, where the coefficients are binomial
expressions (iterated finite differences). Our results can be used for series
summation, for series transformation, or for asymptotic expansions involving
Stirling numbers of the second kind. In certain cases we obtain asymptotic
expansions involving Bernoulli polynomials, poly-Bernoulli polynomials, or
Euler polynomials. We also discuss connections to Euler series transformations
and other series transformation formulas.
|
We discuss relation between the cluster integrable systems and spin chains in
the context of their correspondence with 5d supersymmetric gauge theories. It
is shown that $\mathfrak{gl}_N$ XXZ-type spin chain on $M$ sites is isomorphic
to a cluster integrable system with $N \times M$ rectangular Newton polygon and
$N \times M$ fundamental domain of a 'fence net' bipartite graph. The Casimir
functions of the Poisson bracket, labeled by the zig-zag paths on the graph,
correspond to the inhomogeneities, on-site Casimirs and twists of the chain,
supplemented by total spin. The symmetricity of cluster formulation implies
natural spectral duality, relating $\mathfrak{gl}_N$-chain on $M$ sites with
the $\mathfrak{gl}_M$-chain on $N$ sites. For these systems we construct
explicitly a subgroup of the cluster mapping class group
$\mathcal{G}_\mathcal{Q}$ and show that it acts by permutations of zig-zags
and, as a consequence, by permutations of twists and inhomogeneities. Finally,
we derive Hirota bilinear equations, describing dynamics of the tau-functions
or A-cluster variables under the action of some generators of
$\mathcal{G}_\mathcal{Q}$.
|
Molecular dynamics (MD) is an important research tool extensively applied in
materials science. Running MD on a graphics processing unit (GPU) is an
attractive new approach for accelerating MD simulations. Currently, GPU
implementations of MD usually run in a one-host-process-one-GPU (OHPOG) scheme.
This scheme may pose a limitation on the system size that an implementation can
handle due to the small device memory relative to the host memory. In this
paper, we present a one-host-process-multiple-GPU (OHPMG) implementation of MD
with embedded-atom-model or semi-empirical tight-binding many-body potentials.
Because more device memory is available in an OHPMG process, the system size
that can be handled is increased to a few million or more atoms. In comparison
with the CPU implementation, in which Newton's third law is applied to improve
the computational efficiency, our OHPMG implementation has achieved a
28.9x~86.0x speedup in double precision, depending on the system size, the
cut-off ranges and the number of GPUs. The implementation can also handle a
group of small boxes in one run by combining the small boxes into a large box.
This approach greatly improves the GPU computing efficiency when a large number
of MD simulations for small boxes are needed for statistical purposes.
|
The recent interest in beta- radionuclides for radio-guided surgery derives
from the feature of the beta radiation to release energy in few millimeters of
tissue. Such feature can be used to locate residual tumors with a probe located
in its immediate vicinity, determining the resection margins with an accuracy
of millimeters. The drawback of this technique is that it does not allow to
identify tumors hidden in more than few mm of tissue. Conversely, the
bremsstrahlung X-rays emitted by the interaction of the beta- radiation with
the nuclei of the tissue are relatively penetrating. To complement the beta-
probes, we have therefore developed a detector based on cadmium telluride, an
X-ray detector with a high quantum efficiency working at room temperature. We
measured the secondary emission of bremsstrahlung photons in a target of
Polymethylmethacrylate (PMMA) with a density similar to living tissue. The
results show that this device allows to detect a 1 ml residual or lymph-node
with an activity of 1 kBq hidden under a layer of 10 mm of PMMA with a 3:1
signal to noise, i.e. with a five sigma discrimination in less than 5 s.
|
We have analyzed an efficient integration of the multi-qubit echo quantum
memory into the quantum computer scheme on the atomic resonant ensembles in
quantum electrodynamics cavity. Here, one atomic ensemble with controllable
inhomogeneous broadening is used for the quantum memory node and other atomic
ensembles characterized by the homogeneous broadening of the resonant line are
used as processing nodes. We have found optimal conditions for efficient
integration of multi-qubit quantum memory modified for this analyzed physical
scheme and we have determined a specified shape of the self temporal modes
providing a perfect reversible transfer of the photon qubits between the
quantum memory node and arbitrary processing nodes. The obtained results open
the way for realization of full-scale solid state quantum computing based on
using the efficient multi-qubit quantum memory.
|
On the affine space containing the space $\mathcal{S}$ of quantum states of
finite-dimensional systems there are contravariant tensor fields by means of
which it is possible to define Hamiltonian and gradient vector fields encoding
relevant geometrical properties of $\mathcal{S}$. Guided by Dirac's analogy
principle, we will use them as inspiration to define contravariant tensor
fields, Hamiltonian and gradient vector fields on the affine space containing
the space of fair probability distributions on a finite sample space and
analyse their geometrical properties.
Most of our considerations will be dealt with for the simple example of a
three-level system.
|
In this talk I review recent progress made in extracting V_{ub} from the cut
electron energy and hadronic mass spectra of inclusive B meson decays utilizing
the data from radiative decays. It is shown that an extraction is possible
without modeling the B meson structure function. I discuss the issues involving
the assumptions of local duality in various extractions. I also comment on the
recent CLEO extraction of V_{ub}.
|
We propose a novel method for continuous-time feature tracking in event
cameras. To this end, we track features by aligning events along an estimated
trajectory in space-time such that the projection on the image plane results in
maximally sharp event patch images. The trajectory is parameterized by $n^{th}$
order B-splines, which are continuous up to $(n-2)^{th}$ derivative. In
contrast to previous work, we optimize the curve parameters in a sliding window
fashion. On a public dataset we experimentally confirm that the proposed
sliding-window B-spline optimization leads to longer and more accurate feature
tracks than in previous work.
|
The Solar Tower Atmospheric Cherenkov Effect Experiment (STACEE) is a new
ground-based atmospheric Cherenkov telescope for gamma-ray astronomy. STACEE
uses the large mirror area of a solar heliostat facility to achieve a low
energy threshold. A prototype experiment which uses 32 heliostat mirrors with a
total mirror area of ~ 1200\unit{m^2} has been constructed. This prototype,
called STACEE-32, was used to search for high energy gamma-ray emission from
the Crab Nebula and Pulsar. Observations taken between November 1998 and
February 1999 yield a strong statistical excess of gamma-like events from the
Crab, with a significance of $+6.75\sigma$ in 43 hours of on-source observing
time. No evidence for pulsed emission from the Crab Pulsar was found, and the
upper limit on the pulsed fraction of the observed excess was < 5.5% at the 90%
confidence level. A subset of the data was used to determine the integral flux
of gamma rays from the Crab. We report an energy threshold of E_{th} = 190 \pm
60\unit{GeV}, and a measured integral flux of I (E > E_{th}) = (2.2 \pm 0.6 \pm
0.2) \times 10^{-10}\unit{photons cm^{-2} s^{-1}}. The observed flux is in
agreement with a continuation to lower energies of the power law spectrum seen
at TeV energies.
|
Geographical information systems are ideal candidates for the application of
parallel programming techniques, mainly because they usually handle large data
sets. To help us deal with complex calculations over such data sets, we
investigated the performance constraints of a classic master-worker parallel
paradigm over a message-passing communication model. To this end, we present a
new approach that employs an external database in order to improve the
calculation/communication overlap, thus reducing the idle times for the worker
processes. The presented approach is implemented as part of a parallel
radio-coverage prediction tool for the GRASS environment. The prediction
calculation employs digital elevation models and land-usage data in order to
analyze the radio coverage of a geographical area. We provide an extended
analysis of the experimental results, which are based on real data from an LTE
network currently deployed in Slovenia. Based on the results of the
experiments, which were performed on a computer cluster, the new approach
exhibits better scalability than the traditional master-worker approach. We
successfully tackled real-world data sets, while greatly reducing the
processing time and saturating the hardware utilization.
|
Let $X$ be a smooth scheme over a finitely generated flat $\mathbb{Z}$-,
$\mathbb{Z}_{(p)}$- or $\mathbb{Z}_p$-algebra $R$. Evaluated at finite
truncation sets $S$, the relative de Rham-Witt complex
$W_S\Omega_{X/R}^{\bullet}$ is a quotient of the de Rham complex
$\Omega^{\bullet}_{W_S(X)/W_S(R)}$, which can be computed affine locally via
explicit, but complicated relations. In this paper we prove that
$W_S\Omega_{X/R}^{\bullet}$ is the torsionless quotient of the usual de Rham
complex $\Omega^{\bullet}_{W_S(X)/W_S(R)}$ on the singular scheme $W_S(X)$.
This result was suggested by comparison with a similar modification of the de
Rham complex in the theory of singular varieties.
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Natural language processing (NLP) is the field that attempts to make human
language accessible to computers, and it relies on applying a mathematical
model to express the meaning of symbolic language. One such model, DisCoCat,
defines how to express both the meaning of individual words as well as their
compositional nature. This model can be naturally implemented on quantum
computers, leading to the field quantum NLP (QNLP). Recent experimental work
used quantum machine learning techniques to map from text to class label using
the expectation value of the quantum encoded sentence. Theoretical work has
been done on computing the similarity of sentences but relies on an unrealized
quantum memory store. The main goal of this thesis is to leverage the DisCoCat
model to design a quantum-based kernel function that can be used by a support
vector machine (SVM) for NLP tasks. Two similarity measures were studied: (i)
the transition amplitude approach and (ii) the SWAP test. A simple NLP meaning
classification task from previous work was used to train the word embeddings
and evaluate the performance of both models. The Python module lambeq and its
related software stack was used for implementation. The explicit model from
previous work was used to train word embeddings and achieved a testing accuracy
of $93.09 \pm 0.01$%. It was shown that both the SVM variants achieved a higher
testing accuracy of $95.72 \pm 0.01$% for approach (i) and $97.14 \pm 0.01$%
for (ii). The SWAP test was then simulated under a noise model defined by the
real quantum device, ibmq_guadalupe. The explicit model achieved an accuracy of
$91.94 \pm 0.01$% while the SWAP test SVM achieved 96.7% on the testing
dataset, suggesting that the kernelized classifiers are resilient to noise.
These are encouraging results and motivate further investigations of our
proposed kernelized QNLP paradigm.
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In this paper, we systematically study the dynamic snap-through behavior of a
pre-deformed elastic ribbon by combining theoretical analysis, discrete
numerical simulations, and experiments. By rotating one of its clamped ends
with controlled angular speed, we observe two snap-through transition paths
among the multiple stable configurations of a ribbon in three-dimensional (3D)
space, which is different from the classical snap-through of a two-dimensional
(2D) bistable beam. Our theoretical model for the static bifurcation analysis
is derived based on the Kirchhoff equations, and dynamical numerical
simulations are conducted using the Discrete Elastic Rods (DER) algorithm. The
planar beam model is also employed for the asymptotic analysis of dynamic
snap-through behaviors. The results show that, since the snap-through processes
of both planar beams and 3D ribbons are governed by the saddle-node
bifurcation, the same scaling law for the delay applies. We further demonstrate
that, in elastic ribbons, by controlling the rotating velocity at the end,
distinct snap-through pathways can be realized by selectively skipping specific
modes, moreover, particular final modes can be strategically achieved. Through
a parametric study using numerical simulations, we construct general phase
diagrams for both mode skipping and selection of snapping ribbons. The work
serves as a benchmark for future investigations on dynamic snap-through of thin
elastic structures and provides guidelines for the novel design of intelligent
mechanical systems.
|
A sensitive optical diffractometry method is developed and utilized for
advanced tomography of laser-induced air plasma formations. Using transverse
diffractometry and Supergaussian plasma distribution modelling we extract the
main parameters of the plasma being the plasma density, width and shape with 20
micrometer spatial resolution throughout the plasma formation. The
experimentally recorded diffraction patterns fitted by the Supergaussian plasma
model are found to capture unprecedentedly delicate traits in the evolution of
the plasma from its effective birth and on. Key features in the spatial
evolution of the plasma such as the 'escape position', the 'turning point' and
the refocusing dynamics of the beam are identified and explored in details. Our
work provides experimental and theoretical access into the highly nonlinear
dynamics of laser-induced air plasma.
|
I present an analysis of the gamma-ray and afterglow energies of the complete
sample of 17 short duration GRBs with prompt X-ray follow-up. I find that 80%
of the bursts exhibit a linear correlation between their gamma-ray fluence and
the afterglow X-ray flux normalized to t=1 d, a proxy for the kinetic energy of
the blast wave ($F_{X,1}~F_{gamma}^1.01). An even tighter correlation is
evident between E_{gamma,iso} and L_{X,1} for the subset of 13 bursts with
measured or constrained redshifts. The remaining 20% of the bursts have values
of F_{X,1}/F_{gamma} that are suppressed by about three orders of magnitude,
likely because of low circumburst densities (Nakar 2007). These results have
several important implications: (i) The X-ray luminosity is generally a robust
proxy for the blast wave kinetic energy, indicating nu_X>nu_c and hence a
circumburst density n>0.05 cm^{-3}; (ii) most short GRBs have a narrow range of
gamma-ray efficiency, with <epsilon_{gamma}>~0.85 and a spread of 0.14 dex; and
(iii) the isotropic-equivalent energies span 10^{48}-10^{52} erg. Furthermore,
I find tentative evidence for jet collimation in the two bursts with the
highest E_{gamma,iso}, perhaps indicative of the same inverse correlation that
leads to a narrow distribution of true energies in long GRBs. I find no clear
evidence for a relation between the overall energy release and host galaxy
type, but a positive correlation with duration may be present, albeit with a
large scatter. Finally, I note that the outlier fraction of 20% is similar to
the proposed fraction of short GRBs from dynamically-formed neutron star
binaries in globular clusters. This scenario may naturally explain the
bimodality of the F_{X,1}/F_{gamma} distribution and the low circumburst
densities without invoking speculative kick velocities of several hundred km/s.
|
A pair of variables that tend to rise and fall either together or in
opposition are said to be monotonically associated. For certain phenomena, this
tendency is causally restricted to a subpopulation, as, for example, an
allergic reaction to an irritant. Previously, Yu et al. (2011) devised a method
of rearranging observations to test paired data to see if such an association
might be present in a subpopulation. However, the computational intensity of
the method limited its application to relatively small samples of data, and the
test itself only judges if association is present in some subpopulation; it
does not clearly identify the subsample that came from this subpopulation,
especially when the whole sample tests positive. The present paper adds a
"top-K" feature (Sampath and Verducci (2013)) based on a multistage ranking
model, that identifies a concise subsample that is likely to contain a high
proportion of observations from the subpopulation in which the association is
supported. Computational improvements incorporated into this top-K tau-path
(TKTP) algorithm now allow the method to be extended to thousands of pairs of
variables measured on sample sizes in the thousands. A description of the new
algorithm along with measures of computational complexity and practical
efficiency help to gauge its potential use in different settings. Simulation
studies catalog its accuracy in various settings, and an example from finance
illustrates its step-by-step use.
|
We study the soldering of two Siegel chiral bosons into one scalar field in a
gravitational background.
|
The motion of faint propagating disturbances (PD) in the solar corona reveals
an intricate structure which must be defined by the magnetic field. Applied to
quiet Sun observations by the Atmospheric Imaging Assembly (AIA)/Solar Dynamics
Observatory (SDO), a novel method reveals a cellular network, with cells of
typical diameters 50\arcsec\ in the cool 304\AA\ channel, and 100\arcsec\ in
the coronal 193\AA\ channel. The 193\AA\ cells can overlie several 304\AA\
cells, although both channels share common source and sink regions. The sources
are points, or narrow corridors, of divergence that occupy the centres of
cells. They are significantly aligned with photospheric network features and
enhanced magnetic elements. This shows that the bright network is important to
the production of PDs, and confirms that the network is host to the source
footpoint of quiet coronal loops. The other footpoint, or the sinks of the PDs,
form the boundaries of the coronal cells. These are not significantly aligned
with the photospheric network - they are generally situated above the dark
internetwork photosphere. They form compact points or corridors, often without
an obvious signature in the underlying photosphere. We argue that these sink
points can either be concentrations of closed field footpoints associated with
minor magnetic elements in the internetwork, or concentrations of
upward-aligned open field. The link between the coronal velocity and magnetic
fields is strengthened by a comparison with a magnetic extrapolation, which
shows several general and specific similarities, thus the velocity maps offer a
valuable additional constraint on models.
|
In a composite model of the weak bosons the excited bosons, in particular the
p-wave bosons, are studied. The state with the lowest mass is identified with
the boson, which has been discovered recently at the "Large Hadron Collider" at
CERN. Specific properties of the excited weak bosons are studied, in particular
their decays into weak bosons and into photons.
|
Short-term load forecasting is a critical element of power systems energy
management systems. In recent years, probabilistic load forecasting (PLF) has
gained increased attention for its ability to provide uncertainty information
that helps to improve the reliability and economics of system operation
performances. This paper proposes a two-stage probabilistic load forecasting
framework by integrating point forecast as a key probabilistic forecasting
feature into PLF. In the first stage, all related features are utilized to
train a point forecast model and also obtain the feature importance. In the
second stage the forecasting model is trained, taking into consideration point
forecast features, as well as selected feature subsets. During the testing
period of the forecast model, the final probabilistic load forecast results are
leveraged to obtain both point forecasting and probabilistic forecasting.
Numerical results obtained from ISO New England demand data demonstrate the
effectiveness of the proposed approach in the hour-ahead load forecasting,
which uses the gradient boosting regression for the point forecasting and
quantile regression neural networks for the probabilistic forecasting.
|
The Feistel Boomerang Connectivity Table (FBCT) was proposed as the feistel
counterpart of the Boomerang Connectivity Table. The entries of the FBCT are
actually related to the second-order zero differential spectrum. Recently,
several results on the second-order zero differential uniformity of some
functions were introduced. However, almost all of them were focused on power
functions, and there are only few results on non-power functions. In this
paper, we investigate the second-order zero differential uniformity of the
swapped inverse functions, which are functions obtained from swapping two
points in the inverse function. We also present the second-order zero
differential spectrum of the swapped inverse functions for certain cases. In
particular, this paper is the first result to characterize classes of non-power
functions with the second-order zero differential uniformity equal to 4, in
even characteristic.
|
Let $R$ be a commutative chain ring. We use a variation of Gr\"obner bases to
study the lattice of ideals of $R[x]$. Let $I$ be a proper ideal of $R[x]$. We
are interested in the following two questions: When is $R[x]/I$ Frobenius? When
is $R[x]/I$ Frobenius and local? We develop algorithms for answering both
questions. When the nilpotency of $\text{rad}\,R$ is small, the algorithms
provide explicit answers to the questions.
|
A real time coding system with lookahead consists of a memoryless source, a
memoryless channel, an encoder, which encodes the source symbols sequentially
with knowledge of future source symbols upto a fixed finite lookahead, d, with
or without feedback of the past channel output symbols and a decoder, which
sequentially constructs the source symbols using the channel output. The
objective is to minimize the expected per-symbol distortion. For a fixed finite
lookahead d>=1 we invoke the theory of controlled markov chains to obtain an
average cost optimality equation (ACOE), the solution of which, denoted by
D(d), is the minimum expected per-symbol distortion. With increasing d, D(d)
bridges the gap between causal encoding, d=0, where symbol by symbol
encoding-decoding is optimal and the infinite lookahead case, d=\infty, where
Shannon Theoretic arguments show that separation is optimal. We extend the
analysis to a system with finite state decoders, with or without noise-free
feedback. For a Bernoulli source and binary symmetric channel, under hamming
loss, we compute the optimal distortion for various source and channel
parameters, and thus obtain computable bounds on D(d). We also identify regions
of source and channel parameters where symbol by symbol encoding-decoding is
suboptimal. Finally, we demonstrate the wide applicability of our approach by
applying it in additional coding scenarios, such as the case where the
sequential decoder can take cost constrained actions affecting the quality or
availability of side information about the source.
|
With the decrease in system inertia, frequency security becomes an issue for
power systems around the world. Energy storage systems (ESS), due to their
excellent ramping capabilities, are considered as a natural choice for the
improvement of frequency response following major contingencies. In this
manuscript, we propose a new strategy for energy storage -- frequency shaping
control -- that allows to completely eliminate the frequency Nadir, one of the
main issue in frequency security, and at the same time tune the rate of change
of frequency (RoCoF) to a desired value. With Nadir eliminated, the frequency
security assessment can be performed via simple algebraic calculations, as
opposed to dynamic simulations for conventional control strategies. Moreover,
our proposed control is also very efficient in terms of the requirements on
storage peak power, requiring up to 40% less power than conventional virtual
inertia approach for the same performance.
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We prove that a (globally) subanalytic p-adic function which is locally
Lipschitz continuous with some constant C is piecewise (globally on each piece)
Lipschitz continuous with possibly some other constant, where the pieces can be
taken subanalytic. We also prove the analogous result for a subanalytic family
of functions depending on p-adic parameters. The statements also hold in a
semi-algebraic set-up and also in finite extensions of the field of p-adic
numbers. These results are p-adic analogues of results of K. Kurdyka over the
real numbers. To encompass the total disconnectedness of p-adic fields, we need
to introduce new methods adapted to the p-adic situation.
|
We study amplified-and-forward (AF)-based two-way relaying (TWR) with
multiple source pairs, which are exchanging information through the relay. Each
source has single antenna and the relay has multi-antenna. The optimal
beamforming matrix structure that achieves maximum
signal-to-interference-plus-noise ratio (SINR) for TWR with multiple source
pairs is derived. We then present two new non-zero-forcing based beamforming
schemes for TWR, which take into consideration the tradeoff between preserving
the desired signals and suppressing inter-pair interference between different
source pairs. Joint grouping and beamforming scheme is proposed to achieve a
better signal-to-interference-plus-noise ratio (SINR) when the total number of
source pairs is large and the signal-to-noise ratio (SNR) at the relay is low.
|
Unified University Inventory System (UUIS), is an inventory system created
for the Imaginary University of Arctica (IUfA) to facilitate its inventory
management, of all the faculties in one system. Team 1 elucidates the functions
of the system and the characteristics of the users who have access to these
functions. It shows the access restrictions to different functionalities of the
system provided to users, who are the staff and students of the University.
Team 1, also, emphasises on the necessary steps required to prevent the
security of the system and its data.
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The concept of nanopublications was first proposed about six years ago, but
it lacked openly available implementations. The library presented here is the
first one that has become an official implementation of the nanopublication
community. Its core features are stable, but it also contains unofficial and
experimental extensions: for publishing to a decentralized server network, for
defining sets of nanopublications with indexes, for informal assertions, and
for digitally signing nanopublications. Most of the features of the library can
also be accessed via an online validator interface.
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We present an adaptation of Stein's method of normal approximation to the
study of both discrete- and continuous-time dynamical systems. We obtain new
correlation-decay conditions on dynamical systems for a multivariate central
limit theorem augmented by a rate of convergence. We then present a scheme for
checking these conditions in actual examples. The principal contribution of our
paper is the method, which yields a convergence rate essentially with the same
amount of work as the central limit theorem, together with a multiplicative
constant that can be computed directly from the assumptions.
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Event-triggered control is often argued to lower the average triggering rate
compared to time-triggered control while still achieving a desired control
goal, e.g., the same performance level. However, this property, often called
consistency, cannot be taken for granted and can be hard to analyze in many
settings. In particular, although numerous decentralized event-triggered
control schemes have been proposed in the past years, their performance
properties with respect to time-triggered control remain mostly unexplored. In
this paper, we therefore examine the performance properties of event-triggered
control (relative to time-triggered control) for a single-integrator consensus
problem with a level-triggering rule. We consider the long-term average
quadratic deviation from consensus as a performance measure. For this setting,
we show that enriching the information the local controllers use improves the
performance of the consensus algorithm but renders a previously consistent
event-triggered control scheme inconsistent. In addition, we do so while
deploying optimal control inputs which we derive for both information cases and
all triggering schemes. With this insight, we can furthermore explain the
relationship between two contrasting consistency results from the literature on
decentralized event-triggered control. We support our theoretical findings with
simulation results.
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Time series forecasting (TSF) is one of the most important tasks in data
science given the fact that accurate time series (TS) predictive models play a
major role across a wide variety of domains including finance, transportation,
health care, and power systems. Real-world utilization of machine learning (ML)
typically involves (pre-)training models on collected, historical data and then
applying them to unseen data points. However, in real-world applications, time
series data streams are usually non-stationary and trained ML models usually,
over time, face the problem of data or concept drift.
To address this issue, models must be periodically retrained or redesigned,
which takes significant human and computational resources. Additionally,
historical data may not even exist to re-train or re-design model with. As a
result, it is highly desirable that models are designed and trained in an
online fashion. This work presents the Online NeuroEvolution-based Neural
Architecture Search (ONE-NAS) algorithm, which is a novel neural architecture
search method capable of automatically designing and dynamically training
recurrent neural networks (RNNs) for online forecasting tasks. Without any
pre-training, ONE-NAS utilizes populations of RNNs that are continuously
updated with new network structures and weights in response to new multivariate
input data. ONE-NAS is tested on real-world, large-scale multivariate wind
turbine data as well as the univariate Dow Jones Industrial Average (DJIA)
dataset. Results demonstrate that ONE-NAS outperforms traditional statistical
time series forecasting methods, including online linear regression, fixed long
short-term memory (LSTM) and gated recurrent unit (GRU) models trained online,
as well as state-of-the-art, online ARIMA strategies.
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Under what circumstances might every extension of a combinatorial structure
contain more copies of another one than the original did? This property, which
we call prolificity, holds universally in some cases (e.g., finite linear
orders) and only trivially in others (e.g., permutations). Integer
compositions, or equivalently layered permutations, provide a middle ground. In
that setting, there are prolific compositions for a given pattern if and only
if that pattern begins and ends with 1. For each pattern, there is an easily
constructed automaton that recognises prolific compositions for that pattern.
Some instances where there is a unique minimal prolific composition for a
pattern are classified.
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The sigma-convergence concept has been up to now used to derive macroscopic
models in full space dimensions. In this work, we generalize it to thin
heterogeneous domains given rise to phenomena in lower space dimensions. More
precisely, we provide a new approach of the sigma-convergence method that is
suitable for the study of phenomena occurring in thin heterogeneous media. This
is made through a systematic study of the sigma-convergence method for thin
heterogeneous domains. Assuming that the thin heterogeneous layer is made of
microstructures that are distributed inside in a deterministic way including as
special cases the periodic and the almost periodic distributions, we make use
of the concept of algebras with mean value to state and prove the main
compactness results. As an illustration, we upscale a Darcy-Lapwood-Brinkmann
micro-model for thin flow. We prove that, according to the magnitude of the
permeability of the porous domain, we obtain as effective models, the Darcy law
in lower dimensions. The effective models are derived through the solvability
of either the local Darcy-Brinkmann problems or the local Hele-Shaw problems.
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In the context of a linear model with a sparse coefficient vector,
exponential weights methods have been shown to be achieve oracle inequalities
for prediction. We show that such methods also succeed at variable selection
and estimation under the necessary identifiability condition on the design
matrix, instead of much stronger assumptions required by other methods such as
the Lasso or the Dantzig Selector. The same analysis yields consistency results
for Bayesian methods and BIC-type variable selection under similar conditions.
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The KLOE detector at DAFNE has collected about 30 pb-1 by the end of year
2000,' allowing, among other things, accurate measurements on several decay
channels of the K0S meson.
With data acquired in the year 2000 run we have measured the ratio of the
branching ratios of the K0S to two charged and neutral pions to 1.5 percent
accuracy.
The branching ratio of the semileptonic decay of the K0S is also measured to
5 percent accuracy, the best measurement of this BR to date.
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Training at the edge utilizes continuously evolving data generated at
different locations. Privacy concerns prohibit the co-location of this
spatially as well as temporally distributed data, deeming it crucial to design
training algorithms that enable efficient continual learning over decentralized
private data. Decentralized learning allows serverless training with spatially
distributed data. A fundamental barrier in such distributed learning is the
high bandwidth cost of communicating model updates between agents. Moreover,
existing works under this training paradigm are not inherently suitable for
learning a temporal sequence of tasks while retaining the previously acquired
knowledge. In this work, we propose CoDeC, a novel communication-efficient
decentralized continual learning algorithm which addresses these challenges. We
mitigate catastrophic forgetting while learning a task sequence in a
decentralized learning setup by combining orthogonal gradient projection with
gossip averaging across decentralized agents. Further, CoDeC includes a novel
lossless communication compression scheme based on the gradient subspaces. We
express layer-wise gradients as a linear combination of the basis vectors of
these gradient subspaces and communicate the associated coefficients. We
theoretically analyze the convergence rate for our algorithm and demonstrate
through an extensive set of experiments that CoDeC successfully learns
distributed continual tasks with minimal forgetting. The proposed compression
scheme results in up to 4.8x reduction in communication costs with
iso-performance as the full communication baseline.
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We outline the evaluation of the cosmological constant in the framework of
the standard field-theoretical treatment of vacuum energy and discuss the
relation between the vacuum energy problem and the gauge-group spontaneous
symmetry breaking. We suggest possible extensions of the 't Hooft-Nobbenhuis
symmetry, in particular, its complexification till duality symmetry and discuss
the compatible implementation on gravity. We propose to use the discrete
time-reflection transform to formulate a framework in which one can eliminate
the huge contributions of vacuum energy into the effective cosmological
constant and suggest that the breaking of time--reflection symmetry could be
responsible for a small observable value of this constant.
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In this investigation of character tables of finite groups we study basic
sets and associated representation theoretic data for complementary sets of
conjugacy classes. For the symmetric groups we find unexpected properties of
characters on restricted sets of conjugacy classes, like beautiful
combinatorial determinant formulae for submatrices of the character table and
Cartan matrices with respect to basic sets; we observe that similar phenomena
occur for the transition matrices between power sum symmetric functions to
bounded partitions and the $k$-Schur functions introduced by Lapointe and
Morse. Arithmetic properties of the numbers occurring in this context are
studied via generating functions.
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We present ab initio two-dimensional extended Hubbard-type multiband models
for EtMe_3Sb[Pd(dmit)_2]_2 and \kappa-(BEDT-TTF)_2Cu(NCS)_2, after a
downfolding scheme based on the constrained random phase approximation (cRPA)
and maximally-localized Wannier orbitals, together with the dimensional
downfolding. In the Pd(dmit)_2 salt, the antibonding state of the highest
occupied molecular orbital (HOMO) and the bonding/antibonding states of the
lowest unoccupied molecular orbital (LUMO) are considered as the orbital
degrees of freedom, while, in the \kappa-BEDT-TTF salt, the
HOMO-antibonding/bonding states are considered. Accordingly, a three-band model
for the Pd(dmit)_2 salt and a two-band model for the \kappa-(BEDT-TTF) salt are
derived. We derive single band models for the HOMO-antibonding state for both
of the compounds as well.
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The number of computers, tablets and smartphones is increasing rapidly, which
entails the ownership and use of multiple devices to perform online tasks. As
people move across devices to complete these tasks, their identities becomes
fragmented. Understanding the usage and transition between those devices is
essential to develop efficient applications in a multi-device world. In this
paper we present a solution to deal with the cross-device identification of
users based on semi-supervised machine learning methods to identify which
cookies belong to an individual using a device. The method proposed in this
paper scored third in the ICDM 2015 Drawbridge Cross-Device Connections
challenge proving its good performance.
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In directed graphs, we investigate the problems of finding: 1) a minimum
feedback vertex set (also called the Feedback Vertex Set problem, or MFVS), 2)
a feedback vertex set inducing an acyclic graph (also called the Vertex
2-Coloring without Monochromatic Cycles problem, or Acyclic FVS) and 3) a
minimum feedback vertex set inducing an acyclic graph (Acyclic MFVS).
We show that these problems are strongly related to (variants of) Monotone
3-SAT and Monotone NAE 3-SAT, where monotone means that all literals are in
positive form. As a consequence, we deduce several NP-completeness results on
restricted versions of these problems. In particular, we define the 2-Choice
version of an optimization problem to be its restriction where the optimum
value is known to be either D or D+1 for some integer D, and the problem is
reduced to decide which of D or D+1 is the optimum value. We show that the
2-Choice versions of MFVS, Acyclic MFVS, Min Ones Monotone 3-SAT and Min Ones
Monotone NAE 3-SAT are NP-complete. The two latter problems are the variants of
Monotone 3-SAT and respectively Monotone NAE 3-SAT requiring that the truth
assignment minimize the number of variables set to true.
Finally, we propose two classes of directed graphs for which Acyclic FVS is
polynomially solvable, namely flow reducible graphs (for which MFVS is already
known to be polynomially solvable) and C1P-digraphs (defined by an adjacency
matrix with the Consecutive Ones Property).
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Volcano plot displays unstandardized signal (e.g. log-fold-change) against
noise-adjusted/standardized signal (e.g. t-statistic or -log10(p-value) from
the t test). We review the basic and an interactive use of the volcano plot,
and its crucial role in understanding the regularized t-statistic. The joint
filtering gene selection criterion based on regularized statistics has a curved
discriminant line in the volcano plot, as compared to the two perpendicular
lines for the "double filtering" criterion. This review attempts to provide an
unifying framework for discussions on alternative measures of differential
expression, improved methods for estimating variance, and visual display of a
microarray analysis result. We also discuss the possibility to apply volcano
plots to other fields beyond microarray.
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This paper considers a hierarchical caching system where a server connects
with multiple mirror sites, each connecting with a distinct set of users, and
both the mirror sites and users are equipped with caching memories. Although
there already exist works studying this setup and proposing coded caching
scheme to reduce transmission loads, two main problems are remained to address:
1) the optimal communication load under the uncoded placement for the first
hop, denoted by $R_1$, is still unknown. 2) the previous schemes are based on
Maddah-Ali and Niesen's data placement and delivery, which requires high
subpacketization level. How to achieve the well tradeoff between transmission
loads and subpacketization level for the hierarchical caching system is
unclear. In this paper, we aim to address these two problems. We first propose
a new combination structure named hierarchical placement delivery array (HPDA),
which characterizes the data placement and delivery for any hierarchical
caching system. Then we construct two classes of HPDAs, where the first class
leads to a scheme achieving the optimal $R_1$ for some cases, and the second
class requires a smaller subpacketization level at the cost of slightly
increasing transmission loads.
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The post-recombination streaming of baryons through dark matter keeps baryons
out of low mass (<10^6 solar masses) halos coherently on scales of a few
comoving Mpc. It has been argued that this will have a large impact on the
21-cm signal before and after reionization, as it raises the minimum mass
required to form ionizing sources. Using a semi-numerical code, we show that
the impact of the baryon streaming effect on the 21-cm signal during
reionization (redshifts z approximately 7-20) depends strongly on the cooling
scenario assumed for star formation, and the corresponding virial temperature
or mass at which stars form. For the canonical case of atomic hydrogen cooling
at 10^4 Kelvin, the minimum mass for star formation is well above the mass of
halos that are affected by the baryon streaming and there are no major changes
to existing predictions. For the case of molecular hydrogen cooling, we find
that reionization is delayed by a change in redshift of approximately 2 and
that more relative power is found in large modes at a given ionization
fraction. However, the delay in reionization is degenerate with astrophysical
assumptions, such as the production rate of UV photons by stars.
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Particle hopping is a common feature in heterogeneous media. We explore such
motion by using the widely applicable formalism of the continuous time random
walk and focus on the statistics of rare events. Numerous experiments have
shown that the decay of the positional probability density function P (X, t),
describing the statistics of rare events, exhibits universal exponential decay.
We show that such universality ceases to exist once the threshold of
exponential distribution of particle hops is crossed. While the mean hop is not
diverging and can attain a finite value; the transition itself is critical. The
exponential universality of rare events arises due to the contribution of all
the different states occupied during the process. Once the reported threshold
is crossed, a single large event determines the statistics. In this realm, the
big jump principle replaces the large deviation principle, and the spatial part
of the decay is unaffected by the temporal properties of rare events.
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Freezing of water is arguably one of the most common phase transitions on
Earth and almost always happens heterogeneously. Despite its importance, we
lack a fundamental understanding of what makes substrates efficient ice
nucleators. Here we address this by computing the ice nucleation (IN) ability
of numerous model hydroxylated substrates with diverse surface hydroxyl (OH)
group arrangements. Overall, for the substrates considered, we find that
neither the symmetry of the OH patterns nor the similarity between a substrate
and ice correlate well with the IN ability. Instead, we find that the OH
density and the substrate-water interaction strength are useful descriptors of
a material's IN ability. This insight allows the rationalization of ice
nucleation ability across a wide range of materials, and can aid the search and
design of novel potent ice nucleators in the future.
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We give a self-contained introduction to the theory of secondary polytopes
and geometric bistellar flips in triangulations of polytopes and point sets, as
well as a review of some of the known results and connections to algebraic
geometry, topological combinatorics, and other areas.
As a new result, we announce the construction of a point set in general
position with a disconnected space of triangulations. This shows, for the first
time, that the poset of strict polyhedral subdivisions of a point set is not
always connected.
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We study the product formula $(fg)(A) = f(A)g(A)$ in the framework of
(unbounded) functional calculus of sectorial operators $A$. We give an abstract
result, and, as corollaries, we obtain new product formulas for the holomorphic
functional calculus, an extended Stieltjes functional calculus and an extended
Hille-Phillips functional calculus. Our results generalise previous work of
Hirsch, Martinez and Sanz, and Schilling.
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How producers of public goods persist in microbial communities is a major
question in evolutionary biology. Cooperation is evolutionarily unstable, since
cheating strains can reproduce quicker and take over. Spatial structure has
been shown to be a robust mechanism for the evolution of cooperation. Here we
study how spatial assortment might emerge from native dynamics and show that
fluid flow shear promotes cooperative behavior. Social structures arise
naturally from our advection-diffusion-reaction model as self-reproducing
Turing patterns. We computationally study the effects of fluid advection on
these patterns as a mechanism to enable or enhance social behavior. Our central
finding is that flow shear enables and promotes social behavior in microbes by
increasing the group fragmentation rate and thereby limiting the spread of
cheating strains. Regions of the flow domain with higher shear admit high
cooperativity and large population density, whereas low shear regions are
devoid of life due to opportunistic mutations.
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A class of solutions of the gravitational field equations describing vacuum
spacetimes outside rotating cylindrical sources is presented. A subclass of
these solutions corresponds to the exterior gravitational fields of rotating
cylindrical systems that emit gravitational radiation. The properties of these
rotating gravitational wave spacetimes are investigated. In particular, we
discuss the energy density of these waves using the gravitational stress-energy
tensor.
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Automatic Speaker Verification (ASV) is the process of identifying a person
based on the voice presented to a system. Different synthetic approaches allow
spoofing to deceive ASV systems (ASVs), whether using techniques to imitate a
voice or recunstruct the features. Attackers try to beat up the ASVs using four
general techniques; impersonation, speech synthesis, voice conversion, and
replay. The last technique is considered as a common and high potential tool
for spoofing purposes since replay attacks are more accessible and require no
technical knowledge from adversaries. In this study, we introduce a novel
replay spoofing countermeasure for ASVs. Accordingly, we used the Constant Q
Cepstral Coefficient (CQCC) features fed into an autoencoder to attain more
informative features and to consider the noise information of spoofed
utterances for discrimination purpose. Finally, different configurations of the
Siamese network were used for the first time in this context for
classification. The experiments performed on ASVspoof challenge 2019 dataset
using Equal Error Rate (EER) and Tandem Detection Cost Function (t-DCF) as
evaluation metrics show that the proposed system improved the results over the
baseline by 10.73% and 0.2344 in terms of EER and t-DCF, respectively.
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In comparison to conventional traffic designs, shared spaces promote a more
pleasant urban environment with slower motorized movement, smoother traffic,
and less congestion. In the foreseeable future, shared spaces will be populated
with a mixture of autonomous vehicles (AVs) and vulnerable road users (VRUs)
like pedestrians and cyclists. However, a driver-less AV lacks a way to
communicate with the VRUs when they have to reach an agreement of a
negotiation, which brings new challenges to the safety and smoothness of the
traffic. To find a feasible solution to integrating AVs seamlessly into
shared-space traffic, we first identified the possible issues that the
shared-space designs have not considered for the role of AVs. Then an online
questionnaire was used to ask participants about how they would like a driver
of the manually driving vehicle to communicate with VRUs in a shared space. We
found that when the driver wanted to give some suggestions to the VRUs in a
negotiation, participants thought that the communications via the driver's body
behaviors were necessary. Besides, when the driver conveyed information about
her/his intentions and cautions to the VRUs, participants selected different
communication methods with respect to their transport modes (as a driver,
pedestrian, or cyclist). These results suggest that novel eHMIs might be useful
for AV-VRU communication when the original drivers are not present. Hence, a
potential eHMI design concept was proposed for different VRUs to meet their
various expectations. In the end, we further discussed the effects of the eHMIs
on improving the sociality in shared spaces and the autonomous driving systems.
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In this paper, we study the theory of geodesics with respect to the
Tanaka-Webster connection in a pseudo-Hermitian manifold, aiming to generalize
some comparison results in Riemannian geometry to the case of pseudo-Hermitian
geometry. Some Hopf-Rinow type, Cartan-Hadamard type and Bonnet-Myers type
results are established.
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Extremely large aperture arrays (ELAAs) and reconfigurable intelligent
surfaces (RISs) are candidate enablers to realize connectivity goals for the
sixth-generation (6G) wireless networks. For instance, ELAAs can provide
orders-of-magnitude higher area throughput compared to what massive
multiple-input multiple-output (MIMO) can deliver through spatial multiplexing,
while RISs can improve the propagation conditions over wireless channels but a
passively reflecting RIS must be large to be effective. Active RIS with
amplifiers can deal with this issue. In this paper, we study the distortion
created by nonlinear amplifiers in both ELAAs and active RIS. We analytically
obtain the angular directions and depth of the nonlinear distortion in both
near- and far-field channels. The results are demonstrated numerically and we
conclude that non-linearities can both create in-band and out-of-band
distortion that is beamformed in entirely new directions and distances from the
transmitter.
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Production-destruction systems (PDS) of ordinary differential equations
(ODEs) are used to describe physical and biological reactions in nature. The
considered quantities are subject to natural laws. Therefore, they preserve
positivity and conservation of mass at the analytical level. In order to
maintain these properties at the discrete level, the so-called modified
Patankar-Runge-Kutta (MPRK) schemes are often used in this context. However, up
to our knowledge, the family of MPRK has been only developed up to third order
of accuracy. In this work, we propose a method to solve PDS problems, but using
the Deferred Correction (DeC) process as a time integration method. Applying
the modified Patankar approach to the DeC scheme results in provable
conservative and positivity preserving methods. Furthermore, we demonstrate
that these modified Patankar DeC schemes can be constructed up to arbitrarily
high order. Finally, we validate our theoretical analysis through numerical
simulations.
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For families of continuous plurisubharmonic functions we show that, in a
local sense, separately bounded above implies bounded above.
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Subsets and Splits