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An algorithm is described that can generate random variants of a time series
or image while preserving the probability distribution of original values and
the pointwise Holder regularity. Thus, it preserves the multifractal properties
of the data. Our algorithm is similar in principle to well-known algorithms
based on the preservation of the Fourier amplitude spectrum and original values
of a time series. However, it is underpinned by a dual-tree complex wavelet
transform rather than a Fourier transform. Our method, which we term the
Iterated Amplitude Adjusted Wavelet Transform (IAAWT) method can be used to
generate bootstrapped versions of multifractal data and, because it preserves
the pointwise Holder regularity but not the local Holder regularity, it can be
used to test hypotheses concerning the presence of oscillating singularities in
a time series, an important feature of turbulence and econophysics data.
Because the locations of the data values are randomized with respect to the
multifractal structure, hypotheses about their mutual coupling can be tested,
which is important for the velocity-intermittency structure of turbulence and
self-regulating processes.
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A pretorsion theory for the category of all categories is presented. The
associated prekernels and precokernels are calculated for every functor.
|
This paper considers the reconstruction of a sparse coefficient vector
{\theta} for a rational transfer function, under a pair of FIR and
Takenaka-Malmquist (TM) bases and from a limited number of linear
frequency-domain measurements. We propose to concatenate a limited number of
FIR and TM basis functions in the representation of the transfer function, and
prove the uniqueness of the sparse representation defined in the infinite
dimensional function space with pairs of FIR and TM bases. The sufficient
condition is given for replacing the l_0 optimal solution by the l_1 optimal
solution using FIR and TM bases with random samples on the upper unit circle,
as the foundation of reconstruction. The simulations verify that l_1
minimization can reconstruct the coefficient vector {\theta} with high
probability. It is shown that the concatenated FIR and TM bases give a much
sparser representation, with much lower reconstruction order than using only
FIR basis functions and less dependency on the knowledge of the true system
poles than using only TM basis functions.
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The paper is concerned with the completeness property of root functions of
the Dirac operator with summable complexvalued potential and non-regular
boundary conditions. We also obtain explicit form for the fundamental solution
system of the considered operator.
|
We investigate the electronic structure of Ca1-xSrxVO3 using careful
state-of-the-art experiments and calculations. Photoemission spectra using
synchrotron radiation reveal a hitherto unnoticed polarization dependence of
the photoemission matrix elements for the surface component leading to a
substantial suppression of its intensity. Bulk spectra extracted with the help
of experimentally determined electron escape depth and estimated suppression of
surface contributions resolve outstanding puzzles concerning the electronic
structure in Ca1-xSrxVO3.
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We present observational evidences that dust in the circumnuclear region of
AGNs has different properties than in the Galactic diffuse interstellar medium.
By comparing the reddening of optical and infrared broad lines and the X-ray
absorbing column density we find that the E(B-V)/N_H ratio is nearly always
lower than Galactic by a factor ranging from ~3 up to ~100. Other observational
results indicate that the Av/N_H ratio is significantly lower than Galactic in
various classes of AGNs including intermediate type 1.8-1.9 Seyferts, hard
X-ray selected and radio selected quasars, broad absorption line QSOs and grism
selected QSOs. The lack of prominent absorption features at 9.7um (silicates)
and at 2175A (carbon dip) in the spectra of Seyfert 2s and of reddened Seyfert
1s, respectively, add further evidence for dust in the circumnuclear region of
AGNs being different from Galactic.
These observational results indicate that the dust composition in the
circumnuclear region of AGNs could be dominated by large grains, which make the
extinction curve flatter, featureless and are responsible for the reduction of
the E(B-V)/N_H and Av/N_H ratios.
Regardless of the physical origin of these phenomena, the reduced dust
absorption with respect to what expected from the gaseous column density should
warn about a mismatch between the optical and the X-ray classification of the
active galactic nuclei in terms of their obscuration.
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Exploring the nucleon's sea quark and gluon structure is a prime objective of
a future electron-ion collider (EIC). Many of the key questions require
accurate differential semi-inclusive (spin/flavor decomposition, orbital
motion) and exclusive (spatial distributions of quarks/gluons) DIS measurements
in the region 0.01 < x < 0.3 and Q^2 ~ few 10 GeV^2. Such measurements could
ideally be performed with a high-luminosity collider of moderate CM energy, s ~
10^3 GeV^2, and relatively symmetric configuration, e.g. E_e/E_p = 5/30-60 GeV.
Specific examples are presented, showing the advantages of this setup
(angular/energy distribution of final-state particles, large-x coverage)
compared to typical high-energy colliders.
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The need to characterize ices coating dust grains in dense interstellar
clouds arises from the importance of ice morphology in facilitating the
diffusion and storage of radicals and reaction products in ices, a well-known
place for the formation of complex molecules. Yet, there is considerable
uncertainty about the structure of ISM ices, their ability to store volatiles
and under what conditions. We measured the infrared absorption spectra of CO on
the pore surface of porous amorphous solid water (ASW), and quantified the
effective pore surface area of ASW. Additionally, we present results obtained
from a Monte Carlo model of ASW in which the morphology of the ice is directly
visualized and quantified. We found that 200 ML of ASW annealed to 20 K has a
total pore surface area that is equivalent to 46 ML. This surface area
decreases linearly with temperature to about 120 K. We also found that (1)
dangling OH bonds only exist on the surface of pores; (2) almost all of the
pores in the ASW are connected to the vacuum--ice interface, and are accessible
for adsorption of volatiles from the gas phase; there are few closed cavities
inside ASW at least up to a thickness of 200 ML; (3) the total pore surface
area is proportional to the total 3-coordinated water molecules in the ASW in
the temperature range 60--120 K. We also discuss the implications on the
structure of ASW and surface reactions in the ice mantle in dense clouds.
|
The flux of photons with energies above 1 TeV from the direction of the
centre and a cloud in the western part of the nearby southern supernova remnant
(SNR) RX J1713.7-3946 is calculated in the ``hadronic scenario'' that aims to
explain the intense VHE radiation from this remnant with the decay of \pi_0
pions produced in nuclear collisions. The expected flux from its centre is
found to fall short by about factor 40 from the one observed by the HESS
collaboration. This discrepancy presents a serious obstacle to the ``hadronic
scenario''. The theoretically expected flux from the molecular cloud exceeds
the one observed by HESS by at least a factor 3. While the size of this
discrepancy might still seem acceptable in the face of various theoretical
uncertainties, the result strongly suggests a strict spatial correlation of the
cloud with an excess of TeV \gamma radiation. The observational lack of such
correlations in the remnant reported by HESS is another counter argument
against the hadronic scenario. In combination these arguments cannot be refuted
by choosing certain parameters for the total energy or acceleration efficiency
of the SNR.
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We describe a simple experimental method to detect electron paramagnetic
resonance (EPR) in polycrystalline 2,2-diphenyl-1-picrylhydrazyl (DPPH) sample,
the standard g-marker for EPR spectroscopy, without using a cavity resonator or
a prefabricated waveguide. It is shown that microwave(MW) current injected into
a layer of silver paint coated on an insulating DPPH sample is able to excite
the paramagnetic resonance in DPPH. As the applied dc magnetic field H is
swept, the high-frequency resistance of the Ag-paint layer, measured at room
temperature with a single port impedance analyzer in the MW frequency range 1
to 2.5 GHz, exhibits a sharp peak at a critical value of the dc field (H =
Hres) while the reactance exhibits a dispersion-like behavior around the same
field value for a given frequency. Hres increases linearly with the frequency
of MW current. We interpret the observed features in the impedance to EPR in
DPPH driven by the Oersted magnetic field arising from the MW current in the
Ag-paint layer. We also confirm the occurrence of EPR in DPPH independently
using a coplanar waveguide-based broadband technique. This technique has the
potential to investigate other EPR active inorganic and organic compounds.
|
This paper develops a control approach with correctness guarantees for the
simultaneous operation of lane keeping and adaptive cruise control. The safety
specifications for these driver assistance modules are expressed in terms of
set invariance. Control barrier functions are used to design a family of
control solutions that guarantee the forward invariance of a set, which implies
satisfaction of the safety specifications. The control barrier functions are
synthesized through a combination of sum-of-squares program and physics-based
modeling and optimization. A real-time quadratic program is posed to combine
the control barrier functions with the performance-based controllers, which can
be either expressed as control Lyapunov function conditions or as black-box
legacy controllers. In both cases, the resulting feedback control guarantees
the safety of the composed driver assistance modules in a formally correct
manner. Importantly, the quadratic program admits a closed-form solution that
can be easily implemented. The effectiveness of the control approach is
demonstrated by simulations in the industry-standard vehicle simulator Carsim.
|
We develop the technique of reduced word manipulation to give a range of
results concerning reduced words and permutations more generally. We prove a
broad connection between pattern containment and reduced words, which
specializes to our previous work for vexillary permutations. We also analyze
general tilings of Elnitsky's polygon, and demonstrate that these are closely
related to the patterns in a permutation. Building on previous work for
commutation classes, we show that reduced word enumeration is monotonically
increasing with respect to pattern containment. Finally, we give several
applications of this work. We show that a permutation and a pattern have
equally many reduced words if and only if they have the same length
(equivalently, the same number of 21-patterns), and that they have equally many
commutation classes if and only if they have the same number of 321-patterns.
We also apply our techniques to enumeration problems of pattern avoidance, and
give a bijection between 132-avoiding permutations of a given length and
partitions of that same size, as well as refinements of this data and a
connection to the Catalan numbers.
|
We analyze the low-energy e-N2 collisions within the framework of the
Modified-Effective Range Theory (MERT) for the long-range potentials, developed
by O'Malley, Spruch and Rosenberg [Journal of Math. Phys. 2, 491 (1961)]. In
comparison to the traditional MERT we do not expand the total cross-section in
the series of the incident momentum \hbar k, but instead we apply the exact
analytical solutions of the Schroedinger equation for the long-range
polarization potential, as proposed in the original formulation of O'Malley et
al. This extends the applicability of MERT up to few eV regime, as we confirm
using some simplified model potential of the electron-molecule interaction. The
parameters of the effective-range expansion (i.e. the scattering length and the
effective range) are determined from experimental, integral elastic cross
sections in the 0.1 - 1.0 eV energy range by fitting procedure. Surprisingly,
our treatment predicts a shape resonance that appears slightly higher than
experimentally well known resonance in the total cross section. Agreement with
the experimentally observed shape-resonance can be improved by assuming the
position of the resonance in a given partial wave. Influence of the quadrupole
potential on resonances is also discussed: we show that it can be disregarded
for N2. In conclusion, the modified-effective range formalism treating the
long-range part of the potential in an exact way, reproduces well both the very
low-energy behavior of the integral cross section as well as the presence of
resonances in the few eV range.
|
On social media platforms, hateful and offensive language negatively impact
the mental well-being of users and the participation of people from diverse
backgrounds. Automatic methods to detect offensive language have largely relied
on datasets with categorical labels. However, comments can vary in their degree
of offensiveness. We create the first dataset of English language Reddit
comments that has fine-grained, real-valued scores between -1 (maximally
supportive) and 1 (maximally offensive). The dataset was annotated using
Best--Worst Scaling, a form of comparative annotation that has been shown to
alleviate known biases of using rating scales. We show that the method produces
highly reliable offensiveness scores. Finally, we evaluate the ability of
widely-used neural models to predict offensiveness scores on this new dataset.
|
The paper establishes a relationship between finite separable extensions and
norm groups of strictly quasilocal fields with Henselian discrete valuations,
which yields a generally nonabelian one-dimensional local class field theory.
|
We review our understanding of the prototype ``Propeller'' system AE Aqr and
we examine its flaring behaviour in detail. The flares are thought to arise
from collisions between high density regions in the material expelled from the
system after interaction with the rapidly rotating magnetosphere of the white
dwarf. We show calculations of the time-dependent emergent optical spectra from
the resulting hot, expanding ball of gas and derive values for the mass,
lengthscale and temperature of the material involved. We see that the fits
suggest that the secondary star in this system has reduced metal abundances and
that, counter-intuitively, the evolution of the fireballs is best modelled as
isothermal.
|
Bots are automated social media users that can be used to amplify
(mis)information and sow harmful discourse. In order to effectively influence
users, bots can be generated to reproduce human user behavior. Indeed, people
tend to trust information coming from users with profiles that fit roles they
expect to exist, such as users with gender role stereotypes. In this work, we
examine differences in the types of identities in profiles of human and bot
accounts with a focus on combinations of identities that represent gender role
stereotypes. We find that some types of identities differentiate between human
and bot profiles, confirming this approach can be a useful in distinguishing
between human and bot accounts on social media. However, contrary to our
expectations, we reveal that gender bias is expressed more in human accounts
than bots overall. Despite having less gender bias overall, we provide examples
of identities with strong associations with gender identities in bot profiles,
such as those related to technology, finance, sports, and horoscopes. Finally,
we discuss implications for designing constructive social media bot detection
training materials.
|
Recent experimental developments towards obtaining a very precise value of
the third neutrino mixing angle, $\theta_{13}$, are summarized. Various
implications of the measured value of this angle are briefly discussed.
|
We characterize pairs of bounded Reinhardt domains in $\CC^2$ between which
there exists a proper holomorphic map and find all proper maps that are not
elementary algebraic.
|
We study general Delaunay-graphs, which are natural generalizations of
Delaunay triangulations to arbitrary families, in particular to pseudo-disks.
We prove that for any finite pseudo-disk family and point set, there is a plane
drawing of their Delaunay-graph such that every edge lies inside every
pseudo-disk that contains its endpoints.
|
This paper presents an integrated perception and control approach to
accomplish safe autonomous navigation in unknown environments. This is achieved
by numerical optimization with constraint learning for instantaneous local
control barrier functions (IL-CBFs) and goal-driven control Lyapunov functions
(GD-CLFs). In particular, the constraints reflecting safety and task
requirements are first online learned from perceptual signals, wherein IL-CBFs
are learned to characterize potential collisions, and GD-CLFs are constructed
to reflect incrementally discovered subgoals. Then, the learned IL-CBFs are
united with GD-CLFs in the context of a quadratic programming optimization,
whose feasibility is improved by enlarging the shared control space. Numerical
simulations are conducted to reveal the effectiveness of our proposed safe
feedback control strategy that could drive the mobile robot to safely reach the
destination incrementally in an uncertain environment.
|
We consider a quantum emitter ("atom") radiating in a one-dimensional (1D)
photonic waveguide in the presence of a single mirror, resulting in a delay
differential equation for the atomic amplitude. We carry out a systematic
analysis of the non-Markovian (NM) character of the atomic dynamics in terms of
refined, recently developed notions of quantum non-Markovianity such as
indivisibility and information back-flow. NM effects are quantified as a
function of the round-trip time and phase shift associated with the atom-mirror
optical path. We find, in particular, that unless an atom-photon bound state is
formed a finite time delay is always required in order for NM effects to be
exhibited. This identifies a finite threshold in the parameter space, which
separates the Markovian and non-Markovian regimes.
|
Traditional cosmological inference using Type Ia supernovae (SNeIa) have used
stretch- and color-corrected fits of SN Ia light curves and assumed a resulting
fiducial mean and symmetric intrinsic dispersion for the resulting relative
luminosity. As systematics become the main contributors to the error budget, it
has become imperative to expand supernova cosmology analyses to include a more
general likelihood to model systematics to remove biases with losses in
precision. To illustrate an example likelihood analysis, we use a simple model
of two populations with a relative luminosity shift, independent intrinsic
dispersions, and linear redshift evolution of the relative fraction of each
population. Treating observationally viable two-population mock data using a
one-population model results in an inferred dark energy equation of state
parameter $w$ that is biased by roughly 2 times its statistical error for a
sample of N $ \gtrsim$ 2500 SNeIa. Modeling the two-population data with a
two-population model removes this bias at a cost of an approximately $\sim20\%$
increase in the statistical constraint on $w$. These significant biases can be
realized even if the support for two underlying SNeIa populations, in the form
of model selection criteria, is inconclusive. With the current
observationally-estimated difference in the two proposed populations, a sample
of N $ \gtrsim$ 10,000 SNeIa is necessary to yield conclusive evidence of two
populations.
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In this paper, we consider the problem of incentive mechanism design for
smart-phone crowd-sourcing. Each user participating in crowd-sourcing submits a
set of tasks it can accomplish and its corresponding bid. The platform then
selects the users and their payments to maximize its utility while ensuring
truthfulness, individual rationality, profitability, and polynomial algorithm
complexity. Both the offline and the online scenarios are considered, where in
the offline case, all users submit their profiles simultaneously, while in the
online case they do it sequentially, and the decision whether to accept or
reject each user is done instantaneously with no revocation. The proposed
algorithms for both the offline and the online case are shown to satisfy all
the four desired properties of an efficient auction. Through extensive
simulation, the performance of the offline and the online algorithm is also
compared.
|
We derive a simple rule to determine surface plasmon energies, based on the
geometrical properties of the surface of the metal. We apply this concept to
obtain the surface plasmon energies in wedges, corners and conical tips. The
results presented here provide simple and straightforward rules to design the
energy of surface plasmons in severals situations of experimental interest such
as in plasmon wave guiding and in tip-enhanced spectroscopies.
|
We present a way of exciting surface plasmon polaritons along non-patterned
metallic surfaces by means of a flat squeezing slab designed with
transformation optics. The slab changes the dispersion relation of incident
light, enabling evanescent coupling to propagating surface plasmons. Unlike
prism couplers, the proposed device does not introduce reflections at its input
interface. Moreover, its compact geometry is suitable for integration. A
feasible dielectric implementation of the coupler is suggested. Finally, we
show that the angular response of the device can be engineered by using a
non-uniform compression factor. As an example, we design a coupler with a
half-power angular bandwidth 2.5 times higher than that of a conventional
dielectric coupler.
|
Fine-tuning large-scale pretrained models is prohibitively expensive in terms
of computational and memory costs. LoRA, as one of the most popular
Parameter-Efficient Fine-Tuning (PEFT) methods, offers a cost-effective
alternative by fine-tuning an auxiliary low-rank model that has significantly
fewer parameters. Although LoRA reduces the computational and memory
requirements significantly at each iteration, extensive empirical evidence
indicates that it converges at a considerably slower rate compared to full
fine-tuning, ultimately leading to increased overall compute and often worse
test performance. In our paper, we perform an in-depth investigation of the
initialization method of LoRA and show that careful initialization (without any
change of the architecture and the training algorithm) can significantly
enhance both efficiency and performance. In particular, we introduce a novel
initialization method, LoRA-GA (Low Rank Adaptation with Gradient
Approximation), which aligns the gradients of low-rank matrix product with
those of full fine-tuning at the first step. Our extensive experiments
demonstrate that LoRA-GA achieves a convergence rate comparable to that of full
fine-tuning (hence being significantly faster than vanilla LoRA as well as
various recent improvements) while simultaneously attaining comparable or even
better performance. For example, on the subset of the GLUE dataset with
T5-Base, LoRA-GA outperforms LoRA by 5.69% on average. On larger models such as
Llama 2-7B, LoRA-GA shows performance improvements of 0.34, 11.52%, and 5.05%
on MT-bench, GSM8K, and Human-eval, respectively. Additionally, we observe up
to 2-4 times convergence speed improvement compared to vanilla LoRA, validating
its effectiveness in accelerating convergence and enhancing model performance.
Code is available at https://github.com/Outsider565/LoRA-GA.
|
In this work we generalize and combine Gibbs and von Neumann approaches to
build, for the first time, a rigorous definition of entropy for hybrid
quantum-classical systems. The resulting function coincides with the two cases
above when the suitable limits are considered. Then, we apply the MaxEnt
principle for this hybrid entropy function and obtain the natural candidate for
the Hybrid Canonical Ensemble (HCE). We prove that the suitable classical and
quantum limits of the HCE coincide with the usual classical and quantum
canonical ensembles since the whole scheme admits both limits, thus showing
that the MaxEnt principle is applicable and consistent for hybrid systems.
|
Abridged: ATLASGAL is an unbiased 870 micron submillimetre survey of the
inner Galactic plane. It provides a large and systematic inventory of all
massive, dense clumps in the Galaxy (>1000 Msun) and includes representative
samples of all embedded stages of high-mass star formation. Here we present the
first detailed census of the properties (velocities, distances, luminosities
and masses) and spatial distribution of a complete sample of ~8000 dense clumps
located in the Galactic disk. We derive highly reliable velocities and
distances to ~97% of the sample and use mid- and far-infrared survey data to
develop an evolutionary classification scheme that we apply to the whole
sample. Comparing the evolutionary subsamples reveals trends for increasing
dust temperatures, luminosities and line-widths as a function of evolution
indicating that the feedback from the embedded proto-clusters is having a
significant impact on the structure and dynamics of their natal clumps. We find
88\,per\,cent are already associated with star formation at some level. We also
find the clump mass to be independent of evolution suggesting that the clumps
form with the majority of their mass in-situ. We estimate the statistical
lifetime of the quiescent stage to be ~5 x 10^4 yr for clump masses ~1000 Msun
decreasing to ~1 x 10^4 yr for clump masses >10000 Msun. We find a strong
correlation between the fraction of clumps associated with massive stars and
peak column density. The fraction is initially small at low column densities
but reaching 100\,per\,cent for column densities above 10^{23} cm^{-2}; there
are no clumps with column density clumps above this value that are not already
associated with massive star formation. All of the evidence is consistent with
a dynamic view of star formation wherein the clumps form rapidly and are
initially very unstable so that star formation quickly ensues.
|
Continuous tracking of boxers across multiple training sessions helps
quantify traits required for the well-known ten-point-must system. However,
continuous tracking of multiple athletes across multiple training sessions
remains a challenge, because it is difficult to precisely segment bout
boundaries in a recorded video stream. Furthermore, re-identification of the
same athlete over different period or even within the same bout remains a
challenge. Difficulties are further compounded when a single fixed view video
is captured in top-view. This work summarizes our progress in creating a system
in an economically single fixed top-view camera. Specifically, we describe
improved algorithm for bout transition detection and in-bout continuous player
identification without erroneous ID updation or ID switching. From our custom
collected data of ~11 hours (athlete count: 45, bouts: 189), our transition
detection algorithm achieves 90% accuracy and continuous ID tracking achieves
IDU=0, IDS=0.
|
Pruning has emerged as a promising approach for compressing large-scale
models, yet its effectiveness in recovering the sparsest of models has not yet
been explored. We conducted an extensive series of 485,838 experiments,
applying a range of state-of-the-art pruning algorithms to a synthetic dataset
we created, named the Cubist Spiral. Our findings reveal a significant gap in
performance compared to ideal sparse networks, which we identified through a
novel combinatorial search algorithm. We attribute this performance gap to
current pruning algorithms' poor behaviour under overparameterization, their
tendency to induce disconnected paths throughout the network, and their
propensity to get stuck at suboptimal solutions, even when given the optimal
width and initialization. This gap is concerning, given the simplicity of the
network architectures and datasets used in our study. We hope that our research
encourages further investigation into new pruning techniques that strive for
true network sparsity.
|
We study the ergodic properties of excited states in a model of interacting
fermions in quasi-one-dimensional chains subjected to a random vector
potential. In the noninteracting limit, we show that arbitrarily small values
of this complex off-diagonal disorder trigger localization for the whole
spectrum; the divergence of the localization length in the single-particle
basis is characterized by a critical exponent $\nu$ which depends on the energy
density being investigated. When short-range interactions are included, the
localization is lost, and the system is ergodic regardless of the magnitude of
disorder in finite chains. Our numerical results suggest a delocalization
scheme for arbitrary small values of interactions. This finding indicates that
the standard scenario of the many-body localization cannot be obtained in a
model with random gauge fields.
|
The lasso has become an important practical tool for high dimensional
regression as well as the object of intense theoretical investigation. But
despite the availability of efficient algorithms, the lasso remains
computationally demanding in regression problems where the number of variables
vastly exceeds the number of data points. A much older method, marginal
regression, largely displaced by the lasso, offers a promising alternative in
this case. Computation for marginal regression is practical even when the
dimension is very high. In this paper, we study the relative performance of the
lasso and marginal regression for regression problems in three different
regimes: (a) exact reconstruction in the noise-free and noisy cases when design
and coefficients are fixed, (b) exact reconstruction in the noise-free case
when the design is fixed but the coefficients are random, and (c)
reconstruction in the noisy case where performance is measured by the number of
coefficients whose sign is incorrect.
In the first regime, we compare the conditions for exact reconstruction of
the two procedures, find examples where each procedure succeeds while the other
fails, and characterize the advantages and disadvantages of each. In the second
regime, we derive conditions under which marginal regression will provide exact
reconstruction with high probability. And in the third regime, we derive rates
of convergence for the procedures and offer a new partitioning of the ``phase
diagram,'' that shows when exact or Hamming reconstruction is effective.
|
The technique of muon spin rotation ({\mu}SR) has emerged in the last few
decades as one of the most powerful methods of obtaining local magnetic
information. To make the technique fully quantitative, it is necessary to have
an accurate estimate of where inside the crystal structure the muon implants.
This can be provided by density functional theory calculations using an
approach that is termed DFT+{\mu}, density functional theory with the implanted
muon included. This article reviews this approach, describes some recent
successes in particular {\mu}SR experiments, and suggests some avenues for
future exploration.
|
Exponential random graph models (ERGMs) are flexible probability models
allowing edge dependency. However, it is known that, to a first-order
approximation, many ERGMs behave like Erd\"os-R\'enyi random graphs, where
edges are independent. In this paper, to distinguish ERGMs from Erd\"os-R\'enyi
random graphs, we consider second-order approximations of ERGMs using two-stars
and triangles. We prove that the second-order approximation indeed achieves
second-order accuracy in the triangle-free case. The new approximation is
formally obtained by Hoeffding decomposition and rigorously justified using
Stein's method.
|
Axions play a central role in many realizations of large field models of
inflation and in recent alternative mechanisms for generating primordial tensor
modes in small field models. If these axions couple to gauge fields, the
coupling produces a tachyonic instability that leads to an exponential
enhancement of the gauge fields, which in turn can decay into observable scalar
or tensor curvature perturbations. Thus, a fully self-consistent treatment of
axions during inflation is important, and in this work we discuss the
perturbative constraints on axions coupled to gauge fields. We show how the
recent proposal of generating tensor modes through these alternative mechanisms
is in tension with perturbation theory in the in-in formalism. Interestingly,
we point out that the constraints are parametrically weaker than one would
estimate based on naive power counting of propagators of the gauge field. In
the case of non-Abelian gauge fields, we derive new constraints on the size of
the gauge coupling, which apply also in certain models of natural large field
inflation, such as alignment mechanisms.
|
We study the twisted bosonization of massive Thirring model to relate to
sine-Gordon model in Moyal spacetime using twisted commutation relations. We
obtain the relevant twisted bosonization rules. We show that there exists dual
rela- tionship between twisted bosonic and fermionic operators. The strong-weak
duality is also observed to be preserved as its commutative counterpart.
|
We establish the gravitational detectability of a Dirac monopole using a
weak-field limit of general relativity, which can be developed from the
Newtonian gravitational potential by including energy as a source. The
resulting potential matches (by construction) the weak-field limit of two
different solutions to Einstein's equations of general relativity: one
associated with the magnetically monopolar spray of field lines emerging from
the half-infinite solenoid that makes up the Dirac monopole, the other
associated with the field-energetic source of the solenoid itself (the Dirac
string). The string's gravitational effect dominates, and we suggest that the
primary strong-field contribution of the Dirac configuration is that of a
half-infinite line of energy, whose GR solution is known.
|
Washing hands, social distancing and staying at home are the preventive
measures set in place to contain the spread of the COVID-19, a disease caused
by SARS-CoV-2. These measures, although straightforward to follow, highlight
the tip of an imbalanced socio-economic and socio-technological iceberg. Here,
a System Dynamic (SD) model of COVID-19 preventive measures and their
correlation with the 17 Sustainable Development Goals (SDGs) is presented. The
result demonstrates a better informed view of the COVID-19 vulnerability
landscape. This novel qualitative approach refreshes debates on the future of
SDGS amid the crisis and provides a powerful mental representation for decision
makers to find leverage points that aid in preventing long-term disruptive
impacts of this health crisis on people, planet and economy. There is a need
for further tailor-made and real-time qualitative and quantitative scientific
research to calibrate the criticality of meeting the SDGS targets in different
countries according to ongoing lessons learned from this health crisis.
|
We solve the O(n) model, defined in terms of self- and mutually avoiding
loops coexisting with voids, on a 3-simplex fractal lattice, using an exact
real space renormalization group technique. As the density of voids is
decreased, the model shows a critical point, and for even lower densities of
voids, there is a dense phase showing power-law correlations, with critical
exponents that depend on n, but are independent of density. At n=-2 on the
dilute branch, a trivalent vertex defect acts as a marginal perturbation. We
define a model of biconnected clusters which allows for a finite density of
such vertices. As n is varied, we get a line of critical points of this
generalized model, emanating from the point of marginality in the original loop
model. We also study another perturbation of adding local bending rigidity to
the loop model, and find that it does not affect the universality class.
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Starting from the effective torsion space-time model, we study its effects on
the top pair production cross section at hadron colliders. We also study the
effect of this model on top pair asymmetries at the Tevatron and the LHC. We
find that torsion space-time can explain forward-backward asymmetry according
to measured anomaly at Tevatron. We find an allowed region in the parameters
space which can satisfy simultaneously all $t\bar{t}$ observables measured at
Tevatron and LHC.
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Longitudinal fMRI datasets hold great promise for the study of
neurodegenerative diseases, but realizing their potential depends on extracting
accurate fMRI-based brain measures in individuals over time. This is especially
true for rare, heterogeneous and/or rapidly progressing diseases, which often
involve small samples whose functional features may vary dramatically across
subjects and over time, making traditional group-difference analyses of limited
utility. One such disease is ALS, which results in extreme motor function loss
and eventual death. Here, we analyze a rich longitudinal dataset containing 190
motor task fMRI scans from 16 ALS patients and 22 age-matched HCs. We propose a
novel longitudinal extension to our cortical surface-based spatial Bayesian
GLM, which has high power and precision to detect activations in individuals.
Using a series of longitudinal mixed-effects models to subsequently study the
relationship between activation and disease progression, we observe an inverted
U-shaped trajectory: at relatively mild disability we observe enlarging
activations, while at higher disability we observe severely diminished
activation, reflecting progression toward complete motor function loss. We
observe distinct trajectories depending on clinical progression rate, with
faster progressors exhibiting more extreme hyper-activation and subsequent
hypo-activation. These differential trajectories suggest that initial
hyper-activation is likely attributable to loss of inhibitory neurons. By
contrast, earlier studies employing more limited sampling designs and using
traditional group-difference analysis approaches were only able to observe the
initial hyper-activation, which was assumed to be due to a compensatory
process. This study provides a first example of how surface-based spatial
Bayesian modeling furthers scientific understanding of neurodegenerative
disease.
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Dynamic affinity scheduling has been an open problem for nearly three
decades. The problem is to dynamically schedule multi-type tasks to
multi-skilled servers such that the resulting queueing system is both stable in
the capacity region (throughput optimality) and the mean delay of tasks is
minimized at high loads near the boundary of the capacity region (heavy-traffic
optimality). As for applications, data-intensive analytics like MapReduce,
Hadoop, and Dryad fit into this setting, where the set of servers is
heterogeneous for different task types, so the pair of task type and server
determines the processing rate of the task. The load balancing algorithm used
in such frameworks is an example of affinity scheduling which is desired to be
both robust and delay optimal at high loads when hot-spots occur. Fluid model
planning, the MaxWeight algorithm, and the generalized $c\mu$-rule are among
the first algorithms proposed for affinity scheduling that have theoretical
guarantees on being optimal in different senses, which will be discussed in the
related work section. All these algorithms are not practical for use in data
center applications because of their non-realistic assumptions. The
join-the-shortest-queue-MaxWeight (JSQ-MaxWeight), JSQ-Priority, and
weighted-workload algorithms are examples of load balancing policies for
systems with two and three levels of data locality with a rack structure. In
this work, we propose the Generalized-Balanced-Pandas algorithm (GB-PANDAS) for
a system with multiple levels of data locality and prove its throughput
optimality. We prove this result under an arbitrary distribution for service
times, whereas most previous theoretical work assumes geometric distribution
for service times. The extensive simulation results show that the GB-PANDAS
algorithm alleviates the mean delay and has a better performance than the
JSQ-MaxWeight algorithm by twofold
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It is shown that the two-step excitation scheme typically used to create an
ultracold Rydberg gas can be described with an effective two-level rate
equation, greatly reducing the complexity of the optical Bloch equations. This
allows us to solve the many-body problem of interacting cold atoms with a Monte
Carlo technique. Our results reproduce the Rydberg blockade effect. However, we
demonstrate that an Autler-Townes double peak structure in the two-step
excitation scheme, which occurs for moderate pulse lengths as used in the
experiment, can give rise to an antiblockade effect. It is observable in a
lattice gas with regularly spaced atoms. Since the antiblockade effect is
robust against a large number of lattice defects it should be experimentally
realizable with an optical lattice created by CO$_{2}$ lasers.
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Human social dilemmas are often shaped by actions involving uncertain goals
and returns that may only be achieved in the future. Climate action, voluntary
vaccination and other prospective choices stand as paramount examples of this
setting. In this context, as well as in many other social dilemmas, uncertainty
may produce non-trivial effects. Whereas uncertainty about collective targets
and their impact were shown to negatively affect group coordination and
success, no information is available about timing uncertainty, i.e. how
uncertainty about when the target needs to be reached affects the outcome as
well as the decision-making. Here we show experimentally, through a collective
dilemma wherein groups of participants need to avoid a tipping point under the
risk of collective loss, that timing uncertainty prompts not only early
generosity but also polarized contributions, in which participants' total
contributions are distributed more unfairly than when there is no uncertainty.
Analyzing participant behavior reveals, under uncertainty, an increase in
reciprocal strategies wherein contributions are conditional on the previous
donations of the other participants, a group analogue of the well-known
Tit-for-Tat strategy. Although large timing uncertainty appears to reduce
collective success, groups that successfully collect the required amount show
strong reciprocal coordination. This conclusion is supported by a game
theoretic model examining the dominance of behaviors in case of timing
uncertainty. In general, timing uncertainty casts a shadow on the future that
leads participants to respond early, encouraging reciprocal behaviors, and
unequal contributions.
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We develop a framework for deriving Dyson-Schwinger Equations (DSEs) and
Bethe-Salpeter Equation (BSE) in QCD at large $N_c$ limit. The starting point
is a modified form (with auxiliary fields) of QCD generating functional. This
framework provides a natural order-by-order truncation scheme for DSEs and BSE,
and the kernels of the equations up to any order are explicitly given. Chiral
symmetry (at chiral limit) is preserved in any order truncation, so it
exemplifies the symmetry preserving truncation scheme. It provides a method to
study DSEs and BSE beyond the Rainbow-Ladder truncation, and is especially
useful to study contributions from non-Abelian dynamics (those arise from gluon
self-interactions). We also derive the equation for the quark-ghost scattering
kernel, and discuss the Slavnov-Taylor identity connecting the quark-gluon
vertex, the quark propagator and the quark-ghost scattering kernel.
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In inertial microfluidics lift forces cause a particle to migrate across
streamlines to specific positions in the cross section of a microchannel. We
control the rotational motion of a particle and demonstrate that this allows to
manipulate the lift-force profile and thereby the particle's equilibrium
positions. We perform two-dimensional simulation studies using the method of
multi-particle collision dynamics. Particles with unconstrained rotational
motion occupy stable equilibrium positions in both halfs of the channel while
the center is unstable. When an external torque is applied to the particle, two
equilibrium positions annihilate by passing a saddle-node bifurcation and only
one stable fixpoint remains so that all particles move to one side of the
channel. In contrast, non-rotating particles accumulate in the center and are
pushed into one half of the channel when the angular velocity is fixed to a
non-zero value.
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We discuss the dynamical effects of bulk viscosity and particle creation on
the early evolution of the Friedmann -Robertson -Walker model in the framework
of open thermodynamical systems. We consider bulk viscosity and Particle
creation as separate irreversible processes. Exact solutions of the Einstein
field equations are obtained by using the "gamma-law" equation of state
$p=(\gamma -1)\rho$, where the adiabatic parameter $\gamma$ varies with scale
factor of the metric. We consider the cosmological model to study the early
phases of the evolution of the universe as it goes from an inflationary phase
to a radiation -dominated era in the presence of bulk viscosity and particle
creation. Analytical solutions are obtained for particle number density and
entropy for all models. It is seen that, by choosing appropriate functions for
particle creation rate and bulk viscous coefficient, the models exhibit
singular and non-singular beginnings.
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Shock tubes are commonly employed to test candidate armor materials, validate
numerical models, and conduct simulated blast experiments in animal models. As
DoD interests desire to field wearable sensors as blast dosimeters, shock tubes
may also serve for calibration and testing of these devices. The high blast
pressures needed for experimental testing of candidate armors are unnecessary
to test these sensors. An inexpensive, efficient, and easily available way of
testing these pressure sensors is desirable. It is known that releasing
compressed gas suddenly can create a repeatable shock front, and the pressures
can be finely tuned by changing the pressure to which the gas is compressed. A
Crosman 0.177 caliber air pistol was used (without loading any pellets) to
compress and release air in one end of a 24 inch long 3/4 inch diameter
standard pipe nipple to simulate a blast wave at the other end of the tube. A
variable number of pumps were used to vary the peak blast pressure. As
expected, the trials where 10 pumps were used to compress the air resulted in
the largest average peak pressure of 101.99 kPa (+/- 2.63 kPa). The design with
7 pumps had the second biggest peak pressure, with an average peak pressure of
89.11 kPa (+/-1.77 kPa). The design with 5 pumps had the third largest peak
pressure, with an average peak pressure of 78.80 kPa (+/-1.74 kPa). 3 pumps
produced an average peak pressure of 61.37 kPa (+/-2.20 kPa). 2 pumps produced
an average peak pressure of 48.11 kPa (+/-1.57 kPa). The design with just 1
pump had the smallest peak pressure and produced an average peak pressure of
30.13 kPa (+/-0.79 kPa). This inexpensive shock tube design had a shot-to-shot
cycle time of between 30 and 45 seconds.
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Background: A critical step in effective care and treatment planning for
severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of the
COVID-19 pandemic, is the assessment of the severity of disease progression.
Chest x-rays (CXRs) are often used to assess SARS-CoV-2 severity, with two
important assessment metrics being extent of lung involvement and degree of
opacity. In this proof-of-concept study, we assess the feasibility of
computer-aided scoring of CXRs of SARS-CoV-2 lung disease severity using a deep
learning system.
Materials and Methods: Data consisted of 396 CXRs from SARS-CoV-2 positive
patient cases. Geographic extent and opacity extent were scored by two
board-certified expert chest radiologists (with 20+ years of experience) and a
2nd-year radiology resident. The deep neural networks used in this study, which
we name COVID-Net S, are based on a COVID-Net network architecture. 100
versions of the network were independently learned (50 to perform geographic
extent scoring and 50 to perform opacity extent scoring) using random subsets
of CXRs from the study, and we evaluated the networks using stratified Monte
Carlo cross-validation experiments.
Findings: The COVID-Net S deep neural networks yielded R$^2$ of 0.664 $\pm$
0.032 and 0.635 $\pm$ 0.044 between predicted scores and radiologist scores for
geographic extent and opacity extent, respectively, in stratified Monte Carlo
cross-validation experiments. The best performing networks achieved R$^2$ of
0.739 and 0.741 between predicted scores and radiologist scores for geographic
extent and opacity extent, respectively.
Interpretation: The results are promising and suggest that the use of deep
neural networks on CXRs could be an effective tool for computer-aided
assessment of SARS-CoV-2 lung disease severity, although additional studies are
needed before adoption for routine clinical use.
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In a bicategory of spans (an example of a 'generic bicategory') the
factorization of a span (s,t) as the span (s,1) followed by (1,t) satisfies a
simple universal property with respect to all factorizations in terms of the
generic bicategory structure. Here we show that this universal property can in
fact be used to characterize bicategories of spans. This characterization of
spans is very different from the others in that it does not mention any
adjointness conditions within the bicategory.
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In this paper we quantify the trade-off between setups optimized to be
ancillary to Phase II Superbeams or Neutrino Factories and experiments tuned
for maximal sensitivity to the subdominant terms of the neutrino transition
probability at the atmospheric scale (``maximum discovery potential''). In
particular, the theta(13) sensitivity is computed for both Phase I superbeams
(JHF-SK and NuMI Off-Axis) and next generation long baseline experiments
(ICARUS, OPERA and MINOS). It is shown that Phase I experiments cannot reach a
sensitivity able to ground (or discourage in a definitive manner) the building
of Phase II projects and that, in case of null result and without a dedicated
$\bar{\nu}$ run, this capability is almost saturated by high energy beams like
CNGS, especially for high values of the ratio $\Delta m^2_{21}/|\Delta
m^2_{31}|$.
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We obtain in this short article the non-asymptotic exact estimations for the
norm of (generalized) weighted Hardy-Littlewood average integral operator in
the so-called Bilateral Grand Lebesgue Spaces. We also give examples to show
the sharpness of these inequalities.
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Astronomical data take on a multitude of forms -- catalogs, data cubes,
images, and simulations. The availability of software for rendering
high-quality three-dimensional graphics lends itself to the paradigm of
exploring the incredible parameter space afforded by the astronomical sciences.
The software program Blender gives astronomers a useful tool for displaying
data in a manner used by three-dimensional (3D) graphics specialists and
animators. The interface to this popular software package is introduced with
attention to features of interest in astronomy. An overview of the steps for
generating models, textures, animations, camera work, and renders is outlined.
An introduction is presented on the methodology for producing animations and
graphics with a variety of astronomical data. Examples from sub-fields of
astronomy with different kinds of data are shown with resources provided to
members of the astronomical community. An example video showcasing the outlined
principles and features is provided along with scripts and files for sample
visualizations.
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A binary constraint system game is a two-player one-round non-local game
defined by a system of Boolean constraints. The game has a perfect quantum
strategy if and only if the constraint system has a quantum satisfying
assignment [R. Cleve and R. Mittal, arXiv:1209.2729]. We show that several
concepts including the quantum chromatic number and the Kochen-Specker sets
that arose from different contexts fit naturally in the binary constraint
system framework. The structure and complexity of the quantum satisfiability
problems for these constraint systems are investigated. Combined with a new
construct called the commutativity gadget for each problem, several classic
NP-hardness reductions are lifted to their corresponding quantum versions. We
also provide a simple parity constraint game that requires $\Omega(\sqrt{n})$
EPR pairs in perfect strategies where $n$ is the number of variables in the
constraint system.
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We report drastically different onset temperatures of the reentrant integer
quantum Hall states in the second and third Landau level. This finding is in
quantitative disagreement with the Hartree-Fock theory of the bubble phases
which is thought to describe these reentrant states. Our results indicate that
the number of electrons per bubble in either the second or the third Landau
level is likely different than predicted.
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Direct imaging observations of planets revealed that wide-orbit ($>10$ au)
giant planets exist even around subsolar-metallicity host stars and do not
require metal-rich environments for their formation. A possible formation
mechanism of wide-orbit giant planets in subsolar-metallicity environments is
the gravitational fragmentation of massive protoplanetary discs. Here, we
follow the long-term evolution of the disc for 1 Myr after its formation, which
is comparable to disc lifetime, by way of a two-dimensional thin-disc
hydrodynamic simulation with the metallicity of 0.1 ${\rm Z}_{\odot}$. We find
a giant protoplanet that survives until the end of the simulation. The
protoplanet is formed by the merger of two gaseous clumps at $\sim$0.5 Myr
after disc formation, and then it orbits $\sim$200 au from the host star for
$\sim$0.5 Myr. The protoplanet's mass is $\sim$10 ${\rm M}_{\rm J}$ at birth
and gradually decreases to 1 ${\rm M}_{\rm J}$ due to the tidal effect from the
host star. The result provides the minimum mass of 1 ${\rm M}_{\rm J}$ for
protoplanets formed by gravitational instability in a subsolar-metallicity
disc. We anticipate that the mass of a protoplanet experiencing reduced mass
loss thanks to the protoplanetary contraction in higher resolution simulations
can increase to $\sim$10 ${\rm M}_{\rm J}$. We argue that the disc
gravitational fragmentation would be a promising pathway to form wide-orbit
giant planets with masses of $\ge1$ ${\rm M}_{\rm J}$ in subsolar-metallicity
environments.
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The main theme of the paper is the dynamics of Hamiltonian diffeomorphisms of
${\mathbb C}{\mathbb P}^n$ with the minimal possible number of periodic points
(equal to $n+1$ by Arnold's conjecture), called here Hamiltonian
pseudo-rotations. We prove several results on the dynamics of pseudo-rotations
going beyond periodic orbits, using Floer theoretical methods. One of these
results is the existence of invariant sets in arbitrarily small punctured
neighborhoods of the fixed points, partially extending a theorem of Le Calvez
and Yoccoz and Franks to higher dimensions. The other is a strong variant of
the Lagrangian Poincar\'e recurrence conjecture for pseudo-rotations. We also
prove the $C^0$-rigidity of pseudo-rotations with exponentially Liouville mean
index vector. This is a higher-dimensional counterpart of a theorem of Bramham
establishing such rigidity for pseudo-rotations of the disk.
|
S.C. Zhang has put forward the idea that high-temperature-superconductors can
be described in the framework of an SO(5)-symmetric theory in which the three
components of the antiferromagnetic order-parameter and the two components of
the two-particle condensate form a five-component order-parameter with SO(5)
symmetry. Interactions small in comparison to this strong interaction introduce
anisotropies into the SO(5)-space and determine whether it is favorable for the
system to be superconducting or antiferromagnetic. Here the view is expressed
that Zhang's derivation of the effective interaction V_{eff} based on his
Hamiltonian H_a is not correct. However, the orthogonality constraints
introduced several pages after this 'derivation' give the key to an effective
interaction very similar to that given by Zhang. It is shown that the
orthogonality constraints are not rigorous constraints, but they maximize the
entropy at finite temperature. If the interaction drives the ground-state to
the largest possible eigenvalues of the operators under consideration
(antiferromagnetic ordering, superconducting condensate, etc.), then the
orthogonality constraints are obeyed by the ground-state, too.
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We perform preliminary studies on a large longitudinal face database
MORPH-II, which is a benchmark dataset in the field of computer vision and
pattern recognition. First, we summarize the inconsistencies in the dataset and
introduce the steps and strategy taken for cleaning. The potential implications
of these inconsistencies on prior research are introduced. Next, we propose a
new automatic subsetting scheme for evaluation protocol. It is intended to
overcome the unbalanced racial and gender distributions of MORPH-II, while
ensuring independence between training and testing sets. Finally, we contribute
a novel global framework for age estimation that utilizes posterior
probabilities from the race classification step to compute a racecomposite age
estimate. Preliminary experimental results on MORPH-II are presented.
|
Expected energy spectra calculations for large volume liquid scintillation
detectors to inverse $\beta$-decay for antineutrinos produced by $^{144}$Ce --
$^{144}$Pr artificial source have been performed.
The calculations were carried out through Monte-Carlo method within GEANT4.10
framework and were purposed to search for neutrino oscillation to sterile
eigenstate with mass about 1 eV.
The analysis of relative sensitivity to oscillation parameters for different
detector shapes has been performed.
|
We give a criterion of classicality for mixed states in terms of expectation
values of a quantum observable. Using group representation theory we identify
all cases when the criterion can be computed exactly in terms of the spectrum
of a single operator.
|
The dynamic nature of resource allocation and runtime conditions on Cloud can
result in high variability in a job's runtime across multiple iterations,
leading to a poor experience. Identifying the sources of such variation and
being able to predict and adjust for them is crucial to cloud service providers
to design reliable data processing pipelines, provision and allocate resources,
adjust pricing services, meet SLOs and debug performance hazards. In this
paper, we analyze the runtime variation of millions of production SCOPE jobs on
Cosmos, an exabyte-scale internal analytics platform at Microsoft. We propose
an innovative 2-step approach to predict job runtime distribution by
characterizing typical distribution shapes combined with a classification model
with an average accuracy of >96%, out-performing traditional regression models
and better capturing long tails. We examine factors such as job plan
characteristics and inputs, resource allocation, physical cluster heterogeneity
and utilization, and scheduling policies.
To the best of our knowledge, this is the first study on predicting
categories of runtime distributions for enterprise analytics workloads at
scale. Furthermore, we examine how our methods can be used to analyze what-if
scenarios, focusing on the impact of resource allocation, scheduling, and
physical cluster provisioning decisions on a job's runtime consistency and
predictability.
|
Robust machine learning is currently one of the most prominent topics which
could potentially help shaping a future of advanced AI platforms that not only
perform well in average cases but also in worst cases or adverse situations.
Despite the long-term vision, however, existing studies on black-box
adversarial attacks are still restricted to very specific settings of threat
models (e.g., single distortion metric and restrictive assumption on target
model's feedback to queries) and/or suffer from prohibitively high query
complexity. To push for further advances in this field, we introduce a general
framework based on an operator splitting method, the alternating direction
method of multipliers (ADMM) to devise efficient, robust black-box attacks that
work with various distortion metrics and feedback settings without incurring
high query complexity. Due to the black-box nature of the threat model, the
proposed ADMM solution framework is integrated with zeroth-order (ZO)
optimization and Bayesian optimization (BO), and thus is applicable to the
gradient-free regime. This results in two new black-box adversarial attack
generation methods, ZO-ADMM and BO-ADMM. Our empirical evaluations on image
classification datasets show that our proposed approaches have much lower
function query complexities compared to state-of-the-art attack methods, but
achieve very competitive attack success rates.
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We show that the space R^n x gl(n,R) with a certain antisymmetric bracket
operation contains all n-dimensional Lie algebras. The bracket does not satisfy
the Jacobi identity, but it does satisfy it for subalgebras which are isotropic
under a certain symmetric bilinear form with values in R^n. We ask what the
corresponding "group-like" object should be. The bracket may be obtained by
linearizing at a point the bracket on TM + T*M introduced by T. Courant for the
definition of Dirac structures, a notion which encompasses Poisson structures,
closed 2-forms, and foliations.
|
The ubiquitous use of face recognition has sparked increasing privacy
concerns, as unauthorized access to sensitive face images could compromise the
information of individuals. This paper presents an in-depth study of the
privacy protection of face images' visual information and against recovery.
Drawing on the perceptual disparity between humans and models, we propose to
conceal visual information by pruning human-perceivable low-frequency
components. For impeding recovery, we first elucidate the seeming paradox
between reducing model-exploitable information and retaining high recognition
accuracy. Based on recent theoretical insights and our observation on model
attention, we propose a solution to the dilemma, by advocating for the training
and inference of recognition models on randomly selected frequency components.
We distill our findings into a novel privacy-preserving face recognition
method, PartialFace. Extensive experiments demonstrate that PartialFace
effectively balances privacy protection goals and recognition accuracy. Code is
available at: https://github.com/Tencent/TFace.
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Previous studies have demonstrated the utility and applicability of machine
learning techniques to jet physics. In this paper, we construct new observables
for the discrimination of jets from different originating particles exclusively
from information identified by the machine. The approach we propose is to first
organize information in the jet by resolved phase space and determine the
effective $N$-body phase space at which discrimination power saturates. This
then allows for the construction of a discrimination observable from the
$N$-body phase space coordinates. A general form of this observable can be
expressed with numerous parameters that are chosen so that the observable
maximizes the signal vs.~background likelihood. Here, we illustrate this
technique applied to discrimination of $H\to b\bar b$ decays from massive $g\to
b\bar b$ splittings. We show that for a simple parametrization, we can
construct an observable that has discrimination power comparable to, or better
than, widely-used observables motivated from theory considerations. For the
case of jets on which modified mass-drop tagger grooming is applied, the
observable that the machine learns is essentially the angle of the dominant
gluon emission off of the $b\bar b$ pair.
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While HDMaps are a crucial component of autonomous driving, they are
expensive to acquire and maintain. Estimating these maps from sensors therefore
promises to significantly lighten costs. These estimations however overlook
existing HDMaps, with current methods at most geolocalizing low quality maps or
considering a general database of known maps. In this paper, we propose to
account for existing maps of the precise situation studied when estimating
HDMaps. We identify 3 reasonable types of useful existing maps (minimalist,
noisy, and outdated). We also introduce MapEX, a novel online HDMap estimation
framework that accounts for existing maps. MapEX achieves this by encoding map
elements into query tokens and by refining the matching algorithm used to train
classic query based map estimation models. We demonstrate that MapEX brings
significant improvements on the nuScenes dataset. For instance, MapEX - given
noisy maps - improves by 38% over the MapTRv2 detector it is based on and by 8%
over the current SOTA.
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A self-consistent model of the chemical evolution of the globular cluster NGC
6752 is presented to test a popular theory that observed abundance anomalies
are due to ``internal pollution'' from intermediate mass asymptotic giant
branch stars. We simulated the chemical evolution of the intracluster medium
under the assumption that the products of Type II SNe are completely expelled
from the globular cluster, whereas material ejected from stars with m < 7 M_sun
is retained, due to their weak stellar winds. By tracing the chemical evolution
of the intracluster gas, we tested an internal pollution scenario in which the
Na- and Al-enhanced ejecta from intermediate mass stars is either accreted onto
the surfaces of other stars, or goes toward forming new stars. The observed
spread in Na and Al was reproduced, but not the O-Na and Mg-Al
anticorrelations. In particular, neither O nor Mg are sufficiently depleted to
account for the observations. We predict that the Mg content of Na-rich cluster
stars should be overwhelmingly dominated by the 25,26Mg isotopes, whereas the
latest data shows only a mild 26Mg enhancement and no correlation with 25Mg.
Furthermore, stars bearing the imprint of intermediate mass stellar ejecta are
predicted to be strongly enhanced in both C and N, in conflict with the
empirical data. We find that while standard AGB stellar models do show the hot
H burning that seems required to explain the observations, this is accompanied
by He burning, producing primary C, N, Mg and Na (via HBB) which do not match
the observations. (Abridged)
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Various mechanisms of thermal photon production are reviewed and their
implications for heavy ion collisions are briefly sketched.
|
We construct $p$-adic $L$-functions for Rankin--Selberg products of
automorphic forms of hermitian type in the anticyclotomic direction for both
root numbers. When the root number is $+1$, the construction relies on global
Bessel periods on definite unitary groups which, due to the recent advances on
the global Gan--Gross--Prasad conjecture, interpolate classical central
$L$-values. When the root number is $-1$, we construct an element in the
Iwasawa Selmer group using the diagonal cycle on the product of unitary Shimura
varieties, and conjecture that its $p$-adic height interpolates derivatives of
cyclotomic $p$-adic $L$-functions. We also propose the nonvanishing conjecture
and the main conjecture in both cases.
|
Starburst galaxies, which are known as "reservoirs" of high-energy
cosmic-rays, can represent an important high-energy neutrino "factory"
contributing to the diffuse neutrino flux observed by IceCube. In this paper,
we revisit the constraints affecting the neutrino and gamma-ray hadronuclear
emissions from this class of astrophysical objects. In particular, we go beyond
the standard prototype-based approach leading to a simple power-law neutrino
flux, and investigate a more realistic model based on a data-driven blending of
spectral indexes, thereby capturing the observed changes in the properties of
individual emitters. We then perform a multi-messenger analysis considering the
extragalactic gamma-ray background (EGB) measured by Fermi-LAT and different
IceCube data samples: the 7.5-year High-Energy Starting Events (HESE) and the
6-year high-energy cascade data. Along with starburst galaxies, we take into
account the contributions from blazars and radio galaxies as well as the
secondary gamma-rays from electromagnetic cascades. Remarkably, we find that,
differently from the highly-constrained prototype scenario, the spectral index
blending allows starburst galaxies to account for up to $40\%$ of the HESE
events at $95.4\%$ CL, while satisfying the limit on the non-blazar EGB
component. Moreover, values of $\mathcal{O}(100~\mathrm{PeV})$ for the maximal
energy of accelerated cosmic-rays by supernovae remnants inside the starburst
are disfavoured in our scenario. In broad terms, our analysis points out that a
better modeling of astrophysical sources could alleviate the tension between
neutrino and gamma-ray data interpretation.
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The performance of a D-Wave Vesuvius quantum annealer was recently compared
to a suite of classical algorithms on a class of constraint satisfaction
instances based on frustrated loops. However, the construction of these
instances leads the maximum coupling strength to increase with problem size. As
a result, larger instances are subject to amplified analog control error, and
are effectively annealed at higher temperatures in both hardware and software.
We generate similar constraint satisfaction instances with limited range of
coupling strength and perform a similar comparison to classical algorithms. On
these instances the D-Wave Vesuvius processor, run with a fixed 20$\mu$s anneal
time, shows a scaling advantage over the software solvers for the hardest
regime studied. This scaling advantage opens the possibility of quantum speedup
on these problems. Our results support the hypothesis that performance of
D-Wave Vesuvius processors is heavily influenced by analog control error, which
can be reduced and mitigated as the technology matures.
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Determining the spin and the parity quantum numbers of the recently
discovered Higgs-like boson at the LHC is a matter of great importance. In this
paper, we consider the possibility of using the kinematics of the tagging jets
in Higgs production via the vector boson fusion (VBF) process to test the
tensor structure of the Higgs-vector boson ($HVV$) interaction and to determine
the spin and CP properties of the observed resonance. We show that an anomalous
$HVV$ vertex, in particular its explicit momentum dependence, drastically
affects the rapidity between the two scattered quarks and their transverse
momenta and, hence, the acceptance of the kinematical cuts that allow to select
the VBF topology. The sensitivity of these observables to different spin-parity
assignments, including the dependence on the LHC center of mass energy, are
evaluated. In addition, we show that in associated Higgs production with a
vector boson some kinematical variables, such as the invariant mass of the
system and the transverse momenta of the two bosons and their separation in
rapidity, are also sensitive to the spin--parity assignments of the Higgs--like
boson.
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In this paper, we propose a polar coding based scheme for set reconciliation
between two network nodes. The system is modeled as a well-known Slepian-Wolf
setting induced by a fixed number of deletions. The set reconciliation process
is divided into two phases: 1) a deletion polar code is employed to help one
node to identify the possible deletion indices, which may be larger than the
number of genuine deletions; 2) a lossless compression polar code is then
designed to feedback those indices with minimum overhead. Our scheme can be
viewed as a generalization of polar codes to some emerging network-based
applications such as the package synchronization in blockchains. Some
connections with the existing schemes based on the invertible Bloom lookup
tables (IBLTs) and network coding are also observed and briefly discussed.
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We introduce RecurrentGemma, an open language model which uses Google's novel
Griffin architecture. Griffin combines linear recurrences with local attention
to achieve excellent performance on language. It has a fixed-sized state, which
reduces memory use and enables efficient inference on long sequences. We
provide a pre-trained model with 2B non-embedding parameters, and an
instruction tuned variant. Both models achieve comparable performance to
Gemma-2B despite being trained on fewer tokens.
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A cloud server spent a lot of time, energy and money to train a Viola-Jones
type object detector with high accuracy. Clients can upload their photos to the
cloud server to find objects. However, the client does not want the leakage of
the content of his/her photos. In the meanwhile, the cloud server is also
reluctant to leak any parameters of the trained object detectors. 10 years ago,
Avidan & Butman introduced Blind Vision, which is a method for securely
evaluating a Viola-Jones type object detector. Blind Vision uses standard
cryptographic tools and is painfully slow to compute, taking a couple of hours
to scan a single image. The purpose of this work is to explore an efficient
method that can speed up the process. We propose the Random Base Image (RBI)
Representation. The original image is divided into random base images. Only the
base images are submitted randomly to the cloud server. Thus, the content of
the image can not be leaked. In the meanwhile, a random vector and the secure
Millionaire protocol are leveraged to protect the parameters of the trained
object detector. The RBI makes the integral-image enable again for the great
acceleration. The experimental results reveal that our method can retain the
detection accuracy of that of the plain vision algorithm and is significantly
faster than the traditional blind vision, with only a very low probability of
the information leakage theoretically.
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We present polarization-independent optical shutters with a sub-millisecond
switching time. The approach utilizes dual-frequency nematics doped with a
dichroic dye. Two nematic cells with orthogonal alignment are driven
simultaneously by a low-frequency or high-frequency electric field to switch
the shutter either into a transparent or a light-absorbing state. The switching
speed is accelerated via special short pulses of high amplitude voltage. The
approach can be used in a variety of electro-optical devices.
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The Pade approximant technique and the variational Monte Carlo method are
applied to determine the ground-state energy of a finite number of charged
bosons in two dimensions confined by a parabolic trap. The particles interact
repulsively through a Coulombic, 1/r, potential. Analytic expressions for the
ground-state energy are obtained. The convergence of the Pade sequence and
comparison with the Monte Carlo results show that the error of the Pade
estimate is less than 4% at any boson density and is exact in the extreme
situations of very dilute and high density.
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Topological superconductivity is central to a variety of novel phenomena
involving the interplay between topologically ordered phases and
broken-symmetry states. The key ingredient is an unconventional order
parameter, with an orbital component containing a chiral $p_x$ + i$p_y$ wave
term. Here we present phase-sensitive measurements, based on the quantum
interference in nanoscale Josephson junctions, realized by using Bi$_2$Te$_3$
topological insulator. We demonstrate that the induced superconductivity is
unconventional and consistent with a sign-changing order parameter, such as a
chiral $p_x$ + i$p_y$ component. The magnetic field pattern of the junctions
shows a dip at zero externally applied magnetic field, which is an
incontrovertible signature of the simultaneous existence of 0 and $\pi$
coupling within the junction, inherent to a non trivial order parameter phase.
The nano-textured morphology of the Bi$_2$Te$_3$ flakes, and the dramatic role
played by thermal strain are the surprising key factors for the display of an
unconventional induced order parameter.
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We calculate the conductance of atomic chains as a function of their length.
Using the Density Matrix Renormalization Group algorithm for a many-body model
which takes into account electron-electron interactions and the shape of the
contacts between the chain and the leads, we show that length-dependent
oscillations of the conductance whose period depends on the electron density in
the chain can result from electron-electron scattering alone. The amplitude of
these oscillations can increase with the length of the chain, in contrast to
the result from approaches which neglect the interactions.
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Given a suitable arithmetic function h, we investigate the average order of h
as it ranges over the values taken by an integral binary form F. A general
upper bound is obtained for this quantity, in which the dependence upon the
coefficients of F is made completely explicit.
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The presence of forming planets embedded in their protoplanetary disks has
been inferred from the detection of multiring structures in such disks. Most of
these suspected planets are undetectable by direct imaging observations at
current measurement sensitivities. Inward migration and accretion might make
these putative planets accessible to the Doppler method, but the actual extent
of growth and orbital evolution remains unconstrained.
Under the premise that the gaps in the disk around HD 163296 originate from
new-born planets, we investigate if and under which circumstances the
gap-opening planets could represent progenitors of the exoplanet population
detected around A-type stars. In particular, we study the dependence of final
planetary masses and orbital parameters on the viscosity of the disk. The
evolution of the embedded planets was simulated throughout the disk lifetime
and up to 100 Myr after the dispersal of the disk, taking the evolving disk
structure and a likely range of disk lifetimes into account.
We find that the final configuration of the planets is largely determined by
the $\alpha$ viscosity parameter of the disk and less dependent on the choice
for the disk lifetime and the initial planetary parameters. If we assume that
planets such as those in HD 163296 evolve to form the observed exoplanet
population of A-type stars, a $\alpha$ parameter on the order of $3.16 \times
10^{-4} \lesssim \alpha \lesssim 10^{-3}$ is required for the disks to induce
sufficiently high migration rates. Depending on whether or not future direct
imaging surveys will uncover a larger number of planets with $m_\mathrm{pl}
\lesssim 3 M_\mathrm{Jup}$ and $a_\mathrm{pl} \gtrsim 10 \mathrm{AU}$ we expect
the $\alpha$ parameter to be at the lower or upper end of this range, always
under the assumption that such disks indeed harbor wide orbit planets.
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Enabling fast charging for lithium ion batteries is critical to accelerating
the green energy transition. As such, there has been significant interest in
tailored fast-charging protocols computed from the solutions of constrained
optimal control problems. Here, we derive necessity conditions for a fast
charging protocol based upon monotone control systems theory.
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This text aims at providing a bird's eye view of system identification with
special attention to nonlinear systems. The driving force is to give a feeling
for the philosophical problems facing those that build mathematical models from
data. Special attention will be given to grey-box approaches in nonlinear
system identification. In this text, grey-box methods use auxiliary information
such as the system steady-state data, possible symmetries, some bifurcations
and the presence of hysteresis. The text ends with a sample of applications. No
attempt is made to be thorough nor to survey such an extensive and mature field
as system identification. In most parts references will be provided for a more
detailed study.
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We report a metal-insulator transition (MIT) in the half-filled multiorbital
antiferromagnet (AF) BaMn$_2$Bi$_2$ that is tunable by a magnetic field
perpendicular to the AF sublattices. Instead of an Anderson-Mott mechanism
usually expected in strongly correlated systems, we find by scaling analyses
that the MIT is driven by an Anderson localization. Electrical and
thermoelectrical transport measurements in combination with electronic band
calculations reveal a strong orbital-dependent correlation effect, where both
weakly and strongly correlated $3d$-derived bands coexist with decoupled charge
excitations. Weakly correlated holelike carriers in the $d_{xy}$-derived band
dominate the transport properties and exhibit the Anderson localization,
whereas other $3d$ bands show clear Mott-like behaviors with their spins
ordered into AF sublattices. The tuning role played by the perpendicular
magnetic field supports a strong spin-spin coupling between itinerant holelike
carriers and the AF fluctuations, which is in sharp contrast to their weak
charge coupling.
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Consider the configuration spaces of manifolds. An influential theorem of
McDuff, Segal and Church shows that the (co)homology of the unordered
configuration space is independent of number of points in a range of degree
called the stable range. We study the another important (and general) property
of unordered configuration spaces of manifolds (not necessarily orientable, and
not necessarily admitting non-vanishing vector field) that is homological
monotonicity in unstable part. We show that the homological dimension of
unordered configuration spaces of manifolds in each degree is monotonically
increasing. Our results show that the monotonicity property is not depend on
the differential structure and orientiability of manifold.
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Given a graph G, a colouring is an assignment of colours to the vertices of G
so that no two adjacent vertices are coloured the same. If all colour classes
have size at most t, then we call the colouring t-bounded, and the t-bounded
chromatic number of G, denoted by $\chi_t(G)$, is the minimum number of colours
in such a colouring. Every colouring of G is then $\alpha(G)$-bounded, where
$\alpha(G)$ denotes the size of a largest independent set.
We study colourings of the random graph G(n, 1/2) and of the corresponding
uniform random graph G(n,m) with $m=\left \lfloor \frac 12 {n \choose 2} \right
\rfloor$. We show that $\chi_t(G(n,m))$ is maximally concentrated on at most
two explicit values for $t = \alpha(G(n,m))-2$. This behaviour stands in stark
contrast to that of the normal chromatic number, which was recently shown not
to be concentrated on any sequence of intervals of length $n^{1/2-o(1)}$.
Moreover, when $t = \alpha(G_{n, 1/2})-1$ and if the expected number of
independent sets of size $t$ is not too small, we determine an explicit
interval of length $n^{0.99}$ that contains $\chi_t(G_{n,1/2})$ with high
probability. Both results have profound consequences: the former is at the core
of the intriguing Zigzag Conjecture on the distribution of $\chi(G_{n, 1/2})$
and justifies one of its main hypotheses, while the latter is an important
ingredient in the proof of a non-concentration result for $\chi(G_{n,1/2})$
that is conjectured to be optimal.
These two results are consequences of a more general statement. We consider a
class of colourings that we call tame, and provide tight bounds for the
probability of existence of such colourings via a delicate second moment
argument. We then apply those bounds to the two aforementioned cases. As a
further consequence of our main result, we prove two-point concentration of the
equitable chromatic number of G(n,m).
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TensorFlow is a popular deep learning framework used by data scientists to
solve a wide-range of machine learning and deep learning problems such as image
classification and speech recognition. It also operates at a large scale and in
heterogeneous environments --- it allows users to train neural network models
or deploy them for inference using GPUs, CPUs and deep learning specific
custom-designed hardware such as TPUs. Even though TensorFlow supports a
variety of optimized backends, realizing the best performance using a backend
may require additional efforts. For instance, getting the best performance from
a CPU backend requires careful tuning of its threading model. Unfortunately,
the best tuning approach used today is manual, tedious, time-consuming, and,
more importantly, may not guarantee the best performance.
In this paper, we develop an automatic approach, called TensorTuner, to
search for optimal parameter settings of TensorFlow's threading model for CPU
backends. We evaluate TensorTuner on both Eigen and Intel's MKL CPU backends
using a set of neural networks from TensorFlow's benchmarking suite. Our
evaluation results demonstrate that the parameter settings found by TensorTuner
produce 2% to 123% performance improvement for the Eigen CPU backend and 1.5%
to 28% performance improvement for the MKL CPU backend over the performance
obtained using their best-known parameter settings. This highlights the fact
that the default parameter settings in Eigen CPU backend are not the ideal
settings; and even for a carefully hand-tuned MKL backend, the settings may be
sub-optimal. Our evaluations also revealed that TensorTuner is efficient at
finding the optimal settings --- it is able to converge to the optimal settings
quickly by pruning more than 90% of the parameter search space.
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We consider neutral evolution of a large population subject to changes in its
population size. For a population with a time-variable carrying capacity we
have computed the distributions of the total branch lengths of its sample
genealogies. Within the coalescent approximation we have obtained a general
expression, Eq. (27), for the moments of these distributions for an arbitrary
smooth dependence of the population size on time. We investigate how the
frequency of population-size variations alters the distributions. This allows
us to discuss their influence on the distribution of the number of mutations,
and on the population homozygosity in populations with variable size.
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In this paper we study the problem of extension of holomorphic sections of
line bundles/vector bundles from reduced unions of strata of divisors. An
extension theorem of Ohsawa--Takegoshi type is proved. As consequences we
deduce several qualitative results on extension from snc divisors and generic
global generation of vector bundles.
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Gyrochronology can yield useful ages for field main-sequence stars, a regime
where other techniques are problematic. Typically, gyrochronology relations are
calibrated using young ($\lesssim 2$ Gyr) clusters, but the constraints at
older ages are scarce, making them potentially inaccurate and imprecise. In
order to test the performance of existing relations, we construct samples of
stellar pairs with coeval components, for a range of ages and with available
rotation periods. These include randomly paired stars in clusters, and wide
binaries in the Kepler field. We design indicators that, based on the measured
rotation periods and expectations from gyrochronology, quantify the
(dis)agreement between the coeval pairs and the gyrochronology calibrations
under scrutiny. Our results show that wide binaries and cluster members are in
better concordance with gyrochronology than samples of randomly paired field
stars, confirming that the relations have predicting power. However, the
agreement with the examined relations decreases for older stars, revealing a
degradation of the examined relations with age, in agreement with recent works.
This highlights the need for novel empirical constraints at older ages that may
allow revised calibrations. Notably, using coeval stars to test gyrochronology
poses the advantage of circumventing the need for age determinations while
simultaneously exploiting larger samples at older ages. Our test is independent
of any specific age-rotation relation, and it can be used to evaluate future
spin-down models. In addition, taking gyrochronology at face value, we note
that our results provide new empirical evidence that the components of field
wide binaries are indeed coeval.
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We study the drift of suspended micro-particles in a viscous liquid pumped
back and forth through a periodic lattice of pores (drift ratchet). In order to
explain the particle drift observed in such an experiment, we present an
one-dimensional deterministic model of Stokes' drag. We show that the stability
of oscillations of particle is related to their amplitude. Under appropriate
conditions, particles may drift and two mechanisms of transport are pointed
out. The first one is due to an spatio-temporal synchronization between the
fluid and particle motions. As results the velocity is locked by the ratio of
the space periodicity over the time periodicity. The direction of the transport
may switch by tuning the parameters. Noteworthy, its emergence is related to a
lattice of 2-periodic orbits but not necessary to chaotic dynamics. The second
mechanism is due to an intermittent bifurcation and leads to a slow transport
composed by long time oscillations following by a relative short transport to
the next pore. Both steps repeat in a quasi-periodic manner. The direction of
this last transport is strongly dependent on the pore geometry.
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Pre-trained language models (PTLM) have achieved impressive results in a
range of natural language understanding (NLU) and generation (NLG) tasks.
However, current pre-training objectives such as masked token prediction (for
BERT-style PTLMs) and masked span infilling (for T5-style PTLMs) do not
explicitly model the relational commonsense knowledge about everyday concepts,
which is crucial to many downstream tasks that need common sense to understand
or generate. To augment PTLMs with concept-centric commonsense knowledge, in
this paper, we propose both generative and contrastive objectives for learning
common sense from the text, and use them as intermediate self-supervised
learning tasks for incrementally pre-training PTLMs (before task-specific
fine-tuning on downstream datasets). Furthermore, we develop a joint
pre-training framework to unify generative and contrastive objectives so that
they can mutually reinforce each other. Extensive experimental results show
that our method, concept-aware language model (CALM), can pack more commonsense
knowledge into the parameters of a pre-trained text-to-text transformer without
relying on external knowledge graphs, yielding better performance on both NLU
and NLG tasks. We show that while only incrementally pre-trained on a
relatively small corpus for a few steps, CALM outperforms baseline methods by a
consistent margin and even comparable with some larger PTLMs, which suggests
that CALM can serve as a general, plug-and-play method for improving the
commonsense reasoning ability of a PTLM.
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We study collective dynamics of networks of mutually coupled identical Lorenz
oscillators near subcritical Hopf bifurcation. This system shows induced
multistable behavior with interesting spatio-temporal dynamics including
synchronization, desynchronization and chimera states. We find this network may
exhibit intermittent behavior due to the complex basin structures, where,
temporal dynamics of the oscillators in the ensemble switches between different
attractors. Consequently, different oscillators may show dynamics that is
intermittently synchronized (or desynchronized), giving rise to {\it
intermittent chimera states}. The behaviour of the intermittent laminar phases
is characterized by the characteristic time spend in the synchronization
manifold, which decays as power law. This intermittent dynamics is quite
general and can be extended for large number of oscillators interacting with
nonlocal, global and local coupling schemes.
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The automated analysis of administrative documents is an important field in
document recognition that is studied for decades. Invoices are key documents
among these huge amounts of documents available in companies and public
services. Invoices contain most of the time data that are presented in tables
that should be clearly identified to extract suitable information. In this
paper, we propose an approach that combines an image processing based
estimation of the shape of the tables with a graph-based representation of the
document, which is used to identify complex tables precisely. We propose an
experimental evaluation using a real case application.
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We present a measurement of the angle phi1 of the CKM Unitarity Triangle
using time-dependent Dalitz analysis of D -> Ks pi+ pi- decays produced in
neutral B meson decay to a neutral D meson and a light meson (B0bar -> D(*)
h0). The method allows a direct extraction of 2phi1 and, therefore, helps to
resolve the ambiguity between 2phi1 and pi-2phi1 in the measurement of sin
2phi1. We obtain sin 2phi1=0.78+-0.44+-0.22 and cos 2phi1=1.87+0.40+0.22
-0.53-0.32 The sign of cos 2phi1 is determined to be positive at 98.3% C.L.
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The dark matter puzzle is one of the most important open problems in modern
physics. The ultra-light axion is a well-motivated dark matter candidate,
conceived to resolve the strong-CP problem of quantum chromodynamics. Numerous
precision experiments are searching for the three non-gravitational
interactions of axion-like dark matter. Some of the searches are approaching
fundamental quantum limits on their sensitivity. This Perspective describes
several approaches that use quantum engineering to circumvent these limits.
Squeezing and single-photon counting can enhance searches for the axion-photon
interaction. Optimization of quantum spin ensemble properties is needed to
realize the full potential of spin-based searches for the
electric-dipole-moment and the gradient interactions of axion dark matter.
Several metrological and sensing techniques, developed in the field of quantum
information science, are finding natural applications in this area of
experimental fundamental physics.
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In the eikonal regime, we analytically calculate quasinormal resonance
frequencies for massless scalar perturbations of the higher-dimensional
Reissner--Nordstr\"{o}m (RN) black holes. Remarkably, we find that the
higher-dimensional RN black holes coupled with the massless scalar fields have
the fastest relaxation rates in the Schwarzschild limit, this is qualitatively
different from the four-dimensional case where the black hole with
non-vanishing charge has the fastest relaxation rate.
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Estimators computed from adaptively collected data do not behave like their
non-adaptive brethren. Rather, the sequential dependence of the collection
policy can lead to severe distributional biases that persist even in the
infinite data limit. We develop a general method -- $\mathbf{W}$-decorrelation
-- for transforming the bias of adaptive linear regression estimators into
variance. The method uses only coarse-grained information about the data
collection policy and does not need access to propensity scores or exact
knowledge of the policy. We bound the finite-sample bias and variance of the
$\mathbf{W}$-estimator and develop asymptotically correct confidence intervals
based on a novel martingale central limit theorem. We then demonstrate the
empirical benefits of the generic $\mathbf{W}$-decorrelation procedure in two
different adaptive data settings: the multi-armed bandit and the autoregressive
time series.
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Subsets and Splits
Filtered Text Samples
Retrieves 100 samples of text containing the specific phrase "You are a helpful assistant", providing limited insight into the dataset.
Helpful Assistant Text Samples
Returns a limited set of rows containing the phrase 'helpful assistant' in the text, providing basic filtering of relevant entries.