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We consider a random walk in an i.i.d. non-negative potential on the
d-dimensional integer lattice. The walk starts at the origin and is conditioned
to hit a remote location y on the lattice. We prove that the expected time
under the annealed path measure needed by the random walk to reach y grows only
linearly in the distance from y to the origin. In dimension one we show the
existence of the asymptotic positive speed.
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Let N be a normal subgroup of a finite group G and consider the set cd(G|N)
of degrees of irreducible characters of G whose kernels do not contain N. A
number of theorems are proved relating the set cd(G|N) to the structure of N.
For example, if N is solvable, its derived length is bounded above by a
function of |cd(G|N)|. Also, if |cd(G|N)| is at most 2, then N is solvable and
its derived length is at most |cd(G|N)|. If G is solvable and |cd(G|N)| = 3,
then the derived length of N is at most 3.
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We construct new embedded self-shrinkers of genus 3, 5, 7, 11 and 19 using
variational methods. Our self-shrinkers resemble doublings of the Platonic
solids and were discovered numerically by D. Chopp in 1994.
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Despite their competitive performance on knowledge-intensive tasks, large
language models (LLMs) still have limitations in memorizing all world knowledge
especially long tail knowledge. In this paper, we study the KG-augmented
language model approach for solving the knowledge graph question answering
(KGQA) task that requires rich world knowledge. Existing work has shown that
retrieving KG knowledge to enhance LLMs prompting can significantly improve
LLMs performance in KGQA. However, their approaches lack a well-formed
verbalization of KG knowledge, i.e., they ignore the gap between KG
representations and textual representations. To this end, we propose an
answer-sensitive KG-to-Text approach that can transform KG knowledge into
well-textualized statements most informative for KGQA. Based on this approach,
we propose a KG-to-Text enhanced LLMs framework for solving the KGQA task.
Experiments on several KGQA benchmarks show that the proposed KG-to-Text
augmented LLMs approach outperforms previous KG-augmented LLMs approaches
regarding answer accuracy and usefulness of knowledge statements.
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Without unrealistic continuity and smoothness assumptions on a distributional
density of one dimensional dataset, constructing an authentic possibly-gapped
histogram becomes rather complex. The candidate ensemble is described via a
two-layer Ising model, and its size is shown to grow exponentially. This
exponential complexity makes any exhaustive search in-feasible and all boundary
parameters local. For data compression via Uniformity, the decoding error
criterion is nearly independent of sample size. These characteristics nullify
statistical model selection techniques, such as Minimum Description Length
(MDL). Nonetheless practical and nearly optimal solutions are algorithmically
computable. A data-driven algorithm is devised to construct such histograms
along the branching hierarchy of a Hierarchical Clustering tree. Such resultant
histograms naturally manifest data's physical information contents:
deterministic structures of bin-boundaries coupled with stochastic structures
of Uniformity within each bin. Without enforcing unrealistic Normality and
constant variance assumptions, an application of possibly-gapped histogram is
devised, called analysis of Histogram (ANOHT), to replace Analysis of Variance
(ANOVA). Its potential applications are foreseen in digital re-normalization
schemes and associative pattern extraction among features of heterogeneous data
types. Thus constructing possibly-gapped histograms becomes a prerequisite for
knowledge discovery, via exploratory data analysis and unsupervised Machine
Learning.
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A review of the path integral approach to quantum cosmology and its relation
to canonical quantisation. The initial derivation of the Hartle-Hawking and
Vilenkin wavefunctions from the Euclidean Einstein-Hilbert action, and later,
from the Lorentzian path integral using Picard-Lefshets Theory is discussed.
Path integral quantisation of the Einstein-Cartan action is then employed to
obtain the wavefunctions for minisuperspace closed FRW universes dominated by
matter and radiation. These calculations are complimentary to those recently
carried out by Magueijo using canonical quantisation of the same action.
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Bootstrap current in stellarators can be presented as a sum of a
collisionless value given by the Shaing-Callen asymptotic formula and an
off-set current, which non-trivially depends on plasma collisionality and
radial electric field. Using NEO-2 modelling, analytical estimates and
semi-analytical studies with help of a propagator method, it is shown that the
off-set current in the $1/\nu$ regime does not converge with decreasing
collisionality $\nu_\ast$ but rather shows oscillations over $\log\nu_\ast$
with an amplitude of the order of the bootstrap current in an equivalent
tokamak. The convergence to the Shaing-Callen limit appears in regimes with
significant orbit precession, in particular, due to a finite radial electric
field, where the off-set current decreases as $\nu_\ast^{3/5}$. The off-set
current strongly increases in case of nearly aligned magnetic field maxima on
the field line where it diverges as $\nu_\ast^{-1/2}$ in the $1/\nu$ regime and
saturates due to the precession at a level exceeding the equivalent tokamak
value by ${v_E^\ast}^{-1/2}$ where $v_E^\ast$ is the perpendicular Mach number.
The latter off-set, however, can be minimized by further aligning local
magnetic field maxima and by fulfilling an extra integral condition of
"equivalent ripples" for the magnetic field. A criterion for the accuracy of
this alignment and of ripple equivalence is derived. In addition, the
possibility of the bootstrap effect at the magnetic axis caused by the above
off-set is also discussed.
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We search for the astrometric signatures of planets and brown dwarfs known
from radial velocity surveys in the improved Hipparcos intermediate astrometric
data provided by van Leeuwen (2007a). Our aim is to put more significant
constraints on the inclination and the longitude of the ascending node than was
possible before, resulting in unambiguous companion masses. We fitted the
astrometric orbits of 310 substellar companions around 258 stars to the
Hipparcos intermediate astrometric data. Even though the astrometric signatures
of the companions cannot be detected in most cases, the Hipparcos data still
provide lower limits on the inclination for all but 67 of the investigated
companions, which translates into upper limits on the masses of the unseen
companions. For nine companions the derived upper mass limit lies in the
planetary and for 75 companions in the brown dwarf mass regime, proving the
substellar nature of those objects. Two of those objects have minimum masses
also in the brown dwarf regime and are thus proven to be brown dwarfs. The
confirmed planets are the ones around Pollux (beta Gem b), epsilon Eri b,
epsilon Ret b, mu Ara b, upsilon And c and d, 47 UMa b, HD 10647 b and HD
147513 b. The confirmed brown dwarfs are HD 137510 b and HD 168443 c. In 20
cases, the astrometric signature of the substellar companion was detected in
the Hipparcos data. Of these 20 companions, three are confirmed as planets or
lightweight brown dwarfs (HD 87833 b, iota Dra b, and gamma Cep b), two as
brown dwarfs (HD 106252 b and HD 168443 b), and four are low-mass stars (BD -04
782 b, HD 112758 b, rho CrB b, and HD169822 b). Of the others, many are either
brown dwarfs or very low mass stars. For epsilon Eri, we derive a solution
which is very similar to the one obtained using Hubble Space Telescope data.
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For a weakly interacting Bose-Einstein condensate in a double well, an
appropriate time-dependent modulation of the trapping potential counter-acts
the "self-trapping" effects of the interactions, thereby allowing tunneling
between the wells. It is demonstrated numerically that this modulation can be
employed for transferring the condensate from one well to the other in a
controlled way. Moreover it allows the production of mesoscopic entangled
states on short time scales.
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It is proved, that if M is a connected, complete submanifold of a complex
space form N and each geodesic of M lies in an 1-dimensional totally geodesic
complex submanifold of N, then M is totally geodesic in N and is a real space
form or a complex space form.
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We derive the super Yang-Mills action of Dp-branes on a torus T^{p-4} from
the nonabelian (2,0) theory with Lie 3-algebra. Our realization is based on Lie
3-algebra with pairs of Lorentzian metric generators. The resultant theory then
has negative norm modes, but it results in a unitary theory by setting VEV's of
these modes. This procedure corresponds to the torus compactification,
therefore by taking a transformation which is equivalent to T-duality, the
Dp-brane action is obtained. We also study type IIA/IIB NS5-brane and
Kaluza-Klein monopole systems by taking other VEV assignments. Such various
compactifications can be realized in the nonabelian (2,0) theory, since both
longitudinal and transverse directions can be compactified, which is different
from the BLG theory. We finally discuss U-duality among these branes, and show
that most of the moduli parameters in U-duality group are recovered. Especially
in D5-brane case, the whole U-duality relation is properly reproduced.
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Strong coupling of electronic and vibrational degrees of freedom entails a
low-bias suppression of the current through single-molecule devices, termed
Franck-Condon blockade. In the limit of slow vibrational relaxation, transport
in the Franck-Condon-blockade regime proceeds via avalanches of large numbers
of electrons, which are interrupted by long waiting times without electron
transfer. The avalanches consist of smaller avalanches, leading to a
self-similar hierarchy which terminates once the number of transferred
electrons per avalanche becomes of the order of unity. Experimental signatures
of self-similar avalanche transport are strongly enhanced current (shot) noise,
as expressed by giant Fano factors, and a power-law noise spectrum. We develop
a theory of the Franck-Condon-blockade regime with particular emphasis on
effects of electron cotunneling through highly excited vibrational states. As
opposed to the exponential suppression of sequential tunneling rates for
low-lying vibrational states, cotunneling rates suffer only a power-law
suppression. This leads to a regime where cotunneling dominates the current for
any gate voltage. Including cotunneling within a rate-equation approach to
transport, we find that both the Franck-Condon blockade and self-similar
avalanche transport remain intact in this regime. We predict that cotunneling
leads to absorption-induced vibrational sidebands in the Coulomb-blockaded
regime as well as intrinsic telegraph noise near the charge degeneracy point.
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We present a novel algorithm for dynamic routing with dedicated path
protection which, as the presented simulation results suggest, can be efficient
and exact. We present the algorithm in the setting of optical networks, but it
should be applicable to other networks, where services have to be protected,
and where the network resources are finite and discrete, e.g., wireless radio
or networks capable of advance resource reservation. To the best of our
knowledge, we are the first to propose an algorithm for this long-standing
fundamental problem, which can be efficient and exact, as suggested by
simulation results. The algorithm can be efficient because it can solve large
problems, and it can be exact because its results are optimal, as demonstrated
and corroborated by simulations. We offer a worst-case analysis to argue that
the search space is polynomially upper bounded. Network operations, management,
and control require efficient and exact algorithms, especially now, when
greater emphasis is placed on network performance, reliability, softwarization,
agility, and return on investment. The proposed algorithm uses our generic
Dijkstra algorithm on a search graph generated "on-the-fly" based on the input
graph. We corroborated the optimality of the results of the proposed algorithm
with brute-force enumeration for networks up to 15 nodes large. We present the
extensive simulation results of dedicated-path protection with signal
modulation constraints for elastic optical networks of 25, 50, and 100 nodes,
and with 160, 320, and 640 spectrum units. We also compare the bandwidth
blocking probability with the commonly-used edge-exclusion algorithm. We had
48,600~simulation runs with about 41 million searches.
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While large volumes of unlabeled data are usually available, associated
labels are often scarce. The unsupervised domain adaptation problem aims at
exploiting labels from a source domain to classify data from a related, yet
different, target domain. When time series are at stake, new difficulties arise
as temporal shifts may appear in addition to the standard feature distribution
shift. In this paper, we introduce the Match-And-Deform (MAD) approach that
aims at finding correspondences between the source and target time series while
allowing temporal distortions. The associated optimization problem
simultaneously aligns the series thanks to an optimal transport loss and the
time stamps through dynamic time warping. When embedded into a deep neural
network, MAD helps learning new representations of time series that both align
the domains and maximize the discriminative power of the network. Empirical
studies on benchmark datasets and remote sensing data demonstrate that MAD
makes meaningful sample-to-sample pairing and time shift estimation, reaching
similar or better classification performance than state-of-the-art deep time
series domain adaptation strategies.
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We investigated the disentanglement dynamics of two-qubit system in
Non-Markovian approach. We showed that only the couple strength with the
environment near to or less than fine-structure constant 1/137, entanglement
appear exponential decay for a certain class of two-qubit entangled state.
While the coupling between qubit and the environment is much larger, system
always appears the sudden-death of entanglement even in the vacuum environment.
|
Wavelet and frames have become a widely used tool in mathematics, physics,
and applied science during the last decade. In this article we discuss the
construction of frames for $L^2(\R^n)$ using the action of closed subgroups
$H\subset \mathrm{GL}(n,\mathbb{R})$ such that $H$ has an open orbit $\cO$ in
$\R^n$ under the action $(h,\omega)\mapsto (h^{-1})^T(\omega)$. If $H$ has the
form $ANR$, where $A$ is simply connected and abelian, $N$ contains a
co-compact discrete subgroup and $R$ is compact containing the stabilizer group
of $\omega\in\cO$ then we construct a frame for the space $L^2_{\cO}(\R^n)$ of
$L^2$-functions whose Fourier transform is supported in $\cO$. We apply this to
the case where $H^T=H$ and the stabilizer is a symmetric subgroup, a case
discussed for the continuous wavelet transform in a paper by Fabec and
Olafsson.
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Approximate Newton methods are a standard optimization tool which aim to
maintain the benefits of Newton's method, such as a fast rate of convergence,
whilst alleviating its drawbacks, such as computationally expensive calculation
or estimation of the inverse Hessian. In this work we investigate approximate
Newton methods for policy optimization in Markov Decision Processes (MDPs). We
first analyse the structure of the Hessian of the objective function for MDPs.
We show that, like the gradient, the Hessian exhibits useful structure in the
context of MDPs and we use this analysis to motivate two Gauss-Newton Methods
for MDPs. Like the Gauss-Newton method for non-linear least squares, these
methods involve approximating the Hessian by ignoring certain terms in the
Hessian which are difficult to estimate. The approximate Hessians possess
desirable properties, such as negative definiteness, and we demonstrate several
important performance guarantees including guaranteed ascent directions,
invariance to affine transformation of the parameter space, and convergence
guarantees. We finally provide a unifying perspective of key policy search
algorithms, demonstrating that our second Gauss-Newton algorithm is closely
related to both the EM-algorithm and natural gradient ascent applied to MDPs,
but performs significantly better in practice on a range of challenging
domains.
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We show that without other further assumption than affine equivariance and
locality, a numerical integrator has an expansion in a generalized form of
Butcher series (B-series) which we call aromatic B-series. We obtain an
explicit description of aromatic B-series in terms of elementary differentials
associated to aromatic trees, which are directed graphs generalizing trees. We
also define a new class of integrators, the class of aromatic Runge-Kutta
methods, that extends the class of Runge-Kutta methods, and have aromatic
B-series expansion but are not B-series methods. Finally, those results are
partially extended to the case of more general affine group equivariance.
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We study generalized multifractality characterizing fluctuations and
correlations of eigenstates in disordered systems of symmetry classes AII, D,
and DIII. Both metallic phases and Andersonlocalization transitions are
considered. By using the non-linear sigma-model approach, we construct
pure-scaling eigenfunction observables. The construction is verified by
numerical simulations of appropriate microscopic models, which also yield
numerical values of the corresponding exponents. In the metallic phases, the
numerically obtained exponents satisfy Weyl symmetry relations as well as
generalized parabolicity (proportionality to eigenvalues of the quadratic
Casimir operator). At the same time, the generalized parabolicity is strongly
violated at critical points of metal-insulator transitions, signalling
violation of local conformal invariance. Moreover, in classes D and DIII, even
the Weyl symmetry breaks down at critical points of metal-insulator
transitions. This last feature is related with a peculiarity of the sigma-model
manifolds in these symmetry classes: they consist of two disjoint components.
Domain walls associated with these additional degrees of freedom are crucial
for ensuring Anderson localization and, at the same time, lead to the violation
of the Weyl symmetry.
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Using 281 pb^-1 of data collected with the CLEO-c detector, we report on
first observations and new measurements of Cabibbo-suppressed decays of D
mesons to 2, 3, 4, and 5 pions. Branching fractions of previously unobserved
modes are measured to be: B(D^0\to
pi^+pi^-pi^0pi^0)=(9.9\pm0.6\pm0.7\pm0.2\pm0.1)x10^-3,
B(D^0\to\pi^+\pi^+\pi^-\pi^-\pi^0)=(4.1\pm0.5\pm0.2\pm0.1\pm0.0)x10^-3,
B(D^+\to\pi^+\pi^0\pi^0)=(4.8\pm0.3\pm0.3\pm0.2)x10^-3,
B(D^+\to\pi^+\pi^+\pi^-\pi^0)=(11.6\pm0.4\pm0.6\pm0.4)x10^-3,
B(D^0\to\eta\pi^0)=(0.62\pm0.14\pm0.05\pm0.01\pm0.01)x10^-3, and
B(D^0\to\omega\pi^+\pi^-)=(1.7\pm0.5\pm0.2\pm0.0\pm0.0)x10^-3. The
uncertainties are from statistics, experimental systematics, normalization and
CP correlations (for D^0 modes only). Improvements in other multi-pion decay
modes are also presented. The D-->pi pi rates allow us to extract the ratio of
isospin amplitudes A(Delta I=3/2)/A(\Delta
I=1/2)=0.420\pm0.014(stat)\pm0.016(syst) and the strong phase shift of
delta_I=(86.4+-2.8+-3.3) degrees, which is quite large and now more precisely
determined.
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We report the direct detection of Lyman Continuum (LyC) emission from 9
galaxies and 1 Active Galactic Nuclei (AGN) at $z$ $\sim$ 1.1-1.6 in the
GOODS-North field using deep observations from the Ultraviolet Imaging
Telescope (UVIT) onboard AstroSat. The absolute escape fraction of the sources
estimated from the far-ultraviolet (FUV) and H$\alpha$ line luminosities using
Monte Carlo (MC) analysis of two Inter-Galactic Medium (IGM) models span a
range $\sim$ 10 - 55 $\%$. The restframe UV wavelength of the sources falls in
the extreme-ultraviolet (EUV) regime $\sim$ 550-700 \AA, the shortest LyC
wavelength range probed so far. This redshift range remains devoid of direct
detections of LyC emission due to the instrumental limitations of previously
available facilities. With UVIT having a very low detector noise, each of these
sources are detected with an individual signal-to-noise ratio (SNR) $>$ 3 while
for the stack of six sources, we achieve an SNR $\sim$ 7.4. The LyC emission is
seen to be offset from the optical centroids and extended beyond the UVIT PSF
of 1.$^{\prime\prime}6$ in most of the sources. This sample fills an important
niche between GALEX and Cosmic Origins Spectrograph (COS) at low-$z$, and HST
WFC3 at high-$z$ and is crucial in understanding the evolution of LyC leakers.
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We report lattice dynamical measurements, made using neutron inelastic
scattering methods, of the relaxor perovskite PbMg1/3Nb2/3O3 (PMN) at momentum
transfers near the edge of the Brillouin zone. Unusual"columns" of phonon
scattering that are localized in momentum, but extended in energy, are seen at
both high-symmetry points along the zone edge: \vec{Q}_R={1/2, 1/2, 1/2} and
\vec{Q}_M={1/2,1/2,0}. These columns soften at ~400 K which is similar to the
onset temperature of the zone-center diffuse scattering, indicating a
competition between ferroelectric and antiferroelectric distortions. We propose
a model for the atomic displacements associated with these phonon modes that is
based on a combination of structure factors and group theoretical analysis.
This analysis suggests that the scattering is not from tilt modes (rotational
modes of oxygen octahedra), but from zone-boundary optic modes that are
associated with the displacement of Pb^{2+} and O^{2-} ions. Whereas similar
columns of scattering have been reported in metallic and (less commonly)
molecular systems, they are unusual in insulating materials, particularly in
ferroelectrics; therefore, the physical origin of this inelastic feature in PMN
is unknown. We speculate that the underlying disorder contributes to this
unique anomaly.
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Mathematical proofs are presented concerning the existence of solutions of
the Maxwell equations with suitable boundary conditions. In particular it is
stated that the well known "delayed potentials" provide effective solutions of
the equations, under reasonable conditions on the sources of the fields.
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Grain growth in planet-forming disks is the first step toward the formation
of planets. The growth of grains and their inward drift leaves a distinct
imprint on the dust surface-density distribution and the resulting
surface-brightness profile of the thermal continuum emission. We determine the
surface-brightness profile of the continuum emission using resolved
observations at millimeter wavelengths of the disk around TW Hya, and infer the
signature of dust evolution on the surface density and dust opacity. Archival
ALMA observations at 820 micron on baselines up to 410 kilolambda are compared
to parametrized disk models to determine the surface-brightness profile. Under
the assumption of a constant dust opacity, a broken radial power law best
describes the dust surface density, with a slope of -0.53 +/- 0.01 from the 4.1
au radius of the (already known) inner hole to a turn-over radius of 47.1 +/-
0.2 au, steepening to -8.0 +/- 0.1 at larger radii. The emission drops below
the detection limit beyond ~60 au. The shape of the dust surface density is
consistent with theoretical expectations for grain growth, fragmentation, and
drift, but its total dust content and its turn-over radius are too large for TW
Hya's age of 8-10 Myr even when taking into account a radially varying dust
opacity. Higher resolution imaging with ALMA of TW Hya and other disks is
required to establish if unseen gaps associated with, e.g., embedded planets
trap grains at large radii or if locally enhanced grain growth associated with
the CO snow line explains the extent of the millimeter-continuum surface
brightness profile. In the latter case, population studies should reveal a
correlation between the location of the CO snow line and the extent of the
millimeter continuum. In the former case, and if CO freeze out promotes planet
formation, this correlation should extend to the location of gaps as well.
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We study some geometrical and topological aspects of the generalised
dimensional reduction of supergravities in D=11 and D=10 dimensions, which give
rise to massive theories in lower dimensions. In these reductions, a global
symmetry is used in order to allow some of the fields to have a non-trivial
dependence on the compactifying coordinates. Global consistency in the internal
space imposes topological restrictions on the parameters of the
compactification as well as the structure of the space itself. Examples that we
consider include the generalised reduction of the type IIA and type IIB
theories on a circle, and also the massive ten-dimensional theory obtained by
the generalised reduction of D=11 supergravity.
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We compute the flux of linear momentum carried by gravitational waves emitted
from spinning binary black holes at 2PN order for generic orbits. In particular
we provide explicit expressions of three new types of terms, namely
next-to-leading order spin-orbit terms at 1.5 PN order, spin-orbit tail terms
at 2PN order, and spin-spin terms at 2PN order. Restricting ourselves to
quasi-circular orbits, we integrate the linear momentum flux over time to
obtain the recoil velocity as function of orbital frequency. We find that in
the so-called superkick configuration the higher-order spin corrections can
increase the recoil velocity up to about a factor 3 with respect to the
leading-order PN prediction. Furthermore, we provide expressions valid for
generic orbits, and accurate at 2PN order, for the energy and angular momentum
carried by gravitational waves emitted from spinning binary black holes.
Specializing to quasi-circular orbits we compute the spin-spin terms at 2PN
order in the expression for the evolution of the orbital frequency and found
agreement with Mik\'oczi, Vas\'uth and Gergely. We also verified that in the
limit of extreme mass ratio our expressions for the energy and angular momentum
fluxes match the ones of Tagoshi, Shibata, Tanaka and Sasaki obtained in the
context of black hole perturbation theory.
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High-resolution patterning of periodic structures over large areas has
several applications in science and technology. One such method, based on the
long-known Talbot effect observed with diffraction gratings, is achromatic
Talbot lithography (ATL). This method offers many advantages over other
techniques, such as high resolution, large depth of focus, high throughput,
etc. Although the technique has been studied in the past, its limits have not
yet been explored. Increasing the efficiency and the resolution of the method
is essential and might enable many applications in science and technology. In
this work, we combine this technique with spatially coherent and
quasi-monochromatic light at extreme ultraviolet (EUV) wavelengths and explore
new mask design schemes in order to enhance its throughput and resolution. We
report on simulations of various mask designs in order to explore their
efficiency. Advanced and optimized nanofabrication techniques have to be
utilized to achieve high quality and efficient masks for ATL. Exposures using
coherent EUV radiation from the Swiss light source (SLS) have been performed,
pushing the resolution limits of the technique for dense hole or dot patterning
down to 40 nm pitch. In addition, through extensive simulations, alternative
mask designs with rings instead of holes are explored for the efficient
patterning of hole/dot arrays. We show that these rings exhibit similar aerial
images to hole arrays, while enabling higher efficiency and thereby increased
throughput for ATL exposures. The mask designs with rings show that they are
less prone to problems associated with pattern collapse during the
nanofabrication process and therefore are promising for achieving higher
resolution.
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The main focus of this paper is to introduce a new method to control
perturbative calculations of CP asymmetric reaction rates in the Boltzmann
equation. CP asymmetries in particle reactions are traditionally calculated in
terms of complex couplings, Feynman integrals, and Cutkosky rules. We use an
expansion of the $S$-matrix unitarity condition instead, obtaining a general
expression for the asymmetries without reference to the imaginary part of the
loops. Asymmetry cancelations implied by CPT and unitarity are manifested in a
diagrammatic way and easy to track at any order of perturbation theory. We
demonstrate the power of this general framework within the right-handed
neutrino and top-quark scattering asymmetries in seesaw type-I leptogenesis.
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Recent works of the authors have demonstrated the usefulness of considering
moduli spaces of Artinian reductions of a given ring when studying standard
graded rings and their Lefschetz properties. This paper illuminates a key
aspect of these works, the behaviour of the canonical module under deformations
in this moduli space. We demonstrate that even when there is no natural
geometry around, we can give a viewpoint that behaves like it, effectively
constructing geometry out of nothing, giving interpretation to intersection
numbers without cycles. Moreover, we explore some properties of this
normalization.
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Existing deep neural network based salient object detection (SOD) methods
mainly focus on pursuing high network accuracy. However, those methods overlook
the gap between network accuracy and prediction confidence, known as the
confidence uncalibration problem. Thus, state-of-the-art SOD networks are prone
to be overconfident. In other words, the predicted confidence of the networks
does not reflect the real probability of correctness of salient object
detection, which significantly hinder their real-world applicability. In this
paper, we introduce an uncertaintyaware deep SOD network, and propose two
strategies from different perspectives to prevent deep SOD networks from being
overconfident. The first strategy, namely Boundary Distribution Smoothing
(BDS), generates continuous labels by smoothing the original binary
ground-truth with respect to pixel-wise uncertainty. The second strategy,
namely Uncertainty-Aware Temperature Scaling (UATS), exploits a relaxed Sigmoid
function during both training and testing with spatially-variant temperature
scaling to produce softened output. Both strategies can be incorporated into
existing deep SOD networks with minimal efforts. Moreover, we propose a new
saliency evaluation metric, namely dense calibration measure C, to measure how
the model is calibrated on a given dataset. Extensive experimental results on
seven benchmark datasets demonstrate that our solutions can not only better
calibrate SOD models, but also improve the network accuracy.
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Based on drizzled F606W and F814W images, we present quantitative structural
parameters in the V-band rest-frame for all galaxies with z<1 and
I_814(AB)<24.5 mag in the Hubble Deep Fields North and South. Our structural
parameters are based on a two-component surface brightness distribution using a
Sersic bulge and an exponential disc. Detailed simulations and comparisons with
previous work are presented. The luminosity-size distribution of early-type
galaxies is consistent with the hypothesis that their structural properties
were already in place by z~1 and have evolved passively since then; early-type
galaxies were ~1.35(+-0.1) mag brighter in rest-frame V-band luminosity at
z~0.7 than now. Compared to present day late-type galaxies, those at z~0.7 with
L_V>0.2x10^{10} h^{-2} L_sun show a moderate decrease (~30(+-10)%) in size (or
interpreted differently, a decrease of ~0.77(+-0.30) mag in the central surface
brightness) at a given luminosity. Finally, we make a comparison of our results
with the infall and hierarchical models.
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In This paper we presented new approach for cursive Arabic text recognition
system. The objective is to propose methodology analytical offline recognition
of handwritten Arabic for rapid implementation. The first part in the writing
recognition system is the preprocessing phase is the preprocessing phase to
prepare the data was introduces and extracts a set of simple statistical
features by two methods : from a window which is sliding long that text line
the right to left and the approach VH2D (consists in projecting every character
on the abscissa, on the ordinate and the diagonals 45{\deg} and 135{\deg}) . It
then injects the resulting feature vectors to Hidden Markov Model (HMM) and
combined the two HMM by multi-stream approach.
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To overcome the data sparsity issue in short text topic modeling, existing
methods commonly rely on data augmentation or the data characteristic of short
texts to introduce more word co-occurrence information. However, most of them
do not make full use of the augmented data or the data characteristic: they
insufficiently learn the relations among samples in data, leading to dissimilar
topic distributions of semantically similar text pairs. To better address data
sparsity, in this paper we propose a novel short text topic modeling framework,
Topic-Semantic Contrastive Topic Model (TSCTM). To sufficiently model the
relations among samples, we employ a new contrastive learning method with
efficient positive and negative sampling strategies based on topic semantics.
This contrastive learning method refines the representations, enriches the
learning signals, and thus mitigates the sparsity issue. Extensive experimental
results show that our TSCTM outperforms state-of-the-art baselines regardless
of the data augmentation availability, producing high-quality topics and topic
distributions.
|
The paper deals with the determination of integral functional quality for
control system of generalized linear dynamic object with exponential activation
function by solving the inverse problem of dynamic programming. The obtained
functionals contain two components that define the power consumption of the
control object and stability of the trajectories of control object. In some
cases these components can not be represented by elementary functions but their
using can improve accuracy and reduce the energy consumption of control object.
|
Structure learning is a core problem in AI central to the fields of
neuro-symbolic AI and statistical relational learning. It consists in
automatically learning a logical theory from data. The basis for structure
learning is mining repeating patterns in the data, known as structural motifs.
Finding these patterns reduces the exponential search space and therefore
guides the learning of formulas. Despite the importance of motif learning, it
is still not well understood. We present the first principled approach for
mining structural motifs in lifted graphical models, languages that blend
first-order logic with probabilistic models, which uses a stochastic process to
measure the similarity of entities in the data. Our first contribution is an
algorithm, which depends on two intuitive hyperparameters: one controlling the
uncertainty in the entity similarity measure, and one controlling the softness
of the resulting rules. Our second contribution is a preprocessing step where
we perform hierarchical clustering on the data to reduce the search space to
the most relevant data. Our third contribution is to introduce an O(n ln n) (in
the size of the entities in the data) algorithm for clustering
structurally-related data. We evaluate our approach using standard benchmarks
and show that we outperform state-of-the-art structure learning approaches by
up to 6% in terms of accuracy and up to 80% in terms of runtime.
|
We propose a parameterized proxy principle from which $\kappa$-Souslin trees
with various additional features can be constructed, regardless of the identity
of $\kappa$. We then introduce the microscopic approach, which is a simple
method for deriving trees from instances of the proxy principle. As a
demonstration, we give a construction of a coherent $\kappa$-Souslin tree that
applies also for $\kappa$ inaccessible.
We then carry out a systematic study of the consistency of instances of the
proxy principle, distinguished by the vector of parameters serving as its
input. Among other things, it will be shown that all known $\diamondsuit$-based
constructions of $\kappa$-Souslin trees may be redirected through this new
proxy principle.
|
We report the results of two XMM-Newton observations of the ultra-compact
low-mass X-ray binary 4U1850-087 located in the galactic globular cluster
NGC6712. A broad emission feature at 0.7keV was detected in an earlier ASCA
observation and explained as the result of an unusual Ne/O abundance ratio in
the absorbing material local to the source. We find no evidence for this
feature and derive Ne/O ratios in the range 0.14-0.21, consistent with that of
the interstellar medium. During the second observation, when the source was 10%
more luminous, there is some evidence for a slightly higher Ne/O ratio and
additional absorption. Changes in the Ne/O abundance ratio have been detected
from another ultra-compact binary, 4U1543-624. We propose that these changes
result from an X-ray induced wind which is evaporated from an O and Ne rich
degenerate donor. As the source X-ray intensity increases so does the amount of
evaporation and hence the column densities and abundance ratio of Ne and O.
|
We present the first fully calibrated H$_2$, 1-0 S(1) image of the entire 30
Doradus nebula. The observations were conducted using the NOAO Extremely
Wide-Field Infrared Imager on the CTIO 4-meter Blanco Telescope. Together with
a NEWFIRM Br$\gamma$ image of 30 Doradus, our data reveal the morphologies of
the warm molecular gas and ionized gas in 30 Doradus. The brightest
H$_2$-emitting area, which extends from the northeast to the southwest of R136,
is a photodissociation region viewed face-on, while many clumps and pillar
features located at the outer shells of 30 Doradus are photodissociation
regions viewed edge-on. Based on the morphologies of H$_2$, Br$\gamma$,
$^{12}$CO, and 8$\mu$m emission, the H$_2$ to Br$\gamma$ line ratio and Cloudy
models, we find that the H$_2$ emission is formed inside the photodissociation
regions of 30 Doradus, 2 - 3 pc to the ionization front of the HII region, in a
relatively low-density environment $<$ 10$^4$ cm$^{-3}$. Comparisons with
Br$\gamma$, 8$\mu$m, and CO emission indicate that H$_2$ emission is due to
fluorescence, and provide no evidence for shock excited emission of this line.
|
The AMADEUS experiment deals with the investigation of the low-energy
kaon-nuclei hadronic interaction at the DA{\Phi}NE collider at LNF-INFN, which
is fundamental to respond longstanding questions in the non-perturbative QCD
strangeness sector. The antikaon-nucleon potential is investigated searching
for signals from possible bound kaonic clusters, which would open the
possibility for the formation of cold dense baryonic matter. The confirmation
of this scenario may imply a fundamental role of strangeness in astrophysics.
AMADEUS step 0 consisted in the reanalysis of 2004/2005 KLOE dataset,
exploiting K- absorptions in H, 4He, 9Be and 12C in the setup materials. In
this paper, together with a review on the multi-nucleon K- absorption and the
particle identification procedure, the first results on the {\Sigma}0-p channel
will be presented including a statistical analysis on the possible accomodation
of a deeply bound state
|
In this paper we show that there exists a family of domains
$\Omega_{\varepsilon}\subseteq\mathbb{R}^N$ with $N\ge2$, such that the
$stable$ solution of the problem \[ \begin{cases} -\Delta u= g(u)&\hbox{in
}\Omega_\varepsilon\\ u>0&\hbox{in }\Omega_\varepsilon\\ u=0&\hbox{on
}\partial\Omega_\varepsilon \end{cases} \] admits $k$ critical points with
$k\ge2$. Moreover the sets $\Omega_\varepsilon's$ are star-shaped and "close"
to a strip as $\varepsilon\to0$. Next, if $g(u)\equiv1$ and $N\ge3$ we exhibit
a family of domain $\Omega_\varepsilon's$ with $positive$ $mean$ $curvature$
and solutions $u_\varepsilon $ which have $k$ critical points with $k\ge2$. In
this case, the domains $\Omega_\varepsilon $ turn out to be "close" to a
cylinder as $\varepsilon\to0$.
|
A method is presented for characterizing the emittance dilution and dynamic
aperture for an arbitrary closed lattice that includes guide field magnet
errors, multipole errors and misalignments. This method, developed and tested
at the Cornell Electron Storage Ring Test Accelerator (CesrTA), has been
applied to the damping ring lattice for the International Linear Collider
(ILC). The effectiveness of beam based emittance tuning is limited by beam
position monitor (BPM) measurement errors, number of corrector magnets and
their placement, and correction algorithm. The specifications for damping ring
magnet alignment, multipole errors, number of BPMs, and precision in BPM
measurements are shown to be consistent with the required emittances and
dynamic aperture. The methodology is then used to determine the minimum number
of position monitors that is required to achieve the emittance targets, and how
that minimum depends on the location of the BPMs. Similarly, the maximum
tolerable multipole errors are evaluated. Finally, the robustness of each BPM
configuration with respect to random failures is explored.
|
The mass-radius relations for white dwarf stars are investigated by solving
the Newtonian as well as Tolman-Oppenheimer-Volkoff (TOV) equations for
hydrostatic equilibrium assuming the electron gas to be non-interacting. We
find that the Newtonian limiting mass of $1.4562M_\odot$ is modified to
$1.4166M_\odot$ in the general relativistic case for $^4_2$He (and $^{12}_{\
6}$C) white dwarf stars. Using the same general relativistic treatment, the
critical mass for $^{56}_{26}$Fe white dwarf is obtained as $1.2230M_\odot$. In
addition, departure from the ideal degenerate equation of state (EoS) is
accounted for by considering Salpeter's EoS along with the TOV equations
yielding slightly lower values for the critical masses, namely
$1.4081M_{\odot}$ for $^4_2$He, $1.3916M_{\odot}$ for $^{12}_{\ 6}$C and
$1.1565M_{\odot}$ for $^{56}_{26}$Fe white dwarfs. We also compare the critical
densities for gravitational instability with the neutronization threshold
densities to find that $^4_2$He and $^{12}_{\ 6}$C white dwarf stars are stable
against neutronization with the critical values of $1.4081M_\odot$ and
$1.3916M_{\odot}$, respectively. However the critical masses for $^{16}_{\
8}$O, $^{20}_{10}$Ne, $^{24}_{12}$Mg, $^{28}_{14}$Si, $^{32}_{16}$S and
$^{56}_{26}$Fe white dwarf stars are lower due to neutronization. Corresponding
to their central densities for neutronization thresholds, we obtain their
maximum stable masses due to neutronization by solving the TOV equation coupled
with the Salpeter EoS.
|
In this work, we tackle the problem of domain generalization for object
detection, specifically focusing on the scenario where only a single source
domain is available. We propose an effective approach that involves two key
steps: diversifying the source domain and aligning detections based on class
prediction confidence and localization. Firstly, we demonstrate that by
carefully selecting a set of augmentations, a base detector can outperform
existing methods for single domain generalization by a good margin. This
highlights the importance of domain diversification in improving the
performance of object detectors. Secondly, we introduce a method to align
detections from multiple views, considering both classification and
localization outputs. This alignment procedure leads to better generalized and
well-calibrated object detector models, which are crucial for accurate
decision-making in safety-critical applications. Our approach is
detector-agnostic and can be seamlessly applied to both single-stage and
two-stage detectors. To validate the effectiveness of our proposed methods, we
conduct extensive experiments and ablations on challenging domain-shift
scenarios. The results consistently demonstrate the superiority of our approach
compared to existing methods. Our code and models are available at:
https://github.com/msohaildanish/DivAlign
|
In the framework of effective string theory (EST), the asymptotic behavior of
a large Wilson loop in confining gauge theories can be expressed via Laplace
determinant with Dirichlet boundary condition on the Wilson contour. For a
general polygonal region, Laplace determinant can be computed using the
conformal anomaly and Schwarz-Christoffel transformation. One can construct
ratios of polygonal Wilson loops whose large-size limit can be expressed via
computable Laplace determinants and is independent of the (confining) gauge
group. These ratios are computed for hexagon polygons both in EST and by Monte
Carlo (MC) lattice simulations for the tree-dimensional lattice Z2 gauge theory
(dual to Ising model) near its critical point. For large hexagon Wilson loops a
perfect agreement is observed between the asymptotic EST expressions and the
lattice MC results.
|
We reconstruct the Hubble function from cosmic chronometers, supernovae, and
baryon acoustic oscillations compiled data sets via the Gaussian process (GP)
method and use it to draw out Horndeski theories that are fully anchored on
expansion history data. In particular, we consider three well-established
formalisms of Horndeski gravity which single out a potential through the
expansion data, namely: quintessence potential, designer Horndeski, and
tailoring Horndeski. We discuss each method in detail and complement it with
the GP reconstructed Hubble function to obtain predictive constraints on the
potentials and the dark energy equation of state.
|
We use Green's canonical syzygy conjecture for generic curves to prove that
the Green-Lazarsfeld gonality conjecture holds for generic curves of genus g,
and gonality d, if $g/3<d<[g/2]+2$.
|
We introduce StoDynProg, a small library created to solve Optimal Control
problems arising in the management of Renewable Power Sources, in particular
when coupled with an Energy Storage System. The library implements generic
Stochastic Dynamic Programming (SDP) numerical methods which can solve a large
class of Dynamic Optimization problems. We demonstrate the library capabilities
with a prototype problem: smoothing the power of an Ocean Wave Energy
Converter. First we use time series analysis to derive a stochastic Markovian
model of this system since it is required by Dynamic Programming. Then, we
briefly describe the "policy iteration" algorithm we have implemented and the
numerical tools being used. We show how the API design of the library is
generic enough to address Dynamic Optimization problems outside the field of
Energy Management. Finally, we solve the power smoothing problem and compare
the optimal control with a simpler heuristic control.
|
Training neural networks with binary weights and activations is a challenging
problem due to the lack of gradients and difficulty of optimization over
discrete weights. Many successful experimental results have been achieved with
empirical straight-through (ST) approaches, proposing a variety of ad-hoc rules
for propagating gradients through non-differentiable activations and updating
discrete weights. At the same time, ST methods can be truly derived as
estimators in the stochastic binary network (SBN) model with Bernoulli weights.
We advance these derivations to a more complete and systematic study. We
analyze properties, estimation accuracy, obtain different forms of correct ST
estimators for activations and weights, explain existing empirical approaches
and their shortcomings, explain how latent weights arise from the mirror
descent method when optimizing over probabilities. This allows to reintroduce
ST methods, long known empirically, as sound approximations, apply them with
clarity and develop further improvements.
|
The set of modular invariants that can be obtained from Galois
transformations is investigated systematically for WZW models. It is shown that
a large subset of Galois modular invariants coincides with simple current
invariants. For algebras of type B and D infinite series of previously unknown
exceptional automorphism invariants are found.
|
Except for the presence of gravitational wave source term, the relativistic
perturbation equations of a zero-pressure irrotational fluid in a flat
Friedmann world model coincide exactly with the Newtonian ones to the second
order in perturbations. Such a relativistic-Newtonian correspondence is
available in a special gauge condition (the comoving gauge) in which all the
variables are equivalently gauge invariant. In this work we compare our results
with the ones in the synchronous gauge which has been used often in the
literature. Although the final equations look simpler in the synchronous gauge,
the variables have remnant gauge modes. Except for the presence of the gauge
mode for the perturbed order variables, however, the equations in the
synchronous gauge are gauge invariant and can be exactly identified as the
Newtonian hydrodynamic equations in the Lagrangian frame. In this regard, the
relativistic equations to the second order in the comoving gauge are the same
as the Newtonian hydrodynamic equations in the Eulerian frame. We resolve
several issues related to the two gauge conditions often to fully nonlinear
orders in perturbations.
|
We propose in this article a framework for compilation of quantified
constraint satisfaction problems (QCSP). We establish the semantics of this
formalism by an interpretation to a QCSP. We specify an algorithm to compile a
QCSP embedded into a search algorithm and based on the inductive semantics of
QCSP. We introduce an optimality property and demonstrate the optimality of the
interpretation of the compiled QCSP.
|
The relation between the thermodynamic entropy production and non-Markovian
evolutions is matter of current research. Here, we study the behavior of the
stochastic entropy production in open quantum systems undergoing unital
non-Markovian dynamics. In particular, for the family of Pauli channels we show
that in some specific time intervals both the average entropy production and
the variance can decrease, provided that the quantum dynamics fails to be
P-divisible. Although the dynamics of the system is overall irreversible, our
result may be interpreted as a transient tendency towards reversibility,
described as a delta peaked distribution of entropy production around zero.
Finally, we also provide analytical bounds on the parameters in the generator
giving rise to the quantum system dynamics, so as to ensure irreversibility
mitigation of the corresponding non-Markovian evolution.
|
When analyzing modern machine learning algorithms, we may need to handle
kernel density estimation (KDE) with intricate kernels that are not designed by
the user and might even be irregular and asymmetric. To handle this emerging
challenge, we provide a strong uniform consistency result with the $L^\infty$
convergence rate for KDE on Riemannian manifolds with Riemann integrable
kernels (in the ambient Euclidean space). We also provide an $L^1$ consistency
result for kernel density estimation on Riemannian manifolds with Lebesgue
integrable kernels. The isotropic kernels considered in this paper are
different from the kernels in the Vapnik-Chervonenkis class that are frequently
considered in statistics society. We illustrate the difference when we apply
them to estimate the probability density function. Moreover, we elaborate the
delicate difference when the kernel is designed on the intrinsic manifold and
on the ambient Euclidian space, both might be encountered in practice. At last,
we prove the necessary and sufficient condition for an isotropic kernel to be
Riemann integrable on a submanifold in the Euclidean space.
|
Fixed parameter tractable algorithms for bounded treewidth are known to exist
for a wide class of graph optimization problems. While most research in this
area has been focused on exact algorithms, it is hard to find decompositions of
treewidth sufficiently small to make these al- gorithms fast enough for
practical use. Consequently, tree decomposition based algorithms have limited
applicability to large scale optimization. However, by first reducing the input
graph so that a small width tree decomposition can be found, we can harness the
power of tree decomposi- tion based techniques in a heuristic algorithm, usable
on graphs of much larger treewidth than would be tractable to solve exactly. We
propose a solution merging heuristic to the Steiner Tree Problem that applies
this idea. Standard local search heuristics provide a natural way to generate
subgraphs with lower treewidth than the original instance, and subse- quently
we extract an improved solution by solving the instance induced by this
subgraph. As such the fixed parameter tractable algorithm be- comes an
efficient tool for our solution merging heuristic. For a large class of sparse
benchmark instances the algorithm is able to find small width tree
decompositions on the union of generated solutions. Subsequently it can often
improve on the generated solutions fast.
|
This paper discusses the theory and application of learning Boolean functions
that are concentrated in the Fourier domain. We first estimate the VC dimension
of this function class in order to establish a small sample complexity of
learning in this case. Next, we propose a computationally efficient method of
empirical risk minimization, and we apply this method to the MNIST database of
handwritten digits. These results demonstrate the effectiveness of our model
for modern classification tasks. We conclude with a list of open problems for
future investigation.
|
One of the principal bottlenecks to atmosphere characterisation in the era of
all-sky surveys is the availability of fast, autonomous and robust atmospheric
retrieval methods. We present a new approach using unsupervised machine
learning to generate informed priors for retrieval of exoplanetary atmosphere
parameters from transmission spectra. We use principal component analysis (PCA)
to efficiently compress the information content of a library of transmission
spectra forward models generated using the PLATON package. We then apply a
$k$-means clustering algorithm in PCA space to segregate the library into
discrete classes. We show that our classifier is almost always able to
instantaneously place a previously unseen spectrum into the correct class, for
low-to-moderate spectral resolutions, $R$, in the range $R~=~30-300$ and noise
levels up to $10$~per~cent of the peak-to-trough spectrum amplitude. The
distribution of physical parameters for all members of the class therefore
provides an informed prior for standard retrieval methods such as nested
sampling. We benchmark our informed-prior approach against a standard
uniform-prior nested sampler, finding that our approach is up to a factor two
faster, with negligible reduction in accuracy. We demonstrate the application
of this method to existing and near-future observatories, and show that it is
suitable for real-world application. Our general approach is not specific to
transmission spectroscopy and should be more widely applicable to cases that
involve repetitive fitting of trusted high-dimensional models to large data
catalogues, including beyond exoplanetary science.
|
New sunspot data composites, some of which are radically different in the
character of their long-term variation, are evaluated over the interval
1845-2014. The method commonly used to calibrate historic sunspot data,
relative to modern-day data, is "daisy-chaining", whereby calibration is passed
from one data subset to the neighbouring one, usually using regressions of the
data subsets for the intervals of their overlap. Recent studies have
illustrated serious pitfalls in these regressions and the resulting errors can
be compounded by their repeated use as the data sequence is extended back in
time. Hence the recent composite data series by Usoskin et al. (2016),
$R_{UEA}$, is a very important advance because it avoids regressions,
daisy-chaining and other common, but invalid, assumptions: this is achieved by
comparing the statistics of "active day" fractions to those for a single
reference dataset. We study six sunspot data series including $R_{UEA}$ and the
new "backbone" data series $R_{BB}$, recently generated by Svalgaard and
Schatten (2016) by employing both regression and daisy-chaining. We show that
all six can be used with a continuity model to reproduce the main features of
the open solar flux variation for 1845-2014, as reconstructed from geomagnetic
activity data. However, some differences can be identified that are consistent
with tests using a basket of other proxies for solar magnetic fields. Using
data from a variety of sunspot observers, we illustrate problems with the
method employed in $R_{BB}$ which cause it to increasingly overestimate sunspot
numbers going back in time and we recommend using $R_{UEA}$ because it employs
more robust procedures that avoid such problems.
|
In this paper we solve the one-particle Schr\"{o}dinger equation in a
magnetic field whose flux lines exhibit mutual linking. To make this problem
analytically tractable, we consider a high-symmetry situation where the
particle moves in a three-sphere $(S^3)$. The vector potential is obtained from
the Berry connection of the two by two Hamiltonian $H(\v{r})=\hat{h}(\v{r})
\cdot\vec{\sigma}$, where $\v{r}\in S^3$, $\hat{h}\in S^2$ and $\vec{\sigma}$
are the Pauli matrices. In order to produce linking flux lines, the map
$\hat{h}:S^3\to S^2$ is made to possess nontrivial homotopy. The problem is
exactly solvable for a particular mapping ($\hat{h}$) . The resulting
eigenfunctions are SO(4) spherical harmonics, the same as those when the
magnetic field is absent. The highly nontrivial magnetic field lifts the
degeneracy in the energy spectrum in a way reminiscent of the Zeeman effect.
|
Breakthroughs in cancer biology have defined new research programs
emphasizing the development of therapies that target specific pathways in tumor
cells. Innovations in clinical trial design have followed with master protocols
defined by inclusive eligibility criteria and evaluations of multiple therapies
and/or histologies. Consequently, characterization of subpopulation
heterogeneity has become central to the formulation and selection of a study
design. However, this transition to master protocols has led to challenges in
identifying the optimal trial design and proper calibration of hyperparameters.
We often evaluate a range of null and alternative scenarios, however there has
been little guidance on how to synthesize the potentially disparate
recommendations for what may be optimal. This may lead to the selection of
suboptimal designs and statistical methods that do not fully accommodate the
subpopulation heterogeneity. This article proposes novel optimization criteria
for calibrating and evaluating candidate statistical designs of master
protocols in the presence of the potential for treatment effect heterogeneity
among enrolled patient subpopulations. The framework is applied to demonstrate
the statistical properties of conventional study designs when treatments offer
heterogeneous benefit as well as identify optimal designs devised to monitor
the potential for heterogeneity among patients with differing clinical
indications using Bayesian modeling.
|
This work is the first attempt to evaluate and compare felderated learning
(FL) and split neural networks (SplitNN) in real-world IoT settings in terms of
learning performance and device implementation overhead. We consider a variety
of datasets, different model architectures, multiple clients, and various
performance metrics. For learning performance, which is specified by the model
accuracy and convergence speed metrics, we empirically evaluate both FL and
SplitNN under different types of data distributions such as imbalanced and
non-independent and identically distributed (non-IID) data. We show that the
learning performance of SplitNN is better than FL under an imbalanced data
distribution, but worse than FL under an extreme non-IID data distribution. For
implementation overhead, we end-to-end mount both FL and SplitNN on Raspberry
Pis, and comprehensively evaluate overheads including training time,
communication overhead under the real LAN setting, power consumption and memory
usage. Our key observations are that under IoT scenario where the communication
traffic is the main concern, the FL appears to perform better over SplitNN
because FL has the significantly lower communication overhead compared with
SplitNN, which empirically corroborate previous statistical analysis. In
addition, we reveal several unrecognized limitations about SplitNN, forming the
basis for future research.
|
This brief note gives a survey on results relating to existence of closed
points on schemes, including an elementary topological characterization of the
schemes with (at least one) closed point.
|
Thermal conductivity $\kappa$ of MgO plays a fundamental role in
understanding the thermal evolution and mantle convection in the interior of
terrestrial planets. However, previous theoretical calculations deviate from
each other and the $\kappa$ of high-pressure B2 phase remains undetermined.
Here, by combining molecular dynamics and deep potential trained with
first-principles data, we systematically investigate the $\kappa$ of MgO from
ambient state to the core-mantle boundary (CMB) of super-Earth with
$5M_{\oplus}$. We point out the significance of 4-phonon scatterings and modify
the conventional thermal conductivity model of MgO by considering the
density-dependent proportion of 3-phonon and 4-phonon scatterings. The $\kappa$
profiles of MgO in Earth and super-Earth are further estimated. For
super-Earth, we predict a significant reduction of $\kappa$ at the B1-B2 phase
transition area near the CMB. This work provides new insights into thermal
transport under extreme conditions and an improved thermal model for
terrestrial planets.
|
Modern Convolutional Neural Networks (CNNs) are complex, encompassing
millions of parameters. Their deployment exerts computational, storage and
energy demands, particularly on embedded platforms. Existing approaches to
prune or sparsify CNNs require retraining to maintain inference accuracy. Such
retraining is not feasible in some contexts. In this paper, we explore the
sparsification of CNNs by proposing three model-independent methods. Our
methods are applied on-the-fly and require no retraining. We show that the
state-of-the-art models' weights can be reduced by up to 73% (compression
factor of 3.7x) without incurring more than 5% loss in Top-5 accuracy.
Additional fine-tuning gains only 8% in sparsity, which indicates that our fast
on-the-fly methods are effective.
|
We study the universal characteristics of the shape of a polymer chain in an
environment with correlated structural obstacles, applying the
field-theoretical renormalization group approach. Our results qualitatively
indicate an increase of the asymmetry of the polymer shape in crowded
environment comparing with the pure solution case.
|
Two-dimensional (2D) transition metal dichalcogenide (TMD) nanosheets exhibit
remarkable electronic and optical properties. The 2D features, sizable
bandgaps, and recent advances in the synthesis, characterization, and device
fabrication of the representative MoS$_2$, WS$_2$, WSe$_2$, and MoSe$_2$ TMDs
make TMDs very attractive in nanoelectronics and optoelectronics. Similar to
graphite and graphene, the atoms within each layer in 2D TMDs are joined
together by covalent bonds, while van der Waals interactions keep the layers
together. This makes the physical and chemical properties of 2D TMDs layer
dependent. In this review, we discuss the basic lattice vibrations of
monolayer, multilayer, and bulk TMDs, including high-frequency optical phonons,
interlayer shear and layer breathing phonons, the Raman selection rule,
layer-number evolution of phonons, multiple phonon replica, and phonons at the
edge of the Brillouin zone. The extensive capabilities of Raman spectroscopy in
investigating the properties of TMDs are discussed, such as interlayer
coupling, spin--orbit splitting, and external perturbations. The interlayer
vibrational modes are used in rapid and substrate-free characterization of the
layer number of multilayer TMDs and in probing interface coupling in TMD
heterostructures. The success of Raman spectroscopy in investigating TMD
nanosheets paves the way for experiments on other 2D crystals and related van
der Waals heterostructures.
|
Let $K$ be a number field and let $f : (\mathbb{P}^1)^n \to (\mathbb{P}^1)^n$
be a dominant endomorphism defined over $K$.
We show that if $V$ is an $f$-invariant subvariety (that is, $f(V)=V$) then
there is a positive integer $s_0$ such that
$ (f^{-s-1}(V)\setminus f^{-s}(V))(K) = \emptyset$ for every integer $s \geq
s_0$, answering the Preimages Question of Matsuzawa, Meng, Shibata, and Zhang
in the case of $(\mathbb{P}^1)^n$.
|
Automatic extraction of buildings in remote sensing images is an important
but challenging task and finds many applications in different fields such as
urban planning, navigation and so on. This paper addresses the problem of
buildings extraction in very high-spatial-resolution (VHSR) remote sensing (RS)
images, whose spatial resolution is often up to half meters and provides rich
information about buildings. Based on the observation that buildings in VHSR-RS
images are always more distinguishable in geometry than in texture or spectral
domain, this paper proposes a geometric building index (GBI) for accurate
building extraction, by computing the geometric saliency from VHSR-RS images.
More precisely, given an image, the geometric saliency is derived from a
mid-level geometric representations based on meaningful junctions that can
locally describe geometrical structures of images. The resulting GBI is finally
measured by integrating the derived geometric saliency of buildings.
Experiments on three public and commonly used datasets demonstrate that the
proposed GBI achieves the state-of-the-art performance and shows impressive
generalization capability. Additionally, GBI preserves both the exact position
and accurate shape of single buildings compared to existing methods.
|
The critical behavior of non-order parameter fields is discussed. We show
that relevant features of the deconfining phase transition can be determined by
monitoring universal properties induced by the order parameter on the physical
excitations. Some of the behaviors we uncover are already supported by lattice
results.
|
Potts spin systems play a fundamental role in statistical mechanics and
quantum field theory, and can be studied within the spin, the Fortuin-Kasteleyn
(FK) bond or the $q$-flow (loop) representation. We introduce a Loop-Cluster
(LC) joint model of bond-occupation variables interacting with $q$-flow
variables, and formulate a LC algorithm that is found to be in the same
dynamical universality as the celebrated Swendsen-Wang algorithm. This leads to
a theoretical unification for all the representations, and numerically, one can
apply the most efficient algorithm in one representation and measure physical
quantities in others. Moreover, by using the LC scheme, we construct a
hierarchy of geometric objects that contain as special cases the $q$-flow
clusters and the backbone of FK clusters, the exact values of whose fractal
dimensions in two dimensions remain as an open question. Our work not only
provides a unified framework and an efficient algorithm for the Potts model,
but also brings new insights into rich geometric structures of the FK clusters.
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Although it was demonstrated that discrete molecular levels determine the
sign and magnitude of the thermoelectric effect in single-molecule junctions,
full electrostatic control of these levels has not been achieved to date. Here,
we show that graphene nanogaps combined with gold microheaters serve as a
testbed for studying single-molecule thermoelectricity. Reduced screening of
the gate electric field compared to conventional metal electrodes allows
control of the position of the dominant transport orbital by hundreds of meV.
We find that the power factor of graphene-fullerene junctions can be tuned over
several orders of magnitude to a value close to the theoretical limit of an
isolated Breit-Wigner resonance. Furthermore, our data suggest that the power
factor of an isolated level is only given by the tunnel coupling to the leads
and temperature. These results open up new avenues for exploring
thermoelectricity and charge transport in individual molecules and highlight
the importance of level alignment and coupling to the electrodes for optimum
energy conversion in organic thermoelectric materials.
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A small-$x$ helicity evolution has been derived in 2016-18 and received an
important modification in 2022. This article discusses its general framework
and summarizes the recent theoretical developments, including the asymptotic
behaviors of helicity PDFs and $g_1$ structure function at small $x$. The
latest fits to various polarized scattering data are also discussed. The
results from this research program will provide important theoretical inputs
for the future polarized small-$x$ measurements at the electron-ion collider
(EIC).
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Using smart wearable devices to monitor patients electrocardiogram (ECG) for
real-time detection of arrhythmias can significantly improve healthcare
outcomes. Convolutional neural network (CNN) based deep learning has been used
successfully to detect anomalous beats in ECG. However, the computational
complexity of existing CNN models prohibits them from being implemented in
low-powered edge devices. Usually, such models are complex with lots of model
parameters which results in large number of computations, memory, and power
usage in edge devices. Network pruning techniques can reduce model complexity
at the expense of performance in CNN models. This paper presents a novel
multistage pruning technique that reduces CNN model complexity with negligible
loss in performance compared to existing pruning techniques. An existing CNN
model for ECG classification is used as a baseline reference. At 60% sparsity,
the proposed technique achieves 97.7% accuracy and an F1 score of 93.59% for
ECG classification tasks. This is an improvement of 3.3% and 9% for accuracy
and F1 Score respectively, compared to traditional pruning with fine-tuning
approach. Compared to the baseline model, we also achieve a 60.4% decrease in
run-time complexity.
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Although the present-day orbital distribution of minor bodies that go around
the Sun between the orbit of Neptune and the Kuiper Cliff is well understood,
past ~50 au from the Sun, our vision gets blurred as objects become fainter and
fainter and their orbital periods span several centuries. Deep imaging using
the largest telescopes can overcome the first issue but the problems derived
from the second one are better addressed using data analysis techniques. Here,
we make use of the heliocentric range and range-rate of the known Kuiper belt
objects and their uncertainties to identify structures in orbital parameter
space beyond the Kuiper Cliff. The distribution in heliocentric range there
closely resembles that of the outer main asteroid belt with a gap at 70 au that
may signal the existence of a dynamical analogue of the Jupiter family comets.
Outliers in the distribution of mutual nodal distances suggest that a massive
perturber is present beyond the heliopause.
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A slight modification to one of Tarski's axioms of plane Euclidean geometry
is proposed. This modification allows another of the axioms to be omitted from
the set of axioms and proven as a theorem. This change to the system of axioms
simplifies the system as a whole, without sacrificing the useful modularity of
some of its axioms. The new system is shown to possess all of the known
independence properties of the system on which it was based; in addition,
another of the axioms is shown to be independent in the new system.
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We present next-to-leading order QCD corrections to production of two $W$
bosons at the LHC in the Randall-Sundrum model. Various kinematical
distributions are obtained to order $\alpha_s$ in QCD by taking into account
all the parton level subprocesses. We estimate the impact of the QCD
corrections on various observables and find that they are significant. We also
show the reduction in factorization scale uncertainty when ${\cal O}(\alpha_s)$
effects are included.
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Let A be a basic connected finite dimensional algebra over a field k and let
Q be the ordinary quiver of A. To any presentation of A with Q and admissible
relations, R. Martinez-Villa and J. A. de La Pena have associated a group
called the fundamental group of this presentation. There may exist different
presentations of A with non isomorphic fundamental groups. In this note, we
show that if the field k has characteristic zero, if Q has no oriented cycles
and if Q has no double bypasses then there exists a privileged presentation of
A such that the fundamental group of any other presentation is the quotient of
the fundamental group of this privileged presentation.
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We combine moduli stabilisation and (chiral) model building in a fully
consistent global set-up in Type IIB/F-theory. We consider compactifications on
Calabi-Yau orientifolds which admit an explicit description in terms of toric
geometry. We build globally consistent compactifications with tadpole and
Freed-Witten anomaly cancellation by choosing appropriate brane set-ups and
world-volume fluxes which also give rise to SU(5)- or MSSM-like chiral models.
We fix all the Kaehler moduli within the Kaehler cone and the regime of
validity of the 4D effective field theory. This is achieved in a way compatible
with the local presence of chirality. The hidden sector generating the
non-perturbative effects is placed on a del Pezzo divisor that does not have
any chiral intersections with any other brane. In general, the vanishing D-term
condition implies the shrinking of the rigid divisor supporting the visible
sector. However, we avoid this problem by generating r<n D-term conditions on a
set of n intersecting divisors. The remaining (n-r) flat directions are fixed
by perturbative corrections to the Kaehler potential. We illustrate our general
claims in an explicit example. We consider a K3-fibred Calabi-Yau with four
Kaehler moduli, that is an hypersurface in a toric ambient space and admits a
simple F-theory up-lift. We present explicit choices of brane set-ups and
fluxes which lead to three different phenomenological scenarios: the first with
GUT-scale strings and TeV-scale SUSY by fine-tuning the background fluxes; the
second with an exponentially large value of the volume and TeV-scale SUSY
without fine-tuning the background fluxes; and the third with a very
anisotropic configuration that leads to TeV-scale strings and two micron-sized
extra dimensions. The K3 fibration structure of the Calabi-Yau three-fold is
also particularly suitable for cosmological purposes.
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Causal discovery can be a powerful tool for investigating causality when a
system can be observed but is inaccessible to experiments in practice. Despite
this, it is rarely used in any scientific or medical fields. One of the major
hurdles preventing the field of causal discovery from having a larger impact is
that it is difficult to determine when the output of a causal discovery method
can be trusted in a real-world setting. Trust is especially critical when human
health is on the line.
In this paper, we report the results of a series of simulation studies
investigating the performance of different resampling methods as indicators of
confidence in discovered graph features. We found that subsampling and sampling
with replacement both performed surprisingly well, suggesting that they can
serve as grounds for confidence in graph features. We also found that the
calibration of subsampling and sampling with replacement had different
convergence properties, suggesting that one's choice of which to use should
depend on the sample size.
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TrueBit is a protocol that uses interactive verification to allow a
resource-constrained computation environment like a blockchain to perform much
larger computations than usual in a trusted way. As long as a single honest
participant is present to verify the computation, an invalid computation cannot
get accepted.
In TrueBit, the presence of such a verifier is incentivised by randomly
injected forced errors. Additionally, in order to counter sybil attacks, the
reward for finding an error drops off exponentially with the number of
challengers.
The main drawback of this mechanism is that it makes it very hard to predict
whether participation will be profitable or not.
To even out the rewards, we propose to randomly select multiple solvers from
a pool and evenly share the fees among them, while still allowing outside
challengers. Furthermore, a proof of independent execution will make it harder
to establish computation pools which share computation results.
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We investigate the shell structure of spherical nuclear bubbles in simple
phenomenological shell model potentials. The shell correction energies for
doubly magic bubbles may be as large as -40 MeV and probably imply a very long
lifetime against spontaneous fission. Beta-stability occurs for ratios of the
neutron number N to the proton number Z which differ markable from the
beta-stability valley of ordinary compact nuclei. The alpha-decay probability
is shown to be very small for proton rich bubbles with a moderately large outer
radius. Metastable islands of nuclear bubbles are shown to exist for nucleon in
the range A=450 - 3000.
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Atomistic simulations are used to study linear complexion formation at
dislocations in a body-centered cubic Fe-Ni alloy. Driven by Ni segregation,
precipitation of the metastable B2-FeNi and stable L10-FeNi phases occurs along
the compression side of edge dislocations. If the Ni segregation is not intense
enough to ensure precipitate growth and coalescence along the dislocation
lines, linear complexions in the form of stable nanoscale precipitate arrays
are observed. Critical conditions such as global composition and temperature
are defined for both linear complexion formation and dislocation-assisted
precipitation.
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This work is a Master thesis supervised by Prof. Dr. H.W. Lenstra. Lenstra
and Silverberg showed that each reduced order has a universal grading, which
can be viewed as the `largest possible grading'. We present an algorithm to
compute the universal grading for a given order $R$, which has runtime
$n^{O(m)}$, where n is the length of the input and m is the size of the minimal
spectrum of $R$. We do this by computing all gradings of the corresponding
reduced $\mathbb{Q}$-algebra with cyclic abelian groups of prime-power order.
We additionally generalize the result of Lenstra and Silverberg that reduced
orders have a universal grading to a broader class of rings.
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Chemical substitution is commonly used to explore new ground states in
materials, yet the role of disorder is often overlooked. In Mn-substituted
BaFe$_{2}$As$_{2}$ (MnBFA), superconductivity (SC) is absent, despite being
observed for nominal hole-doped phases. Instead, a glassy magnetic phase
emerges, associated with the $S=5/2$ Mn local spins. In this work, we present a
comprehensive investigation of the electronic structure of MnBFA using
angle-resolved photoemission spectroscopy (ARPES). We find that Mn causes a
small and orbital-specific reduction of the electron pockets, only partially
disrupting nesting conditions. Based upon the analysis of the spectral
properties, we observe, for all bands, an increase in the electronic scattering
rate as a function of Mn content. This is interpreted as increasing band
incoherence, which we propose as the primary contributor to the suppression of
the magnetic order in MnBFA. This finding connects the MnBFA electronic band
structure properties to the glassy magnetic behavior observed in these
materials and suggests that SC is absent because of the collective magnetic
impurity behavior that scatters the Fe-derived excitations. Additionally, our
analysis shows that the binding energy ($E_{B}$) dependence of the imaginary
part of the self-energy [$\text{Im}\Sigma(E_{B})$] is best described by a
fractional scaling ($\text{Im}\Sigma(E_{B})\propto\sqrt{-E_{B}}$). These
results indicate that Mn tunes MnBFA into an electronic disordered phase
between the correlated Hund's metal in BaFe$_{2}$As$_{2}$ and the Hund's
insulator in BaMn$_{2}$As$_{2}$.
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We consider the recursion operators with nonlocal terms of special form for
evolution systems in (1+1) dimensions, and extend them to well-defined
operators on the space of nonlocal symmetries associated with the so-called
universal Abelian coverings over these systems. The extended recursion
operators are shown to leave this space invariant. These results apply, in
particular, to the recursion operators of the majority of known today
(1+1)-dimensional integrable evolution systems. We also present some related
results and describe the extension of them and of the above results to
(1+1)-dimensional systems of PDEs transformable into the evolutionary form.
Some examples and applications are given.
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For any site of definition $\mathcal C$ of a Grothendieck topos $\mathcal E$,
we define a notion of a $\mathcal C$-ary Lawvere theory $\tau: \mathscr C \to
\mathscr T$ whose category of models is a stack over $\mathcal E$. Our
definitions coincide with Lawvere's finitary theories when $\mathcal
C=\aleph_0$ and $\mathcal E = \operatorname{\mathbf {Set}}$. We construct a
fibered category $\operatorname{\mathbf {Mod}}^{\mathscr T}$ of models as a
stack over $\mathcal E$ and prove that it is $\mathcal E$-complete and
$\mathcal E$-cocomplete. We show that there is a free-forget adjunction $F
\dashv U: \operatorname{\mathbf {Mod}}^{\mathscr T} \rightleftarrows \mathscr
E$. If $\tau$ is a commutative theory in a certain sense, then we obtain a
``locally monoidal closed'' structure on the category of models, which enhances
the free-forget adjunction to an adjunction of symmetric monoidal $\mathcal
E$-categories. Our results give a general recipe for constructing a monoidal
$\mathcal E$-cosmos in which one can do enriched $\mathcal E$-category theory.
As an application, we describe a convenient category of linear spaces generated
by the theory of Lebesgue integration.
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EGRET gamma-ray archival data used with GALPROP software show two ringlike
structures in Milky Way Plane which roughly tally with distribution of stars
([1] & references therein). To understand fully the implications of this and
similar results on detailed structure and rotation curve of especially Milky
Way Disk as well as rotation curves of other galaxies as derived from spatially
resolved spectroscopic data-cubes, a re-examination of the basis of the
connection between mass density and rotation curve is warranted. Kenneth F.
Nicholson's approach [2], which uses only Newtonian dynamics & gravity, is
presented.
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We use stellar proper motions (PM) from Gaia Data Release 2 for studying the
internal kinematics of Milky Way globular clusters. In addition to statistical
measurement errors, there are significant spatially correlated systematic
errors, which cannot be ignored when studying the internal kinematics. We
develop a mathematically consistent procedure for incorporating the spatial
correlations in any model-fitting approach, and use it to determine rotation
and velocity dispersion profiles of a few dozen clusters. We confirm detection
of rotation in the sky plane for ~10 clusters reported in previous studies, and
discover a few more clusters with rotation amplitudes exceeding ~0.05 mas/yr.
However, in more than half of these cases the significance of this rotation
signature is rather low when taking into account the systematic errors. We find
that the PM dispersion is not sensitive to systematic errors in PM, however, it
is quite sensitive to the selection criteria on the input sample, most
importantly, in crowded central regions. When using the cleanest possible
samples, PM dispersion can be reliably measured down to 0.1 mas/yr for ~60
clusters.
|
We describe a method for the identification of models for dynamical systems
from observational data. The method is based on the concept of symbolic
regression and uses genetic programming to evolve a system of ordinary
differential equations (ODE). The novelty is that we add a step of
gradient-based optimization of the ODE parameters. For this we calculate the
sensitivities of the solution to the initial value problem (IVP) using
automatic differentiation. The proposed approach is tested on a set of 19
problem instances taken from the literature which includes datasets from
simulated systems as well as datasets captured from mechanical systems. We find
that gradient-based optimization of parameters improves predictive accuracy of
the models. The best results are obtained when we first fit the individual
equations to the numeric differences and then subsequently fine-tune the
identified parameter values by fitting the IVP solution to the observed
variable values.
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We investigate spin dependent transport in hybrid
superconductor(S)--normal-metal(N)--ferromagnet(F) structures under conditions
of proximity effect. We demonstrate the feasibility of the absolute spin-valve
effect for a certain interval of voltages in a system consisting of two coupled
tri-layer structures. Our results are also valid for non-collinear magnetic
configurations of the ferromagnets.
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Recent success in deep learning has generated immense interest among
practitioners and students, inspiring many to learn about this new technology.
While visual and interactive approaches have been successfully developed to
help people more easily learn deep learning, most existing tools focus on
simpler models. In this work, we present GAN Lab, the first interactive
visualization tool designed for non-experts to learn and experiment with
Generative Adversarial Networks (GANs), a popular class of complex deep
learning models. With GAN Lab, users can interactively train generative models
and visualize the dynamic training process's intermediate results. GAN Lab
tightly integrates an model overview graph that summarizes GAN's structure, and
a layered distributions view that helps users interpret the interplay between
submodels. GAN Lab introduces new interactive experimentation features for
learning complex deep learning models, such as step-by-step training at
multiple levels of abstraction for understanding intricate training dynamics.
Implemented using TensorFlow.js, GAN Lab is accessible to anyone via modern web
browsers, without the need for installation or specialized hardware, overcoming
a major practical challenge in deploying interactive tools for deep learning.
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The celebrated Primitive Normal Basis Theorem states that for any $n\ge 2$
and any finite field $\mathbb F_q$, there exists an element $\alpha\in \mathbb
F_{q^n}$ that is simultaneously primitive and normal over $\mathbb F_q$. In
this paper, we prove some variations of this result, completing the proof of a
conjecture proposed by Anderson and Mullen (2014). Our results also imply the
existence of elements of $\mathbb F_{q^n}$ with multiplicative order
$(q^n-1)/2$ and prescribed trace over $\mathbb F_q$.
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We study the homological algebra in the category $\mathcal{P}_p$ of strict
polynomial functors of degree $p$ over a field of positive characteristic $p$.
We determine the decomposition matrix of our category and we calculate the
Ext-groups between functors important from the point of view of representation
theory. Our results include computations of the Ext-algebras of simple functors
and Schur functors. We observe that the category $\mathcal{P}_p$ has a
Kazhdan-Lusztig theory and we show that the DG algebras computing the
Ext-algebras for simple functors and Schur functors are formal. These last
results allow one to describe the bounded derived category of $\mathcal{P}_p$
as derived categories of certain explicitly described graded algebras. We also
generalize our results to all blocks of $p$-weight $1$ in $\mathcal{P}_e$ for
$e>p.$
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Data on e+e- -> piplus-piminus-Upsilon(1S,2S,3S) show a large increase in
branching fractions near Upsilon(10860). A suggestion of Ali et al. is to
interpret this as evidence for a tetraquark, Yb(10890) = b-bbar. However, it
may also be interpreted in terms of Upsilon(10860) -> B-B*, B*B* and BsB*s
above the open-b threshold, followed by de-excitation processes such as $BB* ->
Upsilon (1S,2S,3S). In the charm sector, a hypothesis open to experimental test
is that X,Y and Z peaks in the mass range 3872 to 3945 MeV may all be due to
regular 3P1 and 3P2 c-cbar states (and perhaps 3P0) mixed with meson-meson.
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The quark surface of a strange star has a very low emissivity for X-ray
photons. I find that a small amount of normal matter at the quark surface with
temperature in the range $10^7\la T_{_S}} \ll mc^2/k\simeq 6\times 10^9$ K is
enough to produce X-rays with high luminosity, $L_X\simeq 10^{32}-
10^{34}(\Delta M/10^{-22}M_\odot)^2 erg s^{-1}$. For the total atmosphere mass
$\Delta M\sim (10^{-20}-10^{-19})M_\odot$, this luminosity may be as high as
the Eddington limit. The mean energy of X-ray photons which are radiated from
such a low-mass atmosphere of a strange star is $\sim 10^2(T_S/10^8 K)^{0.45}
\simeq 30-300$ times larger than the mean energy of X-ray photons which are
radiated from the surface of both a neutron star and a strange star with a
massive normal-matter envelope, $\Delta M\sim 10^{-5}M_\odot$, for a fixed
temperature at the stellar core. This raises the possibility that some black
hole candidates with hard X-ray spectra are, in fact, such strange stars with a
low-mass atmosphere. The X-ray emission from single strange stars is estimated.
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In this paper, we propose a trainable multiplication layer (TML) for a neural
network that can be used to calculate the multiplication between the input
features. Taking an image as an input, the TML raises each pixel value to the
power of a weight and then multiplies them, thereby extracting the higher-order
local auto-correlation from the input image. The TML can also be used to
extract co-occurrence from the feature map of a convolutional network. The
training of the TML is formulated based on backpropagation with constraints to
the weights, enabling us to learn discriminative multiplication patterns in an
end-to-end manner. In the experiments, the characteristics of the TML are
investigated by visualizing learned kernels and the corresponding output
features. The applicability of the TML for classification and neural network
interpretation is also evaluated using public datasets.
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The Software-Defined Air-Ground integrated Vehicular (SD-AGV) networks have
emerged as a promising paradigm, which realize the flexible on-ground resource
sharing to support innovative applications for UAVs with heavy computational
overhead. In this paper, we investigate a vehicular cloud-assisted task
scheduling problem in SD-AGV networks, where the computation-intensive tasks
carried by UAVs, and the vehicular cloud are modeled via graph-based
representation. To map each component of the graph tasks to a feasible vehicle,
while achieving the trade-off among minimizing UAVs' task completion time,
energy consumption, and the data exchange cost among moving vehicles, we
formulate the problem as a mixed-integer non-linear programming problem, which
is Np-hard. Moreover, the constraint associated with preserving task structures
poses addressing the subgraph isomorphism problem over dynamic vehicular
topology, that further complicates the algorithm design. Motivated by which, we
propose an efficient decoupled approach by separating the template (feasible
mappings between components and vehicles) searching from the transmission power
allocation. For the former, we present an efficient algorithm of searching for
all the isomorphic subgraphs with low computation complexity. For the latter,
we introduce a power allocation algorithm by applying $p$-norm and convex
optimization techniques. Extensive simulations demonstrate that the proposed
approach outperforms the benchmark methods considering various problem sizes.
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Developing intelligent persuasive conversational agents to change people's
opinions and actions for social good is the frontier in advancing the ethical
development of automated dialogue systems. To do so, the first step is to
understand the intricate organization of strategic disclosures and appeals
employed in human persuasion conversations. We designed an online persuasion
task where one participant was asked to persuade the other to donate to a
specific charity. We collected a large dataset with 1,017 dialogues and
annotated emerging persuasion strategies from a subset. Based on the
annotation, we built a baseline classifier with context information and
sentence-level features to predict the 10 persuasion strategies used in the
corpus. Furthermore, to develop an understanding of personalized persuasion
processes, we analyzed the relationships between individuals' demographic and
psychological backgrounds including personality, morality, value systems, and
their willingness for donation. Then, we analyzed which types of persuasion
strategies led to a greater amount of donation depending on the individuals'
personal backgrounds. This work lays the ground for developing a personalized
persuasive dialogue system.
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Maddox, et al. (2022) establish a new win probability estimation for college
basketball and compared the results with previous methods of Stern (1994),
Desphande and Jensen (2016) and Benz (2019). This paper proposes modifications
to the approach of Maddox, et al. (2022) for the NBA game and investigates the
performance of the model. Enhancements to the model are developed, and the
resulting adjusted model is compared with existing methods and to the ESPN
counterpart. To illustrate utility, all methods are applied to the November 23,
2019 game between the Chicago Bulls and Charlotte Hornets.
|
Splitting of Cooper pairs has recently been realized experimentally for
s-wave Cooper pairs. A split Cooper pair represents an entangled two-electron
pair state which has possible application in on-chip quantum computation.
Likewise the spin-activity of interfaces in nanoscale tunnel junctions has been
investigated theoretically and experimentally in recent years. However, the
possible implications of spin-active interfaces in Cooper pair splitters so far
have not been investigated. We analyse the current and the cross correlation of
currents in a superconductor ferromagnet beamsplitter including spin-active
scattering. Using the Hamiltonian formalism we calculate the cumulant
generating function of charge transfer. As a first step, we discuss
characteristics of the conductance for crossed Andreev reflection in
superconductor ferromagnet beamsplitters with s-wave and p-wave superconductors
and no spin-active scattering. In a second step, we consider spin-active
scattering and show how to realize p-wave splitting only using a s-wave
superconductor via the process of spin-flipped crossed Andreev reflection. We
present results for the conductance and cross correlations. Spin-activity of
interfaces in Cooper pair splitters allows for new features in ordinary s-wave
Cooper pair splitters, that can otherwise only be realised by using p-wave
superconductors. In particular it provides access to Bell states different from
the typical spin singlet state.
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Nuclear stellar cluster (NSCs) are known to exist around massive black holes
(MBHs) in galactic nuclei. Two formation scenarios were suggested for their
origin: Build-up of NSCs and Continuous in-situ star-formation. Here we study
the effects of star formation on the build-up of NSCs and its implications for
their long term evolution and their resulting structure. We show that
continuous star-formation can lead to the build-up of an NSC with properties
similar to those of the Milky-way NSC. We also find that the general structure
of the old stellar population in the NSC with in-situ star-formation could be
very similar to the steady-state Bahcall-Wolf cuspy structure. However, its
younger stellar population do not yet achieve a steady state. In
particular,formed/evolved NSCs with in-situ star-formation contain differential
age-segregated stellar populations which are not yet fully mixed. Younger
stellar populations formed in the outer regions of the NSC have a cuspy
structure towards the NSC outskirts, while showing a core-like distribution
inwards; with younger populations having larger core sizes.
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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.