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We report new results from our effort to identify obscured Wolf-Rayet stars
in the Galaxy. Candidates were selected by their near-infrared (2MASS) and
mid-infrared (Spitzer/GLIMPSE) color excesses, which are consistent with
free-free emission from ionized stellar winds and thermal excess from hot dust.
We have confirmed 12 new Wolf-Rayet stars in the Galactic disk, including 9 of
the nitrogen subtype (WN), and 3 of the carbon subtype (WC); this raises the
total number of Wolf-Rayet stars discovered with our approach to 27. We
classify one of the new stars as a possible dust-producing WC9d+OBI
colliding-wind binary, as evidenced by an infrared excess resembling that of
known WC9d stars, the detection of OBI features superimposed on the WC9
spectrum, and hard X-ray emission detected by XMM-Newton. A WC8 star in our
sample appears to be a member of the stellar cluster Danks 1, in contrast to
the rest of the confirmed Wolf-Rayet stars that generally do not appear to
reside within dense stellar clusters. Either the majority of the stars are
runaways from clusters, or they formed in relative isolation. We briefly
discuss prospects for the expansion and improvement of the search for
Wolf-Rayet stars throughout the Milky Way Galaxy.
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With the first direct detections of gravitational waves (GWs) from the
coalescence of compact binaries observed by the advanced LIGO and VIRGO
interferometers, the era of GW astronomy has begun. Whilst there is strong
evidence that the observed GWs are connected to the merger of two black holes
(BH) or two neutron stars (NS), future detections may present a less consistent
picture. Indeed, the possibility that the observed GW signal was created by a
merger of exotic compact objects (ECOs) such as boson stars (BS) or axion stars
(AS) has not yet been fully excluded. For a detailed understanding of the late
stages of the coalescence full 3D numerical relativity simulations are
essential. In this paper, we extend the infrastructure of the numerical
relativity code BAM, to permit the simultaneous simulation of baryonic matter
with bosonic scalar fields, thus enabling the study of BS-BS, BS-NS, and BS-BH
mergers. We present a large number of single star evolutions to test the newly
implemented routines, and to quantify the numerical challenges of such
simulations, which we find to partially differ from the default NS case. We
also compare head-on BS-BS simulations with independent numerical relativity
codes, namely the SpEC and the GRChombo codes, and find good general agreement.
Finally, we present what are, to the best of our knowledge, the first full NR
simulations of BS-NS mergers, a first step towards identifying the hallmarks of
BS-NS interactions in the strong gravity regime, as well as possible GW and
electromagnetic observables.
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The thermal conductivity of the heavy-fermion superconductor CeCoIn_5 has
been studied in a magnetic field rotating within the 2D planes. A clear
fourfold symmetry of the thermal conductivity which is characteristic of a
superconducting gap with nodes along the (+-pi,+-pi)-directions is resolved.
The thermal conductivity measurement also reveals a first order transition at
H_c2, indicating a Pauli limited superconducting state. These results indicate
that the symmetry most likely belongs to d_{x^2-y^2}, implying that the
anisotropic antiferromagnetic fluctuation is relevant to the superconductivity.
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The recovery of a signal from the magnitudes of its transformation, like the
Fourier transform, is known as the phase retrieval problem and is of big
relevance in various fields of engineering and applied physics. In this paper,
we present a fast inertial/momentum based algorithm for the phase retrieval
problem and we prove a convergence guarantee for the new algorithm and for the
Fast Griffin-Lim algorithm, whose convergence remained unproven in the past
decade. In the final chapter, we compare the algorithm for the Short Time
Fourier transform phase retrieval with the Griffin-Lim algorithm and FGLA and
to other iterative algorithms typically used for this type of problem.
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Unpacking and comprehending how black-box machine learning algorithms make
decisions has been a persistent challenge for researchers and end-users.
Explaining time-series predictive models is useful for clinical applications
with high stakes to understand the behavior of prediction models. However,
existing approaches to explain such models are frequently unique to data where
the features do not have a time-varying component. In this paper, we introduce
WindowSHAP, a model-agnostic framework for explaining time-series classifiers
using Shapley values. We intend for WindowSHAP to mitigate the computational
complexity of calculating Shapley values for long time-series data as well as
improve the quality of explanations. WindowSHAP is based on partitioning a
sequence into time windows. Under this framework, we present three distinct
algorithms of Stationary, Sliding and Dynamic WindowSHAP, each evaluated
against baseline approaches, KernelSHAP and TimeSHAP, using perturbation and
sequence analyses metrics. We applied our framework to clinical time-series
data from both a specialized clinical domain (Traumatic Brain Injury - TBI) as
well as a broad clinical domain (critical care medicine). The experimental
results demonstrate that, based on the two quantitative metrics, our framework
is superior at explaining clinical time-series classifiers, while also reducing
the complexity of computations. We show that for time-series data with 120 time
steps (hours), merging 10 adjacent time points can reduce the CPU time of
WindowSHAP by 80% compared to KernelSHAP. We also show that our Dynamic
WindowSHAP algorithm focuses more on the most important time steps and provides
more understandable explanations. As a result, WindowSHAP not only accelerates
the calculation of Shapley values for time-series data, but also delivers more
understandable explanations with higher quality.
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Covers are a kind of quasiperiodicity in strings. A string $C$ is a cover of
another string $T$ if any position of $T$ is inside some occurrence of $C$ in
$T$. The shortest and longest cover arrays of $T$ have the lengths of the
shortest and longest covers of each prefix of $T$, respectively. The literature
has proposed linear-time algorithms computing longest and shortest cover arrays
taking border arrays as input. An equivalence relation $\approx$ over strings
is called a substring consistent equivalence relation (SCER) iff $X \approx Y$
implies (1) $|X| = |Y|$ and (2) $X[i:j] \approx Y[i:j]$ for all $1 \le i \le j
\le |X|$. In this paper, we generalize the notion of covers for SCERs and prove
that existing algorithms to compute the shortest cover array and the longest
cover array of a string $T$ under the identity relation will work for any SCERs
taking the accordingly generalized border arrays.
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Many studies have shown that Physarum polycephalum slime mold is able to find
the shortest path in a maze. In this paper we study this behavior in a network,
using a hyperbolic model of chemotaxis. Suitable transmission and boundary
conditions at each node are considered to mimic the behavior of such an
organism in the feeding process. Several numerical tests are presented for
special network geometries to show the qualitative agreement between our model
and the observed behavior of the mold.
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We perform a detailed study of mesonic properties in a class of holographic
models of QCD, which is described by the Yang-Mills plus Chern-Simons action.
By decomposing the 5 dimensional gauge field into resonances and integrating
out the massive ones, we reproduce the Chiral Perturbative Theory Lagrangian up
to ${\cal O}(p^6)$ and obtain all the relevant low energy constants (LECs). The
numerical predictions of the LECs show minor model dependence, and agree
reasonably with the determinations from other approaches. Interestingly,
various model-independent relations appear among them. Some of these relations
are found to be the large-distance limits of universal relations between form
factors of the anomalous and even-parity sectors of QCD.
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The charged Higgs associated production with a W boson has a smooth cross
section as a function of the charged Higgs mass at muon colliders. The cross
section in Minimal Supersymmetric Standard Model is about 25fb in the range 200
GeV < mH < 400 GeV with tanbeta = 50. This is much larger than the
corresponding cross section at an e+e- collider which reaches a fraction of
femtobarn. The observability of this channel at a muon collider has been
recently studied in an earlier work leading to the result that with 1 ab-1, a
5sigma signal can be observed throughout the aforementioned mass range. In this
paper, results of a study based on a general two Higgs doublet model (type II
and III) are presented and the cross section of this process in the most
sensitive parameter space is evaluated. It is concluded that the cross section
of this process increases with increasing neutral Higgs boson masses involved
in the s-channel diagram and can be as large as several picobarn with tanbeta =
50. The region of "physical Higgs boson mass" parameter space which could lead
to a 5 sigma signal at 50fb-1 is specified.
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Buildings have been introduced by J. Tits in order to study semi-simple
algebraic groups from a geometrical point of view. One of the most important
results in the theory of buildings is the classification of irreducible
spherical buildings of rank at least 3. About 25 years ago, M. Ronan and J.
Tits defined the class of twin buildings, which generalize spherical buildings
in a natural way. The motivation of their definition is provided by the theory
of Kac-Moody groups.
A 2-spherical twin building is uniquely determined by its local structure in
almost all cases: The so-called foundation is the union of the rank 2 residues
which contain an (arbitrary) chamber. Therefore, the classification of
2-spherical twin buildings reduces to the classification of all foundations
which can be realized as the local structure of such a twin building. We call
such a foundation "integrable".
By a result of Tits, an integrable foundation is Moufang, which means that
the rank 2 buildings in the foundation are Moufang polygons, and that the
glueings are compatible with the Moufang structures induced on the rank 1
residues. As a consequence, the classification of Moufang polygons and the
solution of the isomorphism problem for Moufang sets are essential to work out
which Moufang polygons fit together in order to form a foundation.
The present thesis contributes to establish complete lists of integrable
foundations for certain types of diagrams, namely for simply laced diagrams and
for 443 triangle diagrams. In this process, we closely follow the approach for
the classification of spherical buildings. However, we have to refine the
techniques used there, since in general, foundations don't only depend on the
diagram and the defining field.
Moreover, one of the main results in the context of Moufang sets is the
solution of the isomorphism problem for Moufang sets of pseudo-quadratic
spaces.
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In a recent work, Fouli and Lin generalized a Villarreal's result and showed
that if each connected components of the line graph of a squarefree monomial
ideal contains at most a unique odd cycle, then this ideal is of linear type.
In this short note, we reprove this result with Villarreal's original ideas
together with a method of Conca and De Negri. We also propose a class of
squarefree monomial ideals of linear type.
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The densities of Yang-Lee zeros for the Ising ferromagnet on the $L\times L$
square lattice are evaluated from the exact grand partition functions
($L=3\sim16$). The properties of the density of Yang-Lee zeros are discussed as
a function of temperature $T$ and system size $L$. The three different classes
of phase transitions for the Ising ferromagnet, first-order phase transition,
second-order phase transition, and Yang-Lee edge singularity, are clearly
distinguished by estimating the magnetic scaling exponent $y_h$ from the
densities of zeros for finite-size systems. The divergence of the density of
zeros at Yang-Lee edge in high temperatures (Yang-Lee edge singularity), which
has been detected only by the series expansion until now for the square-lattice
Ising ferromagnet, is obtained from the finite-size data. The identification of
the orders of phase transitions in small systems is also discussed using the
density of Yang-Lee zeros.
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This is a sequel to our previous paper (joint with Furusho). It will give a
more natural framework for constructing elements in the Hopf algebra of framed
mixed Tate motives according to Bloch and Kriz. This framework allows us to
extend our previous results to interpret all multiple zeta values (including
the divergent ones) and the multiple polylogarithms in one variable as elements
of this Hopf algebra. It implies that the pro-unipotent completion of the
torsor of paths on projective line minus three points, is a mixed Tate motive
in the sense of Bloch-Kriz. Also It allows us to interpret the multiple
logarithm as an element of this Hopf algebra as long as the products of
consecutive arguments are not 1.
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In this paper we prove the existence of a uniform bound for Frobenius test
exponents for parameter ideals of a local ring $(R, \frak m)$ of prime
characteristic in the following cases: (1) $R$ is generalized Cohen-Macaulay.
Our proof is much more simpler than the original proof of Huneke, Katzman,
Sharp and Yao, (2) The Frobenius actions on all lower local cohomologies
$H^i_{\frak m}(R)$, $i < \dim R$, are nilpotent.
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We study geometry of two-dimensional models of conformal space-time based on
the group of Moebius transformation. The natural geometric invariants, called
cycles, are used to linearise Moebius action. Conformal completion of the
space-time is achieved through an addition of a zero-radius cycle at infinity.
We pay an attention to the natural condition of non-reversibility of time arrow
in order to get a correct compactification in the hyperbolic case.
|
Selecting suitable charge transport layers and suppressing non-radiative
recombination at interfaces to the absorber layer are vital to maximize the
efficiency of halide perovskite solar cells. In this work, high-quality
perovskite thin films and devices are fabricated with different fullerene-based
electron transport layers and different self-assembled monolayers as hole
transport layers. We then perform a comparative study of a significant variety
of different electrical, optical and photoemission-based characterization
techniques to quantify the properties of the solar cells, the individual layers
and importantly the interfaces between them. In addition, we highlight the
limitations and problems of the different measurements, the insights gained by
combining different methods and the different strategies to extract information
from the experimental raw data.
|
The geometric, aesthetic, and mathematical elegance of origami is being
recognized as a powerful pathway to self-assembly of micro and nano-scale
machines with programmable mechanical properties. The typical approach to
designing the mechanical response of an ideal origami machine is to include
mechanisms where mechanical constraints transform applied forces into a desired
motion along a narrow set of degrees of freedom. In fact, to date, most design
approaches focus on building up complex mechanisms from simple ones in ways
that preserve each individual mechanism's degree of freedom (DOF), with
examples ranging from simple robotic arms to homogenous arrays of identical
vertices, such as the well-known Miura-ori. However, such approaches typically
require tight fabrication tolerances, and often suffer from parasitic
compliance. In this work, we demonstrate a technique in which
high-degree-of-freedom mechanisms associated with single vertices are
heterogeneously combined so that the coupled phase spaces of neighboring
vertices are pared down to a controlled range of motions. This approach has the
advantage that it produces mechanisms that retain the DOF at each vertex, are
robust against fabrication tolerances and parasitic compliance, but
nevertheless effectively constrain the range of motion of the entire machine.
We demonstrate the utility of this approach by mapping out the configuration
space for the modified Miura-ori vertex of degree 6, and show that when strung
together, their combined configuration spaces create mechanisms that isolate
deformations, constrain the configuration topology of neighboring vertices, or
lead to sequential bistable folding throughout the entire origami sheet.
|
For the SPECTRAP experiment at GSI, Germany, detectors with Single-Photon
counting capability in the visible and near-infrared regime are required. For
the wavelength region up to 1100 nm we investigate the performance of 2x2 mm^2
avalanche photo diodes (APDs) of type S0223 manufactured by Radiation
Monitoring Devices. To minimize thermal noise, the APDs are cooled to
approximately -170 deg. C using liquid nitrogen. By operating the diodes close
to the breakdown voltage it is possible to achieve relative gains in excess of
2x10^4. Custom-made low noise preamplifiers are used to read out the devices.
The measurements presented in this paper have been obtained at a relative gain
of 2.2x10^4. At a discriminator threshold of 6 mV the resulting dark count rate
is in the region of 230/s. With these settings the studied APDs are able to
detect single photons at 628 nm wavelength with a photo detection efficiency of
(67+-7)%. Measurements at 1020 nm wavelength have been performed using the
attenuated output of a grating spectrograph with a light bulb as photon source.
With this setup the photo detection efficiency at 1020 nm has been determined
to be (13+-3)%, again at a threshold of 6 mV.
|
Recently, it is increasingly popular to equip mobile RGB cameras with
Time-of-Flight (ToF) sensors for active depth sensing. However, for
off-the-shelf ToF sensors, one must tackle two problems in order to obtain
high-quality depth with respect to the RGB camera, namely 1) online calibration
and alignment; and 2) complicated error correction for ToF depth sensing. In
this work, we propose a framework for jointly alignment and refinement via deep
learning. First, a cross-modal optical flow between the RGB image and the ToF
amplitude image is estimated for alignment. The aligned depth is then refined
via an improved kernel predicting network that performs kernel normalization
and applies the bias prior to the dynamic convolution. To enrich our data for
end-to-end training, we have also synthesized a dataset using tools from
computer graphics. Experimental results demonstrate the effectiveness of our
approach, achieving state-of-the-art for ToF refinement.
|
We consider the inverse problem of H\"oldder-stably determining the time- and
space-dependent coefficients of the Schr\"odinger equation on a simple
Riemannian manifold with boundary of dimension $n\geq2$ from knowledge of the
Dirichlet-to-Neumann map. Assuming the divergence of the magnetic potential is
known, we show that the electric and magnetic potentials can be H\"older-stably
recovered from these data. Here we also remove the smallness assumption for the
solenoidal part of the magnetic potential present in previous results.
|
The main purpose of this paper is to present a general method for the
controllability of the stability of a system of fractional-order differential
equations around its equilibrium states. This method is applied to analyze and
control the fractional stability of the fractional 2-dimensional fractional
Toda lattice with one linear control.
|
We derive Kubo formulae for first-order spin hydrodynamics based on
non-equilibrium statistical operators method. In first-order spin
hydrodynamics, there are two new transport coefficients besides the ordinary
ones appearing in first-order viscous hydrodynamics. They emerge due to the
incorporation of the spin degree of freedom into fluids and the spin-orbital
coupling. Zubarev's non-equilibrium statistical operator method can be well
applied to investigate these quantum effects in fluids. The Kubo formulae,
based on the method of non-equilibrium statistical operators, are related to
equilibrium (imaginary-time) infrared Green's functions, and all the transport
coefficients can be determined when the microscopic theory is specified.
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We clarify three aspects of non-compact elliptic genera. Firstly, we give a
path integral derivation of the elliptic genus of the cigar conformal field
theory from its non-linear sigma-model description. The result is a manifestly
modular sum over a lattice. Secondly, we discuss supersymmetric quantum
mechanics with a continuous spectrum. We regulate the theory and analyze the
dependence on the temperature of the trace weighted by the fermion number. The
dependence is dictated by the regulator. From a detailed analysis of the
dependence on the infrared boundary conditions, we argue that in non-compact
elliptic genera right-moving supersymmetry combined with modular covariance is
anomalous. Thirdly, we further clarify the relation between the flat space
elliptic genus and the infinite level limit of the cigar elliptic genus.
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The environmental effect is commonly used to explain the excess of gas-poor
galaxies in galaxy clusters. Meanwhile, the presence of gas-poor galaxies at
cluster outskirts, where galaxies have not spent enough time to feel the
cluster environmental effect, hints for the presence of pre-processing. Using
cosmological hydrodynamic simulations on 16 clusters, we investigate the
mechanisms of gas depletion of galaxies found inside clusters. The gas
depletion mechanisms can be categorized into three channels based on where and
when they took place. First, 34$\%$ of our galaxies are gas poor before
entering clusters (`pre-processing'). They are mainly satellites that have
undergone the environmental effect inside group halos. Second, 43$\%$ of the
sample became quickly gas deficient in clusters before the first pericentric
pass (`fast cluster processing'). Some of them were group satellites that are
low in gas at the time of cluster entry compared to the galaxies directly
coming from the field. Even the galaxies with large gas fractions take this
channel if they fall into massive clusters ($> 10^{14.5}\, \rm M_{\odot}$) or
approach cluster centers through radial orbits. Third, 24$\%$ of our sample
retain gas even after their first pericentric pass (`slow cluster processing')
as they fall into the less massive clusters and/or have circular orbits. The
relative importance of each channel varies with a cluster's mass, while the
exact degree of significance is subject to large uncertainties. Group
pre-processing accounts for a third of the total gas depletion; but it also
determines the gas fraction of galaxies at their cluster entry which in turn
determines whether a galaxy should take the fast or the slow cluster
processing.
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This paper presents an approach to tackle the re-identification problem. This
is a challenging problem due to the large variation of pose, illumination or
camera view. More and more datasets are available to train machine learning
models for person re-identification. These datasets vary in conditions: cameras
numbers, camera positions, location, season, in size, i.e. number of images,
number of different identities. Finally in labeling: there are datasets
annotated with attributes while others are not. To deal with this variety of
datasets we present in this paper an approach to take information from
different datasets to build a system which performs well on all of them. Our
model is based on a Convolutional Neural Network (CNN) and trained using
multitask learning. Several losses are used to extract the different
information available in the different datasets. Our main task is learned with
a classification loss. To reduce the intra-class variation we experiment with
the center loss. Our paper ends with a performance evaluation in which we
discuss the influence of the different losses on the global re-identification
performance. We show that with our method, we are able to build a system that
performs well on different datasets and simultaneously extracts attributes. We
also show that our system outperforms recent re-identification works on two
datasets.
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The non-perturbative dynamics of quantum field theories is studied using
theoretical tools inspired by string formalism. Two main lines are developed:
the analysis of stringy instantons in a class of four-dimensional N=2 gauge
theories and the holographic study of the minimal model for a strongly coupled
unbalanced superconductor.
The field theory instanton calculus admits a natural and efficient
description in terms of D-brane models. In addition, the string viewpoint
offers the possibility of generalizing the ordinary instanton configurations.
Even though such generalized, or stringy, instantons would be absent in a
purely field-theoretical, low-energy treatment, we demonstrate that they do
alter the IR effective description of the brane dynamics by introducing
contributions related to the string scale. In the first part of this thesis we
compute explicitly the stringy instanton corrections to the effective
prepotential in a class of quiver gauge theories.
In the second part of the thesis, we present a detailed analysis of the
minimal holographic setup yielding an effective description of a superconductor
with two Abelian currents. The model contains a scalar field whose condensation
produces a spontaneous symmetry breaking which describes the transition to a
superfluid phase. This system has important applications in both QCD and
condensed matter physics; moreover, it allows us to study mixed electric-spin
transport properties (i.e. spintronics) at strong coupling.
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We present a new method to identify connected components on triangular grids
used in atmosphere and climate models to discretize the horizontal dimension.
In contrast to structured latitude-longitude grids, triangular grids are
unstructured and the neighbors of a grid cell do not simply follow from the
grid cell index. This complicates the identification of connected components
compared to structured grids. Here, we show that this complication can be
addressed by involving the mathematical tool of cubulation, which allows one to
map the 2-d cells of the triangular grid onto the vertices of the 3-d cells of
a cubic grid. Because the latter is structured, connected components can be
readily identified by previously developed software packages for cubic grids.
Computing the cubulation can be expensive, but importantly needs to be done
only once for a given grid. We implement our method in a Python package that we
name TriCCo and make available via pypi, gitlab and zenodo. We document the
package and demonstrate its application using simulation output from the ICON
atmosphere model. Finally, we characterize its computational performance and
compare it to graph-based identifications of connected components using
breadth-first search. The latter shows that TriCCo is ready for triangular
grids with up to 500,000 cells, but that its speed and memory requirement
should be improved for the application to larger grids.
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The measured masses of the Higgs boson and top quark indicate that the
effective potential of the standard model either develops an unstable
electroweak vacuum or stands stable all the way up to the Planck scale. In the
latter case in which the top quark mass is about $2\sigma$ below its present
central value, the Higgs boson can be the inflaton with the help of a large
nonminimal coupling to curvature in four dimensions. We propose a scenario in
which the Higgs boson can be the inflaton in a five-dimensional Gauss-Bonnet
braneworld model to solve both the unitarity and stability problems which
usually plague Higgs inflation. We find that in order for Higgs inflation to
happen successfully in the Gauss-Bonnet regime, the extra dimension scale must
appear roughly in the range between the TeV scale and the instability scale of
standard model. At the tree level, our model can give rise to a naturally small
nonminimal coupling $\xi\sim\mathcal{O}(1)$ for the Higgs quartic coupling
$\lambda\sim\mathcal{O}(0.1)$ if the extra dimension scale lies at the TeV
scale. At the loop level, the inflationary predictions at the tree level are
preserved. Our model can be confronted with future experiments and observations
from both particle physics and cosmology.
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A quantum-kinetic approach to the ultrafast dynamics of carrier
multiplication in semiconductor quantum dots is presented. We investigate the
underlying dynamics in the electronic subband occupations and the time-resolved
optical emission spectrum, focusing on the interplay between the light-matter
and the Coulomb interaction. We find a transition between qualitatively
differing behaviors of carrier multiplication, which is controlled by the ratio
of the interaction induced time scale and the pulse duration of the exciting
light pulse. On short time scales, i.e., before intra-band relaxation, this
opens the possibility of detecting carrier multiplication without refering to
measurements of (multi-)exciton lifetimes.
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For a Grothendieck category having a noetherian generator, we prove that
there are only finitely many minimal atoms. This is a noncommutative analogue
of the fact that every noetherian scheme has only finitely many irreducible
components. It is also shown that each minimal atom is represented by a
compressible object.
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The $B(E2;0^+\to2^+)$ value in $^{68}$Ni has been measured using Coulomb
excitation at safe energies. The $^{68}$Ni radioactive beam was
post-accelerated at the ISOLDE facility (CERN) to 2.9 MeV/u. The emitted
$\gamma$ rays were detected by the MINIBALL detector array. A kinematic
particle reconstruction was performed in order to increase the measured c.m.
angular range of the excitation cross section. The obtained value of
2.8$^{+1.2}_{-1.0}$ 10$^2$ e$^2$fm$^4$ is in good agreement with the value
measured at intermediate energy Coulomb excitation, confirming the low
$0^+\to2^+$ transition probability.
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Optical nanoresonators are fundamental building blocks in a number of
nanotechnology applications (e.g. in spectroscopy) due to their ability to
efficiently confine light at the nanoscale. Recently, nanoresonators based on
the excitation of phonon polaritons (PhPs) $-$ light coupled to lattice
vibrations $-$ in polar crystals (e.g. SiC, or h-BN) have attracted much
attention due to their strong field confinement, high-quality factors, and
potential to enhance the photonic density of states at mid-infrared (IR)
frequencies. Here, we go one step further by introducing PhPs nanoresonators
that not only exhibit these extraordinary properties but also incorporate a new
degree of freedom $-$ twist tuning, i.e. the possibility to be spectrally
controlled by a simple rotation. To that end, we both take advantage of the
low-loss in-plane hyperbolic propagation of PhPs in the van der Waals crystal
$\alpha$-MoO$_3$, and realize dielectric engineering of a pristine
$\alpha$-MoO$_3$ slab placed on top of metal ribbon grating, which preserves
the high-quality of the polaritonic resonances. By simple rotating the
$\alpha$-MoO$_3$ slab in the plane (from 0 to 45$^{\circ}$), we demonstrate via
far- and near-field measurements that the narrow polaritonic resonances (with
quality factors Q up to 200) can be tuned in a broad range (up to 32 cm$^{-1}$,
i.e up 6 ~ times its full width at half maximum, FWHM ~ 5 cm$^{-1}$). Our
results open the door to the development of tunable low-loss nanotechnologies
at IR frequencies with application in sensing, emission or photodetection.
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A $(G,[k_1,\dots,k_t],\lambda)$ {\it partitioned difference family} (PDF) is
a partition $\cal B$ of an additive group $G$ into sets ({\it blocks}) of sizes
$k_1$, \dots, $k_t$, such that the list of differences of ${\cal B}$ covers
exactly $\lambda$ times every non-zero element of $G$. It is called {\it
Hadamard} (HPDF) if the order of $G$ is $2\lambda$. The study of HPDFs is
motivated by the fact that each of them gives rise, recursively, to infinitely
many other PDFs. Apart from the {\it elementary} HPDFs consisting of a Hadamard
difference set and its complement, only one HPDF was known. In this article we
present three new examples in several groups and we start a general
investigation on the possible existence of HPDFs with assigned parameters by
means of simple arguments.
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We calculate the in-plane modes of the vortex lattice in a rotating Bose
condensate from the Thomas-Fermi to the mean-field quantum Hall regimes. The
Tkachenko mode frequency goes from linear in the wavevector, $k$, for lattice
rotational velocities, $\Omega$, much smaller than the lowest sound wave
frequency in a finite system, to quadratic in $k$ in the opposite limit. The
system also supports an inertial mode of frequency $\ge 2\Omega$. The
calculated frequencies are in good agreement with recent observations of
Tkachenko modes at JILA, and provide evidence for the decrease in the shear
modulus of the vortex lattice at rapid rotation.
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The evolution over time of the non-linear slip behavior of a
polydimethylsiloxane (PDMS) polymer melt on a weakly adsorbing surface made of
short non-entangled PDMS chains densely end-grafted to the surface of a fused
silica prism has been measured. The critical shear rate at which the melt
enters the nonlinear slip regime has been shown to increase with time. The
adsorption kinetics of the melt on the same surface has been determined
independently using ellipsometry. We show that the evolution of slip can be
explained by the slow adsorption of melt chains using the Brochard-de Gennes's
model.
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In this letter I present results from a correlation analysis of three galaxy
redshift catalogs: the SSRS2, the CfA2 and the PSCz. I will focus on the
observation that the amplitude of the two--point correlation function rises if
the depth of the sample is increased. There are two competing explanations for
this observation, one in terms of a fractal scaling, the other based on
luminosity segregation. I will show that there is strong evidence that the
observed growth is due to a luminosity dependent clustering of the galaxies.
|
This paper introduces a novel skiagraphic method for shading toroidal forms
in architectural illustrations, addressing the challenges of traditional
techniques. Skiagraphy projects 3D objects onto 2D surfaces to display
geometric properties. Traditional shading of tori involves extensive manual
calculations and multiple projections, leading to high complexity and
inaccuracies. The proposed method simplifies this by focusing on the elevation
view, eliminating the need for multiple projections and complex math. Utilizing
descriptive geometry, it reduces labor and complexity. Accuracy was validated
through comparisons with SketchUp-generated shading and various torus
configurations. This technique streamlines shading toroidal shapes while
maintaining the artistic value of traditional illustration. Additionally, it
has potential applications in 3D model generation from architectural shade
casts, contributing to the evolving field of architectural visualization and
representation.
|
Recently, a rigorous yet concise formula has been derived to evaluate the
information flow, and hence the causality in a quantitative sense, between time
series. To assess the importance of a resulting causality, it needs to be
normalized. The normalization is achieved through distinguishing three types of
fundamental mechanisms that govern the marginal entropy change of the flow
recipient. A normalized or relative flow measures its importance relative to
other mechanisms. In analyzing realistic series, both absolute and relative
information flows need to be taken into account, since the normalizers for a
pair of reverse flows belong to two different entropy balances; it is quite
normal that two identical flows may differ a lot in relative importance in
their respective balances. We have reproduced these results with several
autoregressive models. We have also shown applications to a climate change
problem and a financial analysis problem. For the former, reconfirmed is the
role of the Indian Ocean Dipole as an uncertainty source to the El Ni\~no
prediction. This might partly account for the unpredictability of certain
aspects of El Ni\~no that has led to the recent portentous but spurious
forecasts of the 2014 "Monster El Ni\~no". For the latter, an unusually strong
one-way causality has been identified from IBM (International Business Machines
Corporation) to GE (General Electric Company) in their early era, revealing to
us an old story, which has almost gone to oblivion, about "Seven Dwarfs"
competing a giant for the mainframe computer market.
|
Besides the target to pursue the narrow bandwidth X-ray pulses, the large
bandwidth free-electron laser pulses are also strongly demanded to satisfy a
wide range of scientific user experiments. In this paper, using the
transversely tilt beam enabled by deflecting cavity and/or corrugated
structure, the potential of large bandwidth X-ray free-electron lasers
generation with the natural gradient of the planar undulator are discussed.
Simulations confirm the theoretical prediction, and X-ray free-electron laser
bandwidth indicates an increase of one order of magnitude with the optimized
parameters.
|
Picking permutations at random, the expected number of k-cycles is known to
be 1/k and is, in particular, independent of the size of the permuted set. This
short note gives similar size-independent statistics of finite general linear
groups: ones that depend only on small minors. The proof technique uses
combinatorics of categories, motivated by representation stability, and applies
simultaneously to symmetric groups, finite linear groups and many other
settings.
|
We calculate the two-boson-exchange (TBE) corrections to the parity asymmetry
of the elastic electron-proton scattering in a model using the formalism of
generalized parton distributions (GPDs).
|
This paper is devoted to a stochastic differential game of functional
forward-backward stochastic differential equation (FBSDE, for short). The
associated upper and lower value functions of the stochastic differential game
are defined by controlled functional backward stochastic differential equations
(BSDEs, for short). Applying the Girsanov transformation method introduced by
Buckdahn and Li [1], the upper and the lower value functions are shown to be
deterministic. We also generalize the Hamilton-Jacobi-Bellman-Isaacs (HJBI, for
short) equations to the path-dependent ones. By establishing the dynamic
programming principal (DPP, for short), the upper and the lower value functions
are shown to be the viscosity solutions of the corresponding upper and the
lower path-dependent HJBI equations, respectively.
|
The quantum phase transition in an atom-molecule conversion system with
atomic hopping between different hyperfine states is studied. In mean field
approximation, we give the phase diagram whose phase boundary only depends on
the atomic hopping strength and the atom-molecule energy detuning but not on
the atomic interaction. Such a phase boundary is further confirmed by the
fidelity of the ground state and the energy gap between the first-excited state
and the ground one. In comparison to mean field approximation, we also study
the quantum phase transition in full quantum method, where the phase boundary
can be affected by the particle number of the system. Whereas, with the help of
finite-size scaling behaviors of energy gap, fidelity susceptibility and the
first-order derivative of entanglement entropy, we show that one can obtain the
same phase boundary by the MFA and full quantum methods in the limit of
$N\rightarrow \infty$. Additionally, our results show that the quantum phase
transition can happens at the critical value of the atomic hopping strength
even if the atom-molecule energy detuning is fixed on a certain value, which
provides one a new way to control the quantum phase transition.
|
Due to the finite kinetic energy in the intermediate $N\Delta$ state the
(internal) energy available for mesonic decay is decreased and consequently the
effective $N\Delta$ width is suppressed in $NN$ scattering. The same can happen
also in $\Delta$$\Delta$ case. Also the $N\Delta$ angular momentum suppresses
the width as well, while the effect of the initial $NN$ angular momentum is
more subtle. The state dependence affects e.g. pion production observables and
can also be seen as the origin of T=1 "dibaryons".
|
This survey is an introduction to asymptotic methods for portfolio-choice
problems with small transaction costs. We outline how to derive the
corresponding dynamic programming equations and simplify them in the small-cost
limit. This allows to obtain explicit solutions in a wide range of settings,
which we illustrate for a model with mean-reverting expected returns and
proportional transaction costs. For even more complex models, we present a
policy iteration scheme that allows to compute the solution numerically.
|
Intersecting D-brane theories motivate the existence of exotic U(1) gauge
bosons that only interact with the Standard Model through kinetic mixing with
hypercharge. We analyze an effective field theory description of this effect
and describe the implications of these exotic gauge bosons on precision
electroweak, LHC and ILC observables.
|
This letter studies the synchrophasor measurement error of electric power
distribution systems with on-line and off-line measurements using graphical and
numerical tests. It demonstrates that the synchrophasor measurement error
follows a non-Gaussian distribution instead of the traditionally-assumed
Gaussian distribution. It suggests the need to use non-Gaussian or Gaussian
mixture models to represent the synchrophasor measurement error. These models
are more realistic to accurately represent the error than the traditional
Gaussian model. The measurements and underlying analysis will be helpful for
the understanding of distribution system measurement characteristics, and also
for the modeling and simulation of distribution system applications.
|
There has been a great interest in magnetic field induced quantum spin
liquids in Kitaev magnets after the discovery of neutron scattering continuum
and half quantized thermal Hall conductivity in the material $\alpha$-RuCl$_3$.
In this work, we provide a semiclassical analysis of the relevant theoretical
models on large system sizes, and compare the results to previous studies on
quantum models with small system sizes. We find a series of competing magnetic
orders with fairly large unit cells at intermediate magnetic fields, which are
most likely missed by previous approaches. We show that quantum fluctuations
are typically strong in these large unit cell orders, while their magnetic
excitations may resemble a scattering continuum and give rise to a large
thermal Hall conductivity. Our work provides an important basis for a thorough
investigation of emergent spin liquids and competing phases in Kitaev magnets.
|
The primary obstacle to developing technologies for low-resource languages is
the lack of usable data. In this paper, we report the adoption and deployment
of 4 technology-driven methods of data collection for Gondi, a low-resource
vulnerable language spoken by around 2.3 million tribal people in south and
central India. In the process of data collection, we also help in its revival
by expanding access to information in Gondi through the creation of linguistic
resources that can be used by the community, such as a dictionary, children's
stories, an app with Gondi content from multiple sources and an Interactive
Voice Response (IVR) based mass awareness platform. At the end of these
interventions, we collected a little less than 12,000 translated words and/or
sentences and identified more than 650 community members whose help can be
solicited for future translation efforts. The larger goal of the project is
collecting enough data in Gondi to build and deploy viable language
technologies like machine translation and speech to text systems that can help
take the language onto the internet.
|
We describe a method of solving the nuclear Skyrme-Hartree-Fock problem by
using a deformed Cartesian harmonic oscillator basis. The complete list of
expressions required to calculate local densities, total energy, and
self-consistent fields is presented, and an implementation of the
self-consistent symmetries is discussed. Formulas to calculate matrix elements
in the Cartesian harmonic oscillator basis are derived for the nuclear and
Coulomb interactions.
|
We consider a wave equation with a potential on the half-line as a model
problem for wave propagation close to an extremal horizon, or the
asymptotically flat end of a black hole spacetime. We propose a definition of
quasinormal frequencies (QNFs) as eigenvalues of the generator of time
translations for a null foliation, acting on an appropriate (Gevrey based)
Hilbert space. We show that this QNF spectrum is discrete in a subset of
$\mathbb{C}$ which includes the region $\{$Re$(s) >-b$, $|$Im $(s)|> K\}$ for
any $b>0$ and some $K=K(b) \gg 1$. As a corollary we establish the
meromorphicity of the scattering resolvent in a sector $|$arg$(s)| <\varphi_0$
for some $\varphi_0 > \frac{2\pi}{3}$, and show that the poles occur only at
quasinormal frequencies according to our definition. This result applies in
situations where the method of complex scaling cannot be directly applied, as
our potentials need not be analytic. Finally, we show that QNFs computed by the
continued fraction method of Leaver are necessarily QNFs according to our new
definition. This paper is a companion to [D. Gajic and C. Warnick, Quasinormal
modes in extremal Reissner-Nordstr\"om spacetimes, preprint (2019)], which
deals with the QNFs of the wave equation on the extremal Reissner-Nordstr\"om
black hole.
|
Collisions of deformed uranium nuclei provide a unique opportunity to study
the spatial dependence of charmonium in-medium effects. By selecting the
orientations of the colliding nuclei, different path lengths through the
nuclear medium could be selected within the same experimental environment. In
addition, higher energy densities can be achieved in U+U collisions relative to
Au+Au collisions. In this paper, we investigate the prospects for charmonium
studies with U+U collisions. We discuss the effects of shadowing and nuclear
absorption on the J/\psi\ yield. We introduce a new observable which could help
distinguish between different types of J/\psi\ interactions in hot and dense
matter.
|
System operators employ operating reserves to deal with unexpected variations
of demand and generation and guarantee the security of supply. However, they
face new challenges to ensure this mission with the increasing share of
renewable generation. This article focuses on the operational approach adopted
by the French transmission system operator RTE for dynamically sizing the
required margins in the dynamic margin monitoring strategy context. It relies
on continuous forecasts of the main drivers of the uncertainties of the system
imbalance. Four types of forecast errors, assumed to be independent, are
considered in this approach: the errors in the wind and photovoltaic power
generation, production of conventional power units, and electricity
consumption. Then, the required margin is the result of comparing the global
forecast error, computed as the convolution of these independent errors, with a
security of supply criterion. This study presents the results of this method
implemented at RTE and used in real-time operation.
|
We define symplectic fractional twists, which generalize Dehn twists, and use
these in open books to investigate contact structures. The resulting contact
structures are invariant under a circle action, and share several similarities
with the invariant contact structures that were studied by Lutz and Giroux. We
show that left-handed fractional twists often give rise to non-fillable contact
manifolds. These manifolds are in fact "algebraically overtwisted", yet they do
not seem to contain bLobs, nor are they directly related to negative
stabilizations. We also show that the Weinstein conjecture holds for the
non-fillable contact manifolds we construct, and we investigate the symplectic
isotopy problem for fractional twists.
|
Synthesis of tri functional electrically conducting, optical and magnetic
nano-chain of Nicore-Aushell has been discussed here. Our Investigation
indicates that such material attached with biomolecule DNA in chain form will
have great potentiality in medical instrument and bio computer device.
|
Greybox fuzzing is a lightweight testing approach that effectively detects
bugs and security vulnerabilities. However, greybox fuzzers randomly mutate
program inputs to exercise new paths; this makes it challenging to cover code
that is guarded by complex checks.
In this paper, we present a technique that extends greybox fuzzing with a
method for learning new inputs based on already explored program executions.
These inputs can be learned such that they guide exploration toward specific
executions, for instance, ones that increase path coverage or reveal
vulnerabilities. We have evaluated our technique and compared it to traditional
greybox fuzzing on 26 real-world benchmarks. In comparison, our technique
significantly increases path coverage (by up to 3X) and detects more bugs (up
to 38% more), often orders-of-magnitude faster.
|
We investigate the influence of the inner profile of lens objects on
gravitational lens statistics taking into account of the effect of
magnification bias and both the evolution and the scatter of halo profiles. We
take the dark halos as the lens objects and consider the following three models
for the density profile of dark halos; SIS (singular isothermal sphere), the
NFW (Navarro Frenk White) profile, and the generalized NFW profile which has a
different slope at smaller radii. The mass function of dark halos is assumed to
be given by the Press-Schechter function. We find that magnification bias for
the NFW profile is order of magnitude larger than that for SIS. We estimate the
sensitivity of the lensing probability of distant sources to the inner profile
of lenses and to the cosmological parameters. It turns out that the lensing
probability is strongly dependent on the inner density profile as well as on
the cosmological constant. We compare the predictions with the largest
observational sample, the Cosmic Lens All-Sky Survey. The absence or presence
of large splitting events in larger surveys currently underway such as the 2dF
and SDSS could set constraints on the inner density profile of dark halos.
|
We study an ensemble of individuals playing the two games of the so-called
Parrondo paradox. In our study, players are allowed to choose the game to be
played by the whole ensemble in each turn. The choice cannot conform to the
preferences of all the players and, consequently, they face a simple
frustration phenomenon that requires some strategy to make a collective
decision. We consider several such strategies and analyze how fluctuations can
be used to improve the performance of the system.
|
We show that the $k$-point bound of de Laat, Machado, Oliveira, and
Vallentin, a hierarchy of upper bounds for the independence number of a
topological packing graph derived from the Lasserre hierarchy, converges to the
independence number.
|
We consider stationary viscous Mean-Field Games systems in the case of local,
decreasing and unbounded coupling. These systems arise in ergodic mean-field
game theory, and describe Nash equilibria of games with a large number of
agents aiming at aggregation. We show how the dimension of the state space, the
behavior of the coupling and the Hamiltonian at infinity affect the existence
and non-existence of regular solutions. Our approach relies on the study of
Sobolev regularity of the invariant measure and a blow-up procedure which is
calibrated on the scaling properties of the system. In very special cases we
observe uniqueness of solutions. Finally, we apply our methods to obtain new
existence results for MFG systems with competition, namely when the coupling is
local and increasing.
|
We study the dynamical stability of Proca-Higgs stars, in spherical symmetry.
These are solutions of the Einstein-Proca-Higgs model, which features a
Higgs-like field coupled to a Proca field, both of which minimally coupled to
the gravitational field. The corresponding stars can be regarded as Proca stars
with self-interactions, while avoiding the hyperbolicity issues of
self-interacting Einstein-Proca models. We report that these configurations are
stable near the Proca limit in the candidate stable branches, but exhibit
instabilities in certain parts of the parameter space, even in the candidate
stable branches, regaining their stability for very strong self-interactions.
This shows that for these models, unlike various examples of scalar boson
stars, self-interactions can deteriorate, rather than improve, the dynamical
robustness of bosonic stars.
|
Data association, the problem of reasoning over correspondence between
targets and measurements, is a fundamental problem in tracking. This paper
presents a graphical model formulation of data association and applies an
approximate inference method, belief propagation (BP), to obtain estimates of
marginal association probabilities. We prove that BP is guaranteed to converge,
and bound the number of iterations necessary. Experiments reveal a favourable
comparison to prior methods in terms of accuracy and computational complexity.
|
In this work we continue our study initiated in \cite{GFGP} on the uniqueness
properties of real solutions to the IVP associated to the Benjamin-Ono (BO)
equation. In particular, we shall show that the uniqueness results established
in \cite{GFGP} do not extend to any pair of non-vanishing solutions of the BO
equation. Also, we shall prove that the uniqueness result established in
\cite{GFGP} under a hypothesis involving information of the solution at three
different times can not be relaxed to two different times.
|
We propose a general framework for the recommendation of possible customers
(users) to advertisers (e.g., brands) based on the comparison between On-line
Social Network profiles. In particular, we represent both user and brand
profiles as trees where nodes correspond to categories and sub-categories in
the associated On-line Social Network. When categories involve posts and
comments, the comparison is based on word embedding, and this allows to take
into account the similarity between topics popular in the brand profile and
user preferences. Results on real datasets show that our approach is
successfull in identifying the most suitable set of users to be used as target
for a given advertisement campaign.
|
Multiple string matching is known as locating all the occurrences of a given
number of patterns in an arbitrary string. It is used in bio-computing
applications where the algorithms are commonly used for retrieval of
information such as sequence analysis and gene/protein identification.
Extremely large amount of data in the form of strings has to be processed in
such bio-computing applications. Therefore, improving the performance of
multiple string matching algorithms is always desirable. Multicore
architectures are capable of providing better performance by parallelizing the
multiple string matching algorithms. The Aho-Corasick algorithm is the one that
is commonly used in exact multiple string matching algorithms. The focus of
this paper is the acceleration of Aho-Corasick algorithm through a multicore
CPU based software implementation. Through our implementation and evaluation of
results, we prove that our method performs better compared to the state of the
art.
|
We discuss techniques and results for the extraction of the nucleon's
spin-dependent parton distributions and their uncertainties from data for
polarized deep-inelastic lepton-nucleon and proton-proton scattering by means
of a global QCD analysis. Computational methods are described that
significantly increase the speed of the required calculations to a level that
allows to perform the full analysis consistently at next-to-leading order
accuracy. We examine how the various data sets help to constrain different
aspects of the quark, anti-quark, and gluon helicity distributions. Uncertainty
estimates are performed using both the Lagrange multiplier and the Hessian
approaches. We use the extracted parton distribution functions and their
estimated uncertainties to predict spin asymmetries for high-transverse
momentum pion and jet production in polarized proton-proton collisions at 500
GeV center-of-mass system energy at BNL-RHIC, as well as for W boson
production.
|
Developing the proper representations for simulating high-speed flows with
strong shock waves, rarefactions, and contact discontinuities has been a
long-standing question in numerical analysis. Herein, we employ neural
operators to solve Riemann problems encountered in compressible flows for
extreme pressure jumps (up to $10^{10}$ pressure ratio). In particular, we
first consider the DeepONet that we train in a two-stage process, following the
recent work of \cite{lee2023training}, wherein the first stage, a basis is
extracted from the trunk net, which is orthonormalized and subsequently is used
in the second stage in training the branch net. This simple modification of
DeepONet has a profound effect on its accuracy, efficiency, and robustness and
leads to very accurate solutions to Riemann problems compared to the vanilla
version. It also enables us to interpret the results physically as the
hierarchical data-driven produced basis reflects all the flow features that
would otherwise be introduced using ad hoc feature expansion layers. We also
compare the results with another neural operator based on the U-Net for low,
intermediate, and very high-pressure ratios that are very accurate for Riemann
problems, especially for large pressure ratios, due to their multiscale nature
but computationally more expensive. Overall, our study demonstrates that simple
neural network architectures, if properly pre-trained, can achieve very
accurate solutions of Riemann problems for real-time forecasting. The source
code, along with its corresponding data, can be found at the following URL:
https://github.com/apey236/RiemannONet/tree/main
|
This research paper presents a thorough economic analysis of Bitcoin and its
impact. We delve into fundamental principles, and technological evolution into
a prominent decentralized digital currency. Analysing Bitcoin's economic
dynamics, we explore aspects such as transaction volume, market capitalization,
mining activities, and macro trends. Moreover, we investigate Bitcoin's role in
economy ecosystem, considering its implications on traditional financial
systems, monetary policies, and financial inclusivity. We utilize statistical
and analytical tools to assess equilibrium , market behaviour, and economic .
Insights from this analysis provide a comprehensive understanding of Bitcoin's
economic significance and its transformative potential in shaping the future of
global finance. This research contributes to informed decision-making for
individuals, institutions, and policymakers navigating the evolving landscape
of decentralized finance.
|
In this paper, we apply the theory of inverse semigroups to the
$C^{*}$-algebra $U[\mathbb{Z}]$ considered in \cite{Cuntz}. We show that the
$C^{*}$-algebra $U[\mathbb{Z}]$ is generated by an inverse semigroup of partial
isometries. We explicity identify the groupoid $\mathcal{G}_{tight}$ associated
to the inverse semigroup and show that $\mathcal{G}_{tight}$ is exactly the
same groupoid obtained in \cite{Cuntz-Li}.
|
The Askaryan Radio Array (ARA) reports an observation of radio emission
coincident with the "Valentine's Day" solar flare on Feb. 15$^{\rm{th}}$, 2011
in the prototype "Testbed" station. We find $\sim2000$ events that passed our
neutrino search criteria during the 70 minute period of the flare, all of which
reconstruct to the location of the sun. A signal analysis of the events reveals
them to be consistent with that of bright thermal noise correlated across
antennas. This is the first natural source of radio emission reported by ARA
that is tightly reconstructable on an event-by-event basis. The observation is
also the first for ARA to point radio from individual events to an
extraterrestrial source on the sky. We comment on how the solar flares, coupled
with improved systematic uncertainties in reconstruction algorithms, could aid
in a mapping of any above-ice radio emission, such as that from cosmic-ray air
showers, to astronomical locations on the sky.
|
OPERA is a neutrino oscillation experiment designed to perform a nu\_tau
appearance search at long distance in the future CNGS beam from CERN to Gran
Sasso. It is based on the nuclear emulsion technique to distinguish among the
neutrino interaction products the track of a tau produced by a nu\_tau and its
decay tracks. The OPERA detector is presently under construction in the Gran
Sasso underground laboratory, 730 km from CERN, and will receive its first
neutrinos in 2006. The experimental technique is reviewed and the development
of the project described. Foreseen performances in measuring nu\_tau appearance
and also in searching for nu\_e appearance are discussed.
|
In this paper, we present a proof of the Riemann hypothesis. We show that
zeros of the Riemann zeta function should be on the line with the real value
1/2, in the region where the real part of complex variable is between 0 and 1.
|
We construct a new class of exact string solutions with a four dimensional
target space metric of signature ($-,+,+,+$) by gauging the independent left
and right nilpotent subgroups with `null' generators of WZNW models for rank 2
non-compact groups $G$. The `null' property of the generators (${\rm Tr }(N_n
N_m)=0$) implies the consistency of the gauging and the absence of
$\a'$-corrections to the semiclassical backgrounds obtained from the gauged
WZNW models. In the case of the maximally non-compact groups ($G= SL(3),
SO(2,2), SO(2,3), G_2$) the construction corresponds to gauging some of the
subgroups generated by the nilpotent `step' operators in the Gauss
decomposition. The rank 2 case is a particular example of a general
construction leading to conformal backgrounds with one time-like direction. The
conformal theories obtained by integrating out the gauge field can be
considered as sigma model analogs of Toda models (their classical equations of
motion are equivalent to Toda model equations). The procedure of `null gauging'
applies also to other non-compact groups.
|
In this paper we study the locus of singular tuples of a complex valued
multisymmetric tensor. The main problem that we focus on is: given the set of
singular tuples of some general tensor, which are all the tensors that admit
those same singular tuples. Assume that the triangular inequality holds, that
is exactly the condition such that the dual variety to the Segre-Veronese
variety is an hypersurface, or equivalently, the hyperdeterminant exists. We
show in such case that, when at least one component has degree odd, this tensor
is projectively unique. On the other hand, if all the degrees are even, the
fiber is an $1$-dimensional space.
|
One-way functions are widely used for encrypting the secret in public key
cryptography, although they are regarded as plausibly one-way but have not been
proven so. Here we discuss the public key cryptosystem based on the system of
higher order Diophantine equations. In this system those Diophantine equations
are used as public keys for sender and recipient, and sender can recover the
secret from the Diophantine equation returned from recipient with a trapdoor.
In general the system of Diophantine equations is hard to solve when it is
positive-dimensional and it implies the Diophantine equations in this
cryptosystem works as a possible one-way function. We also discuss some
problems on implementation, which are caused from additional complexity
necessary for constructing Diophantine equations in order to prevent from
attacking by tamperers.
|
Spannotation is an open source user-friendly tool developed for image
annotation for semantic segmentation specifically in autonomous navigation
tasks. This study provides an evaluation of Spannotation, demonstrating its
effectiveness in generating accurate segmentation masks for various
environments like agricultural crop rows, off-road terrains and urban roads.
Unlike other popular annotation tools that requires about 40 seconds to
annotate an image for semantic segmentation in a typical navigation task,
Spannotation achieves similar result in about 6.03 seconds. The tools utility
was validated through the utilization of its generated masks to train a U-Net
model which achieved a validation accuracy of 98.27% and mean Intersection Over
Union (mIOU) of 96.66%. The accessibility, simple annotation process and
no-cost features have all contributed to the adoption of Spannotation evident
from its download count of 2098 (as of February 25, 2024) since its launch.
Future enhancements of Spannotation aim to broaden its application to complex
navigation scenarios and incorporate additional automation functionalities.
Given its increasing popularity and promising potential, Spannotation stands as
a valuable resource in autonomous navigation and semantic segmentation. For
detailed information and access to Spannotation, readers are encouraged to
visit the project's GitHub repository at
https://github.com/sof-danny/spannotation
|
The shaping of nuclear spin polarization profiles and the induction of
nuclear resonances are demonstrated within a parabolic quantum well using an
externally applied gate voltage. Voltage control of the electron and hole wave
functions results in nanometer-scale sheets of polarized nuclei positioned
along the growth direction of the well. RF voltages across the gates induce
resonant spin transitions of selected isotopes. This depolarizing effect
depends strongly on the separation of electrons and holes, suggesting that a
highly localized mechanism accounts for the observed behavior.
|
Discussion on "Random-projection ensemble classification" by T. Cannings and
R. Samworth. We believe that the proposed approach can find many applications
in economics such as credit scoring (e.g. Altman (1968)) and can be extended to
more general type of classifiers. In this discussion we would like to draw
authors attention to the copula-based discriminant analysis (Han et al. (2013)
and He et al. (2016)).
|
We develop a theory of quantum harmonic analysis on lattices in
$\mathbb{R}^{2d}$. Convolutions of a sequence with an operator and of two
operators are defined over a lattice, and using corresponding Fourier
transforms of sequences and operators we develop a version of harmonic analysis
for these objects. We prove analogues of results from classical harmonic
analysis and the quantum harmonic analysis of Werner, including Tauberian
theorems and a Wiener division lemma. Gabor multipliers from time-frequency
analysis are described as convolutions in this setting. The quantum harmonic
analysis is thus a conceptual framework for the study of Gabor multipliers, and
several of the results include results on Gabor multipliers as special cases.
|
The practical damage of silicon bipolar devices subjected to mixed ionization
and displacement irradiations is usually evaluated by the sum of separated
ionization and displacement damages. However, recent experiments show clear
difference between the practical and summed damages, indicating significant
irradiation synergistic effects (ISEs). Understanding the behaviors and
mechanisms of ISEs is essential to predict the practical damages. In this work,
we first make a brief review on the state of the art, critically emphasizing on
the difficulty encountered in previous models to understand the dose rate
dependence of the ISEs. We then introduce in detail our models explaining this
basic phenomenon, which can be described as follows. Firstly, we show our
experimental works on PNP and NPN transistors. A variable neutron fluence and
$\gamma$-ray dose setup is adopted. Fluence-dependent `tick'-like and sublinear
dose profiles are observed for PNP and NPN transistors, respectively. Secondly,
we describe our theoretical investigations on the positive ISE in NPN
transistors. We propose an atomistic model of transformation and annihilation
of $\rm V_2$ displacement defects in p-type silicon under ionization
irradiation, which is totally different from the traditional picture of Coulomb
interaction of oxide trapped charges in silica on charge carriers in irradiated
silicon. The predicted novel dose and fluence dependences are fully verified by
the experimental data. Thirdly, the mechanism of the observed negative ISE in
PNP transistors is investigated in a similar way as in the NPN transistor case.
The difference is that in n-type silicon, VO displacement defects also undergo
an ionization-induced transformation and annihilation process. Our results show
that, the evolution of displacement defects due to carrier-enhanced defect
diffusion and reaction is the dominating mechanism of the ISEs.
|
The present work introduces floodlight, an open source Python package built
to support and automate team sport data analysis. It is specifically designed
for the scientific analysis of spatiotemporal tracking data, event data, and
game codes in disciplines such as match and performance analysis, exercise
physiology, training science, and collective movement behavior analysis. It is
completely provider- and sports-independent and includes a high-level interface
suitable for programming beginners. The package includes routines for most
aspects of the data analysis process, including dedicated data classes, file
parsing functionality, public dataset APIs, pre-processing routines, common
data models and several standard analysis algorithms previously used in the
literature, as well as basic visualization functionality. The package is
intended to make team sport data analysis more accessible to sport scientists,
foster collaborations between sport and computer scientists, and strengthen the
community's culture of open science and inclusion of previous works in future
works.
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Textural and structural features can be regraded as "two-view" feature sets.
Inspired by the recent progress in multi-view learning, we propose a novel
two-view classification method that models each feature set and optimizes the
process of merging these views efficiently. Examples of implementation of this
approach in classification of real-world data are presented, with special
emphasis on medical images. We firstly decompose fully-textured images into two
layers of representation, corresponding to natural stochastic textures (NST)
and structural layer, respectively. The structural, edge-and-curve-type,
information is mostly represented by the local spatial phase, whereas, the pure
NST has random phase and is characterized by Gaussianity and self-similarity.
Therefore, the NST is modeled by the 2D self-similar process, fractional
Brownian motion (fBm). The Hurst parameter, characteristic of fBm, specifies
the roughness or irregularity of the texture. This leads us to its estimation
and implementation along other features extracted from the structure layer, to
build the "two-view" features sets used in our classification scheme. A shallow
neural net (NN) is exploited to execute the process of merging these feature
sets, in a straightforward and efficient manner.
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The main result of this note is an efficient presentation of the
$S^1$-equivariant cohomology ring of Peterson varieties (in type $A$) as a
quotient of a polynomial ring by an ideal $\mathcal{J}$, in the spirit of the
well-known Borel presentation of the cohomology of the flag variety. Our result
simplifies previous presentations given by Harada-Tymoczko and Bayegan-Harada.
In particular, our result gives an affirmative answer to a conjecture of
Bayegan and Harada that the defining ideal $\mathcal{J}$ is generated by
quadratics.
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The aim of this paper is to give not only an explicit upper bound of the
total Q-curvature but also an induced isoperimetric deficit formula for the
complete conformal metrics on $\mathbb R^n$, $n\ge 3$ with scalar curvature
being nonnegative near infinity and Q-curvature being absolutely convergent.
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The nonextensive statistics based on the $q$-entropy
$S_q=-\frac{\sum_{i=1}^v(p_i-p_i^q)}{1-q}$ has been so far applied to systems
in which the $q$ value is uniformly distributed. For the systems containing
different $q$'s, the applicability of the theory is still a matter of
investigation. The difficulty is that the class of systems to which the theory
can be applied is actually limited by the usual nonadditivity rule of entropy
which is no more valid when the systems contain non uniform distribution of $q$
values. In this paper, within the framework of the so called incomplete
information theory, we propose a more general nonadditivity rule of entropy
prescribed by the zeroth law of thermodynamics. This new nonadditivity
generalizes in a simple way the usual one and can be proved to lead uniquely to
the $q$-entropy.
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Few-shot and one-shot learning have been the subject of active and intensive
research in recent years, with mounting evidence pointing to successful
implementation and exploitation of few-shot learning algorithms in practice.
Classical statistical learning theories do not fully explain why few- or
one-shot learning is at all possible since traditional generalisation bounds
normally require large training and testing samples to be meaningful. This
sharply contrasts with numerous examples of successful one- and few-shot
learning systems and applications.
In this work we present mathematical foundations for a theory of one-shot and
few-shot learning and reveal conditions specifying when such learning schemes
are likely to succeed. Our theory is based on intrinsic properties of
high-dimensional spaces. We show that if the ambient or latent decision space
of a learning machine is sufficiently high-dimensional than a large class of
objects in this space can indeed be easily learned from few examples provided
that certain data non-concentration conditions are met.
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We prove that a compact quaternionic-K\"{a}hler manifold of dimension $4n\geq
8$ admitting a conformal-Killing 2-form which is not Killing, is isomorphic to
the quaternionic projective space, with its standard quaternionic-K\"{a}hler
structure.
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We compare theoretical and experimental predictions of two main classes of
models addressing fermion mass hierarchies and flavour changing neutral
currents (FCNC) effects in supersymmetry: Froggatt-Nielsen (FN) U(1) gauged
flavour models and Nelson-Strassler/extra dimensional models with hierarchical
wave functions for the families. We show that whereas the two lead to identical
predictions in the fermion mass matrices, the second class generates a stronger
suppression of FCNC effects. We prove that, whereas at first sight the FN setup
is more constrained due to anomaly cancelation conditions, imposing unification
of gauge couplings in the second setup generates conditions which precisely
match the mixed anomaly constraints in the FN setup. Finally, we provide an
economical extra dimensional realisation of the hierarchical wave functions
scenario in which the leptonic FCNC can be efficiently suppressed due to the
strong coupling (CFT) origin of the electron mass.
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Recently, Grabowska and Kaplan constructed a four-dimensional lattice
formulation of chiral gauge theories on the basis of the chiral overlap
operator. At least in the tree-level approximation, the left-handed fermion is
coupled only to the original gauge field~$A$, while the right-handed one is
coupled only to the gauge field~$A_\star$, a deformation of~$A$ by the gradient
flow with infinite flow time. In this paper, we study the fermion one-loop
effective action in their formulation. We show that the continuum limit of this
effective action contains local interaction terms between $A$ and~$A_\star$,
even if the anomaly cancellation condition is met. These non-vanishing terms
would lead an undesired perturbative spectrum in the formulation.
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User-driven applications belong to the new type of programs, in which users
get the full control of WHAT, WHEN, and HOW must appear on the screen. Such
programs can exist only if the screen view is organized not according with the
predetermined scenario, written by the developers, but if any screen object can
be moved, resized, and reconfigured by any user at any moment. This article
describes the algorithm, by which an object of an arbitrary shape can be turned
into moveable and resizable. It also explains some rules of such design and the
technique, which can be useful in many cases. Both the individual movements of
objects and their synchronous movements are analysed. After discussing the
individually moveable controls, different types of groups are analysed and the
arbitrary grouping of controls is considered.
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Motivated by applications from computer vision to bioinformatics, the field
of shape analysis deals with problems where one wants to analyze geometric
objects, such as curves, while ignoring actions that preserve their shape, such
as translations, rotations, or reparametrizations. Mathematical tools have been
developed to define notions of distances, averages, and optimal deformations
for geometric objects. One such framework, which has proven to be successful in
many applications, is based on the square root velocity (SRV) transform, which
allows one to define a computable distance between spatial curves regardless of
how they are parametrized. This paper introduces a supervised deep learning
framework for the direct computation of SRV distances between curves, which
usually requires an optimization over the group of reparametrizations that act
on the curves. The benefits of our approach in terms of computational speed and
accuracy are illustrated via several numerical experiments.
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We report C, N, and Si isotopic data for 59 highly 13C-enriched presolar
submicron- to micron-sized SiC grains from the Murchison meteorite, including
eight putative nova grains (PNGs) and 29 15N-rich (14N/15N<=solar) AB grains,
and their Mg-Al, S, and Ca-Ti isotope data when available. These 37 grains are
enriched in 13C, 15N and 26Al with the PNGs showing more extreme enhancements.
The 15N-rich AB grains show systematically higher 26Al and 30Si excesses than
the 14N-rich AB grains. Thus, we propose to divide the AB grains into groups 1
(14N/15N<solar) and 2 (14N/15N>=solar). For the first time, we have obtained
both S and Ti isotopic data for five AB1 grains and one PNG, and found 32S
and/or 50Ti enhancements. Interestingly, one AB1 grain had the largest 32S and
50Ti excesses, strongly suggesting a neutron-capture nucleosynthetic origin of
the 32S excess and thus the initial presence of radiogenic 32Si (t1/2=153 yr).
More importantly, we found that the 15N and 26Al excesses of AB1 grains form a
trend that extends to the region in the N-Al isotope plot occupied by C2
grains, strongly indicating a common stellar origin for both AB1 and C2 grains.
Comparison of supernova models with the AB1 and C2 grain data indicates that
these grains came from SNe that experienced H ingestion into the He/C zones of
their progenitors.
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Learning object-centric representations from complex natural environments
enables both humans and machines with reasoning abilities from low-level
perceptual features. To capture compositional entities of the scene, we
proposed cyclic walks between perceptual features extracted from vision
transformers and object entities. First, a slot-attention module interfaces
with these perceptual features and produces a finite set of slot
representations. These slots can bind to any object entities in the scene via
inter-slot competitions for attention. Next, we establish entity-feature
correspondence with cyclic walks along high transition probability based on the
pairwise similarity between perceptual features (aka "parts") and slot-binded
object representations (aka "whole"). The whole is greater than its parts and
the parts constitute the whole. The part-whole interactions form cycle
consistencies, as supervisory signals, to train the slot-attention module. Our
rigorous experiments on \textit{seven} image datasets in \textit{three}
\textit{unsupervised} tasks demonstrate that the networks trained with our
cyclic walks can disentangle foregrounds and backgrounds, discover objects, and
segment semantic objects in complex scenes. In contrast to object-centric
models attached with a decoder for the pixel-level or feature-level
reconstructions, our cyclic walks provide strong learning signals, avoiding
computation overheads and enhancing memory efficiency. Our source code and data
are available at:
\href{https://github.com/ZhangLab-DeepNeuroCogLab/Parts-Whole-Object-Centric-Learning/}{link}.
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We study the possible exotic states with $J^{PC} = 0^{+-}$ using the
tetraquark interpolating currents with the QCD sum rule approach. The extracted
masses are around 4.85 GeV for the charmonium-like states and 11.25 GeV for the
bottomomium-like states. There is no working region for the light tetraquark
currents, which implies the light $0^{+-}$ state may not exist below 2 GeV.
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In this paper, we address the problem of motion planning and control at the
limits of handling, under locally varying traction conditions. We propose a
novel solution method where traction variations over the prediction horizon are
represented by time-varying tire force constraints, derived from a predictive
friction estimate. A constrained finite time optimal control problem is solved
in a receding horizon fashion, imposing these time-varying constraints.
Furthermore, our method features an integrated sampling augmentation procedure
that addresses the problems of infeasibility and sensitivity to local minima
that arise at abrupt constraint alterations, e.g., due to sudden friction
changes.
We validate the proposed algorithm on a Volvo FH16 heavy-duty vehicle, in a
range of critical scenarios. Experimental results indicate that traction
adaptive motion planning and control improves the vehicle's capacity to avoid
accidents, both when adapting to low local traction, by ensuring dynamic
feasibility of the planned motion, and when adapting to high local traction, by
realizing high traction utilization.
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Recent developments show that Large Language Models (LLMs) produce
state-of-the-art performance on natural language (NL) to code generation for
resource-rich general-purpose languages like C++, Java, and Python. However,
their practical usage for structured domain-specific languages (DSLs) such as
YAML, JSON is limited due to domain-specific schema, grammar, and
customizations generally unseen by LLMs during pre-training. Efforts have been
made to mitigate this challenge via in-context learning through relevant
examples or by fine-tuning. However, it suffers from problems, such as limited
DSL samples and prompt sensitivity but enterprises maintain good documentation
of the DSLs. Therefore, we propose DocCGen, a framework that can leverage such
rich knowledge by breaking the NL-to-Code generation task for structured code
languages into a two-step process. First, it detects the correct libraries
using the library documentation that best matches the NL query. Then, it
utilizes schema rules extracted from the documentation of these libraries to
constrain the decoding. We evaluate our framework for two complex structured
languages, Ansible YAML and Bash command, consisting of two settings:
Out-of-domain (OOD) and In-domain (ID). Our extensive experiments show that
DocCGen consistently improves different-sized language models across all six
evaluation metrics, reducing syntactic and semantic errors in structured code.
We plan to open-source the datasets and code to motivate research in
constrained code generation.
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Falling raindrops are usually considered purely negative factors for
traditional optical imaging because they generate not only rain streaks but
also rain fog, resulting in a decrease in the visual quality of images.
However, this work demonstrates that the image degradation caused by falling
raindrops can be eliminated by the raindrops themselves. The temporal
second-order correlation properties of the photon number fluctuation introduced
by falling raindrops has a remarkable attribute: the rain streak photons and
rain fog photons result in the absence of a stable second-order photon number
correlation, while this stable correlation exists for photons that do not
interact with raindrops. This fundamental difference indicates that the noise
caused by falling raindrops can be eliminated by measuring the second-order
photon number fluctuation correlation in the time domain. The simulation and
experimental results demonstrate that the rain removal effect of this method is
even better than that of deep learning methods when the integration time of
each measurement event is short. This high-efficient quantum rain removal
method can be used independently or integrated into deep learning algorithms to
provide front-end processing and high-quality materials for deep learning.
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The financial industry poses great challenges with risk modeling and profit
generation. These entities are intricately tied to the sophisticated prediction
of stock movements. A stock forecaster must untangle the randomness and
ever-changing behaviors of the stock market. Stock movements are influenced by
a myriad of factors, including company history, performance, and
economic-industry connections. However, there are other factors that aren't
traditionally included, such as social media and correlations between stocks.
Social platforms such as Reddit, Facebook, and X (Twitter) create opportunities
for niche communities to share their sentiment on financial assets. By
aggregating these opinions from social media in various mediums such as posts,
interviews, and news updates, we propose a more holistic approach to include
these "media moments" within stock market movement prediction. We introduce a
method that combines financial data, social media, and correlated stock
relationships via a graph neural network in a hierarchical temporal fashion.
Through numerous trials on current S&P 500 index data, with results showing an
improvement in cumulative returns by 28%, we provide empirical evidence of our
tool's applicability for use in investment decisions.
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Based on 471 million BB pairs collected with the BABAR detector at the PEP-II
e+e- collider, we perform a series of measurements on rare decays B->K(*)l+l-,
where l+l- is either e+e- or mu+mu-. The measurements include total branching
fractions, and partial branching fractions in six bins of di-lepton
mass-squared. We also measure isospin asymmetries in the same six bins.
Furthermore, we measure direct CP and lepton flavor asymmetries for di-lepton
mass below and above the J/Psi resonance. Our measurements show good agreement
with both Standard Model predictions and measurements from other experiments.
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The current trend of scaling language models involves increasing both
parameter count and training dataset size. Extrapolating this trend suggests
that training dataset size may soon be limited by the amount of text data
available on the internet. Motivated by this limit, we investigate scaling
language models in data-constrained regimes. Specifically, we run a large set
of experiments varying the extent of data repetition and compute budget,
ranging up to 900 billion training tokens and 9 billion parameter models. We
find that with constrained data for a fixed compute budget, training with up to
4 epochs of repeated data yields negligible changes to loss compared to having
unique data. However, with more repetition, the value of adding compute
eventually decays to zero. We propose and empirically validate a scaling law
for compute optimality that accounts for the decreasing value of repeated
tokens and excess parameters. Finally, we experiment with approaches mitigating
data scarcity, including augmenting the training dataset with code data or
removing commonly used filters. Models and datasets from our 400 training runs
are freely available at https://github.com/huggingface/datablations.
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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.