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This paper is a thorough study of a digital broadcasting system adapted to
the small mountainous island of Mauritius. A digital LAN was designed with
MPEG-2 signals. The compressed signals were transmitted using DVB-T and QAM
modulators. QAM-16 and QAM-64 modulators were designed and tested with a
simulator under critical conditions of AWGN and phase noises. Results obtained
from simulation have shown that Digital video broadcast with a single frequency
network (SFN) is possible in Mauritius with QAM-64 and QAM-16 modulators
applying COFDM mode of transmission. However, this study has also shown that
QAM-16 modulator had a better performance at low AWGN values (less than 12 dB)
and can be adopted for Mauritius Island, provided that the number of
transmitted channels is not high enough.
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We have shown that the $B-L$ generation due to the decay of the thermally
produced superheavy fields can explain the Baryon assymmetry in the universe if
the superheavy fields are heavier than $10^{13-14}$ GeV. Note that although the
superheavy fields have non-vanishing charges under the standard model gauge
interactions, the thermally prduced baryon asymmetry is sizable. The $B-L$
violating effective operators induced by integrating the superheavy fields have
dimension 7, while the operator in the famous leptogenesis has dimension 5.
Therefore, the constraints from the nucleon stability can be easily satisfied.
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Environmental contours are widely used as basis for design of structures
exposed to environmental loads. The basic idea of the method is to decouple the
environmental description from the structural response. This is done by
establishing an envelope of environmental conditions, such that any structure
tolerating loads on this envelope will have a failure probability smaller than
a prescribed value. Specifically, given an $n$-dimensional random variable
$\mathbf{X}$ and a target probability of failure $p_{e}$, an environmental
contour is the boundary of a set $\mathcal{B} \subset \mathbb{R}^{n}$ with the
following property: For any failure set $\mathcal{F} \subset \mathbb{R}^{n}$,
if $\mathcal{F}$ does not intersect the interior of $\mathcal{B}$, then the
probability of failure, $P(\mathbf{X} \in \mathcal{F})$, is bounded above by
$p_{e}$. As is common for many real-world applications, we work under the
assumption that failure sets are convex.
In this paper, we show that such environmental contours may be regarded as
boundaries of Voronoi cells. This geometric interpretation leads to new
theoretical insights and suggests a simple novel construction algorithm that
guarantees the desired probabilistic properties. The method is illustrated with
examples in two and three dimensions, but the results extend to environmental
contours in arbitrary dimensions. Inspired by the Voronoi-Delaunay duality in
the numerical discrete scenario, we are also able to derive an analytical
representation where the environmental contour is considered as a
differentiable manifold, and a criterion for its existence is established.
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Recent observations of the cosmic microwave background (CMB) indicate that a
successful theory of cosmological inflation needs to have flat potential of the
inflaton scalar field. Realizing the inflaton to be a pseudo-Nambu Goldstone
boson (pNGB) could ensure the flatness and the sub-Planckian scales related to
the dynamics of the paradigm. In this work, we have taken the most general form
of such a scenario: Goldstone inflation, and studied the model in the
noncanonical domain. Natural inflation is a limiting case of this model, which
is also studied here in the noncanonical regime. Our result is compared with
the recent release by Planck collaboration and it is shown that for some
combination of the model parameters, a Goldstone inflationary model in the
noncanonical realisation obeys the current observational bounds. Then, we
studied the era of reheating after the end of inflation. For different choice
of model parameters, constraints on the reheating temperature ($T_{\rm re}$)
and number of e-folds during reheating($N_{\rm re}$) for the allowed
inflationary observables (e.g. scalar spectral index($n_s$) and tensor to
scalar ratio($r$)) are predicted for this model.
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In this work, we consider a tight binding lattice with two non-Hermitian
impurities. The system is described by a non-Hermitian generalization of the
Aubry Andre model. We show for the first time that there exists topologically
nontrivial edge states with real spectra in the PT symmetric region.
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A search for pair production of second-generation scalar leptoquarks in the
final state with two muons and two jets is performed using proton-proton
collision data at sqrt(s) = 7 TeV collected by the CMS detector at the LHC. The
data sample used corresponds to an integrated luminosity of 34 inverse
picobarns. The number of observed events is in good agreement with the
predictions from the standard model processes. An upper limit is set on the
second-generation leptoquark cross section times beta^2 as a function of the
leptoquark mass, and leptoquarks with masses below 394 GeV are excluded at a
95% confidence level for beta = 1, where beta is the leptoquark branching
fraction into a muon and a quark. These limits are the most stringent to date.
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We discuss stability for a class of learning algorithms with respect to noisy
labels. The algorithms we consider are for regression, and they involve the
minimization of regularized risk functionals, such as L(f) := 1/N sum_i
(f(x_i)-y_i)^2+ lambda ||f||_H^2. We shall call the algorithm `stable' if, when
y_i is a noisy version of f*(x_i) for some function f* in H, the output of the
algorithm converges to f* as the regularization term and noise simultaneously
vanish. We consider two flavors of this problem, one where a data set of N
points remains fixed, and the other where N -> infinity. For the case where N
-> infinity, we give conditions for convergence to f_E (the function which is
the expectation of y(x) for each x), as lambda -> 0. For the fixed N case, we
describe the limiting 'non-noisy', 'non-regularized' function f*, and give
conditions for convergence. In the process, we develop a set of tools for
dealing with functionals such as L(f), which are applicable to many other
problems in learning theory.
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We present a first result towards the use of entailment in- side relational
dual tableau-based decision procedures. To this end, we introduce a fragment of
RL(1) which admits a restricted form of composition, (R ; S) or (R ; 1), where
the left subterm R of (R ; S) is only allowed to be either the constant 1, or a
Boolean term neither containing the complement operator nor the constant 1,
while in the case of (R ; 1), R can only be a Boolean term involving relational
variables and the operators of intersection and of union. We prove the
decidability of the fragment by defining a dual tableau- based decision
procedure with a suitable blocking mechanism and where the rules to decompose
compositional formulae are modified so to deal with the constant 1 while
preserving termination. The fragment properly includes the logics presented in
previous work and, therefore, it allows one to express, among others, the
multi-modal logic K with union and intersection of accessibility relations, and
the description logic ALC with union and intersection of roles.
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Beginning with Anderson (1972), spontaneous symmetry breaking (SSB) in
infinite quantum systems is often put forward as an example of (asymptotic)
emergence in physics, since in theory no finite system should display it. Even
the correspondence between theory and reality is at stake here, since numerous
real materials show SSB in their ground states (or equilibrium states at low
temperature), although they are finite. Thus against what is sometimes called
`Earman's Principle', a genuine physical effect (viz. SSB) seems theoretically
recovered only in some idealization (namely the thermodynamic limit),
disappearing as soon as the the idealization is removed. We review the
well-known arguments that (at first sight) no finite system can exhibit SSB,
using the formalism of algebraic quantum theory in order to control the
thermodynamic limit and unify the description of finite- and infinite-volume
systems. Using the striking mathematical analogy between the thermodynamic
limit and the classical limit, we show that a similar situation obtains in
quantum mechanics (which typically forbids SSB) versus classical mechanics
(which allows it). This discrepancy between formalism and reality is quite
similar to the measurement problem, and hence we address it in the same way,
adapting an argument of the author and Reuvers (2013) that was originally
intended to explain the collapse of the wave-function within conventional
quantum mechanics. Namely, exponential sensitivity to (asymmetric)
perturbations of the (symmetric) dynamics as the system size increases causes
symmetry breaking already in finite but very large quantum systems. This
provides continuity between finite- and infinite-volume descriptions of quantum
systems featuring SSB and hence restores Earman's Principle (at least in this
particularly threatening case).
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We propose a new scenario for compound chondrule formation named as
"fragment-collision model," in the framework of the shock-wave heating model. A
molten cm-sized dust particle (parent) is disrupted in the high-velocity gas
flow. The extracted fragments (ejectors) are scattered behind the parent and
the mutual collisions between them will occur. We modeled the disruption event
by analytic considerations in order to estimate the probability of the mutual
collisions assuming that all ejectors have the same radius. We found that the
estimated collision probability, which is the probability of collisions
experienced by an ejector in one disruption event, can account for the observed
fraction of compound chondrules. In addition, the model predictions are
qualitatively consistent with other observational data (oxygen isotopic
composition, textural types, and size ratios of constituents). Based on these
results, we concluded that this new model can be one of the strongest
candidates for the compound chondrule formation.
It should be noted that all collisions do not necessarily lead to the
compound chondrule formation. The formation efficiency and the future works
which should be investigated in the forthcoming paper are also discussed.
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We sketch the basic ideas of the lattice regularization in Quantum Field
Theory, the corresponding Monte Carlo simulations, and applications to Quantum
Chromodynamics (QCD). This approach enables the numerical measurement of
observables at the non-perturbative level. We comment on selected results, with
a focus on hadron masses and the link to Chiral Perturbation Theory. At last we
address two outstanding issues: topological freezing and the sign problem.
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Advances of deep learning for Artificial Neural Networks(ANNs) have led to
significant improvements in the performance of digital signal processing
systems implemented on digital chips. Although recent progress in low-power
chips is remarkable, neuromorphic chips that run Spiking Neural Networks (SNNs)
based applications offer an even lower power consumption, as a consequence of
the ensuing sparse spike-based coding scheme. In this work, we develop a
SNN-based Voice Activity Detection (VAD) system that belongs to the building
blocks of any audio and speech processing system. We propose to use the bin
encoding, a novel method to convert log mel filterbank bins of single-time
frames into spike patterns. We integrate the proposed scheme in a bilayer
spiking architecture which was evaluated on the QUT-NOISE-TIMIT corpus. Our
approach shows that SNNs enable an ultra low-power implementation of a VAD
classifier that consumes only 3.8$\mu$W, while achieving state-of-the-art
performance.
|
Code comment generation which aims to automatically generate natural language
descriptions for source code, is a crucial task in the field of automatic
software development. Traditional comment generation methods use
manually-crafted templates or information retrieval (IR) techniques to generate
summaries for source code. In recent years, neural network-based methods which
leveraged acclaimed encoder-decoder deep learning framework to learn comment
generation patterns from a large-scale parallel code corpus, have achieved
impressive results. However, these emerging methods only take code-related
information as input. Software reuse is common in the process of software
development, meaning that comments of similar code snippets are helpful for
comment generation. Inspired by the IR-based and template-based approaches, in
this paper, we propose a neural comment generation approach where we use the
existing comments of similar code snippets as exemplars to guide comment
generation. Specifically, given a piece of code, we first use an IR technique
to retrieve a similar code snippet and treat its comment as an exemplar. Then
we design a novel seq2seq neural network that takes the given code, its AST,
its similar code, and its exemplar as input, and leverages the information from
the exemplar to assist in the target comment generation based on the semantic
similarity between the source code and the similar code. We evaluate our
approach on a large-scale Java corpus, which contains about 2M samples, and
experimental results demonstrate that our model outperforms the
state-of-the-art methods by a substantial margin.
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Recent strides in nonlinear model predictive control (NMPC) underscore a
dependence on numerical advancements to efficiently and accurately solve
large-scale problems. Given the substantial number of variables characterizing
typical whole-body optimal control (OC) problems - often numbering in the
thousands - exploiting the sparse structure of the numerical problem becomes
crucial to meet computational demands, typically in the range of a few
milliseconds. Addressing the linear-quadratic regulator (LQR) problem is a
fundamental building block for computing Newton or Sequential Quadratic
Programming (SQP) steps in direct optimal control methods. This paper
concentrates on equality-constrained problems featuring implicit system
dynamics and dual regularization, a characteristic of advanced interiorpoint or
augmented Lagrangian solvers. Here, we introduce a parallel algorithm for
solving an LQR problem with dual regularization. Leveraging a rewriting of the
LQR recursion through block elimination, we first enhanced the efficiency of
the serial algorithm and then subsequently generalized it to handle parametric
problems. This extension enables us to split decision variables and solve
multiple subproblems concurrently. Our algorithm is implemented in our
nonlinear numerical optimal control library ALIGATOR. It showcases improved
performance over previous serial formulations and we validate its efficacy by
deploying it in the model predictive control of a real quadruped robot.
|
High-order Gaussian beams with multiple propagation modes have been studied
for free-space optical communications. Fast classification of beams using a
diffractive deep neural network, D2NN, has been proposed. D2NN optimization is
important because it has numerous hyperparameters, such as interlayer distances
and mode combinations. In this study, we classify Hermite-Gaussian beams, which
are high-order Gaussian beams, using a D2NN, and automatically tune one of its
hyperparameters known as the interlayer distance. We used the tree-structured
Parzen estimator, a hyperparameter auto-tuning algorithm, to search for the
best model. Results indicated that classification accuracy obtained by
auto-tuning hyperparameters was higher than that obtained by manually setting
interlayer distances at equal intervals. In addition, we confirmed that
accuracy by auto-tuning improves as the number of classification modes
increases.
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The moduli space $\mathcal{G}^r_{g,d} \to \mathcal{M}_g$ parameterizing
algebraic curves with a linear series of degree $d$ and rank $r$ has expected
relative dimension $\rho = g - (r+1)(g-d+r)$. Classical Brill-Noether theory
concerns the case $\rho \geq 0$; we consider the non-surjective case $\rho <
0$. We prove the existence of components of this moduli space with the expected
relative dimension when $0 > \rho \geq -g+3$, or $0 > \rho \geq -C_r g +
\mathcal{O}(g^{5/6})$, where $C_r$ is a constant depending on the rank of the
linear series such that $C_r \to 3$ as $r \to \infty$. These results are proved
via a two-marked-point generalization suitable for inductive arguments, and the
regeneration theorem for limit linear series.
|
We give a construction of generalized cluster varieties and generalized
cluster scattering diagrams for reciprocal generalized cluster algebras, the
latter of which were defined by Chekhov and Shapiro. These constructions are
analogous to the structures given for ordinary cluster algebras in the work of
Gross, Hacking, Keel, and Kontsevich. As a consequence of these constructions,
we are also able to construct theta functions for generalized cluster algebras,
again in the reciprocal case, and demonstrate a number of their structural
properties.
|
We study the optical and electrical properties of silver films with a graded
thickness obtained through metallic evaporation in vacuum on a tilted substrate
to evaluate their use as semitransparent electrical contacts. We measure their
ellipsometric coefficients, optical transmissions and electrical conductivity
for different widths, and we employ an efficient recursive method to calculate
their macroscopic dielectric function, their optical properties and their
microscopic electric fields. The topology of very thin films corresponds to
disconnected islands, while very wide films are simply connected. For
intermediate widths the film becomes semicontinuous, multiply connected, and
its microscopic electric field develops hotspots at optical resonances which
appear near the percolation threshold of the conducting phase, yielding large
ohmic losses that increase the absorptance above that of a corresponding
homogeneous film. Optimizing the thickness of the film to maximize its
transmittance above the percolation threshold of the conductive phase we
obtained a film with transmittance T = 0.41 and a sheet resistance
$R_{\square}^{\mathrm{max}}\approx2.7\Omega$. We also analyze the observed
emission frequency shift of porous silicon electroluminescent devices when Ag
films are used as solid electrical contacts in replacement of electrolytic
ones.
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We present a general formulation for ray-tracing calculations in curved
space-time. The formulation takes full account of relativistic effects in the
photon transport and the relative motions of the emitters and the
light-of-sight absorbing material. We apply the formulation to calculate the
emission from accretion disks and tori around rotating black holes.
|
Ultracold molecules are associated from an atomic Bose-Einstein condensate by
ramping a magnetic field across a Feshbach resonance. The reverse ramp
dissociates the molecules. The kinetic energy released in the dissociation
process is used to measure the widths of 4 Feshbach resonances in 87Rb. This
method to determine the width works remarkably well for narrow resonances even
in the presence of significant magnetic-field noise. In addition, a
quasi-mono-energetic atomic wave is created by jumping the magnetic field
across the Feshbach resonance.
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Motivated by the motion of biopolymers and membranes in solution, this
article presents a formulation of the equations of motion for curves and
surfaces in a viscous fluid. We focus on geometrical aspects and simple
variational methods for calculating internal stresses and forces, and we derive
the full nonlinear equations of motion. In the case of membranes, we pay
particular attention to the formulation of the equations of hydrodynamics on a
curved, deforming surface. The formalism is illustrated by two simple case
studies: (1) the twirling instability of straight elastic rod rotating in a
viscous fluid, and (2) the pearling and buckling instabilities of a tubular
liposome or polymersome.
|
Our earlier papers explore the nature of large wave vector spin waves in
ultrathin ferromagnets, and also the properties and damping of spin waves of
zero wave vector, at the center of the two dimensional Brillouin zone, with
application to FMR studies. The present paper explores the behavior of spin
waves in such films at intermediate wave vectors, which connect the two
regimes. For the case of Fe films on Au(100), we study the wave vector
dependence of the linewidth of the lowest frequency mode, to find that it
contains a term which varies as the fourth power of the wave vector. It is
argued that this behavior is expected quite generally. We also explore the
nature of the eigenvectors of the two lowest lying modes of the film, as a
function of wave vector. Interestingly, as wave vector increases, the lowest
mode localizes onto the interface between the film and the substrate, while the
second mode evolves into a surface spin wave, localized on the outer layer. We
infer similar behavior for a Co film on Cu(100), though this evolution occurs
at rather larger wave vectors where, as we have shown previously, the modes are
heavily damped with the consequence that identification of distinct eigenmodes
is problematical.
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Causal models communicate our assumptions about causes and effects in
real-world phe- nomena. Often the interest lies in the identification of the
effect of an action which means deriving an expression from the observed
probability distribution for the interventional distribution resulting from the
action. In many cases an identifiability algorithm may return a complicated
expression that contains variables that are in fact unnecessary. In practice
this can lead to additional computational burden and increased bias or
inefficiency of estimates when dealing with measurement error or missing data.
We present graphical criteria to detect variables which are redundant in
identifying causal effects. We also provide an improved version of a well-known
identifiability algorithm that implements these criteria.
|
Shubnikov-de Haas (SdH) oscillations are observed in Bi2Se3 flakes with high
carrier concentration and low bulk mobility. These oscillations probe the
protected surface states and enable us to extract their carrier concentration,
effective mass and Dingle temperature. The Fermi momentum obtained is in
agreement with angle resolved photoemission spectroscopy (ARPES) measurements
performed on crystals from the same batch. We study the behavior of the Berry
phase as a function of magnetic field. The standard theoretical considerations
fail to explain the observed behavior.
|
We consider the problem of optimal recovery of true ranking of $n$ items from
a randomly chosen subset of their pairwise preferences. It is well known that
without any further assumption, one requires a sample size of $\Omega(n^2)$ for
the purpose. We analyze the problem with an additional structure of relational
graph $G([n],E)$ over the $n$ items added with an assumption of
\emph{locality}: Neighboring items are similar in their rankings. Noting the
preferential nature of the data, we choose to embed not the graph, but, its
\emph{strong product} to capture the pairwise node relationships. Furthermore,
unlike existing literature that uses Laplacian embedding for graph based
learning problems, we use a richer class of graph
embeddings---\emph{orthonormal representations}---that includes (normalized)
Laplacian as its special case. Our proposed algorithm, {\it Pref-Rank},
predicts the underlying ranking using an SVM based approach over the chosen
embedding of the product graph, and is the first to provide \emph{statistical
consistency} on two ranking losses: \emph{Kendall's tau} and \emph{Spearman's
footrule}, with a required sample complexity of $O(n^2
\chi(\bar{G}))^{\frac{2}{3}}$ pairs, $\chi(\bar{G})$ being the \emph{chromatic
number} of the complement graph $\bar{G}$. Clearly, our sample complexity is
smaller for dense graphs, with $\chi(\bar G)$ characterizing the degree of node
connectivity, which is also intuitive due to the locality assumption e.g.
$O(n^\frac{4}{3})$ for union of $k$-cliques, or $O(n^\frac{5}{3})$ for random
and power law graphs etc.---a quantity much smaller than the fundamental limit
of $\Omega(n^2)$ for large $n$. This, for the first time, relates ranking
complexity to structural properties of the graph. We also report experimental
evaluations on different synthetic and real datasets, where our algorithm is
shown to outperform the state-of-the-art methods.
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Despite many years of efforts, attempts to reach the quantum regime of
topological surface states (TSS) on an electrically tunable topological
insulator (TI) platform have so far failed on binary TI compounds such as
Bi2Se3 due to high density of interfacial defects. Here, utilizing an optimal
buffer layer on a gatable substrate, we demonstrate the first electrically
tunable quantum Hall effects (QHE) on TSS of Bi2Se3. On the n-side,
well-defined QHE shows up, but it diminishes near the charge neutrality point
(CNP) and completely disappears on the p-side. Furthermore, around the CNP the
system transitions from a metallic to a highly resistive state as the magnetic
field is increased, whose temperature dependence indicates presence of an
insulating ground state at high magnetic fields.
|
We show that for any uncountable cardinal $\lambda$, the category of sets of
cardinality at least $\lambda$ and monomorphisms between them cannot appear as
the category of point of a topos, in particular is not the category of models
of a $L_{(\infty,\omega)}$-theory. More generally we show that for any regular
cardinal $\kappa < \lambda$ it is neither the category of $\kappa$-points of a
$\kappa$-topos, in particular, not the category of models of a
$L_{(\infty,\kappa)}$-theory. The proof relies on the construction of a
categorified version of the Scott topology, which constitute a left adjoint to
the functor sending any topos to its category of points and the computation of
this left adjoint evaluated on the category of sets of cardinality at least
$\lambda$ and monomorphisms between them. The same techniques also applies to a
few other categories. At least to the category of vector spaces of with bounded
below dimension and the category of algebraic closed fields of fixed
characteristic with bounded below transcendence degree.
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Let (M, g) be an asymptotically flat static vacuum initial data set with
non-empty compact boundary. We prove that (M, g) is isometric to a spacelike
slice of a Schwarzschild spacetime under the mere assumption that the boundary
of (M, g) has zero mean curvature, hence generalizing a classic result of
Bunting and Masood-ul-Alam. In the case that the boundary has constant positive
mean curvature and satisfies a stability condition, we derive an upper bound of
the ADM mass of (M, g) in terms of the area and mean curvature of the boundary.
Our discussion is motivated by Bartnik's quasi-local mass definition.
|
State-of-the-art methods for handwriting recognition are based on Long Short
Term Memory (LSTM) recurrent neural networks (RNN), which now provides very
impressive character recognition performance. The character recognition is
generally coupled with a lexicon driven decoding process which integrates
dictionaries. Unfortunately these dictionaries are limited to hundred of
thousands words for the best systems, which prevent from having a good language
coverage, and therefore limit the global recognition performance. In this
article, we propose an alternative to the lexicon driven decoding process based
on a lexicon verification process, coupled with an original cascade
architecture. The cascade is made of a large number of complementary networks
extracted from a single training (called cohort), making the learning process
very light. The proposed method achieves new state-of-the art word recognition
performance on the Rimes and IAM databases. Dealing with gigantic lexicon of 3
millions words, the methods also demonstrates interesting performance with a
fast decision stage.
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This Letter presents a novel structured light system model that effectively
considers local lens distortion by pixel-wise rational functions. We leverage
the stereo method for initial calibration and then estimate the rational model
for each pixel. Our proposed model can achieve high measurement accuracy within
and outside the calibration volume, demonstrating its robustness and accuracy.
|
The apps installed on a smartphone can reveal much information about a user,
such as their medical conditions, sexual orientation, or religious beliefs.
Additionally, the presence or absence of particular apps on a smartphone can
inform an adversary who is intent on attacking the device. In this paper, we
show that a passive eavesdropper can feasibly identify smartphone apps by
fingerprinting the network traffic that they send. Although SSL/TLS hides the
payload of packets, side-channel data such as packet size and direction is
still leaked from encrypted connections. We use machine learning techniques to
identify smartphone apps from this side-channel data. In addition to merely
fingerprinting and identifying smartphone apps, we investigate how app
fingerprints change over time, across devices and across different versions of
apps. Additionally, we introduce strategies that enable our app classification
system to identify and mitigate the effect of ambiguous traffic, i.e., traffic
in common among apps such as advertisement traffic. We fully implemented a
framework to fingerprint apps and ran a thorough set of experiments to assess
its performance. We fingerprinted 110 of the most popular apps in the Google
Play Store and were able to identify them six months later with up to 96%
accuracy. Additionally, we show that app fingerprints persist to varying
extents across devices and app versions.
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We discuss $4$-dimensional achiral Lefschetz fibrations bounding
$3$-dimensional open books and study their Lefschetz fibration (LF) embedding
in a bounded $6$-dimensional manifold, in the sense of Ghanwat--Pancholi. As an
application we give another proof of the fact that every closed orientable
$4$-manifold embeds in $S^2 \times S^2 \times S^2$ . We also show that every
achiral Lefschetz fibration with hyperelliptic monodromy admits LF embedding in
$D^6 = D^2 \times D^4$ and discuss an obstruction to such LF embeddings.
|
This paper summarizes the discussions, presentations, and activity of the
Future Light Sources Workshop 2012 (FLS 2012) working group dedicated to
Electron Sources. The focus of the working group was to discuss concepts and
technologies that might enable much higher peak and average brightness from
electron beam sources. Furthermore the working group was asked to consider
methods to greatly improve the robustness of operation and lower the costs of
providing electrons.
|
In this paper, we report a capillary-based M-Z interferometer that could be
used for precise detection of variations in refractive indices of gaseous
samples. This sensing mechanism is quite straightforward. Cladding and core
modes of a capillary are simultaneously excited by coupling coherent laser
beams to the capillary cladding and core, respectively. Interferogram would be
generated as the light transmitted from the core interferes with the light
transmitted from the cladding. Variations in refractive index of the air
filling the core lead to variations in phase difference between the core and
cladding modes, thus shifting the interference fringes. Using a photodiode
together with a narrow slit, we could analyze the fringe shifts. The resolution
of the sensor was found to be 1*10-8 RIU, that is comparable to the highest
resolution obtained by other interferometric sensors reported in previous
literatures. Finally, we also analyze the temperature cross sensitivity of the
sensor. The advantages of our sensor include very low cost, high sensitivity,
straightforward sensing mechanism, and ease of fabrication.
|
AT2019wey is a Galactic low mass X-ray binary with a candidate black hole
accretor first discovered as an optical transient by ATLAS in December 2019. It
was then associated with an X-ray source discovered by SRG in March 2020. After
observing a brightening in X-rays in August 2020, VLA observations of the
source revealed an optically thin spectrum that subsequently shifted to
optically thick, as the source continued to brighten in radio. This motivated
observations of the source with the VLBA. We found a resolved source that we
interpret to be a steady compact jet, a feature associated with black hole
X-ray binary systems in the hard X-ray spectral state. The jet power is
comparable to the accretion-disk X-ray luminosity. Here, we summarize the
results from these observations.
|
Natural language offers a highly intuitive interface for image editing. In
this paper, we introduce the first solution for performing local (region-based)
edits in generic natural images, based on a natural language description along
with an ROI mask. We achieve our goal by leveraging and combining a pretrained
language-image model (CLIP), to steer the edit towards a user-provided text
prompt, with a denoising diffusion probabilistic model (DDPM) to generate
natural-looking results. To seamlessly fuse the edited region with the
unchanged parts of the image, we spatially blend noised versions of the input
image with the local text-guided diffusion latent at a progression of noise
levels. In addition, we show that adding augmentations to the diffusion process
mitigates adversarial results. We compare against several baselines and related
methods, both qualitatively and quantitatively, and show that our method
outperforms these solutions in terms of overall realism, ability to preserve
the background and matching the text. Finally, we show several text-driven
editing applications, including adding a new object to an image,
removing/replacing/altering existing objects, background replacement, and image
extrapolation. Code is available at:
https://omriavrahami.com/blended-diffusion-page/
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Biunit pairs are introduced as pairs of elements in a semiheap that
generalize the notion of unit. Families of functions generalizing involutions
and conjugations, called switches and warps, are investigated. The main theorem
establishes that there is a one-to-one correspondence between monoids equipped
with a particular switch and semiheaps with a biunit pair. This generalizes a
well-established result in semiheap theory that connects involuted semigroups
and semiheaps with biunit elements. Furthermore, diheaps are introduced as
semiheaps whose elements belong to biunit pairs and they are shown to be
non-isomorphic but warp-equivalent to heaps.
|
Plasmonic antennas are attractive optical structures for many applications in
nano and quantum technologies. By providing enhanced interaction between a
nanoemitter and light, they efficiently accelerate and direct spontaneous
emission. One challenge, however, is the precise nanoscale positioning of the
emitter in the structure. Here we present a laser etching protocol that
deterministically positions a single colloidal CdSe/CdS core/shell quantum dot
emitter inside a subwavelength plasmonic patch antenna with three-dimensional
nanoscale control. By exploiting the properties of metal-insulator-metal
structures at the nanoscale, the fabricated single emitter antenna exhibits an
extremely high Purcell factor (>72) and brightness enhancement by a factor of
70. Due to the unprecedented quenching of Auger processes and the strong
acceleration of multiexciton emission, more than 4 photons per pulse can be
emitted by a single quantum dot. Our technology permits the fabrication of
bright room-temperature single-emitter sources emitting either multiple or
single photons.
|
We present the first lattice QCD calculation of coupled $\pi\omega$ and
$\pi\phi$ scattering, incorporating coupled $S$ and $D$-wave $\pi\omega$ in
$J^P=1^+$. Finite-volume spectra in three volumes are determined via a
variational analysis of matrices of two-point correlation functions, computed
using large bases of operators resembling single-meson, two-meson and
three-meson structures, with the light-quark mass corresponding to a pion mass
of $m_\pi \approx 391$ MeV. Utilizing the relationship between the discrete
spectrum of finite-volume energies and infinite-volume scattering amplitudes,
we find a narrow axial-vector resonance ($J^{PC}=1^{+-}$), the analogue of the
$b_1$ meson, with mass $m_{R}\approx1380$ MeV and width $\Gamma_{R}\approx 91$
MeV. The resonance is found to couple dominantly to $S$-wave $\pi\omega$, with
a much-suppressed coupling to $D$-wave $\pi\omega$, and a negligible coupling
to $\pi\phi$ consistent with the `OZI rule'. No resonant behavior is observed
in $\pi\phi$, indicating the absence of a putative low-mass $Z_s$ analogue of
the $Z_c$ claimed in $\pi J/\psi$. In order to minimally present the contents
of a unitary three-channel scattering matrix, we introduce an $n$-channel
generalization of the traditional two-channel Stapp parameterization.
|
Generalized category discovery (GCD) is a recently proposed open-world task.
Given a set of images consisting of labeled and unlabeled instances, the goal
of GCD is to automatically cluster the unlabeled samples using information
transferred from the labeled dataset. The unlabeled dataset comprises both
known and novel classes. The main challenge is that unlabeled novel class
samples and unlabeled known class samples are mixed together in the unlabeled
dataset. To address the GCD without knowing the class number of unlabeled
dataset, we propose a co-training-based framework that encourages clustering
consistency. Specifically, we first introduce weak and strong augmentation
transformations to generate two sufficiently different views for the same
sample. Then, based on the co-training assumption, we propose a consistency
representation learning strategy, which encourages consistency between
feature-prototype similarity and clustering assignment. Finally, we use the
discriminative embeddings learned from the semi-supervised representation
learning process to construct an original sparse network and use a community
detection method to obtain the clustering results and the number of categories
simultaneously. Extensive experiments show that our method achieves
state-of-the-art performance on three generic benchmarks and three fine-grained
visual recognition datasets. Especially in the ImageNet-100 data set, our
method significantly exceeds the best baseline by 15.5\% and 7.0\% on the
\texttt{Novel} and \texttt{All} classes, respectively.
|
We report on two quantitative, morphological estimators of the filamentary
structure of the Cosmic Web, the so-called global and local skeletons. The
first, based on a global study of the matter density gradient flow, allows us
to study the connectivity between a density peak and its surroundings, with
direct relevance to the anisotropic accretion via cold flows on galactic halos.
From the second, based on a local constraint equation involving the
derivatives of the field, we can derive predictions for powerful statistics,
such as the differential length and the relative saddle to extrema counts of
the Cosmic web as a function of density threshold (with application to
percolation of structures and connectivity), as well as a theoretical framework
to study their cosmic evolution through the onset of gravity-induced
non-linearities.
|
We present a simple method to quantitatively capture the heterogeneity in the
degree distribution of a network graph using a single parameter $\sigma$. Using
an exponential transformation of the shape parameter of the Weibull
distribution, this control parameter allows the degree distribution to be
easily interpolated between highly symmetric and highly heterogeneous
distributions on the unit interval. This parameterization of heterogeneity also
recovers several other canonical distributions as intermediate special cases,
including the Gaussian, Rayleigh, and exponential distributions. We then
outline a general graph generation algorithm to produce graphs with a desired
amount of heterogeneity. The utility of this formulation of a heterogeneity
parameter is demonstrated with examples relating to epidemiological modeling
and spectral analysis.
|
We prove that the 2d Euler equation is globally well-posed in a space of
vector fields having spatial asymptotic expansion at infinity of any a priori
given order. The asymptotic coefficients of the solutions are holomorphic
functions of $t$, do not involve (spacial) logarithmic terms, and develop even
when the initial data has fast decay at infinity. We discuss the evolution in
time of the asymptotic terms and their approximation properties.
|
Recent neural networks based surface reconstruction can be roughly divided
into two categories, one warping templates explicitly and the other
representing 3D surfaces implicitly. To enjoy the advantages of both, we
propose a novel 3D representation, Neural Vector Fields (NVF), which adopts the
explicit learning process to manipulate meshes and implicit unsigned distance
function (UDF) representation to break the barriers in resolution and topology.
This is achieved by directly predicting the displacements from surface queries
and modeling shapes as Vector Fields, rather than relying on network
differentiation to obtain direction fields as most existing UDF-based methods
do. In this way, our approach is capable of encoding both the distance and the
direction fields so that the calculation of direction fields is
differentiation-free, circumventing the non-trivial surface extraction step.
Furthermore, building upon NVFs, we propose to incorporate two types of shape
codebooks, \ie, NVFs (Lite or Ultra), to promote cross-category reconstruction
through encoding cross-object priors. Moreover, we propose a new regularization
based on analyzing the zero-curl property of NVFs, and implement this through
the fully differentiable framework of our NVF (ultra). We evaluate both NVFs on
four surface reconstruction scenarios, including watertight vs non-watertight
shapes, category-agnostic reconstruction vs category-unseen reconstruction,
category-specific, and cross-domain reconstruction.
|
We investigate the $k$-error linear complexity of pseudorandom binary
sequences of period $p^{\mathfrak{r}}$ derived from the Euler quotients modulo
$p^{\mathfrak{r}-1}$, a power of an odd prime $p$ for $\mathfrak{r}\geq 2$.
When $\mathfrak{r}=2$, this is just the case of polynomial quotients (including
Fermat quotients) modulo $p$, which has been studied in an earlier work of
Chen, Niu and Wu. In this work, we establish a recursive relation on the
$k$-error linear complexity of the sequences for the case of $\mathfrak{r}\geq
3$. We also state the exact values of the $k$-error linear complexity for the
case of $\mathfrak{r}=3$. From the results, we can find that the $k$-error
linear complexity of the sequences (of period $p^{\mathfrak{r}}$) does not
decrease dramatically for $k<p^{\mathfrak{r}-2}(p-1)^2/2$.
|
Autonomous mobile robots (e.g., warehouse logistics robots) often need to
traverse complex, obstacle-rich, and changing environments to reach multiple
fixed goals (e.g., warehouse shelves). Traditional motion planners need to
calculate the entire multi-goal path from scratch in response to changes in the
environment, which result in a large consumption of computing resources. This
process is not only time-consuming but also may not meet real-time requirements
in application scenarios that require rapid response to environmental changes.
In this paper, we provide a novel Multi-Goal Motion Memory technique that
allows robots to use previous planning experiences to accelerate future
multi-goal planning in changing environments. Specifically, our technique
predicts collision-free and dynamically-feasible trajectories and distances
between goal pairs to guide the sampling process to build a roadmap, to inform
a Traveling Salesman Problem (TSP) solver to compute a tour, and to efficiently
produce motion plans. Experiments conducted with a vehicle and a snake-like
robot in obstacle-rich environments show that the proposed Motion Memory
technique can substantially accelerate planning speed by up to 90\%.
Furthermore, the solution quality is comparable to state-of-the-art algorithms
and even better in some environments.
|
We develop a theoretical approach for nuclear spectral functions at high
missing momenta and removal energies based on the multi-nucleon short-range
correlation~(SRC) model. The approach is based on the effective Feynman
diagrammatic method which allows to account for the relativistic effects
important in the SRC domain. In addition to two-nucleon SRC with center of mass
motion we derive also the contribution of three-nucleon SRCs to the nuclear
spectral functions. The latter is modeled based on the assumption that 3N SRCs
are a product of two sequential short range NN interactions. This approach
allowed us to express the 3N SRC part of the nuclear spectral function as a
convolution of two NN SRCs. Thus the knowledge of 2N SRCs allows us to model
both two- and three-nucleon SRC contributions to the spectral function. The
derivations of the spectral functions are based on the two theoretical
frameworks in evaluating covariant Feynman diagrams: In the first, referred as
virtual nucleon approximation, we reduce Feynman diagrams to the time ordered
noncovariant diagrams by evaluating nucleon spectators in the SRC at their
positive energy poles, neglecting explicitly the contribution from vacuum
diagrams. In the second approach, referred as light-front approximation, we
formulate the boost invariant nuclear spectral function in the light-front
reference frame in which case the vacuum diagrams are generally suppressed and
the bound nucleon is described by its light-cone variables such as momentum
fraction, transverse momentum and invariant mass.
|
Ubiquitous systems with End-Edge-Cloud architecture are increasingly being
used in healthcare applications. Federated Learning (FL) is highly useful for
such applications, due to silo effect and privacy preserving. Existing FL
approaches generally do not account for disparities in the quality of local
data labels. However, the clients in ubiquitous systems tend to suffer from
label noise due to varying skill-levels, biases or malicious tampering of the
annotators. In this paper, we propose Federated Opportunistic Computing for
Ubiquitous Systems (FOCUS) to address this challenge. It maintains a small set
of benchmark samples on the FL server and quantifies the credibility of the
client local data without directly observing them by computing the mutual
cross-entropy between performance of the FL model on the local datasets and
that of the client local FL model on the benchmark dataset. Then, a credit
weighted orchestration is performed to adjust the weight assigned to clients in
the FL model based on their credibility values. FOCUS has been experimentally
evaluated on both synthetic data and real-world data. The results show that it
effectively identifies clients with noisy labels and reduces their impact on
the model performance, thereby significantly outperforming existing FL
approaches.
|
We study by kinetic Monte Carlo simulations the dynamic behavior of a
Ziff-Gulari-Barshad model with CO desorption for the reaction CO + O $\to$
CO$_2$ on a catalytic surface. Finite-size scaling analysis of the fluctuations
and the fourth-order order-parameter cumulant show that below a critical CO
desorption rate, the model exhibits a nonequilibrium first-order phase
transition between low and high CO coverage phases. We calculate several points
on the coexistence curve. We also measure the metastable lifetimes associated
with the transition from the low CO coverage phase to the high CO coverage
phase, and {\it vice versa}. Our results indicate that the transition process
follows a mechanism very similar to the decay of metastable phases associated
with {\it equilibrium} first-order phase transitions and can be described by
the classic Kolmogorov-Johnson-Mehl-Avrami theory of phase transformation by
nucleation and growth. In the present case, the desorption parameter plays the
role of temperature, and the distance to the coexistence curve plays the role
of an external field or supersaturation. We identify two distinct regimes,
depending on whether the system is far from or close to the coexistence curve,
in which the statistical properties and the system-size dependence of the
lifetimes are different, corresponding to multidroplet or single-droplet decay,
respectively. The crossover between the two regimes approaches the coexistence
curve logarithmically with system size, analogous to the behavior of the
crossover between multidroplet and single-droplet metastable decay near an
equilibrium first-order phase transition.
|
Aircraft models may be considered as flat if one neglects some terms
associated to aerodynamics. Computational experiments in Maple show that in
some cases a suitably designed feed-back allows to follow such trajectories,
when applied to the non-flat model. However some maneuvers may be hard or even
impossible to achieve with this flat approximation. In this paper, we propose
an iterated process to compute a more achievable trajectory, starting from the
flat reference trajectory. More precisely, the unknown neglected terms in the
flat model are iteratively re-evaluated using the values obtained at the
previous step. This process may be interpreted as a new trajectory
parametrization, using an infinite number of derivatives, a property that may
be called \emph{generalized flatness}. We illustrate the pertinence of this
approach in flight conditions of increasing difficulties, from single engine
flight, to aileron roll.
|
Exact expressions for ensemble averaged Madelung energies of finite volumes
are derived. The extrapolation to the thermodynamic limit converges
unconditionally and can be used as a parameter-free real-space summation method
of Madelung constants. In the large volume limit, the surface term of the
ensemble averaged Madelung energy has a universal form, independent of the
crystal structure. The scaling of the Madelung energy with system size provides
a simple explanation for the structural phase transition observed in cesium
halide clusters.
|
Underlying many Bayesian inference techniques that seek to approximate the
posterior as a Gaussian distribution is a fundamental linear algebra problem
that must be solved for both the mean and key entries of the covariance. Even
when the true posterior is not Gaussian (e.g., in the case of nonlinear
measurement functions) we can use variational schemes that repeatedly solve
this linear algebra problem at each iteration. In most cases, the question is
not whether a solution to this problem exists, but rather how we can exploit
problem-specific structure to find it efficiently. Our contribution is to
clearly state the Fundamental Linear Algebra Problem of Gaussian Inference
(FLAPOGI) and to provide a novel presentation (using Kronecker algebra) of the
not-so-well-known result of Takahashi et al. (1973) that makes it possible to
solve for key entries of the covariance matrix. We first provide a global
solution and then a local version that can be implemented using local message
passing amongst a collection of agents calculating in parallel. Contrary to
belief propagation, our local scheme is guaranteed to converge in both the mean
and desired covariance quantities to the global solution even when the
underlying factor graph is loopy; in the case of synchronous updates, we
provide a bound on the number of iterations required for convergence. Compared
to belief propagation, this guaranteed convergence comes at the cost of
additional storage, calculations, and communication links in the case of loops;
however, we show how these can be automatically constructed on the fly using
only local information.
|
Binary pulsars provide some of the tightest current constraints on modified
theories of gravity and these constraints will only get tighter as radio
astronomers continue timing these systems. These binary pulsars are
particularly good at constraining scalar-tensor theories in which gravity is
mediated by a scalar field in addition to the metric tensor. Scalar-tensor
theories can predict large deviations from General Relativity due to the fact
that they allow for violation of the strong-equivalence principle through a
phenomenon known as scalarization. This effect appears directly in the timing
model for binary pulsars, and as such, it can be tightly constrained through
precise timing. In this paper, we investigate these constraints for two
scalar-tensor theories and a large set of realistic equations of state. We
calculate the constraints that can be placed by saturating the current
$1\sigma$ bounds on single post-Keplerian parameters, as well as employing
Bayesian methods through Markov-Chain-Monte-Carlo simulations to explore the
constraints that can be achieved when one considers all measured parameters
simultaneously. Our results demonstrate that both methods are able to place
similar constraints and that they are both indeed dominated by the measurements
of the orbital period decay. The Bayesian approach, however, allows one to
simultaneously explore the posterior distributions of not only the theory
parameters but of the masses as well.
|
We discuss scalar quantum field theories in a Lorentz-invariant
three-dimensional noncommutative space-time. We first analyze the one-loop
diagrams of the two-point functions, and show that the non-planar diagrams are
finite and have infrared singularities from the UV/IR mixing. The scalar
quantum field theories have the problem that the violation of the momentum
conservation from the non-planar diagrams does not vanish even in the
commutative limit. A way to obtain an exact translational symmetry by
introducing an infinite number of tensor fields is proposed. The translational
symmetry transforms local fields into non-local ones in general. We also
discuss an analogue of thermodynamics of free scalar field theory in the
noncommutative space-time.
|
Graphs naturally appear in several real-world contexts including social
networks, the web network, and telecommunication networks. While the analysis
and the understanding of graph structures have been a central area of study in
algorithm design, the rapid increase of data sets over the last decades has
posed new challenges for designing efficient algorithms that process
large-scale graphs. These challenges arise from two usual assumptions in
classical algorithm design, namely that graphs are static and that they fit
into a single machine. However, in many application domains, graphs are subject
to frequent changes over time, and their massive size makes them infeasible to
be stored in the memory of a single machine.
Driven by the need to devise new tools for overcoming such challenges, this
thesis focuses on two areas of modern algorithm design that directly deal with
processing massive graphs, namely dynamic graph algorithms and graph
sparsification. We develop new algorithmic techniques from both dynamic and
sparsification perspective for a multitude of graph-based optimization problems
which lie at the core of Spectral Graph Theory, Graph Partitioning, and Metric
Embeddings. Our algorithms are faster than any previous one and design smaller
sparsifiers with better (approximation) quality. More importantly, this work
introduces novel reduction techniques that show unexpected connections between
seemingly different areas such as dynamic graph algorithms and graph
sparsification.
|
The Sivers function, an asymmetric transverse-momentum distribution of the
quarks in a transversely polarized nucleon, is calculated in the MIT bag model.
The bag quark wave functions contain both $S$-wave and $P$-wave components, and
their interference leads to nonvanishing Sivers function in the presence of the
final state interactions. We approximate these interactions through one-gluon
exchange. An estimate of another transverse momentum dependent distribution
$h_1^\perp$ is also performed in the same model.
|
We try to apply the cosmic crystallography method of Lehoucq, Lachieze-Rey,
and Luminet to a universe model with closed spatial section of negative
curvature. But the sharp peaks predicted for Einstein-de Sitter closed models
do not appear in our hyperbolic example. So we turn to a variant of that
method, by subtracting from the distribution of distances between images in the
closed model, the similar distribution in Friedmann's open model. The result is
a plot with much oscillation in small scales, modulated by a long wavelength
quasi-sinusoidal pattern.
|
This paper presents a robust multi-class multi-object tracking (MCMOT)
formulated by a Bayesian filtering framework. Multi-object tracking for
unlimited object classes is conducted by combining detection responses and
changing point detection (CPD) algorithm. The CPD model is used to observe
abrupt or abnormal changes due to a drift and an occlusion based spatiotemporal
characteristics of track states. The ensemble of convolutional neural network
(CNN) based object detector and Lucas-Kanede Tracker (KLT) based motion
detector is employed to compute the likelihoods of foreground regions as the
detection responses of different object classes. Extensive experiments are
performed using lately introduced challenging benchmark videos; ImageNet VID
and MOT benchmark dataset. The comparison to state-of-the-art video tracking
techniques shows very encouraging results.
|
The correspondences between logarithmic operators in the CFTs on the boundary
of AdS_3 and on the world-sheet and dipole fields in the bulk are studied using
the free field formulation of the SL(2,C)/SU(2) WZNW model. We find that
logarithmic operators on the boundary are related to operators on the
world-sheet which are in indecomposable representations of SL(2). The
Knizhnik-Zamolodchikov equation is used to determine the conditions for those
representations to appear in the operator product expansions of the model.
|
We explain how perturbative string theory can be viewed as an exactly
renormalizable Weyl invariant quantum mechanics in the worldsheet
representation clarifying why string scattering amplitudes are both finite and
unambiguously normalized and explaining the origin of UV-IR relations in
spacetime. As applications we examine the worldsheet representation of
nonperturbative type IB states and of string solitons. We conclude with an
analysis of the thermodynamics of a free closed string gas establishing the
absence of the Hagedorn phase transition. We show that the 10D heterotic
strings share a stable finite temperature ground state with gauge group
SO(16)xSO(16). The free energy at the self-dual Kosterlitz-Thouless phase
transition is minimized with finite entropy and positive specific heat. The
open and closed string gas transitions to a confining long string phase at a
temperature at or below the string scale in the presence of an external
electric field.
|
Important gains have recently been obtained in object detection by using
training objectives that focus on {\em hard negative} examples, i.e., negative
examples that are currently rated as positive or ambiguous by the detector.
These examples can strongly influence parameters when the network is trained to
correct them. Unfortunately, they are often sparse in the training data, and
are expensive to obtain. In this work, we show how large numbers of hard
negatives can be obtained {\em automatically} by analyzing the output of a
trained detector on video sequences. In particular, detections that are {\em
isolated in time}, i.e., that have no associated preceding or following
detections, are likely to be hard negatives. We describe simple procedures for
mining large numbers of such hard negatives (and also hard {\em positives})
from unlabeled video data. Our experiments show that retraining detectors on
these automatically obtained examples often significantly improves performance.
We present experiments on multiple architectures and multiple data sets,
including face detection, pedestrian detection and other object categories.
|
Artificial Intelligence (AI) systems such as autonomous vehicles, facial
recognition, and speech recognition systems are increasingly integrated into
our daily lives. However, despite their utility, these AI systems are
vulnerable to a wide range of attacks such as adversarial, backdoor, data
poisoning, membership inference, model inversion, and model stealing attacks.
In particular, numerous attacks are designed to target a particular model or
system, yet their effects can spread to additional targets, referred to as
transferable attacks. Although considerable efforts have been directed toward
developing transferable attacks, a holistic understanding of the advancements
in transferable attacks remains elusive. In this paper, we comprehensively
explore learning-based attacks from the perspective of transferability,
particularly within the context of cyber-physical security. We delve into
different domains -- the image, text, graph, audio, and video domains -- to
highlight the ubiquitous and pervasive nature of transferable attacks. This
paper categorizes and reviews the architecture of existing attacks from various
viewpoints: data, process, model, and system. We further examine the
implications of transferable attacks in practical scenarios such as autonomous
driving, speech recognition, and large language models (LLMs). Additionally, we
outline the potential research directions to encourage efforts in exploring the
landscape of transferable attacks. This survey offers a holistic understanding
of the prevailing transferable attacks and their impacts across different
domains.
|
Spiking neural networks (SNNs) can be run on neuromorphic devices with
ultra-high speed and ultra-low energy consumption because of their binary and
event-driven nature. Therefore, SNNs are expected to have various applications,
including as generative models being running on edge devices to create
high-quality images. In this study, we build a variational autoencoder (VAE)
with SNN to enable image generation. VAE is known for its stability among
generative models; recently, its quality advanced. In vanilla VAE, the latent
space is represented as a normal distribution, and floating-point calculations
are required in sampling. However, this is not possible in SNNs because all
features must be binary time series data. Therefore, we constructed the latent
space with an autoregressive SNN model, and randomly selected samples from its
output to sample the latent variables. This allows the latent variables to
follow the Bernoulli process and allows variational learning. Thus, we build
the Fully Spiking Variational Autoencoder where all modules are constructed
with SNN. To the best of our knowledge, we are the first to build a VAE only
with SNN layers. We experimented with several datasets, and confirmed that it
can generate images with the same or better quality compared to conventional
ANNs. The code is available at https://github.com/kamata1729/FullySpikingVAE
|
We investigate the production of cosmic ray (CR) protons at cosmological
shocks by performing, for the first time, numerical simulations of large scale
structure formation that include directly the acceleration, transport and
energy losses of the high energy particles. CRs are injected at shocks
according to the thermal leakage model and, thereafter, accelerated to a
power-law distribution as indicated by the test particle limit of the diffusive
shock acceleration theory. The evolution of the CR protons accounts for losses
due to adiabatic expansion/compression, Coulomb collisions and inelastic p-p
scattering. Our results suggest that CR protons produced at shocks formed in
association with the process of large scale structure formation could amount to
a substantial fraction of the total pressure in the intra-cluster medium. Their
presence should be easily revealed by GLAST through detection of gamma-ray flux
from the decay of neutral pions produced in inelastic p-p collisions of such CR
protons with nuclei of the intra-cluster gas. This measurement will allow a
direct determination of the CR pressure contribution in the intra-cluster
medium. We also find that the spatial distribution of CR is typically more
irregular than that of the thermal gas because it is more influenced by the
underlying distribution of shocks. This feature is reflected in the appearance
of our gamma-ray synthetic images. Finally, the average CR pressure
distribution appears statistically slightly more extended than the thermal
pressure.
|
We study invariant Einstein metrics on the indicated homogeneous manifolds
$M$, the corresponding algebraic Einstein equations $E$, the associated with
$M$ and $E$ Newton polytopes $P(M)$, and the integer volumes $\nu = \nu(P(M))$
of it (the Newton numbers). We show that $\nu = 80, 152,...,152$ respectively.
It is claimed that the numbers $\epsilon = \epsilon(M)$ of complex solutions of
$E$ equals $ \nu - 18, \nu - 18, \nu,..., \nu $. The results are consistent
with classification of non K\"ahler invariant Einstein metrics on $G_2/T^2$
obtained recently by Y.Sakane, A. Arvanitoyeorgos, and I. Chrysikos. We present
also a short description of all invariant complex Einstein metrics on $
SU_4/T^3 $. We prove existence of Riemannian non K\"ahler invariant Einstein
metrics on $G_2/T^2$-like K\"ahler homogeneous spaces $ E_6/T^2\cdot(A_2)^2,
E_7/T^2\cdot A_5, E_8/T^2\cdot E_6, F_4/T^2\cdot A_2$, where $ T^2\cdot A_5
\subset A_2\cdot A_5\subset E_7 $ and some other results.
|
We discuss the current picture of the standard model's scalar sector at
strong coupling. We compare the pattern observed in the scalar sector in
perturbation theory up to two-loop with the nonperturbative solution obtained
by a next-to-leading order 1/N expansion. In particular, we analyze two
resonant Higgs scattering processes, ff -> H -> f'f' and ff -> H -> ZZ, WW. We
describe the ingredients of the nonperturbative calculation, such as the
tachyonic regularization, the higher order 1/N intermediate renormalization,
and the numerical methods for evaluating the graphs.
We discuss briefly the perspectives and usefulness of extending these
nonperturbative methods to other theories.
|
We give piecewise affine maps on the unit cube whose symbolic representation
is the Dyck shift. This leads to a different way of verifying the chaotic
nature of this system, including the computation of entropy.
|
In the recent years, public use of artistic effects for editing and
beautifying images has encouraged researchers to look for new approaches to
this task. Most of the existing methods apply artistic effects to the whole
image. Exploitation of neural network vision technologies like object detection
and semantic segmentation could be a new viewpoint in this area. In this paper,
we utilize an instance segmentation neural network to obtain a class mask for
separately filtering the background and foreground of an image. We implement a
top prior-mask selection to let us select an object class for filtering
purpose. Different artistic effects are used in the filtering process to meet
the requirements of a vast variety of users. Also, our method is flexible
enough to allow the addition of new filters. We use pre-trained Mask R-CNN
instance segmentation on the COCO dataset as the segmentation network.
Experimental results on the use of different filters are performed. System's
output results show that this novel approach can create satisfying artistic
images with fast operation and simple interface.
|
We show the existence of a natural Dirichlet-to-Neumann map on Riemannian
manifolds with boundary and bounded geometry, such that the bottom of the
Dirichlet spectrum is positive. This map regarded as a densely defined operator
in the $L^2$-space of the boundary admits Friedrichs extension. We focus on the
spectrum of this operator on covering spaces and total spaces of Riemannian
principal bundles over compact manifolds.
|
We present preliminary results of a study of Delta S = 2 matrix elements
originating from physics beyond the Standard Model. Using 2+1 flavour Domain
Wall Fermions we obtain the non-perturbative renormalisation (mixing) matrix in
the RI scheme. We also discuss plans for the chiral extrapolation of the
renormalised matrix elements in a partially quenched set-up.
|
The two-particle correlation function employed in Hanbury-Brown Twiss
interferometry and femtoscopy is traditionally parameterized by a Gaussian
form. Other forms, however, have also been used, including the somewhat more
general L\'evy form. Here we consider a variety of effects present in realistic
femtoscopic studies which may modify the shape of the correlation function and
thereby influence the physical interpretation of a given parameterization.
|
The accumulation of residual stress during welding and additive manufacturing
is an important effect that can significantly anticipate the workpiece failure.
In this work we exploit the physical and analytical transparency of a 1.5D
model to show that the deposition of thermally expanded material onto an
elastic substrate leads to the accumulation of strain incompatibility. This
field, which is the source of residual stress in the system, introduces memory
of the construction history even in the absence of plastic deformations. The
model is then applied to describe the onset and the progression of residual
stresses during deposition, their evolution upon cooling, and the fundamental
role played by the velocity of the moving heat source.
|
Supervised learning, while deployed in real-life scenarios, often encounters
instances of unknown classes. Conventional algorithms for training a supervised
learning model do not provide an option to detect such instances, so they
miss-classify such instances with 100% probability. Open Set Recognition (OSR)
and Non-Exhaustive Learning (NEL) are potential solutions to overcome this
problem. Most existing methods of OSR first classify members of existing
classes and then identify instances of new classes. However, many of the
existing methods of OSR only makes a binary decision, i.e., they only identify
the existence of the unknown class. Hence, such methods cannot distinguish test
instances belonging to incremental unseen classes. On the other hand, the
majority of NEL methods often make a parametric assumption over the data
distribution, which either fail to return good results, due to the reason that
real-life complex datasets may not follow a well-known data distribution. In
this paper, we propose a new online non-exhaustive learning model, namely,
Non-Exhaustive Gaussian Mixture Generative Adversarial Networks (NE-GM-GAN) to
address these issues. Our proposed model synthesizes Gaussian mixture based
latent representation over a deep generative model, such as GAN, for
incremental detection of instances of emerging classes in the test data.
Extensive experimental results on several benchmark datasets show that
NE-GM-GAN significantly outperforms the state-of-the-art methods in detecting
instances of novel classes in streaming data.
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At online retail platforms, it is crucial to actively detect the risks of
transactions to improve customer experience and minimize financial loss. In
this work, we propose xFraud, an explainable fraud transaction prediction
framework which is mainly composed of a detector and an explainer. The xFraud
detector can effectively and efficiently predict the legitimacy of incoming
transactions. Specifically, it utilizes a heterogeneous graph neural network to
learn expressive representations from the informative heterogeneously typed
entities in the transaction logs. The explainer in xFraud can generate
meaningful and human-understandable explanations from graphs to facilitate
further processes in the business unit. In our experiments with xFraud on real
transaction networks with up to 1.1 billion nodes and 3.7 billion edges, xFraud
is able to outperform various baseline models in many evaluation metrics while
remaining scalable in distributed settings. In addition, we show that xFraud
explainer can generate reasonable explanations to significantly assist the
business analysis via both quantitative and qualitative evaluations.
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Heralded near-deterministic multi-qubit controlled phase gates with
integrated error detection have recently been proposed by Borregaard et al.
[Phys. Rev. Lett. 114, 110502 (2015)]. This protocol is based on a single
four-level atom (a heralding quartit) and $N$ three-level atoms (operational
qutrits) coupled to a single-resonator mode acting as a cavity bus. Here we
generalize this method for two distant resonators without the cavity bus
between the heralding and operational atoms. Specifically, we analyze the
two-qubit controlled-Z gate and its multi-qubit-controlled generalization
(i.e., a Toffoli-like gate) acting on the two-lowest levels of $N$ qutrits
inside one resonator, with their successful actions being heralded by an
auxiliary microwave-driven quartit inside the other resonator. Moreover, we
propose a circuit-quantum-electrodynamics realization of the protocol with flux
and phase qudits in linearly-coupled transmission-line resonators with
dissipation. These methods offer a quadratic fidelity improvement compared to
cavity-assisted deterministic gates.
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Cartesian differential categories come equipped with a differential
combinator that formalizes the derivative from multi-variable differential
calculus, and also provide the categorical semantics of the differential
$\lambda$-calculus. An important source of examples of Cartesian differential
categories are the coKleisli categories of the comonads of differential
categories, where the latter concept provides the categorical semantics of
differential linear logic. In this paper, we generalize this construction by
introducing Cartesian differential comonads, which are precisely the comonads
whose coKleisli categories are Cartesian differential categories, and thus
allows for a wider variety of examples of Cartesian differential categories. As
such, we construct new examples of Cartesian differential categories from
Cartesian differential comonads based on power series, divided power algebras,
and Zinbiel algebras.
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Assume $AD+V=L(\mathbb{R})$. Let $\kappa=\utilde{\delta}^2_1$, the supremum
of all $\utilde{\Delta}^2_1$ prewellorderings. We prove that extenders on the
sequence of $\H$ that have critical point $\kappa$ are generated by countably
complete measures. This provides a partial reversal of Woodin's result that the
$<\Theta$-strongness of $\kappa$ in $\H$ is witnessed by $\kappa$-complete
ultrafilters on $\k$. The aforementioned characterization of extenders works in
a more general setting for all cutpoint measurable cardinals of $\H$ in all
models of determinacy where the fine structural analysis of $\H$ has been
carried out. For example, it holds in the minimal model of the Largest Suslin
Axiom. It also gives a simple proof of a theorem of Steel that the successor
members of the Solovay sequence are cutpoints in $\H$ (in models where $\H$
analysis is carried out).
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We show that the quasi-skutterudite superconductor Sr_3Ir_4Sn_{13} undergoes
a structural transition from a simple cubic parent structure, the I-phase, to a
superlattice variant, the I'-phase, which has a lattice parameter twice that of
the high temperature phase. We argue that the superlattice distortion is
associated with a charge density wave transition of the conduction electron
system and demonstrate that the superlattice transition temperature T* can be
suppressed to zero by combining chemical and physical pressure. This enables
the first comprehensive investigation of a superlattice quantum phase
transition and its interplay with superconductivity in a cubic charge density
wave system.
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In this work, the zero-temperature limit of the thermodynamic spin-density
functional theory is investigated. The coarse-grained approach to the
equilibrium density operator is used to describe the equilibrium state. The
characteristic functions of a macrostate are introduced and their
zero-temperature limits are investigated. A detailed discussion of the
spin-grand-canonical ensemble in the entropy and energy representations is
performed. The maps between the state function variables at 0K limit are
rigorously studied for both representations. In the spin-canonical ensemble,
the energy surface and the discontinuity pattern are investigated. Finally,
based on the maps between the state function variables at 0K limit, the
Hohenberg-Kohn theorem for the systems with non-integer electron and spin
numbers at zero-temperature limit is formulated.
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Rough membership function defines the measurement of relationship between
conditional and decision attribute from an Information system. In this paper we
propose a new method to construct rough graph through rough membership function
$\omega_{G}^F(f)$. Rough graph identifies the pattern between the objects with
imprecise and uncertain information. We explore the operations and properties
of rough graph in various stages of its structure.
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We propose a neural network model for MDG and optical SNR estimation in SDM
transmission. We show that the proposed neural-network-based solution estimates
MDG and SNR with high accuracy and low complexity from features extracted after
DSP.
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We have created a knowledge graph based on major data sources used in
ecotoxicological risk assessment. We have applied this knowledge graph to an
important task in risk assessment, namely chemical effect prediction. We have
evaluated nine knowledge graph embedding models from a selection of geometric,
decomposition, and convolutional models on this prediction task. We show that
using knowledge graph embeddings can increase the accuracy of effect prediction
with neural networks. Furthermore, we have implemented a fine-tuning
architecture that adapts the knowledge graph embeddings to the effect
prediction task and leads to better performance. Finally, we evaluate certain
characteristics of the knowledge graph embedding models to shed light on the
individual model performance.
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Recent advances in distributed learning raise environmental concerns due to
the large energy needed to train and move data to/from data centers. Novel
paradigms, such as federated learning (FL), are suitable for decentralized
model training across devices or silos that simultaneously act as both data
producers and learners. Unlike centralized learning (CL) techniques, relying on
big-data fusion and analytics located in energy hungry data centers, in FL
scenarios devices collaboratively train their models without sharing their
private data. This article breaks down and analyzes the main factors that
influence the environmental footprint of FL policies compared with classical
CL/Big-Data algorithms running in data centers. The proposed analytical
framework takes into account both learning and communication energy costs, as
well as the carbon equivalent emissions; in addition, it models both vanilla
and decentralized FL policies driven by consensus. The framework is evaluated
in an industrial setting assuming a real-world robotized workplace. Results
show that FL allows remarkable end-to-end energy savings (30%-40%) for wireless
systems characterized by low bit/Joule efficiency (50 kbit/Joule or lower).
Consensus-driven FL does not require the parameter server and further reduces
emissions in mesh networks (200 kbit/Joule). On the other hand, all FL policies
are slower to converge when local data are unevenly distributed (often 2x
slower than CL). Energy footprint and learning loss can be traded off to
optimize efficiency.
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We calculate the magnetic moments of light nuclei ($A < 20$) using the
auxiliary field diffusion Monte Carlo method and local two- and three-nucleon
forces with electromagnetic currents from chiral effective field theory. For
all nuclei under consideration, we also calculate the ground-state energies and
charge radii. We generally find a good agreement with experimental values for
all of these observables. For the electromagnetic currents, we explore the
impact of employing two different power countings, and study theoretical
uncertainties stemming from the truncation of the chiral expansion
order-by-order for select nuclei within these two approaches. We find that it
is crucial to employ consistent power countings for interactions and currents
to achieve a systematic order-by-order convergence.
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For static fluid spheres, the condition of hydrostatic equilibrium is given
by the generalized Tolman--Oppenheimer--Volkoff (TOV) equation, a Riccati
equation in the radial pressure. For a perfect fluid source, it is known that
finding a new solution from an existing solution requires solving a Bernoulli
equation, if the density profile is kept the same. In this paper, we consider
maps between static (an)isotropic fluid spheres with the same (arbitrary)
density profile and present solution-generating techniques to find new
solutions from existing ones. The maps, in general, require solving an
associated Riccati equation, which, unlike the Bernoulli equation, cannot be
solved by quadrature. In any case, it can be shown that the output solution is
not, in general, regular for a given regular input solution. However, if
pressure anisotropy is kept the same, the new solution is both regular and can
be found by solving a Bernoulli equation. We give a few examples where the
generalized TOV equation, under algebraic constraints, can be converted into a
Bernoulli equation and thus, solved exactly. We discuss the physical
significance of these Bernoulli equations. Since the density profile remains
the same in our approach, the spatial line element is identical for all
solutions, which facilitates direct comparison between various equilibrium
configurations using fluid variables as functions of the radial coordinate.
Finally, combining with the previous study on generation algorithms, we show
how this study leads us to a new three-parameter family of exact solutions that
satisfy all desirable physical conditions.
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We investigate the cosmological behavior of mimetic F(R) gravity. This
scenario is the F(R) extension of usual mimetic gravity classes, which are
based on re-parametrizations of the metric using new, but not propagating,
degrees of freedom, that can lead to a wider family of solutions. Performing a
detailed dynamical analysis for exponential, power-law, and arbitrary F(R)
forms, we extracted the corresponding critical points. Interestingly enough, we
found that although the new features of mimetic F(R) gravity can affect the
universe evolution at early and intermediate times, at late times they will not
have any effect, and the universe will result at stable states that coincide
with those of usual F(R) gravity. However, this feature holds for the late-time
background evolution only. On the contrary, the behavior of the perturbations
is expected to be different since the new term contributes to the perturbations
even if it does not contribute at the background level.
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We consider the allocation of limited resources to heterogeneous customers
who arrive in an online fashion. We would like to allocate the resources
"fairly", so that no group of customers is marginalized in terms of their
overall service rate. We study whether this is possible to do so in an online
fashion, and if so, what a good online allocation policy is.
We model this problem using online bipartite matching under stationary
arrivals, a fundamental model in the literature typically studied under the
objective of maximizing the total number of customers served. We instead study
the objective of maximizing the minimum service rate across all groups, and
propose two notions of fairness: long-run and short-run.
For these fairness objectives, we analyze how competitive online algorithms
can be, in comparison to offline algorithms which know the sequence of demands
in advance. For long-run fairness, we propose two online heuristics (Sampling
and Pooling) which establish asymptotic optimality in different regimes (no
specialized supplies, no rare demand types, or imbalanced supply/demand). By
contrast, outside all of these regimes, we show that the competitive ratio of
online algorithms is between 0.632 and 0.732. For short-run fairness, we show
for complete bipartite graphs that the competitive ratio of online algorithms
is between 0.863 and 0.942; we also derive a probabilistic rejection algorithm
which is asymptotically optimal in the total demand.
Depending on the overall scarcity of resources, either our Sampling or
Pooling heuristics could be desirable. The most difficult situation for online
allocation occurs when the total supply is just enough to serve the total
demand.
We simulate our algorithms on a public ride-hailing dataset, which both
demonstrates the efficacy of our heuristics and validates our managerial
insights.
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Metal mixing plays critical roles in the enrichment of metals in galaxies.
The abundance of elements such as Mg, Fe, and Ba in metal-poor stars help us
understand the metal mixing in galaxies. However, the efficiency of metal
mixing in galaxies is not yet understood. Here we report a series of
$N$-body/smoothed particle hydrodynamics simulations of dwarf galaxies with
different efficiencies of metal mixing using turbulence-induced mixing model.
We show that metal mixing apparently occurs in dwarf galaxies from Mg and Ba
abundance. We find that the scaling factor for metal diffusion larger than 0.01
is necessary to reproduce the observation of Ba abundance in dwarf galaxies.
This value is consistent with the value expected from turbulence theory and
experiment. We also find that timescale of metal mixing is less than 40 Myr.
This timescale is shorter than that of typical dynamical times of dwarf
galaxies. We demonstrate that the determination of a degree of scatters of Ba
abundance by the observation will help us to constrain the efficiency of metal
mixing more precisely.
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A free-floating planet is a planetary-mass object that orbits around a
non-stellar massive object (e.g. a brown dwarf) or around the Galactic Center.
The presence of exomoons orbiting free-floating planets has been theoretically
predicted by several models. Under specific conditions, these moons are able to
retain an atmosphere capable of ensuring the long-term thermal stability of
liquid water on their surface. We model this environment with a one-dimensional
radiative-convective code coupled to a gas-phase chemical network including
cosmic rays and ion-neutral reactions. We find that, under specific conditions
and assuming stable orbital parameters over time, liquid water can be formed on
the surface of the exomoon. The final amount of water for an Earth-mass
exomonoon is smaller than the amount of water in Earth oceans, but enough to
host the potential development of primordial life. The chemical equilibrium
time-scale is controlled by cosmic rays, the main ionization driver in our
model of the exomoon atmosphere.
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Inducing chirality in optically and electronically active materials is
interesting for applications in sensing and quantum information transmission.
Two-dimensional (2D) transition metal chalcogenides (TMDs) possess excellent
electronic and optical properties but are achiral. Here we demonstrate
chirality induction in atomically thin layers of 2D MoS2 by functionalization
with chiral thiol molecules. Analysis of X-ray absorption near-edge structure
and Raman optical activity with circularly polarized excitation suggest
chemical and electronic interactions that leads chirality transfer from the
molecules to the MoS2. We confirm chirality induction in 2D MoS2 with circular
dichroism measurements that show absorption bands at wavelengths of 380-520 nm
and 520-600 nm with giant molar ellipticity of 10^8 deg cm2/dmol 2-3 orders of
magnitude higher than 3D chiral materials. Phototransistors fabricated from
atomically thin chiral MoS2 for detection of circularly polarized light exhibit
responsivity of >10^2 A/W and maximum anisotropy g-factor of 1.98 close to the
theoretical maximum of 2.0, which indicates that the chiral states of photons
are fully distinguishable by the photodetectors. Our results demonstrate that
it is possible achieve chirality induction in monolayer MoS2 by molecular
functionalization and realise ultra-sensitive detectors for circularly
polarized photons.
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We investigate theoretically a topological vortex phase transition induced by
a superradiant phase transition in an atomic Bose-Einstein condensate driven by
a Laguerre-Gaussian optical mode. We show that superradiant radiation can
either carry zero angular momentum, or be in a rotating Laguerre-Gaussian mode
with angular momentum. The conditions leading to these two regimes are
determined in terms of the width for the pump laser and the condensate size for
the limiting cases where the recoil energy is both much smaller and larger than
the atomic interaction energy.
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Like generic multi-task learning, continual learning has the nature of
multi-objective optimization, and therefore faces a trade-off between the
performance of different tasks. That is, to optimize for the current task
distribution, it may need to compromise performance on some previous tasks.
This means that there exist multiple models that are Pareto-optimal at
different times, each addressing a distinct task performance trade-off.
Researchers have discussed how to train particular models to address specific
trade-off preferences. However, existing algorithms require training overheads
proportional to the number of preferences -- a large burden when there are
multiple, possibly infinitely many, preferences. As a response, we propose
Imprecise Bayesian Continual Learning (IBCL). Upon a new task, IBCL (1) updates
a knowledge base in the form of a convex hull of model parameter distributions
and (2) obtains particular models to address task trade-off preferences with
zero-shot. That is, IBCL does not require any additional training overhead to
generate preference-addressing models from its knowledge base. We show that
models obtained by IBCL have guarantees in identifying the Pareto optimal
parameters. Moreover, experiments on standard image classification and NLP
tasks support this guarantee. Statistically, IBCL improves average per-task
accuracy by at most 23% and peak per-task accuracy by at most 15% with respect
to the baseline methods, with steadily near-zero or positive backward transfer.
Most importantly, IBCL significantly reduces the training overhead from
training 1 model per preference to at most 3 models for all preferences.
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We present narrow-band near-infrared images of a sample of 11 Galactic
planetary nebulae (PNe) obtained in the molecular hydrogen (H$_{2}$) 2.122
$\mu$m and Br$\gamma$ 2.166 $\mu$m emission lines and the $K_{\rm c}$ 2.218
$\mu$m continuum. These images were collected with the Wide-field InfraRed
Camera (WIRCam) on the 3.6m Canada-France-Hawaii Telescope (CFHT); their
unprecedented depth and wide field of view allow us to find extended nebular
structures in H$_{2}$ emission in several PNe, some of these being the first
detection. The nebular morphologies in H$_{2}$ emission are studied in analogy
with the optical images, and indication on stellar wind interactions is
discussed. In particular, the complete structure of the highly asymmetric halo
in NGC6772 is witnessed in H$_{2}$, which strongly suggests interaction with
the interstellar medium. Our sample confirms the general correlation between
H$_{2}$ emission and the bipolarity of PNe. The knotty/filamentary fine
structures of the H$_{2}$ gas are resolved in the inner regions of several
ring-like PNe, also confirming the previous argument that H2 emission mostly
comes from knots/clumps embedded within fully ionized material at the
equatorial regions. Moreover, the deep H$_{2}$ image of the butterfly-shaped
Sh1-89, after removal of field stars, clearly reveals a tilted ring structure
at the waist. These high-quality CFHT images justify follow-up detailed
morpho-kinematic studies that are desired to deduce the true physical
structures of a few PNe in the sample.
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The moduli spaces of trigonal curves of odd genus $g>4$ are proven to be
rational.
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We present a general scheme for the calculation of the Renyi entropy of a
subsystem in quantum many-body models that can be efficiently simulated via
quantum Monte Carlo. When the simulation is performed at very low temperature,
the above approach delivers the entanglement Renyi entropy of the subsystem,
and it allows to explore the crossover to the thermal Renyi entropy as the
temperature is increased. We implement this scheme explicitly within the
Stochastic Series expansion as well as within path-integral Monte Carlo, and
apply it to quantum spin and quantum rotor models. In the case of quantum
spins, we show that relevant models in two dimensions with reduced symmetry (XX
model or hardcore bosons, transverse-field Ising model at the quantum critical
point) exhibit an area law for the scaling of the entanglement entropy.
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Supervised trackers trained on labeled data dominate the single object
tracking field for superior tracking accuracy. The labeling cost and the huge
computational complexity hinder their applications on edge devices.
Unsupervised learning methods have also been investigated to reduce the
labeling cost but their complexity remains high. Aiming at lightweight
high-performance tracking, feasibility without offline pre-training, and
algorithmic transparency, we propose a new single object tracking method,
called the green object tracker (GOT), in this work. GOT conducts an ensemble
of three prediction branches for robust box tracking: 1) a global object-based
correlator to predict the object location roughly, 2) a local patch-based
correlator to build temporal correlations of small spatial units, and 3) a
superpixel-based segmentator to exploit the spatial information of the target
frame. GOT offers competitive tracking accuracy with state-of-the-art
unsupervised trackers, which demand heavy offline pre-training, at a lower
computation cost. GOT has a tiny model size (<3k parameters) and low inference
complexity (around 58M FLOPs per frame). Since its inference complexity is
between 0.1%-10% of DL trackers, it can be easily deployed on mobile and edge
devices.
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Bounded time series consisting of rates or proportions are often encountered
in applications. This manuscript proposes a practical approach to analyze
bounded time series, through a beta regression model. The method allows the
direct interpretation of the regression parameters on the original response
scale, while properly accounting for the heteroskedasticity typical of bounded
variables. The serial dependence is modeled by a Gaussian copula, with a
correlation matrix corresponding to a stationary autoregressive and moving
average process. It is shown that inference, prediction, and control can be
carried out straightforwardly, with minor modifications to standard analysis of
autoregressive and moving average models. The methodology is motivated by an
application to the influenza-like-illness incidence estimated by the
Google${}^\circledR$ Flu Trends project.
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A large-scale gradient in the metal abundance has been detected with ASCA
from an X-ray bright cluster of galaxies AWM7. The metal abundance shows a peak
of 0.5 solar at the center and smoothly declines to <~ 0.2 solar at a radius of
500 kpc. The gas temperature is found to be constant at 3.8 keV. The radial
distribution of iron can be fit with a beta-model with beta ~ 0.8 assuming the
same core radius (115 kpc) as that of the intracluster medium. The metal
distribution in AWM7 suggests that the gas injected from galaxies is not
efficiently mixed in the cluster space and traces the distribution of galaxies.
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The launching of up to four astrometric missions in the next decade will
enable us to make measurements of stars in tidal streamers from Galactic
satellites of sufficient accuracy to place strong constraints on the mass
distribution in the Milky Way. In this paper we simulate observations of debris
populations in order to assess the required properties of any data set chosen
to implement this experiment. We apply our results to find the desired target
properties of stars (e.g., accuracy of velocity and proper motion measurements)
associated with the dwarf spheroidal satellites of the Milky Way.
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We design an arbitrary high-order accurate nodal discontinuous Galerkin
spectral element approximation for the nonlinear two dimensional shallow water
equations with non-constant, possibly discontinuous, bathymetry on
unstructured, possibly curved, quadrilateral meshes. The scheme is derived from
an equivalent flux differencing formulation of the split form of the equations.
We prove that this discretisation exactly preserves the local mass and
momentum. Furthermore, combined with a special numerical interface flux
function, the method exactly preserves the mathematical entropy, which is the
total energy for the shallow water equations. By adding a specific form of
interface dissipation to the baseline entropy conserving scheme we create a
provably entropy stable scheme. That is, the numerical scheme discretely
satisfies the second law of thermodynamics. Finally, with a particular
discretisation of the bathymetry source term we prove that the numerical
approximation is well-balanced. We provide numerical examples that verify the
theoretical findings and furthermore provide an application of the scheme for a
partial break of a curved dam test problem.
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