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We study the strong magnetic field limit for a nonlinear Iwatsuka-type model,
i.e. a nonlinear Schr\"odinger equation in two spatial dimensions with a
magnetic vector potential that only depends on the $x$-coordinate. Using a
high-frequency averaging technique, we show that this equation can be
effectively described by a nonlocal nonlinear model, which is no longer
dispersive. We also prove that, in this asymptotic regime, inhomogeneous
nonlinearities are confined along the $y$-axis.
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Answering in negative a question of M. Hru\v{s}\'ak, we construct a Borel
ideal not extendable to any $F_\sigma$ ideal and such that it is not
Kat\v{e}tov above the ideal $\mathrm{conv}$.
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Mechanical disorder in solids, which is generated by a broad range of
physical processes and controls various material properties, appears in a wide
variety of forms. Defining unified and measurable dimensionless quantifiers,
allowing quantitative comparison of mechanical disorder across widely different
physical systems, is therefore an important goal. Two such coarse-grained
dimensionless quantifiers (among others) appear in the literature, one is
related to the spectral broadening of discrete phononic bands in finite-size
systems (accessible through computer simulations) and the other is related the
spatial fluctuations of the shear modulus in macroscopically large systems. The
latter has been recently shown to determine the amplitude of wave attenuation
rates in the low-frequency limit (accessible through laboratory experiments).
Here, using two alternative and complementary theoretical approaches linked to
the vibrational spectra of solids, we derive a basic scaling relation between
the two dimensionless quantifiers. This scaling relation, which is supported by
simulational data, shows that the two apparently distinct quantifiers are in
fact intrinsically related, giving rise to a unified quantifier of mechanical
disorder in solids. We further discuss the obtained results in the context of
the unjamming transition taking place in soft sphere packings at low confining
pressures, in addition to their implications for our understanding of the
low-frequency vibrational spectra of disordered solids in general, and in
particular those of glassy systems.
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The problem of Bayesian reduced rank regression is considered in this paper.
We propose, for the first time, to use Langevin Monte Carlo method in this
problem. A spectral scaled Student prior distrbution is used to exploit the
underlying low-rank structure of the coefficient matrix. We show that our
algorithms are significantly faster than the Gibbs sampler in high-dimensional
setting. Simulation results show that our proposed algorithms for Bayesian
reduced rank regression are comparable to the state-of-the-art method where the
rank is chosen by cross validation.
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Multi-regional interaction among neuronal populations underlies the brain's
processing of rich sensory information in our daily lives. Recent neuroscience
and neuroimaging studies have increasingly used naturalistic stimuli and
experimental design to identify such realistic sensory computation in the
brain. However, existing methods for cross-areal interaction analysis with
dimensionality reduction, such as reduced-rank regression and canonical
correlation analysis, have limited applicability and interpretability in
naturalistic settings because they usually do not appropriately 'demix' neural
interactions into those associated with different types of task parameters or
stimulus features (e.g., visual or audio). In this paper, we develop a new
method for cross-areal interaction analysis that uses the rich task or stimulus
parameters to reveal how and what types of information are shared by different
neural populations. The proposed neural demixed shared component analysis
combines existing dimensionality reduction methods with a practical neural
network implementation of functional analysis of variance with latent
variables, thereby efficiently demixing nonlinear effects of continuous and
multimodal stimuli. We also propose a simplifying alternative under the
assumptions of linear effects and unimodal stimuli. To demonstrate our methods,
we analyzed two human neuroimaging datasets of participants watching
naturalistic videos of movies and dance movements. The results demonstrate that
our methods provide new insights into multi-regional interaction in the brain
during naturalistic sensory inputs, which cannot be captured by conventional
techniques.
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Speaker embeddings extracted with deep 2D convolutional neural networks are
typically modeled as projections of first and second order statistics of
channel-frequency pairs onto a linear layer, using either average or attentive
pooling along the time axis. In this paper we examine an alternative pooling
method, where pairwise correlations between channels for given frequencies are
used as statistics. The method is inspired by style-transfer methods in
computer vision, where the style of an image, modeled by the matrix of
channel-wise correlations, is transferred to another image, in order to produce
a new image having the style of the first and the content of the second. By
drawing analogies between image style and speaker characteristics, and between
image content and phonetic sequence, we explore the use of such channel-wise
correlations features to train a ResNet architecture in an end-to-end fashion.
Our experiments on VoxCeleb demonstrate the effectiveness of the proposed
pooling method in speaker recognition.
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For a team of robots to work collaboratively, it is crucial that each robot
have the ability to determine the position of their neighbors, relative to
themselves, in order to execute tasks autonomously. This letter presents an
algorithm for determining the three-dimensional relative position between two
mobile robots, each using nothing more than a single ultra-wideband
transceiver, an accelerometer, a rate gyro, and a magnetometer. A sliding
window filter estimates the relative position at selected keypoints by
combining the distance measurements with acceleration estimates, which each
agent computes using an on-board attitude estimator. The algorithm is
appropriate for real-time implementation, and has been tested in simulation and
experiment, where it comfortably outperforms standard estimators. A positioning
accuracy of less than 1 meter is achieved with inexpensive sensors.
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Room Temperature Ionic Liquids (RTILs) are molten salts which exhibit uniques
physical and chemical properties, commonly harnessed for lubrication and energy
applications. The pure ionic nature of RTIL leads to strong electrostatic
interactions among the liquid, furthermore exalted in the presence of
interfaces and confinement. In this work, we use a tuning-fork based dynamic
Surface Force Tribometer (TF-SFT), which allows probing both the rheological
and the tribological properties of RTILs films confined between a millimetric
sphere and a surface, over a wide range of confinements. When the RTIL is
confined between metallic surfaces, we evidence an abrupt change of its
rheological properties below a threshold confinement. This is reminiscent of a
recently reported confinement induced capillary freezing, here observed with a
wide contact area. In parallel, we probe the tribological response of the film
under imposed nanometric shear deformation and unveil a yielding behaviour of
the interfacial solid phase below this threshold confinement. This is
characterized by a transition from an elastic to a plastic regime, exhibiting
striking similarities with the response of glassy materials. This transition to
yielding of the RTIL in metallic confinement leads overall to a reduction in
friction and offers a self-healing protection of the surfaces avoiding direct
contact, with obvious applications in tribology.
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Recently, it has been shown theoretically that fluorescence microscopy using
random illuminations (RIM) yields a doubled lateral resolution and an improved
optical sectioning. Moreover, an algorithm called algoRIM, based on variance
matching, has been successfully validated on numerous biological applications.
Here, we propose a proof of uniqueness of the RIM variance equation, which
corresponds to a first theoretical validation of algoRIM.
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Reliable evaluation of adversarial defenses is a challenging task, currently
limited to an expert who manually crafts attacks that exploit the defense's
inner workings or approaches based on an ensemble of fixed attacks, none of
which may be effective for the specific defense at hand. Our key observation is
that adaptive attacks are composed of reusable building blocks that can be
formalized in a search space and used to automatically discover attacks for
unknown defenses. We evaluated our approach on 24 adversarial defenses and show
that it outperforms AutoAttack, the current state-of-the-art tool for reliable
evaluation of adversarial defenses: our tool discovered significantly stronger
attacks by producing 3.0\%-50.8\% additional adversarial examples for 10
models, while obtaining attacks with slightly stronger or similar strength for
the remaining models.
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In this paper we compare the experimental HERA data with the next-to-leading
order approach (NLO) of Ref.[C.~Contreras, E.~Levin, R.~Meneses and
M.~Sanhueza,Eur. Phys. J. C 80 (2020) no.11, 1029). This approach includes the
re-summed NLO corrections to the kernel of the evolution equation, the correct
asymptotic behaviour in the NLO at $\tau = r^2 Q^2_s \,\gg\,1$; the impact
parameter dependence of the saturation scale in accord with the Froissarrt
theorem as well as the non-linear corrections. In this paper, we successfully
describe the experimental data with the quality, which is not worse, than in
the leading order fits with larger number of the phenomenological parameters.
It is demonstrated, that the data could be described, taking into account both
the diffusion on $\ln(k_T)$, which stems from perturbative QCD, and the
Gribov's diffusion in impact parameters. It is shown an ability to describe the
data at rather large values of $\alpha_S$.
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Soft glassy materials such as mayonnaise, wet clays, or dense microgels
display under external shear a solid-to-liquid transition. Such a shear-induced
transition is often associated with a non-monotonic stress response, in the
form of a stress maximum referred to as "stress overshoot". This ubiquitous
phenomenon is characterized by the coordinates of the maximum in terms of
stress $\sigma_\text{M}$ and strain $\gamma_\text{M}$ that both increase as
weak power laws of the applied shear rate. Here we rationalize such power-law
scalings using a continuum model that predicts two different regimes in the
limit of low and high applied shear rates. The corresponding exponents are
directly linked to the steady-state rheology and are both associated with the
nucleation and growth dynamics of a fluidized region. Our work offers a
consistent framework for predicting the transient response of soft glassy
materials upon start-up of shear from the local flow behavior to the global
rheological observables.
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Temporal correspondence - linking pixels or objects across frames - is a
fundamental supervisory signal for the video models. For the panoptic
understanding of dynamic scenes, we further extend this concept to every
segment. Specifically, we aim to learn coarse segment-level matching and fine
pixel-level matching together. We implement this idea by designing two novel
learning objectives. To validate our proposals, we adopt a deep siamese model
and train the model to learn the temporal correspondence on two different
levels (i.e., segment and pixel) along with the target task. At inference time,
the model processes each frame independently without any extra computation and
post-processing. We show that our per-frame inference model can achieve new
state-of-the-art results on Cityscapes-VPS and VIPER datasets. Moreover, due to
its high efficiency, the model runs in a fraction of time (3x) compared to the
previous state-of-the-art approach.
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Lines and circles pose significant scalability challenges in synthetic
geometry. A line with $n$ points implies ${n \choose 3}$ collinearity atoms, or
alternatively, when lines are represented as functions, equality among ${n
\choose 2}$ different lines. Similarly, a circle with $n$ points implies ${n
\choose 4}$ cocyclicity atoms or equality among ${n \choose 3}$ circumcircles.
We introduce a new mathematical concept of $k$-equivalence relations, which
generalizes equality ($k=1$) and includes both lines ($k=2$) and circles
($k=3$), and present an efficient proof-producing procedure to compute the
closure of a $k$-equivalence relation.
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We present a study of the incidence of active galactic nucleus (AGN) in a
sample of major merging systems at 0.3<z<2.5. Galaxies in this merger sample
have projected separations between 3 to 15 kpc and are selected from the
CANDELS/3D-HST catalogs using a peak-finding algorithm. AGNs in mergers and
non-mergers are identified on the basis of their X-ray emission, optical lines,
mid-infrared colors, and radio emission. Among galaxies with adequate
measurements to find potential AGNs, we find a similar fraction of AGNs in
mergers (16.4%) compared to the fraction found in non-merging galaxies (15.4%).
In mergers, this fraction is obtained by assuming that, in unresolved
observations, only one of the merging galaxies is the AGN source. The
similarity between the fractions is possibly due to the higher availability of
cold gas at high redshifts, where the excess of nuclear activity as a result of
merging is less important than at lower redshifts. Star-forming galaxies have a
higher incidence of AGNs than quiescent galaxies. In particular, starbursts in
mergers are the most common sites of AGN activity since they present higher AGN
fractions and black hole accretion rates. We find no clear correlation between
the black hole accretion rate and the galaxy properties (i.e., star-formation
rate, stellar mass) in mergers and non-mergers. However, mergers seem to have a
higher correlation with star formation than non-mergers, which possibly
indicates that the merging process is starting to influence the star formation
and AGN activity even at this pre-coalescence stage.
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We consider the weighted Sobolev spaces associated with non-isotropic
dilations of Calder\'on-Torchinsky and characterize the spaces by the square
functions of Marcinkiewicz type including those defined with repeated uses of
averaging operation.
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We forecast the reionization history constraints, inferred from Lyman-alpha
damping wing absorption features, for a future sample of $\sim 20$ $z \geq 6$
gamma-ray burst (GRB) afterglows. We describe each afterglow spectrum by a
three-parameter model. First, L characterizes the size of the ionized region
(the "bubble size") around a GRB host halo. Second, $\langle{x_{\rm
HI}\rangle}$ is the volume-averaged neutral fraction outside of the ionized
bubble around the GRB, which is approximated as spatially uniform. Finally,
$N_{\mathrm{HI}}$ denotes the column-density of a local damped Lyman-alpha
absorber (DLA) associated with the GRB host galaxy. The size distribution of
ionized regions is extracted from a numerical simulation of reionization, and
evolves strongly across the Epoch of Reionization (EoR). The model DLA column
densities follow the empirical distribution determined from current GRB
afterglow spectra. We use a Fisher matrix formalism to forecast the
$\langle{x_{\rm HI}(z)\rangle}$ constraints that can be obtained from follow-up
spectroscopy of afterglows with SNR = 20 per R=3,000 resolution element at the
continuum. We find that the neutral fraction may be determined to better than
10-15\% (1-$\sigma$) accuracy from this data across multiple independent
redshift bins at $z \sim 6-10$, spanning much of the EoR, although the
precision degrades somewhat near the end of reionization. A more futuristic
survey with $80$ GRB afterglows at $z \geq 6$ can improve the precision here by
a factor of $2$ and extend measurements out to $z \sim 14$. We further discuss
how these constraints may be combined with estimates of the escape fraction of
ionizing photons, derived from the DLA column density distribution towards GRBs
extracted at slightly lower redshift. This combination will help in testing
whether we have an accurate census of the sources that reionized the universe.
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The first chapter is a critical review and a case study in eBusiness, with
special attention to the digital currencies resource and its possibilities. 2.
chapter attempts to incorporate the UTAUT model with perceived risk theory to
explore its impact on the intention to use m-government services. 3. chapter
aims to assess the level of gender inclusivity in the municipal e-procurement
processes in the City of Johannesburg as a case study. It uses a GAD approach.
4. chapter examines the impediments that derail the intensive uptake of
eLearning programmes in a particular higher education institution. The study
adopted an inductive research paradigm that followed a qualitative research
strategy. Data were collected by means of one-on-one in-depth interviews from
selected faculty members at a nominated institution of higher learning. 5.
chapter investigated the role of KMS in enhancing the export performance of
firms operating within the manufacturing sector in Zimbabwe. The study used a
quantitative approach in which a survey questionnaire was distributed to 555
managers drawn from 185 manufacturing firms based in Harare. Data analyses
involved the use of descriptive statistics, Spearman correlations and
regression analysis. In the sixth chapter, a survey was undertaken on 131 SMEs
from the Pelagonija region in order to determine the current level of SME
digitalization within the region. It is aimed to compare with the EU average
and to make conclusions on the impact of the SME digitalization on region GDP
growth as well as revenues collection. The last chapter s purpose was to
develop a measuring and modelling framework, an instrument of IBSQ for the
South African banking sector. Snowball and convenience sampling, both
non-probability techniques were used to recruit participants for the study. A
total of 310 Internet banking customer responses were utilised in the analysis.
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An almost Fuchsian manifold is a hyperbolic 3-manifold of the type $S\times
\mathbb{R}$ which admits a closed minimal surface (homeomorphic to $S$) with
the maximum principal curvature $\lambda_0 <1$, while a weakly almost Fuchsian
manifold is of the same type but it admits a closed minimal surface with
$\lambda_0 <= 1$. We first prove that any weakly almost Fuchsian manifold is in
fact quasi-Fuchsian, and we construct a Canary-Storm type compactification for
the weakly almost Fuchsian space. We apply these results to prove uniform upper
bounds on the volume of the convex core and Hausdorff dimension for the limit
set of weakly almost Fuchsian manifolds and to give examples of quasi-Fuchsian
manifolds which admit unique minimal surfaces (resp. stable minimal surfaces)
without being almost Fuchsian (resp. weakly almost Fuchsian). We also show that
for every $g$ there is an $\epsilon$ depending only on $g$ such that if a
closed hyperbolic 3-manifold fibers over the circle with fiber a surface of
genus $g$, then any embedded minimal surface isotopic to the fiber has the
maximum principal curvature $\lambda_0$ larger than $1+\epsilon$.
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We solve time-harmonic Maxwell's equations in anisotropic, spatially
homogeneous media in intersections of $L^p$-spaces. The material laws are
time-independent. The analysis requires Fourier restriction-extension estimates
for perturbations of Fresnel's wave surface. This surface can be decomposed
into finitely many components of the following three types: smooth surfaces
with non-vanishing Gaussian curvature, smooth surfaces with Gaussian curvature
vanishing along one-dimensional submanifolds, but without flat points, and
surfaces with conical singularities. Our estimates are based on new
Bochner-Riesz estimates with negative index for non-elliptic surfaces.
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Previously we constructed Calabi-Yau threefolds by a differential-geometric
gluing method using Fano threefolds with their smooth anticanonical $K3$
divisors in arXiv:1305.0074. In this article we further consider the
diffeomorphic types of the resulting Calabi-Yau threefolds starting from
different pairs of Fano threefolds of Picard number one.
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Electric field measurements of the Time Domain Sampler (TDS) receiver, part
of the Radio and Plasma Waves (RPW) instrument on board Solar Orbiter, often
exhibit very intense broadband wave emissions at frequencies below 20~kHz in
the spacecraft frame. In this paper, we present a year-long study of
electrostatic fluctuations observed in the solar wind at an interval of
heliocentric distances from 0.5 to 1~AU. The RPW/TDS observations provide a
nearly continuous data set for a statistical study of intense waves below the
local plasma frequency. The on-board and continuously collected and processed
properties of waveform snapshots allow for the mapping plasma waves at
frequencies between 200~Hz and 20~kHz. We used the triggered waveform snapshots
and a Doppler-shifted solution of the dispersion relation for wave mode
identification in order to carry out a detailed spectral and polarization
analysis. Electrostatic ion-acoustic waves are the common wave emissions
observed between the local electron and proton plasma frequency in the soler
wind. The occurrence rate of ion-acoustic waves peaks around perihelion at
distances of 0.5~AU and decreases with increasing distances, with only a few
waves detected per day at 0.9~AU. Waves are more likely to be observed when the
local proton moments and magnetic field are highly variable. A more detailed
analysis of more than 10000 triggered waveform snapshots shows the mean wave
frequency at about 3 kHz and wave amplitude about 2.5 mV/m. The wave amplitude
varies as 1/R^(1.38) with the heliocentric distance. The relative phase
distribution between two components of the E-field shows a mostly linear wave
polarization. Electric field fluctuations are closely aligned with the
directions of the ambient field lines. Only a small number (3%) of ion-acoustic
waves are observed at larger magnetic discontinuities.
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Human mobility is crucial to understand the transmission pattern of COVID-19
on spatially embedded geographic networks. This pattern seems unpredictable,
and the propagation appears unstoppable, resulting in over 350,000 death tolls
in the U.S. by the end of 2020. Here, we create the spatiotemporal inter-county
mobility network using 10 TB (Terabytes) trajectory data of 30 million smart
devices in the U.S. in the first six months of 2020. We investigate its bound
percolation by removing the weakly connected edges. The mobility network
becomes vulnerable and prone to reach its criticality and thus experience
surprisingly abrupt phase transitions. Despite the complex behaviors of the
mobility network, we devised a novel approach to identify a small, manageable
set of recurrent critical bridges, connecting the giant component and the
second-largest component. These adaptive links, located across the United
States, played a key role as valves connecting components in divisions and
regions during the pandemic. Beyond, our numerical results unveil that network
characteristics determine the critical thresholds and the bridge locations. The
findings provide new insights into managing and controlling the connectivity of
mobility networks during unprecedented disruptions. The work can also
potentially offer practical future infectious diseases both globally and
locally.
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In the perovskite oxide SrCrO$_{3}$ the interplay between crystal structure,
strain and orbital ordering enables a transition from a metallic to an
insulating electronic structure under certain conditions. We identified a
narrow window of oxygen partial pressure in which highly strained SrCrO$_{3}$
thin films can be grown using radio-frequency (RF) off-axis magnetron
sputtering on three different substrates,
(LaAlO$_{3}$)$_{0.3}$-(Sr$_{2}$TaAlO$_{6}$)$_{0.7}$ (LSAT), SrTiO$_{3}$ (STO)
and DyScO$_{3}$ (DSO). X-ray diffraction and atomic force microscopy confirmed
the quality of the films and a metal-insulator transition driven by the
substrate induced strain was demonstrated.
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In this survey we provide an overview of our recent results concerning Ricci
de Turck flow on spaces with isolated conical singularities. The crucial
characteristic of the flow is that it preserves the conical singularity. Under
certain conditions, Ricci flat metrics with isolated conical singularities are
stable and positive scalar curvature is preserved under the flow. We also
discuss the relation to Perelman's entropies in the singular setting, and
outline open questions and future reseach directions.
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For given quantum (non-commutative) spaces $\mathbb{P}$ and $\mathbb{O}$ we
study the quantum space of maps $\mathbb{M}_{\mathbb{P},\mathbb{O}}$ from
$\mathbb{P}$ to $\mathbb{O}$. In case of finite quantum spaces these objects
turn out to be behind a large class of maps which generalize the classical
$\mathrm{qc}$-correlations known from quantum information theory to the setting
of quantum input and output sets. We prove a number of important functorial
properties of the mapping
$(\mathbb{P},\mathbb{O})\mapsto\mathbb{M}_{\mathbb{P},\mathbb{O}}$ and use them
to study various operator algebraic properties of the $\mathrm{C}^*$-algebras
$\operatorname{C}(\mathbb{M}_{\mathbb{P},\mathbb{O}})$ such as the lifting
property and residual finite dimensionality. Inside
$\operatorname{C}(\mathbb{M}_{\mathbb{P},\mathbb{O}})$ we construct a universal
operator system $\mathbb{S}_{\mathbb{P},\mathbb{O}}$ related to $\mathbb{P}$
and $\mathbb{O}$ and show, among other things, that the embedding
$\mathbb{S}_{\mathbb{P},\mathbb{O}}\subset\operatorname{C}(\mathbb{M}_{\mathbb{P},\mathbb{O}})$
is hyperrigid, $\operatorname{C}(\mathbb{M}_{\mathbb{P},\mathbb{O}})$ is the
$\mathrm{C}^*$-envelope of $\mathbb{S}_{\mathbb{P},\mathbb{O}}$ and that a
large class of non-signalling correlations on the quantum sets $\mathbb{P}$ and
$\mathbb{O}$ arise from states on
$\operatorname{C}(\mathbb{M}_{\mathbb{P},\mathbb{O}})\otimes_{\rm{max}}\operatorname{C}(\mathbb{M}_{\mathbb{P},\mathbb{O}})$
as well as states on the commuting tensor product
$\mathbb{S}_{\mathbb{P},\mathbb{O}}\otimes_{\rm{c}}\mathbb{S}_{\mathbb{P},\mathbb{O}}$.
Finally we introduce and study the notion of a synchronous correlation with
quantum input and output sets, prove several characterizations of such
correlations and their relation to traces on
$\operatorname{C}(\mathbb{M}_{\mathbb{P},\mathbb{O}})$.
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Along with the increasing popularity of agile software development, software
work has become much more social than ever. Contemporary software teams rely on
a variety of collaborative practices, such as pair programming, the topic of
our study. Many agilists advocated the importance of collocation, face-to-face
interaction, and physical artefacts incorporated in the shared workspace, which
the COVID-19 pandemic made unavailable; most software companies around the
world were forced to send their engineers to work from home. As software
projects and teams overnight turned into dis-tributed collaborations, we
question what happened to the pair programming practice in the work-from-home
mode. This paper reports on a longitudinal study of remote pair programming in
two companies. We conducted 38 interviews with 30 engineers from Norway,
Sweden, and the USA, and used the results of a survey in one of the case
companies. Our study is unique as we collected the data longitudinally in
April/May 2020, Sep/Oct 2020, and Jan/Feb 2021. We found that pair programming
has decreased and some interviewees report not pairing at all for almost a full
year. The experiences of those who paired vary from actively co-editing the
code by using special tools to more passively co-reading and discussing the
code and solutions by sharing the screen. Finally, we found that the interest
in and the use of PP over time, since the first months of forced work from home
to early 2021, has admittedly increased, also as a social practice.
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We study Naruse-Newton coefficients, which are obtained from expanding
descent polynomials in a Newton basis introduced by Jiradilok and McConville.
These coefficients $C_0, C_1, \ldots$ form an integer sequence associated to
each finite set of positive integers. For fixed nonnegative integers $a<b$, we
examine the set $R_{a, b}$ of all ratios $\frac{C_a}{C_b}$ over finite sets of
positive integers. We characterize finite sets for which $\frac{C_a}{C_b}$ is
minimized and provide a construction to prove $R_{a, b}$ is unbounded above. We
use this construction to obtain results on the closure of $R_{a, b}$. We also
examine properties of Naruse-Newton coefficients associated with doubleton
sets, such as unimodality and log-concavity. Finally, we find an explicit
formula for all ratios $\frac{C_a}{C_b}$ of Naruse-Newton coefficients
associated with ribbons of staircase shape.
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The tilted balance among competing interactions can yield a rich variety of
ground states of quantum matter. In most Ce-based heavy fermion systems, this
can often be qualitatively described by the famous Doniach phase diagram, owing
to the competition between the Kondo screening and the
Ruderman-Kittel-Kasuya-Yoshida exchange interaction. Here, we report an unusual
pressure-temperature phase diagram beyond the Doniach one in CeCuP2. At ambient
pressure, CeCuP2 displays typical heavy-fermion behavior, albeit with a very
low carrier density. With lowering temperature, it shows a crossover from a non
Fermi liquid to a Fermi liquid at around 2.4 K. But surprisingly, the Kondo
coherence temperature decreases with increasing pressure, opposite to that in
most Ce-based heavy fermion compounds. Upon further compression, two
superconducting phases are revealed. At 48.0 GPa, the transition temperature
reaches 6.1 K, the highest among all Ce-based heavy fermion superconductors. We
argue for possible roles of valence tuning and fluctuations associated with its
special crystal structure in addition to the hybridization effect. These
unusual phase diagrams suggest that CeCuP2 is a novel platform for studying the
rich heavy fermions physics beyond the conventional Doniach paradigm.
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We present a compression algorithm for parton densities using synthetic
replicas generated from the training of a Generative Adversarial Network (GAN).
The generated replicas are used to further enhance the statistics of a given
Monte Carlo PDF set prior to compression. This results in a compression
methodology that is able to provide a compressed set with smaller number of
replicas and a more adequate representation of the original probability
distribution. We also address the question of whether the GAN could be used as
an alternative mechanism to avoid the fitting of large number of replicas.
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Travellers in autonomous vehicles (AVs) need not to walk to the destination
any more after parking like those in conventional human-driven vehicles (HVs).
Instead, they can drop off directly at the destination and AVs can cruise for
parking autonomously. It is a revolutionary change that such parking autonomy
of AVs may increase the potential parking span substantially and affect the
spatial parking equilibrium. Given this, from urban planners' perspective, it
is of great necessity to reconsider the planning of parking supply along the
city. To this end, this paper is the first to examine the spatial parking
equilibrium considering the mix of AVs and HVs with parking cruising effect. It
is found that the equilibrium solution of travellers' parking location choices
can be biased due to the ignorance of cruising effects. On top of that, the
optimal parking span of AVs at given parking supply should be no less than that
at equilibrium. Besides, the optimal parking planning to minimize the total
parking cost is also explored in a bi-level parking planning design problem
(PPDP). While the optimal differentiated pricing allows the system to achieve
optimal parking distribution, this study suggests that it is beneficial to
encourage AVs to cruise further to park by reserving less than enough parking
areas for AVs.
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We propose a nanoscale device consisting of a double quantum dot with strong
intra- and inter- dot Coulomb repulsions. In this design, the current can only
flow through the lower dot, but is triggered by the gate-controlled occupancy
of the upper dot. At low temperatures, our calculations predict the double dot
to pass through a narrow Kondo regime, resulting in highly sensitive switching
characteristics between three well-defined states : insulating, normal
conduction and resonant conduction.
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In this paper we explore a special class of metric spaces called smocked
metric spaces and study their tangent cones at infinity. We prove that under
the right hypotheses, the rescaled limits of balls converge in both the
Gromov-Hausdorff and Intrinsic Flat sense to normed spaces. This paper will be
applied in upcoming work by Kazaras and Sormani concerning Gromov's conjectures
on the properties of GH and SWIF limits of Riemannian manifolds with positive
scalar curvature.
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We consider training models on private data that are distributed across user
devices. To ensure privacy, we add on-device noise and use secure aggregation
so that only the noisy sum is revealed to the server. We present a
comprehensive end-to-end system, which appropriately discretizes the data and
adds discrete Gaussian noise before performing secure aggregation. We provide a
novel privacy analysis for sums of discrete Gaussians and carefully analyze the
effects of data quantization and modular summation arithmetic. Our theoretical
guarantees highlight the complex tension between communication, privacy, and
accuracy. Our extensive experimental results demonstrate that our solution is
essentially able to match the accuracy to central differential privacy with
less than 16 bits of precision per value.
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Among spin-crossover complexes, Fe-porphyrin (FeP) stands out for molecular
spintronic applications: An intricate, yet favourable balance between ligand
fields, charge transfer, and the Coulomb interaction makes FeP highly
manipulable, while its planar structure facilitates device integration. Here,
we theoretically design a mechanical spin-switch device in which external
strain triggers the intrinsic magneto-structural coupling of FeP through a
purely organic embedding. Exploiting the chemical compatibility and
stretchability of graphene nanoribbon electrodes, we overcome common
reliability and reproducibility issues of conventional inorganic setups. The
competition between the Coulomb interaction and distortion-induced changes in
ligand fields requires methodologies beyond the state-of-the-art: Combining
density functional theory with many-body techniques, we demonstrate
experimentally feasible tensile strain to trigger a low-spin ($S=1$) to
high-spin ($S=2$) crossover. Concomitantly, the current through the device
toggles by over an order of magnitude, adding a fully planar mechanical
current-switch unit to the panoply of molecular spintronics.
|
In this paper, we study the topological properties of complex polynomial
Hamiltonian differential systems of degree $n$ having an isochronous center of
Morse type. Firstly, we prove that if the critical level curve possessing an
isochronous center contains only a single singular point, then the vanishing
cycle associated to this center represents a zero homology cycle on the compact
Riemann surface of a generic level curve. Our result provides a positive answer
to a question asked by L. Gavrilov under a quite simple condition and can be
applied to achieve an equivalent description of the Jacobian conjecture on
$\mathbb{C}^2$. Secondly, we obtain a very simple but useful
necessary condition for isochronicity of Hamiltonian systems, which is that
the $(n+1)$-degree part of the Hamiltonian function must have a factor with
multipicity no less than $(n+1)/2$. Thirdly, we show a relation between
Gavrilov's question and the conjecture proposed by X. Jarque and J.
Villadelprat on the non-isochronicity of real Hamiltonian systems of even
degree $n$.
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This paper analyzes the performance of maximum-ratio transmission
(MRT)/maximum-ratio combining (MRC) scheme in a dual-hop non-orthogonal
multiple access (NOMA) full-duplex (FD) relay networks in the presence of
residual hardware impairments (RHIs). The effects of channel estimation errors
(CEEs) and imperfect successive interference cancellation are also considered
for a realistic performance analysis. In the network, the base station and
multiple users utilize MRT and MRC, respectively, while a dedicated relay
consisting of two antennas, one for receiving and the other for broadcasting,
operates in amplify-and-forward mode. For performance criterion, exact outage
probability (OP) expression is derived for Nakagami-m fading channels.
Furthermore, a tight lower bound and asymptotic expressions are also derived to
provide more insights into the obtained OP in terms of diversity order and
array gain. The obtained numerical results demonstrate the importance of
loop-interference cancellation process at FD relay in order for the
investigated system to perform better than half-duplex-NOMA counterpart. Also,
a performance trade-off between the MRT and MRC schemes is observed in the
presence of CEEs among users. Furthermore, it is shown that RHIs have a
significant effect on the performance of users with lower power coefficients,
however it does not change the diversity order. RHIs and CEEs have the most and
least deterioration effects on the system performance, respectively.
|
This paper reviews the first NTIRE challenge on quality enhancement of
compressed video, with a focus on the proposed methods and results. In this
challenge, the new Large-scale Diverse Video (LDV) dataset is employed. The
challenge has three tracks. Tracks 1 and 2 aim at enhancing the videos
compressed by HEVC at a fixed QP, while Track 3 is designed for enhancing the
videos compressed by x265 at a fixed bit-rate. Besides, the quality enhancement
of Tracks 1 and 3 targets at improving the fidelity (PSNR), and Track 2 targets
at enhancing the perceptual quality. The three tracks totally attract 482
registrations. In the test phase, 12 teams, 8 teams and 11 teams submitted the
final results of Tracks 1, 2 and 3, respectively. The proposed methods and
solutions gauge the state-of-the-art of video quality enhancement. The homepage
of the challenge: https://github.com/RenYang-home/NTIRE21_VEnh
|
Deep neural networks have achieved promising performance in supervised point
cloud applications, but manual annotation is extremely expensive and
time-consuming in supervised learning schemes. Unsupervised domain adaptation
(UDA) addresses this problem by training a model with only labeled data in the
source domain but making the model generalize well in the target domain.
Existing studies show that self-supervised learning using both source and
target domain data can help improve the adaptability of trained models, but
they all rely on hand-crafted designs of the self-supervised tasks. In this
paper, we propose a learnable self-supervised task and integrate it into a
self-supervision-based point cloud UDA architecture. Specifically, we propose a
learnable nonlinear transformation that transforms a part of a point cloud to
generate abundant and complicated point clouds while retaining the original
semantic information, and the proposed self-supervised task is to reconstruct
the original point cloud from the transformed ones. In the UDA architecture, an
encoder is shared between the networks for the self-supervised task and the
main task of point cloud classification or segmentation, so that the encoder
can be trained to extract features suitable for both the source and the target
domain data. Experiments on PointDA-10 and PointSegDA datasets show that the
proposed method achieves new state-of-the-art performance on both
classification and segmentation tasks of point cloud UDA. Code will be made
publicly available.
|
Distant supervision (DS) is a well established technique for creating
large-scale datasets for relation extraction (RE) without using human
annotations. However, research in DS-RE has been mostly limited to the English
language. Constraining RE to a single language inhibits utilization of large
amounts of data in other languages which could allow extraction of more diverse
facts. Very recently, a dataset for multilingual DS-RE has been released.
However, our analysis reveals that the proposed dataset exhibits unrealistic
characteristics such as 1) lack of sentences that do not express any relation,
and 2) all sentences for a given entity pair expressing exactly one relation.
We show that these characteristics lead to a gross overestimation of the model
performance. In response, we propose a new dataset, DiS-ReX, which alleviates
these issues. Our dataset has more than 1.5 million sentences, spanning across
4 languages with 36 relation classes + 1 no relation (NA) class. We also modify
the widely used bag attention models by encoding sentences using mBERT and
provide the first benchmark results on multilingual DS-RE. Unlike the competing
dataset, we show that our dataset is challenging and leaves enough room for
future research to take place in this field.
|
Coronal upflows at the edges of active regions (AR), which are a possible
source of slow solar wind, have been found to connect with dynamics in the
transition region. To infer at what scale transition region dynamics connect to
AR upflows, we investigate the statistical properties of the small-scale
dynamics in the transition region underneath the upflows at the edge of AR NOAA
11934. With observations from the Interface Region Imaging Spectragraph (IRIS),
we found that the Si IV 1403\,\AA\ Doppler map consists of numerous
blue-shifted and red-shifted patches mostly with sizes less than 1\,$Mm^2$. The
blue-shifted structures in the transition region tend to be brighter than the
red-shifted ones, but their nonthermal velocities have no significant
difference. With the SWAMIS feature tracking procedure, in IRIS slit-jaw
1400\,\AA\ images we found that dynamic bright dots with an average size of
about 0.3\,$Mm^2$ and lifetimes mostly less than 200\,s spread all over the
region. Most of the bright dots appear to be localised, without clear signature
of propagation of plasma to a long distance on the projection plane. Surge-like
motions with speeds about 15 km/s could be seen in some events at the
boundaries of the upflow region, where the magnetic field appear to be
inclined. We conclude that the transition region dynamics connecting to coronal
upflows should occur in very fine scale, suggesting that the corresponding
coronal upflows should also be highly-structured. It is also plausible that the
transition region dynamics might just act as stimulation at the coronal base
that then drives the upflows in the corona.
|
The probabilistic learning on manifolds (PLoM) introduced in 2016 has solved
difficult supervised problems for the ``small data'' limit where the number N
of points in the training set is small. Many extensions have since been
proposed, making it possible to deal with increasingly complex cases. However,
the performance limit has been observed and explained for applications for
which $N$ is very small (50 for example) and for which the dimension of the
diffusion-map basis is close to $N$. For these cases, we propose a novel
extension based on the introduction of a partition in independent random
vectors. We take advantage of this novel development to present improvements of
the PLoM such as a simplified algorithm for constructing the diffusion-map
basis and a new mathematical result for quantifying the concentration of the
probability measure in terms of a probability upper bound. The analysis of the
efficiency of this novel extension is presented through two applications.
|
Confidentiality hinders the publication of authentic, labeled datasets of
personal and enterprise data, although they could be useful for evaluating
knowledge graph construction approaches in industrial scenarios. Therefore, our
plan is to synthetically generate such data in a way that it appears as
authentic as possible. Based on our assumption that knowledge workers have
certain habits when they produce or manage data, generation patterns could be
discovered which can be utilized by data generators to imitate real datasets.
In this paper, we initially derived 11 distinct patterns found in real
spreadsheets from industry and demonstrate a suitable generator called Data
Sprout that is able to reproduce them. We describe how the generator produces
spreadsheets in general and what altering effects the implemented patterns
have.
|
The use of classical computers to simulate quantum computing has been
successful in aiding the study of quantum algorithms and circuits that are too
complex to examine analytically. Current implementations of quantum computing
simulators are limited to two-level quantum systems. Recent advances in
high-dimensional quantum computing systems have demonstrated the viability of
working with multi-level superposition and entanglement. These advances allow
an agile increase in the number of dimensions of the system while maintaining
quantum entanglement, achieving higher encoding of information and making
quantum algorithms less vulnerable to decoherence and computational errors. In
this paper, we introduce QuantumSkynet, a novel high-dimensional cloud-based
quantum computing simulator. This platform allows simulations of qudit-based
quantum algorithms. We also propose a unified generalization of
high-dimensional quantum gates, which are available for simulations in
QuantumSkynet. Finally, we report simulations and their results for qudit-based
versions of the Deutsch--Jozsa and quantum phase estimation algorithms using
QuantumSkynet.
|
The two-field equations governing fully nonlinear dynamics of the drift wave
(DW) and geodesic acoustic mode (GAM) in the toroidal geometry are derived in
nonlinear gyrokinetic framework. Two stages with distinctive features are
identified and analyzed. In the linear growth stage, the set of nonlinear
equations can be reduced to the intensively studied parametric decay
instability (PDI), accounting for the spontaneous resonant excitation of GAM by
DW. The main results of previous works on spontaneous GAM excitation, e.g., the
much enhanced GAM group velocity and the nonlinear growth rate of GAM, are
reproduced from numerical solution of the two-field equations. In the fully
nonlinear stage, soliton structures are observed to form due to the balancing
of the self-trapping effect by the spontaneously excited GAM and kinetic
dispersiveness of DW. The soliton structures enhance turbulence spreading from
DW linearly unstable to stable region, exhibiting convective propagation
instead of typical linear dispersive process, and is thus, expected to induce
core-edge interaction and nonlocal transport.
|
Due to the mobility and frequent disconnections, the correctness of mobile
interaction systems, such as mobile robot systems and mobile payment systems,
are often difficult to analyze. This paper introduces three critical properties
of systems, called system connectivity, interaction soundness and data
validity, and presents a related modeling and analysis method, based on a kind
of Petri nets called VPN. For a given system, a model including component nets
and interaction structure nets is constructed by using VPNs. The component net
describes the internal process of each component, while the interaction
structure net reflects the dynamic interaction between components. Based on
this model, three properties are defined and analyzed. The case study of a
practical mobile payment system shows the effectiveness of the proposed method.
|
Point defects in insulators are considered promising candidates for quantum
technologies. In keeping with this, we present an extensive optically-detected
magnetic resonance (ODMR) study at room-temperature on individual TR12 centers
(ZPL at 471nm), which are known in the literature since 1956. In this work we
found TR12 centers to show a strong ODMR signal under optical saturation. These
observed defects were created in high-purity epitaxial layers of diamond by
standard irradiation and annealing processes. From the analysis of the ODMR
spectra along with antibunching measurements and coherent population trapping,
we proposed the energy level structure of TR12 center, consisting of ground
state and excited state singlets complemented by a metastable triplet in
between. Mapping the fluorescence dependence of the center on an external
magnetic field and on the polarization of laser excitation, allows us to
identify twelve inequivalent orientations for TR12 centers. This includes the
exact orientations of the dipole transition and the triplet axes in the diamond
lattice in full agreement with the results of modeling based on the proposed
level structure. Furthermore, a static Jahn-Teller effect was detected through
fluorescence switching between two levels at low optical excitation power,
directly observable in the real-time fluorescence signal for various
polarization of laser excitation. Based on these results we discuss the
prospects of the TR12 center in diamond for quantum sensing and quantum
information processing.
|
The aims of this article is to generalize some useful Besicovitch-Morse type
covering lemmas in complete Riemannian manifolds and try to find more spaces
such that the so-called BCP and WBCP are equivalent while these two properties
are weaker and still useful. We also get interest in the best constants of
Besicovitch-type covering properties in Euclidean spaces and sorted out the
best results of related problems before giving a new proof of Besicovitch
covering theorem in the one-dimensional case.
|
Since a rigorous microscopic treatment of a nematic fluid system based on a
pairwise interaction potential is immensely complex we had introduced a simple
mean field potential which was a modification of the Maier-Saupe potential in a
previous paper. Building up on that here we have modified that potential to
take into account the various aspects of a smectic A-nematic phase transition.
In particular we have studied the dependence of the phase transition on the
coupling coefficient between the nematic and smectic order parameters which in
turn depends on the length of alkyl chain, existence of tricritical point,
variation of entropy and specific heat as well as the dependence of the phase
transition on pressure.
|
Learned networks in the domain of visual recognition and cognition impress in
part because even though they are trained with datasets many orders of
magnitude smaller than the full population of possible images, they exhibit
sufficient generalization to be applicable to new and previously unseen data.
Although many have examined issues regarding generalization from several
perspectives, we wondered If a network is trained with a biased dataset that
misses particular samples corresponding to some defining domain attribute, can
it generalize to the full domain from which that training dataset was
extracted? It is certainly true that in vision, no current training set fully
captures all visual information and this may lead to Selection Bias. Here, we
try a novel approach in the tradition of the Thought Experiment. We run this
thought experiment on a real domain of visual objects that we can fully
characterize and look at specific gaps in training data and their impact on
performance requirements. Our thought experiment points to three conclusions:
first, that generalization behavior is dependent on how sufficiently the
particular dimensions of the domain are represented during training; second,
that the utility of any generalization is completely dependent on the
acceptable system error; and third, that specific visual features of objects,
such as pose orientations out of the imaging plane or colours, may not be
recoverable if not represented sufficiently in a training set. Any currently
observed generalization in modern deep learning networks may be more the result
of coincidental alignments and whose utility needs to be confirmed with respect
to a system's performance specification. Our Thought Experiment Probe approach,
coupled with the resulting Bias Breakdown can be very informative towards
understanding the impact of biases.
|
The expanding application in Micro-Air Vehicles has encouraged many
researchers to understand the unsteady flow around a flapping foil at a low
Reynolds number. We numerically investigate an incompressible unsteady flow
around a two-dimensional pitching airfoil (SD7003) at high reduced frequency in
the laminar regime. This study interrogates the effect of different unsteady
parameters, namely amplitude (A), reduced frequency (k), Reynolds number (Re),
and asymmetry parameter (S) for pitching motion on the force coefficients. The
inviscid theoretical model is utilized to calculate the lift coefficient for
sinusoidal motion in the viscous regime, and a comparison is made with the
numerical results. The theoretical analysis identifies the influence of the
non-circulatory lift over circulatory lift at a high reduced frequency.
Further, the results indicate that the reduced frequency (k) and asymmetry
parameter (S) have a significant impact on the instantaneous and time-averaged
force coefficients as well as on the vortex structure in the wake. Finally, the
Fast Fourier Transformation (FFT) analysis is carried out over a simulated case
with fixed amplitude and Reynolds number for distinct k and S values. The
findings confirm that the dominant frequency in the flow (k*) has a direct
correlation to the airfoil pitching frequency (k).
|
Numerous efforts have been invested in improving the effectiveness of bug
localization techniques, whereas little attention is paid to making these tools
run more efficiently in continuously evolving software repositories. This paper
first analyzes the information retrieval model behind a classic bug
localization tool, BugLocator, and builds a mathematical foundation
illustrating that the model can be updated incrementally when codebase or bug
reports evolve. Then, we present IncBL, a tool for Incremental Bug Localization
in evolving software repositories. IncBL is evaluated on the Bugzbook dataset,
and the results show that IncBL can significantly reduce the running time by
77.79% on average compared with the re-computing the model, while maintaining
the same level of accuracy. We also implement IncBL as a Github App that can be
easily integrated into open-source projects on GitHub. Users can deploy and use
IncBL locally as well. The demo video for IncBL can be viewed at
https://youtu.be/G4gMuvlJSb0, and the source code can be found at
https://github.com/soarsmu/IncBL.
|
Recently, DETR and Deformable DETR have been proposed to eliminate the need
for many hand-designed components in object detection while demonstrating good
performance as previous complex hand-crafted detectors. However, their
performance on Video Object Detection (VOD) has not been well explored. In this
paper, we present TransVOD, an end-to-end video object detection model based on
a spatial-temporal Transformer architecture. The goal of this paper is to
streamline the pipeline of VOD, effectively removing the need for many
hand-crafted components for feature aggregation, e.g., optical flow, recurrent
neural networks, relation networks. Besides, benefited from the object query
design in DETR, our method does not need complicated post-processing methods
such as Seq-NMS or Tubelet rescoring, which keeps the pipeline simple and
clean. In particular, we present temporal Transformer to aggregate both the
spatial object queries and the feature memories of each frame. Our temporal
Transformer consists of three components: Temporal Deformable Transformer
Encoder (TDTE) to encode the multiple frame spatial details, Temporal Query
Encoder (TQE) to fuse object queries, and Temporal Deformable Transformer
Decoder to obtain current frame detection results. These designs boost the
strong baseline deformable DETR by a significant margin (3%-4% mAP) on the
ImageNet VID dataset. TransVOD yields comparable results performance on the
benchmark of ImageNet VID. We hope our TransVOD can provide a new perspective
for video object detection. Code will be made publicly available at
https://github.com/SJTU-LuHe/TransVOD.
|
Video question answering (Video QA) presents a powerful testbed for
human-like intelligent behaviors. The task demands new capabilities to
integrate video processing, language understanding, binding abstract linguistic
concepts to concrete visual artifacts, and deliberative reasoning over
spacetime. Neural networks offer a promising approach to reach this potential
through learning from examples rather than handcrafting features and rules.
However, neural networks are predominantly feature-based - they map data to
unstructured vectorial representation and thus can fall into the trap of
exploiting shortcuts through surface statistics instead of true systematic
reasoning seen in symbolic systems. To tackle this issue, we advocate for
object-centric representation as a basis for constructing spatio-temporal
structures from videos, essentially bridging the semantic gap between low-level
pattern recognition and high-level symbolic algebra. To this end, we propose a
new query-guided representation framework to turn a video into an evolving
relational graph of objects, whose features and interactions are dynamically
and conditionally inferred. The object lives are then summarized into resumes,
lending naturally for deliberative relational reasoning that produces an answer
to the query. The framework is evaluated on major Video QA datasets,
demonstrating clear benefits of the object-centric approach to video reasoning.
|
With the recent developments in neural networks, there has been a resurgence
in algorithms for the automatic generation of simulation ready electronic
circuits from hand-drawn circuits. However, most of the approaches in
literature were confined to classify different types of electrical components
and only a few of those methods have shown a way to rebuild the circuit
schematic from the scanned image, which is extremely important for further
automation of netlist generation. This paper proposes a real-time algorithm for
the automatic recognition of hand-drawn electrical circuits based on object
detection and circuit node recognition. The proposed approach employs You Only
Look Once version 5 (YOLOv5) for detection of circuit components and a novel
Hough transform based approach for node recognition. Using YOLOv5 object
detection algorithm, a mean average precision (mAP0.5) of 98.2% is achieved in
detecting the components. The proposed method is also able to rebuild the
circuit schematic with 80% accuracy with a near-real time performance of 0.33s
per schematic generation.
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We present a novel deep neural network (DNN) architecture for compressing an
image when a correlated image is available as side information only at the
decoder. This problem is known as distributed source coding (DSC) in
information theory. In particular, we consider a pair of stereo images, which
generally have high correlation with each other due to overlapping fields of
view, and assume that one image of the pair is to be compressed and
transmitted, while the other image is available only at the decoder. In the
proposed architecture, the encoder maps the input image to a latent space,
quantizes the latent representation, and compresses it using entropy coding.
The decoder is trained to extract the common information between the input
image and the correlated image, using only the latter. The received latent
representation and the locally generated common information are passed through
a decoder network to obtain an enhanced reconstruction of the input image. The
common information provides a succinct representation of the relevant
information at the receiver. We train and demonstrate the effectiveness of the
proposed approach on the KITTI and Cityscape datasets of stereo image pairs.
Our results show that the proposed architecture is capable of exploiting the
decoder-only side information, and outperforms previous work on stereo image
compression with decoder side information.
|
Ly-alpha emitting galaxies and giant Ly-alpha blobs (LABs) have been
extensively observed to study the formation history of galaxies. However, the
origin of their extended Ly-alpha emission, especially of LABs, remains
controversial. Polarization signals from some LABs have been discovered, and
this is commonly interpreted as strong evidence supporting that the extended
Ly-alpha emission originates from the resonance scattering. The Monte Carlo
Ly-alpha radiative transfer code LaRT is updated to investigate the
polarization of Ly-alpha using the Stokes vector formalism. We apply LaRT to a
few models to explore the fundamental polarization properties of Ly-alpha.
Interestingly, individual Ly-alpha photon packets are found to be almost
completely polarized by a sufficient number of scatterings (N_scatt > 10^4-10^5
in a static medium) or Doppler shifts induced by gas motion, even starting from
unpolarized light. It is also found that the polarization pattern can exhibit a
non-monotonically increasing pattern in some cases, besides the commonly-known
trend that the polarization monotonically increases with radius. The
polarization properties are primarily determined by the degree of polarization
of individual photon packets and the anisotropy of the Ly-alpha radiation
field, which are eventually controlled by the medium's optical depth and
velocity field. If once Ly-alpha photon packets achieve ~100% polarization, the
radial profile of polarization appears to correlate with the surface brightness
profile. A steep surface brightness profile tends to yield a rapid increase of
the linear polarization near the Ly-alpha source location. In contrast, a
shallow surface brightness profile gives rise to a slowly increasing
polarization pattern.
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In this work we elaborate on holographic description of the path-integral
optimization in conformal field theories (CFT) using Hartle-Hawking wave
functions in Anti-de Sitter spacetimes. We argue that the maximization of the
Hartle-Hawking wave function is equivalent to the path-integral optimization
procedure in CFT. In particular, we show that metrics that maximize gravity
wave functions computed in particular holographic geometries, precisely match
those derived in the path-integral optimization procedure for their dual CFT
states. The present work is a detailed version of \cite{Boruch:2020wax} and
contains many new results such as analysis of excited states in various
dimensions including JT gravity, and a new way of estimating holographic
path-integral complexity from Hartle-Hawking wave functions. Finally, we
generalize the analysis to Lorentzian Anti-de Sitter and de Sitter geometries
and use it to shed light on path-integral optimization in Lorentzian CFTs.
|
The possibility of long-baseline quantum experiments in space makes it
necessary to better understand the time evolution of relativistic quantum
particles in a weakly varying gravitational field. We explain why conventional
treatments by traditional quantum optics and atomic physics based on quantum
mechanics may become inadequate when faced with issues related to locality,
simultaneity, signaling, causality, etc. Quantum field theory is needed. Adding
the effects of gravitation, we are led to Quantum Field Theory in Curved
Spacetime (QFTCST). This well-established theory should serve as the canonical
reference theory to a large class of proposed space experiments testing the
foundations of gravitation and quantum theory, and the basic notions of quantum
information theory in relativistic settings.
This is the first in a series of papers treating near-term quantum optics and
matter waves experiments in space from the perspective of QFTCST. We analyze
the quantum motion of photons and of scalar massive particles using QFTCST with
application to interferometer experiments. Our main result is that, for
photons, the weak gravitational field is to leading order completely equivalent
to an inhomogeneous dielectric, thus allowing for a description of quantum
optics experiments in curved space using familiar notions from the theory of
optical media. We also discuss interference experiments that probe first-order
quantum coherence, the importance of a covariant particle detection theory, and
the relevance of time of arrival measurements. For massive particles with
internal structure, we identify a novel gravity-induced phase shift that
originates from the different gravitational masses attributed to the excited
internal states. This phase shift can in principle be measured in space
experiments.
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Different AdS-Rindler wedges can be mapped to each other using bulk
isometries. In this paper we address how the boundary representations
corresponding to the AdS-Rindler wedges transform under such isometries. We
show that when a bulk wedge is mapped to another using a bulk isometry, their
boundary representations are mapped by the conformal transformation
corresponding to the isometry. We comment on the import of this result on the
relation between AdS/CFT and quantum error correction.
|
Markov Population Models are a widespread formalism used to model the
dynamics of complex systems, with applications in Systems Biology and many
other fields. The associated Markov stochastic process in continuous time is
often analyzed by simulation, which can be costly for large or stiff systems,
particularly when a massive number of simulations has to be performed (e.g. in
a multi-scale model). A strategy to reduce computational load is to abstract
the population model, replacing it with a simpler stochastic model, faster to
simulate. Here we pursue this idea, building on previous works and constructing
a generator capable of producing stochastic trajectories in continuous space
and discrete time. This generator is learned automatically from simulations of
the original model in a Generative Adversarial setting. Compared to previous
works, which rely on deep neural networks and Dirichlet processes, we explore
the use of state of the art generative models, which are flexible enough to
learn a full trajectory rather than a single transition kernel.
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In this paper, we exploit the capability of multi-agent deep reinforcement
learning (MA-DRL) technique to generate a transmit power pool (PP) for Internet
of things (IoT) networks with semi-grant-free non-orthogonal multiple access
(SGF-NOMA). The PP is mapped with each resource block (RB) to achieve
distributed transmit power control (DPC). We first formulate the resource
(sub-channel and transmit power) selection problem as stochastic Markov game,
and then solve it using two competitive MA-DRL algorithms, namely double deep Q
network (DDQN) and Dueling DDQN. Each GF user as an agent tries to find out the
optimal transmit power level and RB to form the desired PP. With the aid of
dueling processes, the learning process can be enhanced by evaluating the
valuable state without considering the effect of each action at each state.
Therefore, DDQN is designed for communication scenarios with a small-size
action-state space, while Dueling DDQN is for a large-size case. Our results
show that the proposed MA-Dueling DDQN based SGF-NOMA with DPC outperforms the
SGF-NOMA system with the fixed-power-control mechanism and networks with pure
GF protocols with 17.5% and 22.2% gain in terms of the system throughput,
respectively. Moreover, to decrease the training time, we eliminate invalid
actions (high transmit power levels) to reduce the action space. We show that
our proposed algorithm is computationally scalable to massive IoT networks.
Finally, to control the interference and guarantee the quality-of-service
requirements of grant-based users, we find the optimal number of GF users for
each sub-channel.
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We propose in this paper a new nonlinear mathematical model of an oscillating
water column. The one-dimensional shallow water equations in the presence of
this device are essentially reformulated as two transmission problems: the
first one is associated with a step in front of the device and the second one
is related to the interaction between waves and a fixed partially-immersed
structure. By taking advantage of free surface Bernoulli's equation, we close
the system by deriving a transmission condition that involves a time-dependent
air pressure inside the chamber of the device, instead of a constant
atmospheric pressure as in the previous work \cite{bocchihevergara2021}. We
then show that the second transmission problem can be reduced to a quasilinear
hyperbolic initial boundary value problem with a semilinear boundary condition
determined by an ODE depending on the trace of the solution to the PDE at the
boundary. Local well-posedness for general problems of this type is established
via an iterative scheme by using linear estimates for the PDE and nonlinear
estimates for the ODE. Finally, the well-posedness of the transmission problem
related to the wave-structure interaction in the oscillating water column is
obtained as an application of the general theory.
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Scene text image super-resolution (STISR) aims to improve the resolution and
visual quality of low-resolution (LR) scene text images, and consequently boost
the performance of text recognition. However, most of existing STISR methods
regard text images as natural scene images, ignoring the categorical
information of text. In this paper, we make an inspiring attempt to embed
categorical text prior into STISR model training. Specifically, we adopt the
character probability sequence as the text prior, which can be obtained
conveniently from a text recognition model. The text prior provides categorical
guidance to recover high-resolution (HR) text images. On the other hand, the
reconstructed HR image can refine the text prior in return. Finally, we present
a multi-stage text prior guided super-resolution (TPGSR) framework for STISR.
Our experiments on the benchmark TextZoom dataset show that TPGSR can not only
effectively improve the visual quality of scene text images, but also
significantly improve the text recognition accuracy over existing STISR
methods. Our model trained on TextZoom also demonstrates certain generalization
capability to the LR images in other datasets.
|
The main goal of this paper is to discuss how to integrate the possibilities
of crowdsourcing platforms with systems supporting workflow to enable the
engagement and interaction with business tasks of a wider group of people.
Thus, this work is an attempt to expand the functional capabilities of typical
business systems by allowing selected process tasks to be performed by
unlimited human resources. Opening business tasks to crowdsourcing, within
established Business Process Management Systems (BPMS) will improve the
flexibility of company processes and allow for lower work-load and greater
specialization among the staff employed on-site. The presented conceptual work
is based on the current international standards in this field, promoted by
Workflows Management Coalition. To this end, the functioning of business
platforms was analysed and their functionality was presented visually, followed
by a proposal and a discussion of how to implement crowdsourcing into workflow
systems.
|
The beautiful structures of single and multi-domain proteins are clearly
ordered in some fashion but cannot be readily classified using group theory
methods that are successfully used to describe periodic crystals. For this
reason, protein structures are considered to be aperiodic, and may have evolved
this way for functional purposes, especially in instances that require a
combination of softness and rigidity within the same molecule. By analyzing the
solved protein structures, we show that orientational symmetry is broken in the
aperiodic arrangement of the secondary structural elements (SSEs), which we
deduce by calculating the nematic order parameter, $P_{2}$. We find that the
folded structures are nematic droplets with a broad distribution of $P_{2}$. We
argue that non-zero values of $P_{2}$, leads to an arrangement of the SSEs that
can resist mechanical forces, which is a requirement for allosteric proteins.
Such proteins, which resist mechanical forces in some regions while being
flexible in others, transmit signals from one region of the protein to another
(action at a distance) in response to binding of ligands (oxygen, ATP or other
small molecules).
|
Both single-laser and two-laser experiments were conducted to look into the
ion-imaging of Br*(2P1/2) and Br(2P3/2) photo-fragmented from
1-bromo-2-methylbutane in the range 232-240 nm via a detection scheme of (2+1)
resonance-enhanced multiphoton ionization. The angular analysis of these
photofragment distributions yields the anisotropy parameter beta = 1.88 +/-
0.06 for the Br* excited state which arises from a parallel transition, while
beta = 0.63 +/- 0.09 for the Br ground state indicates the contribution from
both a perpendicular transition and a non-adiabatic transition. When a hexapole
coupled with an orienting field was implemented, the parent molecules are
spatially oriented to yield an orientation efficiency |<cos theta>| of 0.15.
Besides the chi angle between the recoil velocity v and the transition dipole
moment mu, orienting molecules allows for the evaluation of the angle alpha
between v and the permanent molecular dipole moment d. The angular analysis of
Br* photofragment distribution yields chi to be 11.5 degrees and alpha in the
range from 160 degrees to 180 degrees with weak dependency. In the two-laser
experiments, the angular anisotropy of Br photofragment distribution was found
to be smaller (0.38 +/- 0.10) when the photolysis wavelength was red-shifted to
240 nm, suggesting the increasing contributions from perpendicular transitions.
|
Particularly important to hurricane risk assessment for coastal regions is
finding accurate approximations of return probabilities of maximum windspeeds.
Since extremes in maximum windspeed have a direct relationship to minimums in
the central pressure, accurate windspeed return estimates rely heavily on
proper modeling of the central pressure minima. Using the HURDAT2 database, we
show that the central pressure minima of hurricane events can be appropriately
modeled by a nonstationary extreme value distribution. We also provide and
validate a Poisson distribution with a nonstationary rate parameter to model
returns of hurricane events. Using our nonstationary models and numerical
simulation techniques from established literature, we perform a simulation
study to model returns of maximum windspeeds of hurricane events along the
North Atlantic Coast. We show that our revised model agrees with current data
and results in an expectation of higher maximum windspeeds for all regions
along the coast with the highest maximum windspeeds occurring in the northern
part of the coast.
|
In this paper, I generalize the Naszodi-Mendonca method in order to identify
changes in marital preferences over multiple dimensions, such as the partners'
race and education level. Similar to the original Naszodi-Mendonca method,
preferences are identified by the generalized method through estimating their
effects on marriage patterns, in particular, on the share of inter-racial
couples, and the share of educationally homogamous couples. This is not a
simple task because marriage patterns are shaped not only by marital
preferences, but also by the distribution of marriageable males and females by
traits. The generalized Naszodi-Mendonca method is designed for constructing
counterfactuals to perform the decomposition. I illustrate the application of
the generalized Naszodi-Mendonca method by decomposing changes in the
prevalence of racial and educational homogamy in the 1980s using US data from
IPUMS.
|
To enable multiple missiles to attack a maneuvering target simultaneously,
fixed-time distributed cooperative guidance laws are proposed in this paper.
Here, we present a novel fixed-time fast nonsingular terminal sliding mode
surface (FNTSMS). In particular, the sliding mode surface not only avoids
singularities but also has the characteristic of a settling time boundary
regardless of the initial conditions. Based on the FNTSMS, we have developed a
distributed guidance law, which has the characteristic of fixed-time
convergence. The guidance law achieves the consensus of range-to-go, relative
velocity along and perpendicular to the line of sight (LOS) direction to
realize the simultaneous attack. In addition, a saturation function is
introduced to avoid the chattering problem caused by the commonly used sign
function. Furthermore, the distributed cooperative guidance law with
communication failure is considered and proved theoretically, which shows that
the proposed guidance law has excellent performance. Finally, the simulation
results verify the performance of the distributed guidance law and its
robustness against communication topology mutations, and explain the phenomenon
in detail.
|
For better clustering performance, appropriate representations are critical.
Although many neural network-based metric learning methods have been proposed,
they do not directly train neural networks to improve clustering performance.
We propose a meta-learning method that train neural networks for obtaining
representations such that clustering performance improves when the
representations are clustered by the variational Bayesian (VB) inference with
an infinite Gaussian mixture model. The proposed method can cluster unseen
unlabeled data using knowledge meta-learned with labeled data that are
different from the unlabeled data. For the objective function, we propose a
continuous approximation of the adjusted Rand index (ARI), by which we can
evaluate the clustering performance from soft clustering assignments. Since the
approximated ARI and the VB inference procedure are differentiable, we can
backpropagate the objective function through the VB inference procedure to
train the neural networks. With experiments using text and image data sets, we
demonstrate that our proposed method has a higher adjusted Rand index than
existing methods do.
|
Deployed machine learning models are confronted with the problem of changing
data over time, a phenomenon also called concept drift. While existing
approaches of concept drift detection already show convincing results, they
require true labels as a prerequisite for successful drift detection.
Especially in many real-world application scenarios-like the ones covered in
this work-true labels are scarce, and their acquisition is expensive.
Therefore, we introduce a new algorithm for drift detection, Uncertainty Drift
Detection (UDD), which is able to detect drifts without access to true labels.
Our approach is based on the uncertainty estimates provided by a deep neural
network in combination with Monte Carlo Dropout. Structural changes over time
are detected by applying the ADWIN technique on the uncertainty estimates, and
detected drifts trigger a retraining of the prediction model. In contrast to
input data-based drift detection, our approach considers the effects of the
current input data on the properties of the prediction model rather than
detecting change on the input data only (which can lead to unnecessary
retrainings). We show that UDD outperforms other state-of-the-art strategies on
two synthetic as well as ten real-world data sets for both regression and
classification tasks.
|
The present study examined 4698 Indian Coronary Artery Disease research
publications, as indexed in Web of Science database during 1990-2019, with a
view to understand their growth rate, global share, citation impact,
international collaborative papers, distribution of publications by broad
subjects, productivity and citation profile of top organizations and authors,
and preferred media of communication. The Indian publications registered an
annual average growth rate of 11.47%, global share of 1.14%, international
collaborative publications share of 38.89% and its citation impact averaged to
25.58 citations per paper. Among broad subjects, Cardiovascular System &
Cardiology contributed the largest publications share of 19.14% in Indian
coronary artery disease output, followed by Neurosciences & Neurology (14.94%),
Pharmacology & Pharmacy (8.51%), etc. during 1990-2019. Among various
organizations and authors contributing to Indian coronary artery disease
research, the top 20 organizations and top 30 authors together contributed
40.70% and 37.29% respectively as their share of Indian publication output and
38.36% and 33.13% respectively as their share of Indian citation output during
1990-2019. Among 1222 contributing journals in Indian coronary artery disease
research, the top 30 journals registered 30.80% share during 1990-2019. There
is an urgent need to increase the publication output, improve research quality
and improve international collaboration. Indian government also needs to come
up with a policy for identification, screening, diagnosis and treatment of
coronary artery disease patients, besides curriculum reform in teaching,
capacity building, patient education and political support are badly needed.
|
Fix $R>1$ and let $A_R=\{1/R\le |z|\le R \}$ be an annulus. Also, let $K(R)$
denote the smallest constant such that $A_R$ is a $K(R)$-spectral set for the
bounded linear operator $T\in \mathcal{B}(H)$ whenever $||T||\le R$ and
$||T^{-1}||\le R.$ We show that $K(R)\ge 2, \text{ for all } R>1. $ This
improves on previous results by Badea, Beckermann and Crouzeix.
|
We describe an improvement on the magnetic scalar potential approach to the
design of an electromagnet, which incorporates the need to wind the coil as a
helix. Any magnetic field that can be described by a magnetic scalar potential
is produced with high fidelity within a Target region; all fields are confined
within a larger Return. The helical winding only affects the field in the
Return.
|
We introduce the use of conditional generative adversarial networks
forgeneralised gravitational wave burst generation in the time
domain.Generativeadversarial networks are generative machine learning models
that produce new databased on the features of the training data set. We
condition the network on fiveclasses of time-series signals that are often used
to characterise gravitational waveburst searches: sine-Gaussian, ringdown,
white noise burst, Gaussian pulse and binaryblack hole merger. We show that the
model can replicate the features of these standardsignal classes and, in
addition, produce generalised burst signals through interpolationand class
mixing. We also present an example application where a convolutional
neuralnetwork classifier is trained on burst signals generated by our
conditional generativeadversarial network. We show that a convolutional neural
network classifier trainedonly on the standard five signal classes has a poorer
detection efficiency than aconvolutional neural network classifier trained on a
population of generalised burstsignals drawn from the combined signal class
space.
|
The analogy between self-similar time series with given Hurst exponent H and
Markovian, Gaussian stochastic processes with multiplicative noise and entropic
index q (Borland, PRE 57, 6, 6634-6642, 1998) allows us to explain the
empirical results reported in (Pavithran et al., EPL, 129 2020 24004) and
(Pavithran et al. Sci. Reports 10.1 (2020) 1-8) with the help of the properties
of the nonextensive entropy Sq of index q: a dominant oscillating mode arises
as H goes to zero in many different systems and its amplitude is proportional
to 1/ H^2 . Thus, a decrease of H acts as precursor of large oscillations of
the state variable, which corresponds to catastrophic events in many problems
of practical interest. In contrast, if H goes to 1 then the time series is
strongly intermittent, fluctuations of the state variable follow a power law
whose exponent depends on H, and exceedingly large event are basically
unpredictable. These predictions agree with observations in problems of
aeroacoustics, aeroelasticity, electric engineering, hydrology, laser physics,
meteorology, plasma physics, plasticity, polemology, seismology and
thermoacoustics.
|
We discuss relations between the initial boundary value problem (IBVP) and
quasi-local Hamiltonians in GR. The latter have traditionally been based on
Dirichlet boundary conditions, which however are shown here to be ill-posed for
the IBVP. We present and analyse several other choices of boundary conditions
which are better behaved with respect to the IBVP and carry out a corresponding
Hamiltonian analysis, using the framework of the covariant phase space method.
|
Providing personalized explanations for recommendations can help users to
understand the underlying insight of the recommendation results, which is
helpful to the effectiveness, transparency, persuasiveness and trustworthiness
of recommender systems. Current explainable recommendation models mostly
generate textual explanations based on pre-defined sentence templates. However,
the expressiveness power of template-based explanation sentences are limited to
the pre-defined expressions, and manually defining the expressions require
significant human efforts. Motivated by this problem, we propose to generate
free-text natural language explanations for personalized recommendation. In
particular, we propose a hierarchical sequence-to-sequence model (HSS) for
personalized explanation generation. Different from conventional sentence
generation in NLP research, a great challenge of explanation generation in
e-commerce recommendation is that not all sentences in user reviews are of
explanation purpose. To solve the problem, we further propose an auto-denoising
mechanism based on topical item feature words for sentence generation.
Experiments on various e-commerce product domains show that our approach can
not only improve the recommendation accuracy, but also the explanation quality
in terms of the offline measures and feature words coverage. This research is
one of the initial steps to grant intelligent agents with the ability to
explain itself based on natural language sentences.
|
We consider the single image super-resolution (SISR) problem, where a
high-resolution (HR) image is generated based on a low-resolution (LR) input.
Recently, generative adversarial networks (GANs) become popular to hallucinate
details. Most methods along this line rely on a predefined single-LR-single-HR
mapping, which is not flexible enough for the SISR task. Also, GAN-generated
fake details may often undermine the realism of the whole image. We address
these issues by proposing best-buddy GANs (Beby-GAN) for rich-detail SISR.
Relaxing the immutable one-to-one constraint, we allow the estimated patches to
dynamically seek the best supervision during training, which is beneficial to
producing more reasonable details. Besides, we propose a region-aware
adversarial learning strategy that directs our model to focus on generating
details for textured areas adaptively. Extensive experiments justify the
effectiveness of our method. An ultra-high-resolution 4K dataset is also
constructed to facilitate future super-resolution research.
|
A long-standing challenge in artificial intelligence is lifelong learning. In
lifelong learning, many tasks are presented in sequence and learners must
efficiently transfer knowledge between tasks while avoiding catastrophic
forgetting over long lifetimes. On these problems, policy reuse and other
multi-policy reinforcement learning techniques can learn many tasks. However,
they can generate many temporary or permanent policies, resulting in memory
issues. Consequently, there is a need for lifetime-scalable methods that
continually refine a policy library of a pre-defined size. This paper presents
a first approach to lifetime-scalable policy reuse. To pre-select the number of
policies, a notion of task capacity, the maximal number of tasks that a policy
can accurately solve, is proposed. To evaluate lifetime policy reuse using this
method, two state-of-the-art single-actor base-learners are compared: 1) a
value-based reinforcement learner, Deep Q-Network (DQN) or Deep Recurrent
Q-Network (DRQN); and 2) an actor-critic reinforcement learner, Proximal Policy
Optimisation (PPO) with or without Long Short-Term Memory layer. By selecting
the number of policies based on task capacity, D(R)QN achieves near-optimal
performance with 6 policies in a 27-task MDP domain and 9 policies in an
18-task POMDP domain; with fewer policies, catastrophic forgetting and negative
transfer are observed. Due to slow, monotonic improvement, PPO requires fewer
policies, 1 policy for the 27-task domain and 4 policies for the 18-task
domain, but it learns the tasks with lower accuracy than D(R)QN. These findings
validate lifetime-scalable policy reuse and suggest using D(R)QN for larger and
PPO for smaller library sizes.
|
We prove that the general linear groups of the integers, Gaussian integers,
and Eisenstein integers satisfy homological stability of slope 1 when using
$\mathbb{Z}[1/2]$-coefficients and of slope $2/3$ when using
$\mathbb{Z}$-coefficients.
|
The sources of reliable, code-level information about vulnerabilities that
affect open-source software (OSS) are scarce, which hinders a broad adoption of
advanced tools that provide code-level detection and assessment of vulnerable
OSS dependencies.
In this paper, we study the extent to which the output of off-the-shelf
static code analyzers can be used as a source of features to represent commits
in Machine Learning (ML) applications. In particular, we investigate how such
features can be used to construct embeddings and train ML models to
automatically identify source code commits that contain vulnerability fixes.
We analyze such embeddings for security-relevant and non-security-relevant
commits, and we show that, although in isolation they are not different in a
statistically significant manner, it is possible to use them to construct a ML
pipeline that achieves results comparable with the state of the art.
We also found that the combination of our method with commit2vec represents a
tangible improvement over the state of the art in the automatic identification
of commits that fix vulnerabilities: the ML models we construct and commit2vec
are complementary, the former being more generally applicable, albeit not as
accurate.
|
We review a combinatoric approach to the Hodge Conjecture for Fermat
Varieties and announce new cases where the conjecture is true.
|
This article studies the impact of online news on social and economic
consumer perceptions through the application of semantic network analysis.
Using almost 1.3 million online articles on Italian media covering a period of
four years, we assessed the incremental predictive power of economic-related
keywords on the Consumer Confidence Index. We transformed news into networks of
co-occurring words and calculated the semantic importance of specific keywords,
to see if words appearing in the articles could anticipate consumers'
judgements about the economic situation. Results show that economic-related
keywords have a stronger predictive power if we consider the current households
and national situation, while their predictive power is less significant with
regards to expectations about the future. Our indicator of semantic importance
offers a complementary approach to estimate consumer confidence, lessening the
limitations of traditional survey-based methods.
|
We establish a direct connection between general tensor networks and deep
feed-forward artificial neural networks. The core of our results is the
construction of neural-network layers that efficiently perform tensor
contractions, and that use commonly adopted non-linear activation functions.
The resulting deep networks feature a number of edges that closely matches the
contraction complexity of the tensor networks to be approximated. In the
context of many-body quantum states, this result establishes that
neural-network states have strictly the same or higher expressive power than
practically usable variational tensor networks. As an example, we show that all
matrix product states can be efficiently written as neural-network states with
a number of edges polynomial in the bond dimension and depth logarithmic in the
system size. The opposite instead does not hold true, and our results imply
that there exist quantum states that are not efficiently expressible in terms
of matrix product states or practically usable PEPS, but that are instead
efficiently expressible with neural network states.
|
Moir\'e super-potentials in two-dimensional materials allow unprecedented
control of the ratio between kinetic and interaction energy. By this, they pave
the way to study a wide variety of strongly correlated physics under a new
light. In particular, the transition metal dichalcogenides (TMDs) are promising
candidate "quantum simulators" of the Hubbard model on a triangular lattice.
Indeed, Mott and generalized Wigner crystals have been observed in such
devices. Here we theoretically propose to extend this model into the
multi-orbital regime by focusing on electron doped systems at filling higher
than 2. As opposed to hole bands, the electronic bands in TMD materials include
two, nearly degenerate species, which can be viewed as two orbitals with
different effective mass and binding energy. Using realistic band-structure
parameters and a slave-rotor mean-field theory, we find that an orbitally
selective Mott (OSM) phase can be stabilized over a wide range of fillings,
where one band is locked in a commensurate Mott state, while the other remains
itinerant with variable density. This scenario thus, realizes the basic
ingredients in the Kondo lattice model: A periodic lattice of localized
magnetic moments interacting with metallic states. We also discuss possible
experimental signatures of the OSM state.
|
Mitchell Feigenbaum discovered an intriguing property of viewing images
through cylindrical mirrors or looking into water. Because the eye is a lens
with an opening of about 5mm, many different rays of reflected images reach the
eye, and need to be interpreted by the visual system. This has the surprising
effect that what one perceives depends on the orientation of the head, whether
it is tilted or not. I explain and illustrate this phenomenon on the example of
a human eye looking at a ruler immersed in water.
|
The restoration lemma by Afek, Bremler-Barr, Kaplan, Cohen, and Merritt
[Dist. Comp. '02] proves that, in an undirected unweighted graph, any
replacement shortest path avoiding a failing edge can be expressed as the
concatenation of two original shortest paths. However, the lemma is
tiebreaking-sensitive: if one selects a particular canonical shortest path for
each node pair, it is no longer guaranteed that one can build replacement paths
by concatenating two selected shortest paths. They left as an open problem
whether a method of shortest path tiebreaking with this desirable property is
generally possible.
We settle this question affirmatively with the first general construction of
restorable tiebreaking schemes. We then show applications to various problems
in fault-tolerant network design. These include a faster algorithm for subset
replacement paths, more efficient fault-tolerant (exact) distance labeling
schemes, fault-tolerant subset distance preservers and $+4$ additive spanners
with improved sparsity, and fast distributed algorithms that construct these
objects. For example, an almost immediate corollary of our restorable
tiebreaking scheme is the first nontrivial distributed construction of sparse
fault-tolerant distance preservers resilient to three faults.
|
We give a new proof for the central limit theorem in probability for the
directed polymer model in a bounded environment with bond disorder in the
interior of the weak disorder phase. In the same setup, we also show that the
large deviation rate function agrees with that of the underlying random walk.
In addition, for the Brownian polymer model, we show that the central limit
theorem holds almost surely in the whole weak disorder phase.
The results are proved using the moment bound from [20] and a new tool
introduced in this paper, which allows a quantitative comparison between the
associated martingales at different inverse temperatures. This comparison is
made precise using the noise operator $T_\rho$ acting on the environment by
independent resampling.
|
A splinter is a notion of singularity that has seen numerous recent
applications, especially in connection with the direct summand theorem, the
mixed characteristic minimal model program, Cohen-Macaulayness of absolute
integral closures and cohomology vanishing theorems. Nevertheless, many basic
questions about these singularities remain elusive. One outstanding problem is
whether the splinter property spreads from a point to an open neighborhood of a
noetherian scheme. Our paper addresses this problem in prime characteristic,
where we show that a locally noetherian scheme that has finite Frobenius or
that is locally essentially of finite type over a quasi-excellent local ring
has an open splinter locus. In particular, all varieties over fields of
positive characteristic have open splinter loci. Intimate connections are
established between the openness of splinter loci and $F$-compatible ideals,
which are prime characteristic analogues of log canonical centers. We prove the
surprising fact that for a large class of noetherian rings with pure (aka
universally injective) Frobenius, the splinter condition is detected by the
splitting of a single generically \'etale finite extension. We also show that
for a noetherian $\textbf{N}$-graded ring over a field, the homogeneous maximal
ideal detects the splinter property.
|
We consider accelerated black hole horizons with and without defects. These
horizons appear in the $C$-metric solution to Einstein equations and in its
generalization to the case where external fields are present. These solutions
realize a variety of physical processes, from the decay of a cosmic string by a
black hole pair nucleation to the creation of a black hole pair by an external
electromagnetic field. Here, we show that such geometries exhibit an infinite
set of symmetries in their near horizon region, generalizing in this way
previous results for smooth isolated horizons. By considering the limit close
to both the black hole and the acceleration horizons, we show that a sensible
set of asymptotic boundary conditions gets preserved by supertranslation and
superrotation transformations. By acting on the geometry with such
transformations, we derive the superrotated, supertranslated version of the
$C$-metric and compute the associated conserved charges.
|
Multi-access coded caching schemes from cross resolvable designs (CRD) have
been reported recently \cite{KNRarXiv}. To be able to compare coded caching
schemes with different number of users and possibly with different number of
caches a new metric called rate-per-user was introduced and it was shown that
under this new metric the schemes from CRDs perform better than the
Maddah-Ali-Niesen scheme in the large memory regime. In this paper a new class
of CRDs is presented and it is shown that the multi-access coded caching
schemes derived from these CRDs perform better than the Maddah-Ali-Niesen
scheme in the entire memory regime. Comparison with other known multi-access
coding schemes is also presented.
|
The security of the Internet rests on a small number of open-source
cryptographic libraries: a vulnerability in any one of them threatens to
compromise a significant percentage of web traffic. Despite this potential for
security impact, the characteristics and causes of vulnerabilities in
cryptographic software are not well understood. In this work, we conduct the
first comprehensive analysis of cryptographic libraries and the vulnerabilities
affecting them. We collect data from the National Vulnerability Database,
individual project repositories and mailing lists, and other relevant sources
for eight widely used cryptographic libraries.
Among our most interesting findings is that only 27.2% of vulnerabilities in
cryptographic libraries are cryptographic issues while 37.2% of vulnerabilities
are memory safety issues, indicating that systems-level bugs are a greater
security concern than the actual cryptographic procedures. In our investigation
of the causes of these vulnerabilities, we find evidence of a strong
correlation between the complexity of these libraries and their (in)security,
empirically demonstrating the potential risks of bloated cryptographic
codebases. We further compare our findings with non-cryptographic systems,
observing that these systems are, indeed, more complex than similar
counterparts, and that this excess complexity appears to produce significantly
more vulnerabilities in cryptographic libraries than in non-cryptographic
software.
|
Recent advances in quantum engineering have given us the ability to design
hybrid systems with novel properties normally not present in the regime they
operate in. The coupling of spin ensembles and magnons to microwave resonators
has for instance lead to a much richer understanding of collective effects in
these systems and their potential quantum applications. We can also hybridize
electron and nuclear spin ensembles together in the solid-state regime to
investigate collective effects normally only observed in the atomic, molecular
and optical world. Here we explore in the solid state regime the dynamics of a
double domain nuclear spin ensemble coupled to the Nambu-Goldstone boson in
GaAs semiconductors and show it exhibits both collective and individual
relaxation (thermalization) on very different time scales. Further the
collective relaxation of the nuclear spin ensemble is what one would expect
from superradiant decay. This opens up the possibility for the exploration of
novel collective behaviour in solid state systems where the natural energies
associated with those spins are much less than the thermal energy.
|
In this paper, we develop a computational multiscale to solve the parabolic
wave approximation with heterogeneous and variable media. Parabolic wave
approximation is a technique to approximate the full wave equation. One benefit
of the method is that: one wave propagation direction can be taken as an
evolution direction, and we then can discretize it using a classical scheme
like Backward Euler. Consequently, we obtain a set of quasi-gas-dynamic (QGD)
models with different heterogeneous permeability fields. Then, we employ
constraint energy minimization generalized multiscale finite element method
(CEM-GMsFEM) to perform spatial discretization for the problem. The resulting
system can be solved by combining the central difference in time evolution. Due
to the variable media, we apply the technique of proper orthogonal
decomposition (POD) to further the dimension of the problem and solve the
corresponding model problem in the POD space instead of in the whole multiscale
space spanned by all possible multiscale basis functions. We prove the
stability of the full discretization scheme and give the convergence analysis
of the proposed approximation scheme. Numerical results verify the
effectiveness of the proposed method.
|
Nickel-based complex oxides have served as a playground for decades in the
quest for a copper-oxide analog of the high-temperature (high-Tc)
superconductivity. They may provide key points towards understanding the
mechanism of the high-Tc and an alternative route for a room-temperature
superconductor. The recent discovery of superconductivity in the infinite-layer
nickelate thin films has put this pursuit to an end. Having complete control in
material preparation and a full understanding of the properties and electronic
structures becomes the center of gravity of current research in nickelates.
Thus far, material synthesis remains challenging. The demonstration of perfect
diamagnetism is still missing, and understanding the role of the interface and
bulk to the superconducting properties is still lacking. Here, we synthesized
high-quality Nd0.8Sr0.2NiO2 thin films with different thicknesses and
investigated the interface and strain effects on the electrical, magnetic and
optical properties. The perfect diamagnetism is demonstrated, confirming the
occurrence of superconductivity in the thin films. Unlike the thick films in
which the normal state Hall coefficient (RH) changes signs from negative to
positive as the temperature decreases, the RH of the films thinner than 6.1-nm
remains negative at the whole temperature range below 300 K, suggesting a
thickness-driven band structure modification. The X-ray spectroscopy reveals
the Ni-O hybridization nature in doped finite-layer nickelates, and the
hybridization is enhanced as the thickness decreases. Consistent with band
structure calculations on nickelate/SrTiO3 interfaces, the interface and strain
effect induce the dominating electron-like band in the ultrathin film, thus
causing the sign-change of the RH.
|
Recommending medications for patients using electronic health records (EHRs)
is a crucial data mining task for an intelligent healthcare system. It can
assist doctors in making clinical decisions more efficiently. However, the
inherent complexity of the EHR data renders it as a challenging task: (1)
Multilevel structures: the EHR data typically contains multilevel structures
which are closely related with the decision-making pathways, e.g., laboratory
results lead to disease diagnoses, and then contribute to the prescribed
medications; (2) Multiple sequences interactions: multiple sequences in EHR
data are usually closely correlated with each other; (3) Abundant noise: lots
of task-unrelated features or noise information within EHR data generally
result in suboptimal performance. To tackle the above challenges, we propose a
multilevel selective and interactive network (MeSIN) for medication
recommendation. Specifically, MeSIN is designed with three components. First,
an attentional selective module (ASM) is applied to assign flexible attention
scores to different medical codes embeddings by their relevance to the
recommended medications in every admission. Second, we incorporate a novel
interactive long-short term memory network (InLSTM) to reinforce the
interactions of multilevel medical sequences in EHR data with the help of the
calibrated memory-augmented cell and an enhanced input gate. Finally, we employ
a global selective fusion module (GSFM) to infuse the multi-sourced information
embeddings into final patient representations for medications recommendation.
To validate our method, extensive experiments have been conducted on a
real-world clinical dataset. The results demonstrate a consistent superiority
of our framework over several baselines and testify the effectiveness of our
proposed approach.
|
Quantum information is typically encoded in the state of a qubit that is
decoupled from the environment. In contrast, waveguide quantum electrodynamics
studies qubits coupled to a mode continuum, exposing them to a loss channel and
causing quantum information to be lost before coherent operations can be
performed. Here we restore coherence by realizing a dark state that exploits
symmetry properties and interactions between four qubits. Dark states decouple
from the waveguide and are thus a valuable resource for quantum information but
also come with a challenge: they cannot be controlled by the waveguide drive.
We overcome this problem by designing a drive that utilizes the symmetry
properties of the collective state manifold allowing us to selectively drive
both bright and dark states. The decay time of the dark state exceeds that of
the waveguide-limited single qubit by more than two orders of magnitude.
Spectroscopy on the second excitation manifold provides further insight into
the level structure of the hybridized system. Our experiment paves the way for
implementations of quantum many-body physics in waveguides and the realization
of quantum information protocols using decoherence-free subspaces.
|
Sparse neural networks have been widely applied to reduce the computational
demands of training and deploying over-parameterized deep neural networks. For
inference acceleration, methods that discover a sparse network from a
pre-trained dense network (dense-to-sparse training) work effectively.
Recently, dynamic sparse training (DST) has been proposed to train sparse
neural networks without pre-training a dense model (sparse-to-sparse training),
so that the training process can also be accelerated. However, previous
sparse-to-sparse methods mainly focus on Multilayer Perceptron Networks (MLPs)
and Convolutional Neural Networks (CNNs), failing to match the performance of
dense-to-sparse methods in the Recurrent Neural Networks (RNNs) setting. In
this paper, we propose an approach to train intrinsically sparse RNNs with a
fixed parameter count in one single run, without compromising performance.
During training, we allow RNN layers to have a non-uniform redistribution
across cell gates for better regularization. Further, we propose SNT-ASGD, a
novel variant of the averaged stochastic gradient optimizer, which
significantly improves the performance of all sparse training methods for RNNs.
Using these strategies, we achieve state-of-the-art sparse training results,
better than the dense-to-sparse methods, with various types of RNNs on Penn
TreeBank and Wikitext-2 datasets. Our codes are available at
https://github.com/Shiweiliuiiiiiii/Selfish-RNN.
|
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