  We provide analogues for non-orientable surfaces with or without boundary or
punctures of several basic theorems in the setting of the Thurston theory of
surfaces which were developed so far only in the case of orientable surfaces.
Namely, we provide natural analogues for non-orientable surfaces of the
Fenchel-Nielsen theorem on the parametrization of the Teichm\"uller space of
the surface, the Dehn-Thurston theorem on the parametrization of measured
foliations in the surface, and the Hatcher-Thurston theorem, which gives a
complete minimal set of moves between pair of pants decompositions of the
surface. For the former two theorems, one in effect drops the twisting number
for any curve in a pants decomposition which is 1-sided, and for the latter,
new elementary moves on pants decompositions are introduced.


  We study the geometry of a $G$-structure $P$ inside the oriented orthonormal
frame bundle ${\rm SO}(M)$ over an oriented Riemannian manifold $M$. We assume
that $G$ is connected and closed, so the quotient ${\rm SO}(n)/G$, where
$n=\dim M$, is a normal homogeneous space and we equip ${\rm SO}(M)$ with the
natural Riemannian structure induced from the structure on $M$ and the Killing
form of ${\rm SO}(n)$. We show, in particular, that minimality of $P$ is
equivalent to harmonicity of an induced section of the homogeneous bundle ${\rm
SO}(M)\times_{{\rm SO}(n)}{\rm SO}(n)/G$, with a Riemannian metric on $M$
obtained as the pull-back with respect to this section of the Riemannian metric
on the considered associated bundle, and to the minimality of the image of this
section. We apply obtained results to the case of almost product structures,
i.e., structures induced by plane fields.


  Variable power transmission in mechanical systems is often achieved by
devices, e.g., clutches and brakes, that use dry friction. In these systems,
the variability in power transmission is brought about by engaging and
disengaging the friction plates. Though commonly used, this method of making
the coupling noisy is not as versatile as their electrical analog. An
alternative method would be to intermittently vary the frictional force. In
this paper, we demonstrate a self-organized way to tune the noise in the
frictional coupling between two surfaces which are in relative motion with each
other. This is achieved by exploiting the complexity that arises from the
frictional interaction of the balls which are placed in a circular groove
between the surfaces. The extent of floppiness in the coupling is related to
the rate at which the balls make transitions between their rolling and sliding
states. If the moving surface is soft and the static surface is hard we show
that with increasing filling fraction of the balls the transitions between
rolling and sliding against the static surface give way to the transitions
between rolling and sliding against the moving surface. As a consequence, the
noise in the coupling is large for both small and large filling fraction with a
dip in the middle. In contrast, the sliding with the static surface is
suppressed if the moving pate is hard and the noise in the coupling decreases
monotonically with the filling fraction of the balls.


  Singular hyperbolicity is a weakened form of hyperbolicity that has been
introduced for vector fields in order to allow non-isolated singularities
inside the non-wandering set. A typical example of a singular hyperbolic set is
the Lorenz attractor. However, in contrast to uniform hyperbolicity, singular
hyperbolicity does not immediately imply robust topological properties, such as
the transitivity.
  In this paper, we prove that open and densely inside the space of $C^1$
vector fields of a compact manifold, any singular hyperbolic attractors is
robustly transitive.


  We define Floer homology theories for oriented, singular knots in S^3 and
show that one of these theories can be defined combinatorially for planar
singular knots.


  We demonstrate a technique that allows to fully control the bandwidth of
entangled photons independently of the frequency band of interest and of the
nonlinear crystal. We show that this technique allows to generate nearly
transform-limited biphotons with almost one octave of bandwidth (hundreds of
THz) which corresponds to correlation times of just a few femtoseconds. The
presented method becomes an enabling tool for attosecond entangled-photons
quantum optics. The technique can also be used to generate paired photons with
a very high degree of entanglement.


  With advances in scientific computing and mathematical modeling, complex
scientific phenomena such as galaxy formations and rocket propulsion can now be
reliably simulated. Such simulations can however be very time-intensive,
requiring millions of CPU hours to perform. One solution is multi-fidelity
emulation, which uses data of different fidelities to train an efficient
predictive model which emulates the expensive simulator. For complex scientific
problems and with careful elicitation from scientists, such multi-fidelity data
may often be linked by a directed acyclic graph (DAG) representing its
scientific model dependencies. We thus propose a new Graphical Multi-fidelity
Gaussian Process (GMGP) model, which embeds this DAG structure (capturing
scientific dependencies) within a Gaussian process framework. We show that the
GMGP has desirable modeling traits via two Markov properties, and admits a
scalable algorithm for recursive computation of the posterior mean and variance
along at each depth level of the DAG. We also present a novel experimental
design methodology over the DAG given an experimental budget, and propose a
nonlinear extension of the GMGP via deep Gaussian processes. The advantages of
the GMGP are then demonstrated via a suite of numerical experiments and an
application to emulation of heavy-ion collisions, which can be used to study
the conditions of matter in the Universe shortly after the Big Bang. The
proposed model has broader uses in data fusion applications with graphical
structure, which we further discuss.


  Because most massive stars have been or will be affected by a companion
during the course of their evolution, we cannot afford to neglect binaries when
discussing the progenitors of supernovae and GRBs. Analyzing linear
polarization in the emission lines of close binary systems allows us to probe
the structures of these systems' winds and mass flows, making it possible to
map the complex morphologies of the mass loss and mass transfer structures that
shape their subsequent evolution. In Wolf-Rayet (WR) binaries, line
polarization variations with orbital phase distinguish polarimetric signatures
arising from lines that scatter near the stars from those that scatter far from
the orbital plane. These far-scattering lines may form the basis for a "binary
line-effect method" of identifying rapidly rotating WR stars (and hence GRB
progenitor candidates) in binary systems.


  We review the numerical analysis' understanding of Krylov subspace methods
for solving (non-hermitian) systems of equations and discuss its implications
for lattice gauge theory computations using the example of the Wilson fermion
matrix. Our thesis is that mature methods like QMR, BiCGStab or restarted GMRES
are close to optimal for the Wilson fermion matrix. Consequently,
preconditioning appears to be the crucial issue for further improvements.


  One of the frontier issues that severely hamper the development of automatic
snore sound classification (ASSC) associates to the lack of sufficient
supervised training data. To cope with this problem, we propose a novel data
augmentation approach based on semi-supervised conditional Generative
Adversarial Networks (scGANs), which aims to automatically learn a mapping
strategy from a random noise space to original data distribution. The proposed
approach has the capability of well synthesizing 'realistic' high-dimensional
data, while requiring no additional annotation process. To handle the mode
collapse problem of GANs, we further introduce an ensemble strategy to enhance
the diversity of the generated data. The systematic experiments conducted on a
widely used Munich-Passau snore sound corpus demonstrate that the scGANs-based
systems can remarkably outperform other classic data augmentation systems, and
are also competitive to other recently reported systems for ASSC.


  We argue that in addition to the Hall conductance and the nondissipative
component of the viscous tensor, there exists a third independent transport
coefficient, which is precisely quantized. It takes constant values along
quantum Hall plateaus. We show that the new coefficient is the Chern number of
a vector bundle over moduli space of surfaces of genus 2 or higher and
therefore cannot change continuously along the plateau. As such, it does not
transpire on a sphere or a torus. In the linear response theory, this
coefficient determines intensive forces exerted on electronic fluid by
adiabatic deformations of geometry and represents the effect of the
gravitational anomaly. We also present the method of computing the transport
coefficients for quantum Hall states.


  We present infrared JHK photometry of the cataclysmic variable SDSS
J123813.73-033933.0 (SDSS1238)and analyze it along with optical spectroscopy,
demonstrating that the binary system is most probably comprised of a massive
white dwarf with Teff=12000+/-1000 K and a brown dwarf of spectral type L4. The
inferred system parameters suggest that this system may have evolved beyond the
orbital period minimum and is a bounce-back system. SDSS1238 stands out among
CVs by exhibiting the cyclical variability (brightenings). These are not
related to specific orbital phases of the binary system and are fainter than
dwarf novae outbursts, that usually occur on longer timescales. This phenomenon
has not been observed extensively and, thus, is poor understood. The new
time-resolved, multi-longitude photometric observations of SDSS1238 allowed us
to observe two consecutive brightenings and to determine their recurrence time.
The period analysis of all observed brightenings during 2007 suggests a typical
timescale that is close to a period of ~9.3 hours. However, the brightenings
modulation is not strictly periodic, possibly maintaining coherence only on
timescales of several weeks. The characteristic variability with double orbital
frequency that clearly shows up during brightenings is also analyzed. The
Doppler mapping of the system shows the permanent presence of a spiral arm
pattern in the accretion disk. A simple model is presented to demonstrate that
spiral arms in the velocity map appear at the location and phase corresponding
to the 2:1 resonance radius and constitute themselves as a double-humped light
curves. The long-term and short-term variability of this CV is discussed
together with the spiral arm structure of an accretion disk in the context of
observational effects taking place in bounce-back systems.


  In order to characterize the channel capacity of a wavelength channel in a
wavelength-division multiplexed (WDM) system, statistical models are needed for
the transmitted signals on the other wavelengths. For example, one could assume
that the transmitters for all wavelengths are configured independently of each
other, that they use the same signal power, or that they use the same
modulation format. In this paper, it is shown that these so-called behavioral
models have a profound impact on the single-wavelength achievable information
rate. This is demonstrated by establishing, for the first time, upper and lower
bounds on the maximum achievable rate under various behavioral models, for a
rudimentary WDM channel model.


  We consider the remainder term in the semiclassical limit formula for the eta
invariant on a metric contact manifold, proving in general that it is
controlled by volumes of recurrence sets of the Reeb flow. This particularly
gives a logarithmic improvement of the remainder for Anosov Reeb flows, while
for certain elliptic flows the improvement is in terms of irrationality
measures of corresponding Floquet exponents.


  We consider a $d$-dimensional SDE with an identity diffusion matrix and a
drift vector being a vector function of bounded variation. We give a
representation for the derivative of the solution with respect to the initial
data.


  We present the results from nearly three years of monitoring of the
variations in dispersion measure (DM) along the line-of-sight to 11 millisecond
pulsars using the Giant Metrewave Radio Telescope (GMRT). These results
demonstrate accuracies of single epoch DM estimates of the order of 5x10^(-4)
cm^(-3) pc. A preliminary comparison with the Parkes Pulsar Timing Array (PPTA)
data shows that the measured DM fluctuations are comparable. We show effects of
DM variations due to the solar wind and solar corona and compare with the
existing models.


  We study the Hall constant in a homogeneous two-dimensional fluid of
correlated electrons immersed in a perpendicular magnetic field, with special
focus on the regime of low carrier density. The model consists of a one-band
tight-binding model and a momentum-independent causal self-energy, representing
interaction-induced correlations effects that are restricted to be local in
space. We write general gauge-invariant equations for the conductivity tensor
at first order in the magnetic field and solve them numerically (analytically
when possible) in a minimal model of anisotropic square lattice with constant
self-energy. Our results show that deviations from the universal behavior of
the Hall constant, as observed in the semiclassical regime, appear upon
entering the quantum regime, where the Fermi energy and interaction are
comparable energy scales.


  In a recent analysis of FMRI datasets [K Mueller et al, Front Hum Neurosci
11:345], the estimated spatial smoothness parameters and the statistical
significance of clusters were found to depend strongly on the resampled voxel
size (for the same data, over a range of 1 to 3 mm) in one popular FMRI
analysis software package (SPM12). High sensitivity of thresholding results on
such an arbitrary parameter as final spatial grid size is an undesirable
feature in a processing pipeline. Here, we examine the stability of spatial
smoothness and cluster-volume threshold estimates with respect to voxel
resampling size in the AFNI software package's pipeline. A publicly available
collection of resting-state and task FMRI datasets from 78 subjects was
analyzed using standard processing steps in AFNI. We found that the spatial
smoothness and cluster-volume thresholds are fairly stable over the voxel
resampling size range of 1 to 3 mm, in contradistinction to the reported
results from SPM12.


  In a quantum dot with dozens of electrons, an approximation beyond
Tamm-Dankoff is used to construct the quantum states with an additional
electron-hole pair, i.e. the "excitonic" states. The lowest states mimic the
non-interacting spectrum, but with excitation gaps renormalized by Coulomb
interactions. At higher excitation energies, the computed density of energy
levels shows an exponential increase with energy. In the interband absorption,
we found a background level in the quasicontinuum of states rising linearly
with the excitation energy. Above this background, there are distinct peaks
related to single resonances or to groups of many states with small interband
dipole moments.


  This volume constitutes the pre-proceedings of the 28th International
Workshop on Functional and Logic Programming (WFLP 2020), organized by the
University of Bologna, Italy, as part of Bologna Federated Conference on
Programming Languages 2020. The international Workshop on Functional and
(constraint) Logic Programming (WFLP) aims at bringing together researchers,
students, and practitioners interested in functional programming, logic
programming, and their integration. WFLP has a reputation for being a lively
and friendly forum, and it is open for presenting and discussing work in
progress, technical contributions, experience reports, experiments, reviews,
and system descriptions.


  We investigated the relation between the two-dimensional minimal gravity
(Lee-Yang series) with boundaries and open intersection theory. It is noted
that the minimal gravity with boundaries is defined in terms of boundary
cosmological constant $\mu_B$ and the open intersection theory in terms of
boundary marked point generating parameter $s$. Based on the conjecture that
the two different descriptions of the generating functions are related by the
Laplace transform, we derive the compact expressions for the generating
function of the intersection theory from that of the minimal gravity on a disk
and on a cylinder.


  We present a robust regression estimator for longitudinal data, which is
especially suited for functional data that has been observed on sparse or
irregular time grids. We show by simulation that the proposed estimators
possess good outlier-resistance properties compared with the traditional
functional least-squares estimator. As an example of application, we study the
relationship between levels of oxides of nitrogen and ozone in the city of San
Francisco.


  We present a coalescent process where three particles merge at each
coagulation step. Using a random walk representation, we prove duality with a
fragmentation process, whose fragmentation law we specify explicitly.
Furthermore, we give a second construction of the coalescence in terms of
random binary forests and study asymptotic properties. Starting from N
particles of unit mass, we obtain under an appropriate rescaling when N tends
to infinity a well-known binary coalescence, the so-called standard additive
coalescent.


  We consider minimization problems for curves of measure, with kinetic and
potential energy and a congestion penalization, as in the functionals that
appear in Mean Field Games with a variational structure. We prove L infinity
regularity results for the optimal density, which can be applied to the
rigorous derivations of equilibrium conditions at the level of each agent's
trajectory, via time-discretization arguments, displacement convexity, and
suitable Moser iterations. Similar L infinity results have already been found
by P.-L. Lions in his course on Mean Field Games, using a proof based on the
use of a (very degenerate) elliptic equation on the dual potential (the value
function) phi, in the case where the initial and final density were prescribed
(planning problem). Here the strategy is highly different, and allows for
instance to prove local-in-time estimates without assumptions on the initial
and final data, and to insert a potential in the dynamics.


  It is proved that the discriminant of $n\times n$ real symmetric matrices can
be written as a sum of squares, where the number of summands equals the
dimension of the space of $n$-variable spherical harmonics of degree $n$. The
representation theory of the orthogonal group is applied to express the
discriminant of three by three real symmetric matrices as a sum of five
squares, and to show that it can not be written as the sum of less than five
squares. It is proved that the discriminant of four by four real symmetric
matrices can be written as a sum of seven squares. These improve results of
Kummer from 1843 and Borchardt from 1846.


  The accretion of massive star clusters via dynamical friction has previously
been established to be a likely scenario for the build up of nuclear stellar
clusters (NSCs). A remaining issue is whether strong external tidal
perturbation may lead to the severe disruption of loosely-bound clusters well
before they sink deeply into the center of their host galaxies. We carry out a
series of $N$-body simulations and verify our early idealized analytic models.
We show if the density profile of the host galaxies can be described by a
power-law distribution with an index, $\alpha <1$, the cluster would be
compressed in the radial direction by the external galactic tidal field. In
contrast, the galactic tidal perturbation is disruptive in regions with a
steep, $\alpha >1$, density fall-off or in the very center where gravity is
dominated by the point-mass potential of super-massive black holes (SMBHs).
This sufficient criterion supplements the conventional necessary
Roche-lobe-filling condition in determining the preservation versus
disintegration of satellite stellar systems. We simulate the disruption of
stellar clusters which venture on nearly-circular, modestly- or
highly-eccentric orbits into the center of galaxies with a range of background
density profiles and SMBHs. We obtain the spatial distribution of the
stellar-cluster remnants. We apply these results to the NSC within a few
parsecs from SMBH Sgr A$^\ast$ at the Galactic Center. Recent observations
indicate the coexistence of two populations of stars with distinctively
separate ages and metallicities. We verify that the subsolar-metalicity
population can be the debris of disrupted stellar clusters.


  In this study, we address the challenge of portfolio optimization, a critical
aspect of managing investment risks and maximizing returns. The mean-CVaR
portfolio is considered a promising method due to today's unstable financial
market crises like the COVID-19 pandemic. It incorporates expected returns into
the CVaR, which considers the expected value of losses exceeding a specified
probability level. However, the instability associated with the input parameter
changes and estimation errors can deteriorate portfolio performance. Therefore
in this study, we propose a Doubly Robust mean-CVaR Portfolio refined approach
to the mean-CVaR portfolio optimization. Our method can solve the instability
problem to simultaneously optimize the multiple levels of CVaRs and define
uncertainty sets for the mean parameter to perform robust optimization.
Theoretically, the proposed method can be formulated as a second-order cone
programming problem which is the same formulation as traditional mean-variance
portfolio optimization. In addition, we derive an estimation error bound of the
proposed method for the finite-sample case. Finally, experiments with benchmark
and real market data show that our proposed method exhibits better performance
compared to existing portfolio optimization strategies.


  A new non-ballistic analytical model for the intrinsic channel region of
MOSFET-like single-walled carbon-nanotube field-effect transistors with ohmic
contacts has been developed which overcomes the limitations of existing models
and extends their applicability toward high bias voltages needed for analog
applications. The new model comprises an improved description of
electron-phonon scattering mechanism taking into account the accumulation of
electrons at the bottom of conduction subband due to back scattering by optical
phonons. The model has been justified by a Boltzmann transport equation solver.
The simulation results are found to be in agreement with experimental data for
highly doped CNTFETs.


  Near-infrared (NIR) and optical polarimetric observations of a selection of
X-ray binaries are presented. The targets were observed using the Very Large
Telescope and the United Kingdom Infrared Telescope. We detect a significant
level (3 sigma) of linear polarisation in four sources. The polarisation is
found to be intrinsic (at the > 3 sigma level) in two sources; GRO J1655-40 (~
4-7% in H and Ks-bands during an outburst) and Sco X-1 (~ 0.1-0.9% in H and K),
which is stronger at lower frequencies. This is likely to be the signature of
optically thin synchrotron emission from the collimated jets in these systems,
whose presence indicates a partially-ordered magnetic field is present at the
inner regions of the jets. In Sco X-1 the intrinsic polarisation is variable
(and sometimes absent) in the H and K-bands. In the J-band (i.e. at higher
frequencies) the polarisation is not significantly variable and is consistent
with an interstellar origin. The optical light from GX 339-4 is also polarised,
but at a level and position angle consistent with scattering by interstellar
dust. The other polarised source is SS 433, which has a low level (0.5-0.8%) of
J-band polarisation, likely due to local scattering. The NIR counterparts of
GRO J0422+32, XTE J1118+480, 4U 0614+09 and Aql X-1 (which were all in or near
quiescence) have a linear polarisation level of < 16% (3 sigma upper limit,
some are < 6%). We discuss how such observations may be used to constrain the
ordering of the magnetic field close to the base of the jet in such systems.


  Molecular dynamics (MD) is a crucial technique for simulating biological
systems, enabling the exploration of their dynamic nature and fostering an
understanding of their functions and properties. To address exploration
inefficiency, emerging enhanced sampling approaches like coarse-graining (CG)
and generative models have been employed. In this work, we propose a
\underline{Frame-to-Frame} generative model with guided
\underline{Flow}-matching (F$3$low) for enhanced sampling, which (a) extends
the domain of CG modeling to the SE(3) Riemannian manifold; (b) retreating CGMD
simulations as autoregressively sampling guided by the former frame via
flow-matching models; (c) targets the protein backbone, offering improved
insights into secondary structure formation and intricate folding pathways.
Compared to previous methods, F$3$low allows for broader exploration of
conformational space. The ability to rapidly generate diverse conformations via
force-free generative paradigm on SE(3) paves the way toward efficient enhanced
sampling methods.


  We explore the situation of intertwined quantum phase transitions (IQPTs),
for which a QPT involving a crossing of two configurations is accompanied by a
shape evolution of each configuration with its own separate QPT. We demonstrate
the relevance of IQPTs to the Zr isotopes, with such coexisting Type I and Type
II QPTs, and ground state shapes changing from spherical to prolate axially
deformed and finally to gamma-unstable. Evidence for this scenario is provided
by a detailed comparison with experimental data, using a definite
symmetry-based conceptual framework.


  We performed numerical simulations of star cluster encounters with
Hernquist's TREECODE in a CRAY YMP-2E computer. We used different initial
conditions (relative positions and velocities, cluster sizes, masses and
concentration degrees) with total number of particles per simulation ranging
from 4608 to 20480. Long term interaction stages (up to 1 Gyr) when the pair
coalesces into a single cluster are compared with isolated LMC clusters.
Evidence is found that when seen in a favourable plane these resulting clusters
show elliptical shapes due to the disruption of one of the companions. These
elliptical shapes are essentially time independent but they do depend on the
initial structural parameters of the pair components. We also analyzed the
fraction of stars that are ejected to the field by the interaction. We found
that this fraction can be almost 50% for the disrupted cluster. These
simulations can represent a possible mechanism to explain the ellipticity
observed on several star clusters in the Magellanic Clouds.


  In this paper, we present the observations of two new GW Vir stars from the
extended \textit{TESS} mission in both 120\,s short-cadence and 20\,s
ultra-short-cadence mode of two pre-white dwarf stars showing hydrogen
deficiency. We performed an asteroseismological analysis of these stars on the
basis of PG~1159 evolutionary models that take into account the complete
evolution of the progenitor stars. We searched for patterns of uniform period
spacings in order to constrain the stellar mass of the stars, and employed the
individual observed periods to search for a representative seismological model.
The analysis of the {\it TESS} light curves of TIC\,333432673 and
TIC\,095332541 reveals the presence of several oscillations with periods
ranging from 350 to 500~s associated to typical gravity ($g$)-modes. From
follow-up ground-based spectroscopy, we find that both stars have similar
effective temperature ($T_\mathrm{eff} = 120,000 \pm 10,000$\,K) and surface
gravity ($\log g = 7.5 \pm 0.5$) but a different He/C composition. On the basis
of PG~1159 evolutionary tracks, we derived a spectroscopic mass of $M_{\star}$
= $0.58^{+0.16}_{-0.08}\,M_{\odot}$ for both stars. Our asteroseismological
analysis of TIC\,333432673 allowed us to find a constant period spacing
compatible with a stellar mass $M_{\star}\sim 0.60-0.61\,M_{\odot}$, and an
asteroseismological model for this star with a stellar mass $M_{\star}$ =
$0.589\pm 0.020$ $M_{\odot}$, and a seismological distance of $d=
459^{+188}_{-156}$ pc. For this star, we find an excellent agreement between
the different methods to infer the stellar mass, and also between the
seismological distance and that measured with {\it Gaia} ($d_{\rm Gaia}=
389^{+5.6}_{-5.2}$ pc). For TIC\,095332541, we have found a possible period
spacing that suggests a stellar mass of $M_{\star}\sim 0.55-0.57\,M_{\odot}$.


  Scalar field self-force effects on a scalar charge orbiting a
Reissner-Nordstr\"om black hole are investigated. The scalar wave equation is
solved analytically in a post-Newtonian framework, and the solution is used to
compute the self-field as well as the components of the self-force at the
particle's location up to 7.5 post-Newtonian order. The energy fluxes radiated
to infinity and down the hole are also evaluated. Comparison with previous
numerical results in the Schwarzschild case shows a good agreement in both
strong-field and weak-field regimes.


  It is well-known that if R is a domain with finite character, each locally
principal nonzero ideal of R is invertible. We address the problem of
understanding when the converse is true and survey some recent results.


  Recent studies of neutral atomic hydrogen (HI) in nearby galaxies found that
all field disk galaxies are HI saturated, in that they carry roughly as much HI
as permitted before this gas becomes gravitationally unstable. By taking this
HI saturation for granted, the atomic gas fraction $f_{\rm atm}$ of galactic
disks can be predicted as a function of the stability parameter
$q=j\sigma/(GM)$, where $M$ and $j$ are the baryonic mass and specific angular
momentum of the disk and $\sigma$ is the HI velocity dispersion Obreschkow et
al. 2016. The log-ratio $\Delta f_q$ between this predictor and the observed
atomic fraction can be seen as a physically motivated `HI deficiency'. While
field disk galaxies have $\Delta f_q \approx0$, objects subject to
environmental removal of HI are expected to have $\Delta f_q>0$. Within this
framework, we revisit the HI deficiencies of satellite galaxies in the Virgo
cluster and in clusters of the EAGLE simulation. We find that observed and
simulated cluster galaxies are HI deficient and that $\Delta f_q$ slightly
increases when getting closer to the cluster centres. The $\Delta f_q$ values
are similar to traditional HI deficiency estimators, but $\Delta f_q$ is more
directly comparable between observations and simulations than morphology-based
deficiency estimators. By tracking the simulated HI deficient cluster galaxies
back in time, we confirm that $\Delta f_q\approx0$ until the galaxies first
enter a halo with $M_{\rm halo}>10^{13} {\rm M_{\odot}}$, at which moment they
quickly lose HI by environmental effects. Finally, we use the simulation to
investigate the links between $\Delta f_q$ and quenching of star formation.


  Mixed norms that promote structured sparsity have numerous applications in
signal processing and machine learning problems. In this work, we present a new
algorithm, based on a Newton root search technique, for computing the
projection onto the $\ell_{\infty,1}$ ball, which has found application in
cognitive neuroscience and classification tasks. Numerical simulations show
that our proposed method is between 8 and 10 times faster on average, and up to
20 times faster for very sparse solutions, than the previous state of the art.
Tests on real functional magnetic resonance image data show that, for some data
distributions, our algorithm can obtain speed improvements by a factor of
between 10 and 100, depending on the implementation.


  We explore the capability of plain Vision Transformers (ViTs) for semantic
segmentation and propose the SegVit. Previous ViT-based segmentation networks
usually learn a pixel-level representation from the output of the ViT.
Differently, we make use of the fundamental component -- attention mechanism,
to generate masks for semantic segmentation. Specifically, we propose the
Attention-to-Mask (ATM) module, in which the similarity maps between a set of
learnable class tokens and the spatial feature maps are transferred to the
segmentation masks. Experiments show that our proposed SegVit using the ATM
module outperforms its counterparts using the plain ViT backbone on the ADE20K
dataset and achieves new state-of-the-art performance on COCO-Stuff-10K and
PASCAL-Context datasets. Furthermore, to reduce the computational cost of the
ViT backbone, we propose query-based down-sampling (QD) and query-based
up-sampling (QU) to build a Shrunk structure. With the proposed Shrunk
structure, the model can save up to $40\%$ computations while maintaining
competitive performance.


  We study equilibrium and nonequilibrium properties of the single-impurity
Anderson model with a power-law pseudogap in the density of states. In
equilibrium, the model is known to display a quantum phase transition from a
generalized Kondo to a local moment phase. In the present work, we focus on the
extension of these phases beyond equilibrium, i.e. under the influence of a
bias voltage. Within the auxiliary master equation approach combined with a
scheme based on matrix product states (MPS) we are able to directly address the
current-carrying steady state. Starting with the equilibrium situation, we
first corroborate our results by comparing with a direct numerical evaluation
of ground state spectral properties of the system by MPS. Here, a scheme to
locate the phase boundary by extrapolating the power-law exponent of the self
energy produces a very good agreement with previous results obtained by the
numerical renormalization group. Our nonequilibrium study as a function of the
applied bias voltage is then carried out for two points on either side of the
phase boundary. In the Kondo regime the resonance in the spectral function is
splitted as a function of the increasing bias voltage. The local moment regime,
instead, displays a dip in the spectrum near the position of the chemical
potentials. Similar features are observed in the corresponding self energies.
The Kondo split peaks approximately obey a power-law behavior as a function of
frequency, whose exponents depend only slightly on voltage. Finally, the
differential conductance in the Kondo regime shows a peculiar maximum at finite
voltages, whose height, however, is below the accuracy level.


  The technology evolution has shown a very impressive performance in the last
years by introducing several technologies that are based on the concept of
component. As time passes, new versions of Component- Based technologies are
released in order to improve services provided by previous ones. One important
issue that regards these technologies is transactional activity. Transactions
are important because they consist in sending different small amounts of
information collected properly in a single combined unit which makes the
process simpler, less expensive and also improves the reliability of the whole
system, reducing its chances to go through possible failures. Different
Component-Based technologies offer different ways of handling transactions. In
this paper, we will review and discuss how transactions are handled in three of
them: COM, EJB and .NET. It can be expected that .NET offers more efficient
mechanisms due to the fact of being released later than the other two
technologies. Nevertheless, COM and EJB are still present in the market and
their services are still widely used. Comparing transaction handling in these
technologies will be helpful to analyze the advantages and disadvantages of
each of them. This comparison and evaluation will be seen in two main
perspectives: performance and security.


  We consider a two-user secure computation problem in which Alice and Bob
communicate interactively in order to compute some deterministic functions of
the inputs. The privacy requirement is that each user should not learn any
additional information about a function of the inputs other than what can be
inferred from its own input and output. For the distribution-free setting,
i.e., when the protocol must be correct and private for any joint input
distribution, we completely characterize the set of all securely computable
functions. When privacy is required only against Bob who computes a function
based on a single transmission from Alice, we show that asymptotically secure
computability is equivalent to perfectly secure computability. Separately, we
consider an eavesdropper who has access to all the communication and should not
learn any information about some function of the inputs (possibly different
from the functions to be computed by the users) and show that interaction may
be necessary for secure computation.


  This paper presents an evaluation framework that attempts to quantify the
"degree of realism" of simulated financial time series, whatever the simulation
method could be, with the aim of discover unknown characteristics that are not
being properly reproduced by such methods in order to improve them. For that
purpose, the evaluation framework is posed as a machine learning problem in
which some given time series examples have to be classified as simulated or
real financial time series. The "challenge" is proposed as an open competition,
similar to those published at the Kaggle platform, in which participants must
send their classification results along with a description of the features and
the classifiers used. The results of these "challenges" have revealed some
interesting properties of financial data, and have lead to substantial
improvements in our simulation methods under research, some of which will be
described in this work.


  A naive celestial dictionary causes massless two-point functions to take the
delta-function forms in the celestial conformal field theory (CCFT). We rectify
the dictionary, involving the shadow transformation so that the two-point
functions follow the standard power-law. In this new definition, we can
smoothly take the massless limit of the massive dictionary. We also compute a
three-point function using the new dictionary and discuss the OPE in CCFT.


  Federated learning (FL) is an important technique for learning models from
decentralized data in a privacy-preserving way. Existing FL methods usually
uniformly sample clients for local model learning in each round. However,
different clients may have significantly different data sizes, and the clients
with more data cannot have more opportunities to contribute to model training,
which may lead to inferior performance. In this paper, instead of client
uniform sampling, we propose a novel data uniform sampling strategy for
federated learning (FedSampling), which can effectively improve the performance
of federated learning especially when client data size distribution is highly
imbalanced across clients. In each federated learning round, local data on each
client is randomly sampled for local model learning according to a probability
based on the server desired sample size and the total sample size on all
available clients. Since the data size on each client is privacy-sensitive, we
propose a privacy-preserving way to estimate the total sample size with a
differential privacy guarantee. Experiments on four benchmark datasets show
that FedSampling can effectively improve the performance of federated learning.


  Nowadays, the product search service of e-commerce platforms has become a
vital shopping channel in people's life. The retrieval phase of products
determines the search system's quality and gradually attracts researchers'
attention. Retrieving the most relevant products from a large-scale corpus
while preserving personalized user characteristics remains an open question.
Recent approaches in this domain have mainly focused on embedding-based
retrieval (EBR) systems. However, after a long period of practice on Taobao, we
find that the performance of the EBR system is dramatically degraded due to
its: (1) low relevance with a given query and (2) discrepancy between the
training and inference phases. Therefore, we propose a novel and practical
embedding-based product retrieval model, named Multi-Grained Deep Semantic
Product Retrieval (MGDSPR). Specifically, we first identify the inconsistency
between the training and inference stages, and then use the softmax
cross-entropy loss as the training objective, which achieves better performance
and faster convergence. Two efficient methods are further proposed to improve
retrieval relevance, including smoothing noisy training data and generating
relevance-improving hard negative samples without requiring extra knowledge and
training procedures. We evaluate MGDSPR on Taobao Product Search with
significant metrics gains observed in offline experiments and online A/B tests.
MGDSPR has been successfully deployed to the existing multi-channel retrieval
system in Taobao Search. We also introduce the online deployment scheme and
share practical lessons of our retrieval system to contribute to the community.


  A classical random walk $(S_t, t\in\mathbb{N})$ is defined by
$S_t:=\displaystyle\sum_{n=0}^t X_n$, where $(X_n)$ are i.i.d. When the
increments $(X_n)_{n\in\mathbb{N}}$ are a one-order Markov chain, a short
memory is introduced in the dynamics of $(S_t)$. This so-called "persistent"
random walk is nolonger Markovian and, under suitable conditions, the rescaled
process converges towards the integrated telegraph noise (ITN) as the
time-scale and space-scale parameters tend to zero (see Herrmann and Vallois,
2010; Tapiero-Vallois, Tapiero-Vallois2}). The ITN process is effectively
non-Markovian too. The aim is to consider persistent random walks $(S_t)$ whose
increments are Markov chains with variable order which can be infinite. This
variable memory is enlighted by a one-to-one correspondence between $(X_n)$ and
a suitable Variable Length Markov Chain (VLMC), since for a VLMC the dependency
from the past can be unbounded.
  The key fact is to consider the non Markovian letter process $(X_n)$ as the
margin of a couple $(X_n,M_n)_{n\ge 0}$ where $(M_n)_{n\ge 0}$ stands for the
memory of the process $(X_n)$. We prove that, under a suitable rescaling,
$(S_n,X_n,M_n)$ converges in distribution towards a time continuous process
$(S^0(t),X(t),M(t))$. The process $(S^0(t))$ is a semi-Markov and Piecewise
Deterministic Markov Process whose paths are piecewise linear.


  Recently, two nearby prominent starburst galaxies, M82 and NGC253, have been
detected as point-like sources with gamma-ray telescopes at TeV energies [1]
[2]. It has been claimed that these detections show that the cosmic ray
intensity in the starburst galaxies is three orders of magnitude higher than in
the Milky Way galaxy, assuming that the observed gamma rays arise due to pion
production of cosmic rays interacting with the ambient gas. The observed
spectrum is flatter than the cosmic ray spectrum in the Milky Way galaxy, and
this could be due to the much higher gas density in the starburst galaxies [3].
The interpretation seems to be in line with the Ginzburg-model of the origin of
cosmic rays according to which the cosmic rays are accelerated in the shells of
supernova remnants. As an immediate corollary it follows that the cosmic ray
driven gamma ray luminosity should scale with the gas density and supernova
rate. At lower energies, gamma-ray measurements with the Fermi LAT instrument
could provide support for this scaling [4]. However, there are nagging doubts
about the interpretation of the observations at very high energies. At the
distance of the observed galaxies, point-like sources cannot be discriminated
from diffuse emission for an angular resolution of the order of 0.1 deg. Hence,
the question about the contribution of unresolved point-like sources to the
gamma-ray luminosity arises.


  This is a discussion on "Sparse graphs using exchangeable random measures" by
Francois Caron and Emily B. Fox, published in Journal of the Royal Statistical
Society, Series B, 2017.


  Low-precision training has emerged as a promising low-cost technique to
enhance the training efficiency of deep neural networks without sacrificing
much accuracy. Its Bayesian counterpart can further provide uncertainty
quantification and improved generalization accuracy. This paper investigates
low-precision sampling via Stochastic Gradient Hamiltonian Monte Carlo (SGHMC)
with low-precision and full-precision gradient accumulators for both strongly
log-concave and non-log-concave distributions. Theoretically, our results show
that, to achieve $\epsilon$-error in the 2-Wasserstein distance for
non-log-concave distributions, low-precision SGHMC achieves quadratic
improvement
($\widetilde{\mathbf{O}}\left({\epsilon^{-2}{\mu^*}^{-2}\log^2\left({\epsilon^{-1}}\right)}\right)$)
compared to the state-of-the-art low-precision sampler, Stochastic Gradient
Langevin Dynamics (SGLD)
($\widetilde{\mathbf{O}}\left({{\epsilon}^{-4}{\lambda^{*}}^{-1}\log^5\left({\epsilon^{-1}}\right)}\right)$).
Moreover, we prove that low-precision SGHMC is more robust to the quantization
error compared to low-precision SGLD due to the robustness of the
momentum-based update w.r.t. gradient noise. Empirically, we conduct
experiments on synthetic data, and {MNIST, CIFAR-10 \& CIFAR-100} datasets,
which validate our theoretical findings. Our study highlights the potential of
low-precision SGHMC as an efficient and accurate sampling method for
large-scale and resource-limited machine learning.


  Cusps of cosmic strings emit strong beams of high-frequency gravitational
waves (GW). As a consequence of these beams, the stochastic ensemble of
gravitational waves generated by a cosmological network of oscillating loops is
strongly non Gaussian, and includes occasional sharp bursts that stand above
the rms GW background. These bursts might be detectable by the planned GW
detectors LIGO/VIRGO and LISA for string tensions as small as $G \mu \sim
10^{-13}$. The GW bursts discussed here might be accompanied by Gamma Ray
Bursts.


  The restoration of an additive function defined on P parallelepipeds via its
derivative with respect to P parallelepipeds is studied. The obtained theorem
is applied to the questions of uniqueness of multiple series with regard to
Haar and Price systems.


  Effective collaboration between humans and AI-based systems requires
effective modeling of the human in the loop, both in terms of the mental state
as well as the physical capabilities of the latter. However, these models can
also open up pathways for manipulating and exploiting the human in the hopes of
achieving some greater good, especially when the intent or values of the AI and
the human are not aligned or when they have an asymmetrical relationship with
respect to knowledge or computation power. In fact, such behavior does not
necessarily require any malicious intent but can rather be borne out of
cooperative scenarios. It is also beyond simple misinterpretation of intents,
as in the case of value alignment problems, and thus can be effectively
engineered if desired. Such techniques already exist and pose several
unresolved ethical and moral questions with regards to the design of autonomy.
In this paper, we illustrate some of these issues in a teaming scenario and
investigate how they are perceived by participants in a thought experiment.


  Both theoretical and experimental analysis are carried out to understand the
physical properties of the fascinating electronic and optical properties of
antimony tungstate (Sb$_2$WO$_6$). The nanosized ($\sim 40-80~nm$) material is
produced using hydrothermal method followed by the SEM and XRD analysis to find
the structural properties. The present calculations using PBEsol and PBE
approximations for exchange-correlation potential are compared with the
experimental structural parameters and in the case of the calculations using
PBEsol approach the predicted crystal parameters and simulated XRD pattern are
in excellent agreement with experimental results. The experimental absorption
spectra measured in the ultraviolet-visible range give the bandgap of $2.42
~eV$, while the most intense peak of photoluminescence spectra is found at
$468nm~(2.65 ~eV)$. Using density functional theory (DFT) technique, the band
structure and density of states for \swo~ are calculated and the calculated
bandgap of $2.62 ~eV$ is in agreement with the experimental finding. From
theoretical partial density of states calculations we identify that the bandgap
is formed between the O$-2p_y$ orbitals bonded with Sb at valence band maxima
and the W$-5d_{x^2-y^2}$ orbital at conduction band minima. The atomic level
transitions responsible for the peaks of absorption spectra and
photoluminescence spectra are identified as well by the means of DFT
calculations. Following the matching of theoretical and experimental
observations, the calculations of optical properties reveal the plasma
frequency to be equal to $13.36 ~eV$.


  Real-world driving involves intricate interactions among vehicles navigating
through dense traffic scenarios. Recent research focuses on enhancing the
interaction awareness of autonomous vehicles to leverage these interactions in
decision-making. These interaction-aware planners rely on neural-network-based
prediction models to capture inter-vehicle interactions, aiming to integrate
these predictions with traditional control techniques such as Model Predictive
Control. However, this integration of deep learning-based models with
traditional control paradigms often results in computationally demanding
optimization problems, relying on heuristic methods. This study introduces a
principled and efficient method for combining deep learning with constrained
optimization, employing knowledge distillation to train smaller and more
efficient networks, thereby mitigating complexity. We demonstrate that these
refined networks maintain the problem-solving efficacy of larger models while
significantly accelerating optimization. Specifically, in the domain of
interaction-aware trajectory planning for autonomous vehicles, we illustrate
that training a smaller prediction network using knowledge distillation speeds
up optimization without sacrificing accuracy.


  In recent work we developed a description of cosmic large scale structure
formation in terms of non-equilibrium ensembles of classical particles, with
time evolution obtained in the framework of a statistical field theory. In
these works, the initial Gaussian correlations between particles have so far
been treated perturbatively or restricted to pure momentum correlations. Here
we treat the correlations between all phase-space coordinates exactly by
adopting a diagrammatic language for the different forms of correlations,
directly inspired by the Mayer cluster expansion. We will demonstrate that
explicit expressions for phase-space density cumulants of arbitrary $n$-point
order, which fully capture the non-linear coupling of free streaming kinematics
due to initial correlations, can be obtained from a simple set of Feynman
rules. These cumulants will be the foundation for further investigations of
interacting perturbation theory.


  To date, the highest fidelity quantum logic gates between two qubits have
been achieved with variations on the geometric-phase gate in trapped ions, with
the two leading variants being the Molmer-Sorensen gate and the light-shift
(LS) gate. Both of these approaches have their respective advantages and
challenges. For example, the latter is technically simpler and is natively
insensitive to optical phases, but it has not been made to work directly on a
clock-state qubit. We present a new technique for implementing the LS gate that
combines the best features of these two approaches: By using a small ($\sim
{\rm MHz}$) detuning from a narrow (dipole-forbidden) optical transition, we
are able to operate an LS gate directly on hyperfine clock states, achieving
gate fidelities of $99.74(4)\%$ using modest laser power at visible
wavelengths. Current gate infidelities appear to be dominated by technical
noise, and theoretical modeling suggests a path towards gate fidelity above
$99.99\%$.


  We present a new algorithm to solve the conjugacy problem in Artin braid
groups, which is faster than the one presented by Birman, Ko and Lee. This
algorithm can be applied not only to braid groups, but to all Garside groups
(which include finite type Artin groups and torus knot groups among others).


  We consider metric ultraproducts of finite groups with respect to some
classes of length functions. All sofic groups embed into these ultraproducts.
We study embeddings of normed groups. We also show that in some natural
situations such an ultraproduct is a simple group.


  Fact verification has attracted a lot of attention in the machine learning
and natural language processing communities, as it is one of the key methods
for detecting misinformation. Existing large-scale benchmarks for this task
have focused mostly on textual sources, i.e. unstructured information, and thus
ignored the wealth of information available in structured formats, such as
tables. In this paper we introduce a novel dataset and benchmark, Fact
Extraction and VERification Over Unstructured and Structured information
(FEVEROUS), which consists of 87,026 verified claims. Each claim is annotated
with evidence in the form of sentences and/or cells from tables in Wikipedia,
as well as a label indicating whether this evidence supports, refutes, or does
not provide enough information to reach a verdict. Furthermore, we detail our
efforts to track and minimize the biases present in the dataset and could be
exploited by models, e.g. being able to predict the label without using
evidence. Finally, we develop a baseline for verifying claims against text and
tables which predicts both the correct evidence and verdict for 18% of the
claims.


  In this note we present a more detailed and explicit exposition of the
definition of a conformal representation of a Leibniz algebra. Recall
(arXiv:math/0611501v3) that Leibniz algebras are exactly Lie dialgebras. The
idea is based on the general fact that every dialgebra that belongs to a
variety $\Var $ can be embedded into a conformal algebra of the same variety.
In particular, we prove that an arbitrary (finite dimensional) Leibniz algebra
has a (finite) faithful conformal representation. As a corollary, we deduce the
analogue of the PBW-theorem for Leibniz algebras.


  We report on the experimental and theoretical investigation of
magnetic-field-induced second harmonic generation (SHG) and two-photon
absorption (TPA) of excited exciton states ($n \geq 3$) of the yellow series in
Cu$_2$O. In this centrosymmetric material, SHG can occur due to constructive
interplay of electric dipole and electric quadrupole/magnetic dipole
transitions for light propagating along the low-symmetry directions [111] or
[112]. By application of a magnetic field in Voigt configuration, SHG gets also
allowed for excitation along the [110]-axis and even the high-symmetry cubic
direction [001]. Combining a symmetry analysis and a microscopic theory, we
uncover the two key contributions to the magnetic-field-induced SHG: the Zeeman
effect and the magneto-Stark effect. We demonstrate systematic dependencies of
the SHG intensity on the linear polarization angles of the ingoing fundamental
laser and the outgoing SHG beam. In general, the resulting contour plots in
combination with a symmetry analysis allow one to determine uniquely the
character of involved transitions. Moreover, we can separate in magnetic field
the Zeeman and the magneto-Stark effect through appropriate choice of the
experimental geometry and polarization configuration. We present a microscopic
theory of the second harmonic generation of excitons in a centrosymmetric cubic
semiconductor taking into account the symmetry and the band structure of
cuprous oxide. Based on the developed microscopic theory we identify the main
contributions to the second-order nonlinear susceptibility of $S$-, $P$- and
$D$-excitons. We analyze the redistribution of SHG intensities between the
excitonic states both in the absence and presence of the magnetic field and
show good agreement with the experimental data. With increasing exciton
principal quantum number the magneto-Stark effect overpowers the influence of
the Zeeman effect.


  The question on connection between the structure of a finite group $G$ and
the properties of the indices of elements of $G$ has been a popular research
topic for many years. The $p$-index $|x^G|_p$ of an element $x$ of a group $G$
is the $p$-part of its index $|x^G|=|G:C_G(x)|$. The presented short note
describes some new results and open problems in this direction, united by the
concept of the $p$-index of a group element.


  Let $p$ and $q$ be distinct primes such that $q+1 | p-1$. In this paper we
find all integer solutions $a$, $b$ to the equation $1/a + 1/b = (q+1)/pq$
using only elementary methods.


  We present results of spin-unrestricted first-principles quantum transport
for a gated C$_{60}$ molecule weakly contacted to Al electrodes, making
emphasis on the role played by the electronic localization and the spin degree
of freedom. As expected, the conductance presents a series of peaks as a
function of a gate voltage, demonstrating that transport in the Coulomb
blockade regime can be properly treated within a first-principles scheme. A
well-known manifestation of the interplay between Coulomb interaction and the
spin degree of freedom in atoms and molecules, the Hund 's rule, determines the
sequence of conductance peaks.


  We give improved hitting sets for two special cases of Read-once Oblivious
Arithmetic Branching Programs (ROABP). First is the case of an ROABP with known
order of the variables. The best previously known hitting set for this case had
size $(nw)^{O(\log n)}$ where $n$ is the number of variables and $w$ is the
width of the ROABP. Even for a constant-width ROABP, nothing better than a
quasi-polynomial bound was known. We improve the hitting-set size for the
known-order case to $n^{O(\log w)}$. In particular, this gives the first
polynomial-size hitting set for constant-width ROABP (known-order). However,
our hitting set only works when the characteristic of the field is zero or
large enough. To construct the hitting set, we use the concept of the rank of
the partial derivative matrix. Unlike previous approaches which build up from
mapping variables to monomials, we map variables to polynomials directly.
  The second case we consider is that of polynomials computable by width-$w$
ROABPs in any order of the variables. The best previously known hitting set for
this case had size $d^{O(\log w)}(nw)^{O(\log \log w)}$, where $d$ is the
individual degree. We improve the hitting-set size to $(ndw)^{O(\log \log w)}$.


  This paper proposes optimal lockdown management policies based on short-term
prediction of active COVID-19 confirmed cases to ensure the availability of
critical medical resources. The optimal time to start the lockdown from the
current time is obtained after maximizing a cost function considering economic
value subject to constraints of availability of medical resources, and maximum
allowable value of daily growth rate and Test Positive Ratio. The estimated
value of required medical resources is calculated as a function of total active
cases. The predicted value of active cases is calculated using an adaptive
short-term prediction model. The proposed approach can be easily implementable
by a local authority. An optimal lockdown case study for Delhi during the
second wave in the month of April 2021 is presented using the proposed
formulation.


  We present a model for the radio evolution of supernova remnants (SNRs)
obtained by using three-dimensional (3D) hydrodynamic simulations, coupled with
nonlinear kinetic theory of cosmic ray (CR) acceleration in SNRs. We model the
radio evolution of SNRs on a global level, by performing simulations for wide
range of the relevant physical parameters, such as the ambient density, the
supernova (SN) explosion energy, the acceleration efficiency and the magnetic
field amplification (MFA) efficiency. We attribute the observed spread of radio
surface brightnesses for corresponding SNR diameters to the spread of these
parameters. In addition to our simulations of type Ia SNRs, we also considered
SNR radio evolution in denser, nonuniform circumstellar environment, modified
by the progenitor star wind. These simulations start with the mass of the
ejecta substantially higher than in the case of a type Ia SN and presumably
lower shock speed. The magnetic field is understandably seen as very important
for the radio evolution of SNRs. In terms of MFA, we include both resonant and
non-resonant modes in our large scale simulations, by implementing models
obtained from first-principles, particle-in-cell (PIC) simulations and
non-linear magnetohydrodynamical (MHD) simulations. We test the quality and
reliability of our models on a sample consisting of Galactic and extragalactic
SNRs. Our simulations give $\Sigma-D$ slopes between -4 and -6 for the full
Sedov regime. Recent empirical slopes obtained for the Galactic samples are
around -5, while for the extragalactic samples are around -4.


  Granular materials are inherently heterogeneous, leading to challenges in
formulating accurate models of sound propagation. In order to quantify acoustic
responses in space and time, we perform experiments in a photoelastic granular
material in which the internal stress pattern (in the form of force chains) is
visible. We utilize two complementary methods, high-speed imaging and
piezoelectric transduction, to provide particle-scale measurements of both the
amplitude and speed of an acoustic wave in the near-field regime. We observe
that the wave amplitude is on average largest within particles experiencing the
largest forces, particularly in those chains radiating away from the source,
with the force-dependence of this amplitude in qualitative agreement with a
simple Hertzian-like model of particle contact area. In addition, we are able
to directly observe rare transient force chains formed by the opening and
closing of contacts during propagation. The speed of the leading edge of the
pulse is in quantitative agreement with predictions for one-dimensional chains,
while the slower speed of the peak response suggests that it contains waves
which have travelled over multiple paths even within just this near-field
region. These effects highlight the importance of particle-scale behaviors in
determining the acoustical properties of granular materials.


  Information theory gives rise to a novel method for causal skeleton discovery
by expressing associations between variables as tensors. This tensor-based
approach reduces the dimensionality of the data needed to test for conditional
independence, e.g., for systems comprising three variables, the causal skeleton
can be determined using pair-wise determined tensors. To arrive at this result,
an additional information measure, path information, is proposed.


  We report an observation of the $B^{\pm} \to J/\psi \eta K^{\pm}$ and $B^0
\to J/\psi \eta K^0_S$ decays using 772$\times 10^{6}$ $B\bar{B}$ pairs
collected at the $\Upsilon(4S)$ resonance with the Belle detector at the KEKB
asymmetric-energy $e^+e^-$ collider. We obtain the branching fractions ${\cal
B}(B^{\pm}\rightarrow J/\psi\eta K^{\pm})=(1.27\pm 0.11{\rm (stat.)\pm 0.11{\rm
(syst.)})}\times10^{-4}$ and ${\cal B}(B^0\to J/\psi \eta K^0_S)=(5.22 \pm 0.78
{\rm(stat.)} \pm 0.49{\rm(syst.)})\times10^{-5}$. We search for a new narrow
charmonium(-like) state $X$ in the $J/\psi \eta$ mass spectrum and find no
significant excess. We set upper limits on the product of branching fractions,
${\cal B}(B^\pm \to XK^\pm){\cal B}(X \to J/\psi \eta)$, at 3872 MeV$/c^2$
where a $C$-odd partner of X(3872) may exist, at $\psi(4040)$ and $\psi(4160)$
assuming their known mass and width, and over a range from 3.8 to 4.8
GeV$/c^2$. % at a 5 MeV$/c^2$ step. The obtained upper limits at 90% confidence
level for $X^{C{\rm -odd}}(3872)$, $\psi(4040)$ and $\psi(4160)$ are 3.8$\times
10^{-6}$, 15.5$\times 10^{-6}$ and 7.4$\times 10^{-6}$, respectively.


  Next Generation Sequencing (NGS), a recently evolved technology, have served
a lot in the research and development sector of our society. This novel
approach is a newbie and has critical advantages over the traditional Capillary
Electrophoresis (CE) based Sanger Sequencing. The advancement of NGS has led to
numerous important discoveries, which could have been costlier and time taking
in case of traditional CE based Sanger sequencing. NGS methods are highly
parallelized enabling to sequence thousands to millions of molecules
simultaneously. This technology results into huge amount of data, which need to
be analysed to conclude valuable information. Specific data analysis algorithms
are written for specific task to be performed. The algorithms in group, act as
a tool in analysing the NGS data. Analysis of NGS data unravels important clues
in quest for the treatment of various life-threatening diseases; improved crop
varieties and other related scientific problems related to human welfare. In
this review, an effort was made to address basic background of NGS
technologies, possible applications, computational approaches and tools
involved in NGS data analysis, future opportunities and challenges in the area.


  Quasi-free photoproduction of $\pi\eta$-pairs has been investigated from
threshold up to incident photon energies of 1.4 GeV, respectively up to
photon-nucleon invariant masses up to 1.9 GeV. Total cross sections, angular
distributions, invariant-mass distributions of the $\pi\eta$ and meson-nucleon
pairs, and beam-helicity asymmetries have been measured for the reactions
$\gamma p\rightarrow p\pi^0\eta$, $\gamma n\rightarrow n\pi^0\eta$, $\gamma
p\rightarrow n\pi^+\eta$, and $\gamma n\rightarrow p\pi^-\eta$ from nucleons
bound inside the deuteron. For the $\gamma p$ initial state data for free
protons have also been analyzed. Finally, the total cross sections for
quasi-free production of $\pi^0\eta$ pairs from nucleons bound in $^3$He nuclei
have been investigated in view of final state interaction (FSI) effects. The
experiments were performed at the tagged photon beam facility of the Mainz MAMI
accelerator using an almost $4\pi$ covering electromagnetic calorimeter
composed of the Crystal Ball and TAPS detectors. The shapes of all differential
cross section data and the asymmetries are very similar for protons and
neutrons and agree with the conjecture that the reactions are dominated by the
sequential $\Delta^{\star}3/2^-\rightarrow\eta\Delta(1232)\rightarrow\pi\eta N$
decay chain, mainly with $\Delta(1700)3/2^-$ and $\Delta(1940)3/2^-$. The
ratios of the magnitude of the total cross sections also agree with this
assumption. However, the absolute magnitudes of the cross sections are reduced
by FSI effects with respect to free proton data.


  Recent experimental results based on multi-electrode and imaging techniques
have reinvigorated the idea that large neural networks operate near a critical
point, between order and disorder. However, evidence for criticality has relied
on the definition of arbitrary order parameters, or on models that do not
address the dynamical nature of network activity. Here we introduce a novel
approach to assess criticality that overcomes these limitations, while
encompassing and generalizing previous criteria. We find a simple model to
describe the global activity of large populations of ganglion cells in the rat
retina, and show that their statistics are poised near a critical point. Taking
into account the temporal dynamics of the activity greatly enhances the
evidence for criticality, revealing it where previous methods would not. The
approach is general and could be used in other biological networks.


  The amplitude of Parametric X-ray radiation emitted coherently by a bunch of
electrons crossing a crystal (CPXR) is calculated. When the bunch density is
modulated with a longitudinal period close to the X-ray wavelenght,
constructive many-electron interferences enhance the intensity by Nb *
|F(K)|^2, where Nb is the number of electrons in the bunch, F(K) the bunch form
factor and K a specified wave vector. CPXR can be used to test the
microbunching in a X-ray free-electron laser.


  The magnetic properties of arrays of nanowires (NWs) and nanotubes (NTs), 150
nm in diameter, electrodeposited inside nanoporous polycarbonate membranes are
investigated. The comparison of the nanoscopic magnetic force microscopy (MFM)
imaging and the macroscopic behavior as measured by alternating gradient force
magnetometry (AGFM) is made. It is shown that MFM is a complementary technique
that provides an understanding of the magnetization reversal characteristics at
the microscopic scale of individual nanostructures. The local hysteresis loops
have been extracted by MFM measurements. The influence of the shape of such
elongated nanostructures on the dipolar coupling and consequently on the
squareness of the hysteresis curves is demonstrated. It is shown that the
nanowires exhibit stronger magnetic interactions than nanotubes. The
non-uniformity of the magnetization states is also revealed by combining the
MFM and AGFM measurements.


  Non-equilibrium steady states for chains of oscillators (masses) connected by
harmonic and anharmonic springs and interacting with heat baths at different
temperatures have been the subject of several studies. In this paper, we show
how some of the results extend to more complicated networks. We establish the
existence and uniqueness of the non-equilibrium steady state, and show that the
system converges to it at an exponential rate. The arguments are based on
controllability and conditions on the potentials at infinity.


  Motivated by charge balancing constraints for rank modulation schemes, we
introduce the notion of balanced permutations and derive the capacity of
balanced permutation codes. We also describe simple interleaving methods for
permutation code constructions and show that they approach capacity


  The increased availability and brilliance of new X-ray facilities have in the
recent years opened up the possibility to characterize the motion of dispersed
nanoparticles in various microfluidic applications. One of these applications
is the process of making strong continuous filaments through hydrodynamic
alignment and assembly of cellulose nanofibrils (CNF) demonstrated by
H{\aa}kansson et al. [Nature communications 5, 2014]. In this process it is
vital to study the alignment of the nanofibrils in the flow, as this in turn
affects the final material properties of the dried filament. Small angle X-ray
scattering (SAXS) is a well-suited characterization technique for this, which
typically provides the alignment in a projected plane perpendicular to the beam
direction. In this work, we demonstrate a simple method to reconstruct the full
three-dimensional (3D) orientation distribution function (ODF) from a
SAXS-experiment through the assumption that the azimuthal angle of the
nanofibril around the flow direction is distributed uniformly; an assumption
that is approximately valid in the flow-focusing process. For demonstrational
purposes, the experimental results from H{\aa}kansson et al. (2014) have been
revised, resulting in a small correction to the presented order parameters. The
results are then directly compared with simple numerical models to describe the
increased alignment of CNF both in the flowing system and during the drying
process. The proposed reconstruction method will allow for further improvements
of theoretical or numerical simulations and consequently open up new
possibilities for optimizing assembly processes, which include flow-alignment
of elongated nanoparticles.


  In the setting of nonparametric multivariate regression with unknown error
variance, we study asymptotic properties of a Bayesian method for estimating a
regression function f and its mixed partial derivatives. We use a random series
of tensor product of B-splines with normal basis coefficients as a prior for f,
and the error variance is either estimated using the empirical Bayes approach
or is endowed with a suitable prior in a hierarchical Bayes approach. We
establish pointwise, L2 and supremum norm posterior contraction rates for f and
its mixed partial derivatives, and show that they coincide with the minimax
rates. Our results cover even the anisotropic situation, where the true
regression function may have different smoothness in different directions.
Using the convergence bounds, we show that pointwise, L2 and supremum norm
credible sets for f and its mixed partial derivatives have guaranteed
frequentist coverage with optimal size. New results on tensor products of
B-splines are also obtained in the course.


  An agent-based model is proposed for analyzing the dynamics that arise from
interactions within social networks, analyzing the individual behavior of each
profile. Said model considers a simplified construction of a social network
while satisfying properties attributed to this type of systems. For that
matter, previously established studies on the matter are taken into account,
while including each profiles preferences, and how they evolve with time, for
the system's dynamic behavior. Results are analyzed based on concepts like the
emergence of clusters, the polarization of the network and the homogeneity of
preferences between connected profiles; and how they may depend on the
characteristics of the profiles and of the network.


  Emission line galaxies (ELGs) are used in several ongoing and upcoming
surveys (SDSS-IV/eBOSS, DESI) as tracers of the dark matter distribution. Using
a new galaxy formation model, we explore the characteristics of [OII] emitters,
which dominate optical ELG selections at $z\simeq 1$. Model [OII] emitters at
$0.5<z<1.5$ are selected to mimic the DEEP2, VVDS, eBOSS and DESI surveys. The
luminosity functions of model [OII] emitters are in reasonable agreement with
observations. The selected [OII] emitters are hosted by haloes with $M_{\rm
halo}\geq 10^{10.3}h^{-1}{\rm M}_{\odot}$, with ~90% of them being central
star-forming galaxies. The predicted mean halo occupation distributions of
[OII] emitters has a shape typical of that inferred for star-forming galaxies,
with the contribution from central galaxies, $\langle N
\rangle_{\left[OII\right]\, cen}$, being far from the canonical step function.
The $\langle N \rangle_{\left[OII\right]\, cen}$ can be described as the sum of
an asymmetric Gaussian for disks and a step function for spheroids, which
plateaus below unity. The model [OII] emitters have a clustering bias close to
unity, which is below the expectations for eBOSS and DESI ELGs. At $z\sim 1$, a
comparison with observed g-band selected galaxy, which are expected to be
dominated by [OII] emitters, indicates that our model produces too few [OII]
emitters that are satellite galaxies. This suggests the need to revise our
modelling of hot gas stripping in satellite galaxies.


  Using recently developed numerical methods, we examine neutral compactified
non-uniform black strings which connect to the Gregory-Laflamme critical point.
By studying the geometry of the horizon we give evidence that this branch of
solutions may connect to the black hole solutions, as conjectured by Kol. We
find the geometry of the topology changing solution is likely to be nakedly
singular at the point where the horizon radius is zero. We show that these
solutions can all be expressed in the coordinate system discussed by Harmark
and Obers.


  Motivation: Enhancers are important cis-regulatory elements that regulate a
wide range of biological functions and enhance the transcription of target
genes. Although many feature extraction methods have been proposed to improve
the performance of enhancer identification, they cannot learn position-related
multiscale contextual information from raw DNA sequences.
  Results: In this article, we propose a novel enhancer identification method
(iEnhancer-ELM) based on BERT-like enhancer language models. iEnhancer-ELM
tokenizes DNA sequences with multi-scale k-mers and extracts contextual
information of different scale k-mers related with their positions via an
multi-head attention mechanism. We first evaluate the performance of different
scale k-mers, then ensemble them to improve the performance of enhancer
identification. The experimental results on two popular benchmark datasets show
that our model outperforms stateof-the-art methods. We further illustrate the
interpretability of iEnhancer-ELM. For a case study, we discover 30 enhancer
motifs via a 3-mer-based model, where 12 of motifs are verified by STREME and
JASPAR, demonstrating our model has a potential ability to unveil the
biological mechanism of enhancer.
  Availability and implementation: The models and associated code are available
at https://github.com/chen-bioinfo/iEnhancer-ELM
  Contact<EMAIL_ADDRESS>  Supplementary information: Supplementary data are available at Bioinformatics
Advances online.


  We generalize Schroedinger's factorization method for Hydrogen from the
conventional separation into angular and radial coordinates to a
Cartesian-based factorization. Unique to this approach, is the fact that the
Hamiltonian is represented as a sum over factorizations in terms of coupled
operators that depend on the coordinates and momenta in each Cartesian
direction. We determine the eigenstates and energies, the wavefunctions in both
coordinate and momentum space, and we also illustrate how this technique can be
employed to develop the conventional confluent hypergeometric equation
approach. The methodology developed here could potentially be employed for
other Hamiltonians that can be represented as the sum over coupled Schroedinger
factorizations.


  We discuss two different types of issues concerning the quantization of
Einstein-Rosen waves. First of all we study in detail the possibility of using
the coherent states corresponding to the dynamics of the auxiliary, free
Hamiltonian appearing in the description of the model to study the full
dynamics of the system. For time periods of arbitrary length we show that this
is only possible for states that are close, in a precise mathematical sense, to
the vacuum. We do this by comparing the quantum evolutions defined by the
auxiliary and physical Hamiltonians on the class of coherent states. In the
second part of the paper we study the structure of n-point functions. As we
will show their detailed behavior differs from the one corresponding to
standard perturbative quantum field theories. We take this as a manifestation
of the fact that the correct approximation scheme for physically interesting
objects in these models does not lead to a power series expansion in the
relevant coupling constant but to a more complicated asymptotic behavior.


  We have serendipitously discovered an extremely lithium-rich star on the red
giant branch of the globular cluster M3 (NGC 5272). An echelle spectrum
obtained with the Keck I HIRES reveals a Li I 6707 Angstrom resonance doublet
of 520 milli-Angstrom equivalent width, and our analysis places the star among
the most Li-rich giants known: log[epsilon(Li)] ~= +3.0. We determine the
elemental abundances of this star, IV-101, and three other cluster members of
similar luminosity and color, and conclude that IV-101 has abundance ratios
typical of giants in M3 and M13 that have undergone significant mixing. We
discuss mechanisms by which a low-mass star may be so enriched in Li, focusing
on the mixing of material processed by the hydrogen-burning shell just below
the convective envelope. While such enrichment could conceivably only happen
rarely, it may in fact regularly occur during giant-branch evolution but be
rarely detected because of rapid subsequent Li depletion.


  Analysing Web graphs has applications in determining page ranks, fighting Web
spam, detecting communities and mirror sites, and more. This study is however
hampered by the necessity of storing a major part of huge graphs in the
external memory, which prevents efficient random access to edge (hyperlink)
lists. A number of algorithms involving compression techniques have thus been
presented, to represent Web graphs succinctly but also providing random access.
Those techniques are usually based on differential encodings of the adjacency
lists, finding repeating nodes or node regions in the successive lists, more
general grammar-based transformations or 2-dimensional representations of the
binary matrix of the graph. In this paper we present two Web graph compression
algorithms. The first can be seen as engineering of the Boldi and Vigna (2004)
method. We extend the notion of similarity between link lists, and use a more
compact encoding of residuals. The algorithm works on blocks of varying size
(in the number of input lines) and sacrifices access time for better
compression ratio, achieving more succinct graph representation than other
algorithms reported in the literature. The second algorithm works on blocks of
the same size, in the number of input lines, and its key mechanism is merging
the block into a single ordered list. This method achieves much more attractive
space-time tradeoffs.


  Applicative bisimulation is a coinductive technique to check program
equivalence in higher-order functional languages. It is known to be sound, and
sometimes complete, with respect to context equivalence. In this paper we show
that applicative bisimulation also works when the underlying language of
programs takes the form of a linear $\lambda$-calculus extended with features
such as probabilistic binary choice, but also quantum data, the latter being a
setting in which linearity plays a role. The main results are proofs of
soundness for the obtained notions of bisimilarity.


  Internet of Things (IoT) devices have become prevalent, embedding
intelligence into our environment. It is projected that over 75 billion IoT
devices will be connected by 2025 worldwide, with the majority being operated
indoors. Dye-sensitized solar cells (DSSC) have recently been optimized for
ambient light, having the capabilities of providing sufficient energy for
self-powered IoT devices. Interaction with digital technologies, termed Human
Computer Interaction (HCI), is often achieved via physical mechanisms (e.g.
remote controls, cell phones) which can hinder the natural interface between
users and IoT devices, a key consideration for HCI. What if the solar cell that
is powering the IoT device can also recognize hand gestures which would allow
the user to naturally interact with the system? Previous attempts to achieve
this have necessarily employed an array of solar cell/photodiodes to detect
directionality. In this work, we demonstrate that by monitoring the
photocurrent output of an asymmetrically patterned monolithic (i.e., single
cell) DSSC, and using machine learning, we can recognize simple hand gestures,
achieving an accuracy prediction of 97.71%. This work shows that, DSSCs are the
perfect choice for self-powered interactive technologies, both in terms of
powering IoT devices in ambient light conditions and having aesthetic qualities
that are prioritized by users. As well as powering interactive technologies,
they can also provide a means of interactive control.


  In this paper we construct the set of quantum mechanical observables in the
Fedosov *-formalism (a coordinate invariant way to do quantum mechanics on any
manifold M) of a single free particle that lives on a constant curvature
manifold with metric signature (p,q). This was done for most but not all
constant curvature manifolds. We show that the algebra of all observables in
n=p+q dimensions is SO(p+1,q+1) in a nonperturbative calculation. A subgroup of
this group is identified as the analogue of the Poincare group in Minkowski
space i.e. it is the space of symmetries on the manifolds considered. We then
write down a Klein-Gordon (KG) equation given by the the equation
p^2|phi>=m^2|phi> for the set of allowed physical states. This result is
consistent with previous results on AdS. Furthermore we lay out the standard
scheme for the free KG field from the single particle theory. Furthermore we
argue that this scheme will work on a general space-time.


  Spatial intermittency in fully developed turbulence is an established feature
of astrophysical plasma fluctuations and in particular apparent in the
interplanetary medium by in situ observations. In this situation the classical
Boltzmann-Gibbs extensive thermo-statistics, applicable when microscopic
interactions and memory are short ranged, fails. Upon generalization of the
entropy function to nonextensivity, accounting for long-range interactions and
thus for correlations in the system, it is demonstrated that the corresponding
probability distributions (PDFs) are members of a family of specific power-law
distributions. In particular, the resulting theoretical bi-kappa functional
reproduces accurately the observed global leptokurtic, non-Gaussian shape of
the increment PDFs of characteristic solar wind variables on all scales.
Gradual decoupling is obtained by enhancing the spatial separation scale
corresponding to increasing kappa-values in case of slow solar wind conditions
where a Gaussian is approached in the limit of large scales. Contrary, the
scaling properties in the high speed solar wind are predominantly governed by
the mean energy or variance of the distribution. The PDFs of solar wind scalar
field differences are computed from WIND and ACE data for different time-lags
and bulk speeds and analyzed within the nonextensive theory. Consequently,
nonlocality in fluctuations, related to both, turbulence and its large scale
driving, should be related to long-range interactions in the context of
nonextensive entropy generalization, providing fundamentally the physical
background of the observed scale dependence of fluctuations in intermittent
space plasmas.


  By means of wide-field (42x28 arcmin) CCD images of the Coma cluster of
galaxies and of control fields, taken at the Canada-France-Hawaii telescope
with the CFH12K and UH8K cameras, we determine the luminosity function
bi-variate in central brightness. We found a clear progression for a steeping
and weakening of the FL going from high surface brightness galaxies (mu~20$
mag/arcsec2) to galaxies of very-faint central brightnesses (mu 24.5
mag/arcsec2). Compact galaxies, usually rejected in the star/galaxy
classification, are found to be a minor population in Coma. A huge population
of low surface brightness galaxies red and faint are discovered, representing
the largest contributor to the LF at faint magnitudes. Among them, there could
be the remnant of high redshift starbursting galaxies responsible for the
Butcher-Oemler effect.


  The magnetic field plays a central role in the formation and evolution of
circumstellar disks. The magnetic field connects the rapidly rotating central
region with the outer envelope and extracts angular momentum from the central
region during gravitational collapse of the cloud core. This process is known
as magnetic braking. Both analytical and multidimensional simulations have
shown that disk formation is strongly suppressed by magnetic braking in
moderately magnetized cloud cores in the ideal magnetohydrodynamic limit. On
the other hand, recent observations have provided growing evidence of a
relatively large disk several tens of astronomical units in size existing in
some Class 0 young stellar objects. This introduces a serious discrepancy
between the theoretical study and observations. Various physical mechanisms
have been proposed to solve the problem of catastrophic magnetic braking, such
as misalignment between the magnetic field and the rotation axis, turbulence,
and non-ideal effect. In this paper, we review the mechanism of magnetic
braking, its effect on disk formation and early evolution, and the mechanisms
that resolve the magnetic braking problem. In particular, we emphasize the
importance of non-ideal effects. The combination of magnetic diffusion and
thermal evolution during gravitational collapse provides a robust formation
process for the circumstellar disk at the very early phase of protostar
formation. The rotation induced by the Hall effect can supply a sufficient
amount of angular momentum for typical circumstellar disks around T Tauri
stars. By examining the combination of the suggested mechanisms, we conclude
that the circumstellar disks commonly form in the very early phase of protostar
formation.


  The nature of transverse instabilities to dark solitons and dispersive shock
waves for the (2+1)-dimensional defocusing nonlinear Schrodinger equation /
Gross-Pitaevskii (NLS / GP) equation is considered. Special attention is given
to the small (shallow) amplitude regime, which limits to the
Kadomtsev-Petviashvili (KP) equation. We study analytically and numerically the
eigenvalues of the linearized NLS / GP equation. The dispersion relation for
shallow solitons is obtained asymptotically beyond the KP limit. This yields 1)
the maximum growth rate and associated wavenumber of unstable perturbations;
and 2) the separatrix between convective and absolute instabilities. The latter
result is used to study the transition between convective and absolute
instabilities of oblique dispersive shock waves (DSWs). Stationary and
nonstationary oblique DSWs are constructed analytically and investigated
numerically by direct simulations of the NLS / GP equation. The instability
properties of oblique DSWs are found to be directly related to those of the
dark soliton. It is found that stationary and nonstationary oblique DSWs have
the same jump conditions in the shallow and hypersonic regimes. These results
have application to controlling nonlinear waves in dispersive media.


  Image restoration under severe weather is a challenging task. Most of the
past works focused on removing rain and haze phenomena in images. However, snow
is also an extremely common atmospheric phenomenon that will seriously affect
the performance of high-level computer vision tasks, such as object detection
and semantic segmentation. Recently, some methods have been proposed for snow
removing, and most methods deal with snow images directly as the optimization
object. However, the distribution of snow location and shape is complex.
Therefore, failure to detect snowflakes / snow streak effectively will affect
snow removing and limit the model performance. To solve these issues, we
propose a Snow Mask Guided Adaptive Residual Network (SMGARN). Specifically,
SMGARN consists of three parts, Mask-Net, Guidance-Fusion Network (GF-Net), and
Reconstruct-Net. Firstly, we build a Mask-Net with Self-pixel Attention (SA)
and Cross-pixel Attention (CA) to capture the features of snowflakes and
accurately localized the location of the snow, thus predicting an accurate snow
mask. Secondly, the predicted snow mask is sent into the specially designed
GF-Net to adaptively guide the model to remove snow. Finally, an efficient
Reconstruct-Net is used to remove the veiling effect and correct the image to
reconstruct the final snow-free image. Extensive experiments show that our
SMGARN numerically outperforms all existing snow removal methods, and the
reconstructed images are clearer in visual contrast. All codes will be
available.


  As robotic swarm systems emerge, it is increasingly important to provide
strong guarantees on energy consumption and safety to maximize system
performance. One approach to achieve these guarantees is through
constraint-driven control, where agents seek to minimize energy consumption
subject to a set of safety and task constraints. In this paper, we provide a
sufficient and necessary condition for an energy-minimizing agent with
integrator dynamics to have a continuous control input at the transition
between unconstrained and constrained trajectories. In addition, we present and
analyze barycentric motion and collision avoidance constraints to be used in
constraint-driven control of swarms.


  A search for pair production of squarks or gluinos decaying via sleptons or
weak bosons is reported. The search targets a final state with exactly two
leptons with same-sign electric charge or at least three leptons without any
charge requirement. The analysed data set corresponds to an integrated
luminosity of 139 fb$^{-1}$ of proton$-$proton collisions collected at a
centre-of-mass energy of 13 TeV with the ATLAS detector at the LHC. Multiple
signal regions are defined, targeting several SUSY simplified models yielding
the desired final states. A single control region is used to constrain the
normalisation of the $WZ$+jets background. No significant excess of events over
the Standard Model expectation is observed. The results are interpreted in the
context of several supersymmetric models featuring R-parity conservation or
R-parity violation, yielding exclusion limits surpassing those from previous
searches. In models considering gluino (squark) pair production, gluino
(squark) masses up to 2.2 (1.7) TeV are excluded at 95% confidence level.


  This paper addresses identification of sparse linear and noise-driven
continuous-time state-space systems, i.e., the right-hand sides in the
dynamical equations depend only on a subset of the states. The key assumption
in this study, is that the sample rate is not high enough to directly infer the
continuous time system from the data. This assumption is relevant in
applications where sampling is expensive or requires human intervention (e.g.,
biomedicine applications). We propose an iterative optimization scheme with
$l_1$-regularization, where the search directions are restricted those that
decrease prediction error in each iteration. We provide numerical examples
illustrating the proposed method; the method outperforms the least squares
estimation for large noise.


  Query reformulation is a key mechanism to alleviate the linguistic chasm of
query in ad-hoc retrieval. Among various solutions, query reduction effectively
removes extraneous terms and specifies concise user intent from long queries.
However, it is challenging to capture hidden and diverse user intent. This
paper proposes Contextualized Query Reduction (ConQueR) using a pre-trained
language model (PLM). Specifically, it reduces verbose queries with two
different views: core term extraction and sub-query selection. One extracts
core terms from an original query at the term level, and the other determines
whether a sub-query is a suitable reduction for the original query at the
sequence level. Since they operate at different levels of granularity and
complement each other, they are finally aggregated in an ensemble manner. We
evaluate the reduction quality of ConQueR on real-world search logs collected
from a commercial web search engine. It achieves up to 8.45% gains in exact
match scores over the best competing model.


  We present a simple communication complexity problem where three parties
benefit from sharing bound entanglement. This demonstrates that entanglement
distillability of the shared state is not necessary in order to surpass
classical communication complexity.


  Omnidirectional image quality assessment (OIQA) aims to predict the
perceptual quality of omnidirectional images that cover the whole
180$\times$360$^{\circ}$ viewing range of the visual environment. Here we
propose a blind/no-reference OIQA method named S$^2$ that bridges the gap
between low-level statistics and high-level semantics of omnidirectional
images. Specifically, statistic and semantic features are extracted in separate
paths from multiple local viewports and the hallucinated global omnidirectional
image, respectively. A quality regression along with a weighting process is
then followed that maps the extracted quality-aware features to a perceptual
quality prediction. Experimental results demonstrate that the proposed S$^2$
method offers highly competitive performance against state-of-the-art methods.


  For graphs $G^<$ and $H^<$ with linearly ordered vertex sets, the \ordered
Ramsey number $r_<(G^<,H^<)$ is the smallest positive integer $N$ such that any
red-blue coloring of the edges of the complete ordered graph $K^<_N$ on $N$
vertices contains either a blue copy of $G^<$ or a red copy of $H^<$. Motivated
by a problem of Conlon, Fox, Lee, and Sudakov (2017), we study the numbers
$r_<(M^<,K^<_3)$ where $M^<$ is an ordered matching on $n$ vertices.
  We prove that almost all $n$-vertex ordered matchings $M^<$ with interval
chromatic number 2 satisfy $r_<(M^<,K^<_3) \in \Omega((n/\log n)^{5/4})$ and
$r_<(M^<,K^<_3) \in O(n^{7/4})$, improving a recent result by Rohatgi (2019).
We also show that there are $n$-vertex ordered matchings $M^<$ with interval
chromatic number at least 3 satisfying $r_<(M^<,K^<_3) \in \Omega((n/\log
n)^{4/3})$, which asymptotically matches the best known lower bound on these
off-diagonal ordered Ramsey numbers for general $n$-vertex ordered matchings.