  Precipitation nowcasting is to predict the future rainfall intensity over a
short period of time, which mainly relies on the prediction of radar echo
sequences. Though convolutional neural network (CNN) and recurrent neural
network (RNN) are widely used to generate radar echo frames, they suffer from
inductive bias (i.e., translation invariance and locality) and seriality,
respectively. Recently, Transformer-based methods also gain much attention due
to the great potential of Transformer structure, whereas short-term
dependencies and autoregressive characteristic are ignored. In this paper, we
propose a variant of Transformer named patches with 3D-temporal convolutional
Transformer network (PTCT), where original frames are split into multiple
patches to remove the constraint of inductive bias and 3D-temporal convolution
is employed to capture short-term dependencies efficiently. After training, the
inference of PTCT is performed in an autoregressive way to ensure the quality
of generated radar echo frames. To validate our algorithm, we conduct
experiments on two radar echo dataset: Radar Echo Guangzhou and HKO-7. The
experimental results show that PTCT achieves state-of-the-art (SOTA)
performance compared with existing methods.


  In this article, we will present that the analytic adjoint ideal sheaves
associated to plurisubharmonic functions are not coherent.


  Recent ultrasound imaging modalities based on ultrasound computed tomography
indicate a huge potential to detect pathologies is tissue due to altered
biomechanical properties. Especially the imaging of speed-of-sound (SoS)
distribution in tissue has shown clinical promise and thus gained increasing
attention in the field -- with several methods proposed based on transmission
mode tomography. SoS imaging using conventional ultrasound (US) systems would
be convenient and easy for clinical translation, but this requires using
conventional US probes with single-sides tissue access and thus pulse-echo
imaging sequences. Recent pulse-echo SoS imaging methods rely on plane wave
(PW) insonifications, which is prone to strong aberration effects for
non-homogeneous tissue composition. In this paper we propose to use diverging
waves (DW) for SoS imaging and thus substantially improve the reconstruction of
SoS distributions. We study this proposition by first plane wavefront
aberrations compared to DW. We then present the sensitivity of both approaches
to major parameterization choices on a set of simulated phantoms. Using the
optimum parameter combination for each method for a given transducer model and
imaging sequence, we analyze the SoS imaging performance comparatively between
the two approaches. Results indicate that using DW instead of PW, the
reconstruction accuracy improves substantially, by over 22% in reconstruction
error (RMSE) and by 55% in contrast (CNR). We also demonstrate improvements in
SoS reconstructions from an actual US acquisition of a breast phantom with
tumor- and cyst-representative inclusions, with high and low SoS contrast,
respectively.


  We construct the theoretical framework for quasi-two-body $D$ meson decays
with the help of pion and kaon electromagnetic form factors, and with which we
study the contributions of the subprocesses $\rho(770,1450)\to K\bar{K}$ for
the three-body $D$ decays within the flavour $SU(3)$ symmetry. Because of the
limitations imposed by phase space and strong coupling, the contributions for
kaon pair from the virtual bound state $\rho(770)$ are channel-dependent and
generally small for the concerned three-body $D$ decays. But some
quasi-two-body processes could still be observed in the Dalitz plot analyses
for related decays, such as $D^0 \to K^-\rho(770)^+ \to K^- K^+K_S^0$ and $D^+
\to K_S^0\rho(770)^+ \to K_S^0 K^+K_S^0$, they are predicted to have the
branching fractions $\mathcal{B}=(0.82\pm0.04)\times 10^{-4}$ and
$\mathcal{B}=0.47^{+0.05}_{-0.03}\times 10^{-4}$, which are $(1.86\pm0.16)\%$
and $(1.84^{+0.21}_{-0.16})\%$, respectively, of the total branching fractions
for the corresponding three-body $D$ decays. We find in this work that the
normal subprocesses like $\rho(1450)^+\to \pi^+\pi^0$ or $\rho(1450)^+\to
K^+\bar{K}^0$, which are bound by the masses of decaying initial states, will
provide virtual contributions in some special decays.


  We study the asymptotic behavior of the appropriately scaled and possibly
perturbed spectral measure $\mu$ of large random real symmetric matrices with
heavy tailed entries. Specifically, consider the N by N symmetric matrix
$Y_N^\sigma$ whose (i,j) entry is $\sigma(i/N,j/N)X_{ij}$ where $(X_{ij},
0<i<j+1<\infty)$ is an infinite array of i.i.d real variables with common
distribution in the domain of attraction of an $\alpha$-stable law,
$0<\alpha<2$, and $\sigma$ is a deterministic function. For a random diagonal
$D_N$ independent of $Y_N^\sigma$ and with appropriate rescaling $a_N$, we
prove that the distribution $\mu$ of $a_N^{-1}Y_N^\sigma + D_N$ converges in
mean towards a limiting probability measure which we characterize. As a special
case, we derive and analyze the almost sure limiting spectral density for
empirical covariance matrices with heavy tailed entries.


  Spectral features from PAH molecules observed in the mid-IR range are
typically used to infer the amount of recent and ongoing star formation (SF) on
kpc scales around AGN where more traditional methods fail. This assumes that
the observed PAH features are excited predominantly by SF. With current
ground-based telescopes and the upcoming JWST, much smaller spatial scales can
be probed and we aim at testing this assumption in the range of few tens to few
hundreds of pc. For that, we spatially map the emitted 11.3{\mu}m PAH surface
flux as a function of distance from 0.4-4 arcsec from the centre in 28 nearby
AGN using ground-based high-angular resolution mid-IR spectroscopy. We detect
and extract the 11.3{\mu}m PAH feature in 13 AGN. The fluxes within each
aperture are scaled to a luminosity-normalised distance from the nucleus to be
able to compare intrinsic spatial scales of AGN radiation spanning about 2
orders of magnitude in luminosity. For this, we establish an empirical relation
between the absorption-corrected X-ray luminosity and the sublimation radius in
these sources. Once normalised, the radial profiles of the emitted PAH surface
flux show similar radial slopes, with a power-law index of approximately -1.1,
and similar absolute values, consistent within a factor of a few of each other
as expected from the uncertainty in the intrinsic scale estimate. We interpret
this as evidence that the profiles are caused by a common compact central
physical process, either the AGN itself or circumnuclear SF linked in strength
to the AGN power. A photoionisation-based model of an AGN exciting dense clouds
in its environment can reproduce the observed radial slope and confirms that
the AGN radiation field is strong enough to explain the observed PAH surface
fluxes within ~10-500 pc of the nucleus. Our results advice caution in the use
of PAH emission as a SF tracer within a kpc around AGN.


  We develop a method of extending actions of compact transformation groups
which is then applied to the problem of preservation of equivariant extensor
property by passing to a subspace of given orbit types.


  We study the recently introduced family of confluent Virasoro fusion kernels
$\mathcal{C}_k(b,\boldsymbol{\theta},\sigma_s,\nu)$. We study their
eigenfunction properties and show that they can be viewed as non-polynomial
generalizations of both the continuous dual $q$-Hahn and the big $q$-Jacobi
polynomials. More precisely, we prove that: (i) $\mathcal{C}_k$ is a joint
eigenfunction of four different difference operators for any positive integer
$k$, (ii) $\mathcal{C}_k$ degenerates to the continuous dual $q$-Hahn
polynomials when $\nu$ is suitably discretized, and (iii) $\mathcal{C}_k$
degenerates to the big $q$-Jacobi polynomials when $\sigma_s$ is suitably
discretized. These observations lead us to propose the existence of a
non-polynomial generalization of the $q$-Askey scheme. The top member of this
non-polynomial scheme is the Virasoro fusion kernel (or, equivalently,
Ruijsenaars' hypergeometric function), and its first confluence is given by the
$\mathcal{C}_k$.


  Trajectories that capture object movement have numerous applications, in
which similarity computation between trajectories often plays a key role.
Traditionally, the similarity between two trajectories is quantified by means
of heuristic measures, e.g., Hausdorff or ERP, that operate directly on the
trajectories. In contrast, recent studies exploit deep learning to map
trajectories to d-dimensional vectors, called embeddings. Then, some distance
measure, e.g., Manhattan or Euclidean, is applied to the embeddings to quantify
trajectory similarity. The resulting similarities are inaccurate: they only
approximate the similarities obtained using the heuristic measures. As distance
computation on embeddings is efficient, focus has been on achieving embeddings
yielding high accuracy.
  Adopting an efficiency perspective, we analyze the time complexities of both
the heuristic and the learning-based approaches, finding that the time
complexities of the former approaches are not necessarily higher. Through
extensive experiments on open datasets, we find that, on both CPUs and GPUs,
only a few learning-based approaches can deliver the promised higher
efficiency, when the embeddings can be pre-computed, while heuristic approaches
are more efficient for one-off computations. Among the learning-based
approaches, the self-attention-based ones are the fastest to learn embeddings
that also yield the highest accuracy for similarity queries. These results have
implications for the use of trajectory similarity approaches given different
application requirements.


  Pulsed-field magnetization experiments (fields $B$ of up to 85 T and
temperatures $T$ down to 0.4 K) are reported on nine organic Cu-based
two-dimensional (2D) Heisenberg magnets. All compounds show a low-$T$
magnetization that is concave as a function of $B$, with a sharp ``elbow''
transition to a constant value at a field $B_{\rm c}$. Monte-Carlo simulations
including a finite interlayer exchange energy $J_{\perp}$ quantitatively
reproduce the data; the concavity indicates the effective dimensionality and
$B_{\rm c}$ is an accurate measure of the in-plane exchange energy $J$. Using
these values and Ne\'el temperatures measured by muon-spin rotation, it is also
possible to obtain a quantitative estimate of $|J_{\perp}/J|$. In the light of
these results, it is suggested that in magnets of the form
[Cu(HF$_2$)(pyz)$_2$]X, where X is an anion, the sizes of $J$ and $J_{\perp}$
are controlled by the tilting of the pyrazine (pyz) molecule with respect to
the 2D planes.


  For a category B with finite products, we first characterize pseudofunctors
from B to Cat whose corresponding opfibration is cartesian monoidal. Among
those, we then characterize the ones which extend to pseudofunctors from
internal groups to 2-groups. If B is additive, this is the case precisely when
the corresponding opfibration has groupoidal fibres.


  We show that every globally asymptotically stable system with a twice
continuously differentiable vector field admits a local polynomial Lyapunov
function on an arbitrary bounded neighborhood of the origin.


  Several studies have been carried out on revealing linguistic features
captured by BERT. This is usually achieved by training a diagnostic classifier
on the representations obtained from different layers of BERT. The subsequent
classification accuracy is then interpreted as the ability of the model in
encoding the corresponding linguistic property. Despite providing insights,
these studies have left out the potential role of token representations. In
this paper, we provide a more in-depth analysis on the representation space of
BERT in search for distinct and meaningful subspaces that can explain the
reasons behind these probing results. Based on a set of probing tasks and with
the help of attribution methods we show that BERT tends to encode meaningful
knowledge in specific token representations (which are often ignored in
standard classification setups), allowing the model to detect syntactic and
semantic abnormalities, and to distinctively separate grammatical number and
tense subspaces.


  We attempt to describe the rapidity distribution of P, P-bar, K+ and K- for
the most central Au+Au collisions at root(sNN) of 62.4 GeV,130 GeV and 200 GeV.
The transverse momentum spectra of strange as well as non-strange hadrons e.g.
P, P-bar, K+, K-, Lambda, Lambda-bar, Cascade,Cascade-bar and (Omega +
Omega-bar) are studied for the whole centrality classes at all the three RHIC
energies. The experimental data of the transverse momentum spectra and the
rapidity distributions are well reproduced. This is done by using a statistical
thermal freeze-out model which incorporates the rapidity (collision) axis as
well as transverse direction boosts developed within an expanding hot and dense
hadronic fluid (fireball) till the final freeze-out. We determine the
thermo-chemical freeze-out conditions particularly in terms of temperature,
baryon chemical potential and collective flow effect parameters for different
particle species. The parameters indicate occurrence of freeze-out of the
singly and doubly strange hyperon species at somewhat earlier times during the
evolution of the fireball. Dependence of the freeze-out parameters on the
degree of centrality is also described and it is found that the kinetic
temperature increases and collective flow effect decreases with decreasing
centrality at all energies studied. The nuclear transparency effect is also
studied and it is clear from our model that the nuclear matter becomes more
transparent at the highest RHIC energy compared to lower RHIC energies. The
contribution of heavier hadronic resonance decay is taken into account.


  Magnesium (Mg) alloys have shown great prospects as both structural and
biomedical materials, while poor corrosion resistance limits their further
application. In this work, to avoid the time-consuming and laborious experiment
trial, a high-throughput computational strategy based on first-principles
calculations is designed for screening corrosion-resistant binary Mg alloy with
intermetallics, from both the thermodynamic and kinetic perspectives. The
stable binary Mg intermetallics with low equilibrium potential difference with
respect to the Mg matrix are firstly identified. Then, the hydrogen adsorption
energies on the surfaces of these Mg intermetallics are calculated, and the
corrosion exchange current density is further calculated by a hydrogen
evolution reaction (HER) kinetic model. Several intermetallics, e.g. Y3Mg, Y2Mg
and La5Mg, are identified to be promising intermetallics which might
effectively hinder the cathodic HER. Furthermore, machine learning (ML) models
are developed to predict Mg intermetallics with proper hydrogen adsorption
energy employing work function (W_f) and weighted first ionization energy
(WFIE). The generalization of the ML models is tested on five new binary Mg
intermetallics with the average root mean square error (RMSE) of 0.11 eV. This
study not only predicts some promising binary Mg intermetallics which may
suppress the galvanic corrosion, but also provides a high-throughput screening
strategy and ML models for the design of corrosion-resistant alloy, which can
be extended to ternary Mg alloys or other alloy systems.


  Deep learning has been demonstrated to achieve excellent results for image
classification and object detection. However, the impact of deep learning on
video analysis (e.g. action detection and recognition) has been limited due to
complexity of video data and lack of annotations. Previous convolutional neural
networks (CNN) based video action detection approaches usually consist of two
major steps: frame-level action proposal detection and association of proposals
across frames. Also, these methods employ two-stream CNN framework to handle
spatial and temporal feature separately. In this paper, we propose an
end-to-end deep network called Tube Convolutional Neural Network (T-CNN) for
action detection in videos. The proposed architecture is a unified network that
is able to recognize and localize action based on 3D convolution features. A
video is first divided into equal length clips and for each clip a set of tube
proposals are generated next based on 3D Convolutional Network (ConvNet)
features. Finally, the tube proposals of different clips are linked together
employing network flow and spatio-temporal action detection is performed using
these linked video proposals. Extensive experiments on several video datasets
demonstrate the superior performance of T-CNN for classifying and localizing
actions in both trimmed and untrimmed videos compared to state-of-the-arts.


  We review recent progress in the theoretical description of anisotropic hard
colloidal particles. The shapes considered range from rods and dumbbells to
rounded cubes, polyhedra and to biaxial particles with arbitrary shape. Our
focus is on both static and dynamical density functional theory and on computer
simulations. We describe recent results for the structure, dynamics and phase
behaviour in the bulk and in various confining geometries, e.g. established by
two parallel walls which reduce the dimensionality of the system to two
dimensions. We also include recent theoretical modelling for active particles,
which are autonomously driven by some intrinsic motor, and highlight their
fascinating nonequilibrium dynamics and collective behaviour.


  Dwarf spheroidal galaxies (dSphs) have been extensively investigated in the
Local Group, but their low luminosity and surface brightness make similar work
in more distant galaxy groups challenging. Modern instrumentation unlocks the
possibility of scrutinizing these faint systems in other environments,
expanding the parameter space of group properties. We use MUSE spectroscopy to
study the properties of 14 known or suspected dSph satellites of Cen A. Twelve
targets are confirmed to be group members based on their radial velocities. Two
targets are background galaxies at 50 Mpc: KK 198 is a face-on spiral galaxy,
and dw1315-45 is an ultra-diffuse galaxy with an effective radius of 2300 pc.
The 12 confirmed dSph members of the Cen A group have old and metal-poor
stellar populations and follow the stellar metallicity-luminosity relation
defined by the dwarf galaxies in the Local Group. In the three brightest dwarf
galaxies (KK 197, KKs 55, and KKs 58), we identify globular clusters, as well
as a planetary nebula in KK 197, although its association with this galaxy
and/or the extended halo of Cen A is uncertain. Using four discrete tracers, we
measure the velocity dispersion and dynamical mass of KK 197. This dSph appears
dark matter dominated and lies on the radial acceleration relation of
star-forming galaxies within the uncertainties. It also is consistent with
predictions stemming from modified Newtonian dynamics (MOND). Surprisingly, in
the dwarf KK 203 we find an extended Halpha ring. Careful examination of Hubble
Space Telescope photometry reveals a very low level of star formation at ages
between 30-300 Myr. The Halpha emission is most likely linked to a 40 Myr old
supernova remnant, although other possibilities for its origin cannot be
entirely ruled out.


  Motivation: Standard algorithms for pairwise protein sequence alignment make
the simplifying assumption that amino acid substitutions at neighboring sites
are uncorrelated. This assumption allows implementation of fast algorithms for
pairwise sequence alignment, but it ignores information that could conceivably
increase the power of remote homolog detection. We examine the validity of this
assumption by constructing extended substitution matrixes that encapsulate the
observed correlations between neighboring sites, by developing an efficient and
rigorous algorithm for pairwise protein sequence alignment that incorporates
these local substitution correlations, and by assessing the ability of this
algorithm to detect remote homologies. Results: Our analysis indicates that
local correlations between substitutions are not strong on the average.
Furthermore, incorporating local substitution correlations into pairwise
alignment did not lead to a statistically significant improvement in remote
homology detection. Therefore, the standard assumption that individual residues
within protein sequences evolve independently of neighboring positions appears
to be an efficient and appropriate approximation.


  We classify the spherical birational sheets in a complex simple
simply-connected algebraic group. We use the classification to show that, when
$G$ is a connected reductive complex algebraic group with simply-connected
derived subgroup, two conjugacy classes $\mathcal{O}_1$, $\mathcal{O}_2$ of $G$
lie in the same birational sheet, up to a shift by a central element of $G$, if
and only if the coordinate rings of $\mathcal{O}_1$ and $\mathcal{O}_2$ are
isomorphic as $G$-modules. As a consequence, we prove a conjecture of Losev for
the spherical subvariety of the Lie algebra of $G$.


  We exhibit the first examples of residually finite non-linear Gromov
hyperbolic groups. Our examples are constructed as amalgamated products of
torsion-free cocompact lattices in the rank 1 Lie group $\mathrm{Sp}(d,1)$,
$d\geq 2$ along maximal cyclic subgroups.


  A discretized version of canonical gravity in (3+1)-d introduced in a
previous paper is further developed, introducing the Liouville form and the
Poisson brackets, and studying them in detail in an explicit parametrization
that shows the nature of the variables when the second class constraints are
imposed. It is then shown that, even leaving aside the difficult question of
imposing the first class constraints on the states, it is impossible to
quantize the model directly, using complex variables and leaving the second
class constraints to fix the metric of the quantum Hilbert, because one cannot
find a metric which makes the area variables hermitean.


  Predicting the long-lasting quasistationary state for a given initial state
is one of central issues in Hamiltonian systems having long-range interaction.
A recently proposed method is based on the Vlasov description and uniformly
redistributes the initial distribution along contours of the asymptotic
effective Hamiltonian, which is defined by the obtained quasistationary state
and is determined self-consistently. The method, to which we refer as the
rearrangement formula, was suggested to give precise prediction under limited
situations. Restricting initial states consisting of spatially homogeneous part
and small perturbation, we numerically reveal two conditions that the
rearrangement formula prefers: One is no Landau damping condition for
unperturbed homogeneous part, and the other comes from the Casimir invariants.
Mechanisms of these conditions are discussed. Clarifying these conditions, we
inform validity to use the rearrangement formula as the response theory for an
external field, and we shed light on improving the theory as a nonequilibrium
statistical mechanics.


  Owing to the resource-constrained feature of Internet of Things (IoT)
devices, offloading tasks from IoT devices to the nearby mobile edge computing
(MEC) servers can not only save the energy of IoT devices but also reduce the
response time of executing the tasks. However, offloading a task to the nearest
MEC server may not be the optimal solution due to the limited computing
resources of the MEC server. Thus, jointly optimizing the offloading decision
and resource management is critical, but yet to be explored. Here, offloading
decision refers to where to offload a task and resource management implies how
much computing resource in an MEC server is allocated to a task. By considering
the waiting time of a task in the communication and computing queues (which are
ignored by most of the existing works) as well as tasks priorities, we propose
the \ul{D}eep reinforcement l\ul{E}arning based offloading de\ul{C}ision and
r\ul{E}source manageme\ul{NT} (DECENT) algorithm, which leverages the advantage
actor critic method to optimize the offloading decision and computing resource
allocation for each arriving task in real-time such that the cumulative
weighted response time can be minimized. The performance of DECENT is
demonstrated via different experiments.


  In an Markov decision process (MDP), unobservable confounders may exist and
have impacts on the data generating process, so that the classic off-policy
evaluation (OPE) estimators may fail to identify the true value function of the
target policy. In this paper, we study the statistical properties of OPE in
confounded MDPs with observable instrumental variables. Specifically, we
propose a two-stage estimator based on the instrumental variables and establish
its statistical properties in the confounded MDPs with a linear structure. For
non-asymptotic analysis, we prove a $\mathcal{O}(n^{-1/2})$-error bound where
$n$ is the number of samples. For asymptotic analysis, we prove that the
two-stage estimator is asymptotically normal with a typical rate of $n^{1/2}$.
To the best of our knowledge, we are the first to show such statistical results
of the two-stage estimator for confounded linear MDPs via instrumental
variables.


  As artificial intelligence and machine learning tools become more accessible,
and scientists face new obstacles to data collection (e.g. rising costs,
declining survey response rates), researchers increasingly use predictions from
pre-trained algorithms as outcome variables. Though appealing for financial and
logistical reasons, using standard tools for inference can misrepresent the
association between independent variables and the outcome of interest when the
true, unobserved outcome is replaced by a predicted value. In this paper, we
characterize the statistical challenges inherent to this so-called ``inference
with predicted data'' problem and elucidate three potential sources of error:
(i) the relationship between predicted outcomes and their true, unobserved
counterparts, (ii) robustness of the machine learning model to resampling or
uncertainty about the training data, and (iii) appropriately propagating not
just bias but also uncertainty from predictions into the ultimate inference
procedure.


  We apply the recently generalized Levinson theorem for potentials with
inverse square singularities [Sheka et al, Phys.Rev.A, v.68, 012707 (2003)] to
Aharonov-Bohm systems in two-dimensions. By this theorem, the number of bound
states in a given m-th partial wave is related to the phase shift and the
magnetic flux. The results are applied to 2D soliton-magnon scattering.


  This is a paper with the aim of give the student a detailed calculation of
magnetic monopole's Dirac theory.


  We investigate the streaming and thermal instabilities of the electron-ion
plasma with homogeneous cold cosmic rays drifting perpendicular to the
background magnetic field in the multi-fluid approach. One-dimensional
perturbations along the magnetic field are considered. The induced return
current of the background plasma and back-reaction of cosmic rays are taken
into account. It is shown that the cosmic ray back-reaction results in the
streaming instability having considerably larger growth rates than that due to
the return current of the background plasma. This increase is by a factor of
the square root of the ratio of the background plasma mass density to the
cosmic ray mass density. The maximal growth rates and corresponding wave
numbers are found. The thermal instability is shown to be not subject to the
action of cosmic rays in the model under consideration. The dispersion relation
for the thermal instability includes ion inertia. In the limit of fast thermal
energy exchange between electrons and ions, the isobaric and isochoric growth
rates are derived. The results obtained can be useful for the investigation of
the electron-ion astrophysical objects such as galaxy clusters including the
dynamics of streaming cosmic rays.


  Pade Approximants can be used to go beyond Vector Meson Dominance in a
systematic approximation. We illustrate this fact with the case of the pion
vector form factor and extract values for the first two coefficients of its
Taylor expansion. Pade Approximants are shown to be a useful and simple tool
for incorporating high-energy information, allowing an improved determination
of these Taylor coefficients.


  U-Net style networks are commonly utilized in unsupervised image registration
to predict dense displacement fields, which for high-resolution volumetric
image data is a resource-intensive and time-consuming task. To tackle this
challenge, we first propose Fourier-Net, which replaces the costly U-Net style
expansive path with a parameter-free model-driven decoder. Instead of directly
predicting a full-resolution displacement field, our Fourier-Net learns a
low-dimensional representation of the displacement field in the band-limited
Fourier domain which our model-driven decoder converts to a full-resolution
displacement field in the spatial domain. Expanding upon Fourier-Net, we then
introduce Fourier-Net+, which additionally takes the band-limited spatial
representation of the images as input and further reduces the number of
convolutional layers in the U-Net style network's contracting path. Finally, to
enhance the registration performance, we propose a cascaded version of
Fourier-Net+. We evaluate our proposed methods on three datasets, on which our
proposed Fourier-Net and its variants achieve comparable results with current
state-of-the art methods, while exhibiting faster inference speeds, lower
memory footprint, and fewer multiply-add operations. With such small
computational cost, our Fourier-Net+ enables the efficient training of
large-scale 3D registration on low-VRAM GPUs. Our code is publicly available at
\url{https://github.com/xi-jia/Fourier-Net}.


  We study the makespan minimization problem with unrelated selfish machines
under the assumption that job sizes are stochastic. We design simple truthful
mechanisms that under various distributional assumptions provide constant and
sublogarithmic approximations to expected makespan. Our mechanisms are
prior-independent in that they do not rely on knowledge of the job size
distributions. Prior-independent approximation mechanisms have been previously
studied for the objective of revenue maximization [Dhangwatnotai, Roughgarden
and Yan'10, Devanur, Hartline, Karlin and Nguyen'11, Roughgarden, Talgam-Cohen
and Yan'12]. In contrast to our results, in prior-free settings no truthful
anonymous deterministic mechanism for the makespan objective can provide a
sublinear approximation [Ashlagi, Dobzinski and Lavi'09].


  Complex networks are graphs representing real-life systems that exhibit
unique characteristics not found in purely regular or completely random graphs.
The study of such systems is vital but challenging due to the complexity of the
underlying processes. This task has nevertheless been made easier in recent
decades thanks to the availability of large amounts of networked data. Link
prediction in complex networks aims to estimate the likelihood that a link
between two nodes is missing from the network. Links can be missing due to
imperfections in data collection or simply because they are yet to appear.
Discovering new relationships between entities in networked data has attracted
researchers' attention in various domains such as sociology, computer science,
physics, and biology. Most existing research focuses on link prediction in
undirected complex networks. However, not all real-life systems can be
faithfully represented as undirected networks. This simplifying assumption is
often made when using link prediction algorithms but inevitably leads to loss
of information about relations among nodes and degradation in prediction
performance. This paper introduces a link prediction method designed explicitly
for directed networks. It is based on the similarity-popularity paradigm, which
has recently proven successful in undirected networks. The presented algorithms
handle the asymmetry in node relationships by modeling it as asymmetry in
similarity and popularity. Given the observed network topology, the algorithms
approximate the hidden similarities as shortest path distances using edge
weights that capture and factor out the links' asymmetry and nodes' popularity.
The proposed approach is evaluated on real-life networks, and the experimental
results demonstrate its effectiveness in predicting missing links across a
broad spectrum of networked data types and sizes.


  We investigate spaces of operators which are invariant under translations or
modulations by lattices in phase space. The natural connection to the
Heisenberg module is considered, giving results on the characterisation of such
operators as limits of finite--rank operators. Discrete representations of
these operators in terms of elementary objects and the composition calculus are
given. Different quantisation schemes are discussed with respect to the
results.


  Magnetic-field-induced changes of the Fermi surface play a central role in
theories of the exotic quantum criticality of YbRh2Si2. We have carried out de
Haas-van Alphen measurements in the magnetic-field range 8 T <= H <= 16 T, and
directly observe field dependence of the extremal Fermi surface areas. Our data
support the theory that a low-field "large" Fermi surface, including the Yb 4f
quasihole, is increasingly spin split until a majority-spin branch undergoes a
Lifshitz transition and disappears at H0 ~ 10 T, without requiring 4f
localization at H0.


  Binaries with circumbinary disks are commonly found among optically bright
post-AGB stars. Although clearly linked to binary interaction processes, the
formation, evolution and fate of these disks are still badly understood. Due to
their compactness, interferometric techniques are required to resolve them.
Here, we discuss our high-quality multiwavelength interferometric data of two
prototypical yet very different post-AGB binaries, AC and 89 Herculis, as well
as the modeling thereof with radiative transfer models. A detailed account of
the data and models of both objects is published in three separate papers
elsewhere; here we focus on comparing the modeling results for the two objects.
In particular we discuss the successes and limitations of the models which were
developed for protoplanetary disks around young stars. We conclude that
multiwavelength high-angular-resolution observations and radiative transfer
disk models are indispensible to understand these complex interacting objects
and their place in the grand scheme of the (binary) evolution of low and
intermediate mass stars.


  In a recent paper, we have pointed out a relation between the Killing spinor
and Einstein equations. Using this relation, new brane solutions of D=11 and
D=10 type IIB supergravity theories are constructed. It is shown that in a
brane solution, the flat world-volume directions, the smeared transverse
directions and the sphere located at a fixed radial distance can be replaced
with any Lorentzian Ricci flat, Euclidean Ricci flat and Einstein manifolds,
respectively. The solution obtained in this fashion is supersymmetric when the
Ricci flat and Einstein manifolds have Killing spinors. We generalize
intersecting brane solutions, in which M5, M2 and D3-branes also wrap over the
cycles determined by the K\"{a}hler forms of Ricci flat K\"{a}hler manifolds.
New, singular, Ricci flat manifolds as (generalized) cones over the U(1)
bundles over Ricci flat K\"{a}hler spaces are constructed. These manifolds have
covariantly constant spinors and give rise to new, supersymmetric, Ricci flat
compactifications of non-gauged supergravity theories. We find M2 and D3-brane
solutions, which asymptotically approach these singular vacua.


  We introduce a new random walk with unbounded memory obtained as a mixture of
the Elephant Random Walk and the Dynamic Random Walk which we call the Dynamic
Elephant Random Walk (DERW). As a consequence of this mixture the distribution
of the increments of the resulting random process is time dependent. We prove a
strong law of large numbers for the DERW and, in a particular case, we provide
an explicit expression for its speed. Finally, we give sufficient conditions
for the central limit theorem and the law of the iterated logarithm to hold.


  Here we generalize our previous model of molecular motors trafficking
subdiffusing cargos in viscoelastic cytosol by (i) including mechanochemical
coupling between cyclic conformational fluctuations of the motor protein driven
by the reaction of ATP hydrolysis and its translational motion within the
simplest two-state model of hand-over-hand motion of kinesin, and also (ii) by
taking into account the anharmonicity of the tether between the motor and cargo
(its maximally possible extension length). It is shown that the major earlier
results such as occurrence of normal versus anomalous transport depending on
the amplitude of binding potential, cargo size and the motor turnover frequency
not only survive in this more realistic model, but the results also look very
similar for the correspondingly adjusted parameters. However, this more
realistic model displays a substantially larger thermodynamic efficiency due to
a bidirectional mechanochemical coupling. For realistic parameters, the maximal
thermodynamic efficiency can be transiently about 50% as observed for kinesins,
and even larger, surprisingly also in a novel strongly anomalous (sub)transport
regime, where the motor enzymatic turnovers become also anomalously slow and
cannot be characterized by a turnover rate. Here anomalously slow dynamics of
the cargo enforces anomalously slow cyclic kinetics of the motor protein.


  We propose a method to achieve scalable quantum computation based on fast
quantum gates on an array of trapped ions, without the requirement of ion
shuttling. Conditional quantum gates are obtained for any neighboring ions
through spin-dependent acceleration of the ions from periodic photon kicks. The
gates are shown to be robust to influence of all the other ions in the array
and insensitive to the ions' temperature.


  The experimental investigation of matter under extreme densities and
temperatures as they occur for example in astrophysical objects and nuclear
fusion applications constitutes one of the most active frontiers at the
interface of material science, plasma physics, and engineering. The central
obstacle is given by the rigorous interpretation of the experimental results,
as even the diagnosis of basic parameters like the temperature T is rendered
highly difficult by the extreme conditions. In this work, we present a simple,
approximation-free method to extract the temperature of arbitrarily complex
materials from scattering experiments, without the need for any simulations or
an explicit deconvolution. This new paradigm can be readily implemented at
modern facilities and corresponding experiments will have a profound impact on
our understanding of warm dense matter and beyond, and open up a gamut of
appealing possibilities in the context of thermonuclear fusion, laboratory
astrophysics, and related disciplines.


  We investigate the effect of possible a-priori "imprinting" effects of
general relativity itself on satellite/spaceraft-based tests of it. We deal
with some performed or proposed time-delay ranging experiments in the Sun's
gravitational field. It turns out that the "imprint" of general relativity on
the Astronomical Unit and the solar gravitational constant GM_{\odot}, not
solved for in the so far performed spacecraft-based time-delay tests, induces
an a-priori bias of the order of 10^-6 in typical solar system ranging
experiments aimed to measuring the space curvature PPN parameter gamma. It is
too small by one order of magnitude to be of concern for the performed Cassini
experiment, but it would affect future planned or proposed tests aiming to
reach a 10^-7-10^-9 accuracy in determining gamma.


  Consider a presentation $\mathcal{P}=<{\bf x}\mid{\bf \bigcup_{i=1}^n r_i}>$.
  Let ${\bf R_i}$ be the normal closure of the set ${\bf r_i}$ in the free
group ${\bf F}$ with basis ${\bf x}$, $\mathcal{P}_i=<{\bf x}\mid{\bf r_i}>$,
${\bf N_i} = \prod_{j\neq i}{\bf R_j}$. In the present article, using geometric
techniques of pictures, generators for $\frac{{\bf R_i}\cap {\bf N_i}}{[{\bf
R_i}, {\bf N_i}]}$, $i=1,...,n$, are obtained from a set of generators over
$\{\mathcal{P}_i\mid i=1,..., n\}$ for $\pi_2(\mathcal{P})$. As a corollary, we
get a sufficient condition for the family $\{{\bf R_1},...,{\bf R_n}\}$ to be
independent.


  In this paper, we propose a full-duplex orthogonal frequency-division
multiplexing (OFDM) device-to-device (D2D) system in two-hop networks, where
multiple full-duplex decode-and-forward (DF) relays assist the transmission
from D2D user equipment (DUE) transmitter to DUE receiver. By such a
transmission mechanism, the signal transmitted by the DUE transmitter does not
need to go through a base station (BS). Meanwhile, due to the adoption of
underlay D2D communication protocol, power control mechanisms are thereby
necessary to be applied to mitigate the interference to conventional cellular
communications. Based on these considerations, we analyze the outage
performance of the proposed system, and derive the exact expressions of outage
probabilities when bulk and per-subcarrier relay selection criteria are
applied. Furthermore, closed-form expressions of outage probabilities are also
obtained for special cases when the instantaneous channel state information
(CSI) between BS and cellular user equipments (CUEs) is not accessible, so that
a static power control mechanism is applied. Subsequently, we also investigate
the outage performance optimization problem by coordinating transmit power
among DUE transmitter and relays, and provide a suboptimal solution, which is
capable of improving the outage performance. All analysis is substantiated by
numerical results provided by Monte Carlo simulations. The analytical and
numerical results demonstrated in this paper can provide an insight into the
full-duplex relay-assisted OFDM D2D systems, and serve as a guideline for its
implementation in next generation networks.


  The search for higher-order feature interactions that are statistically
significantly associated with a class variable is of high relevance in fields
such as Genetics or Healthcare, but the combinatorial explosion of the
candidate space makes this problem extremely challenging in terms of
computational efficiency and proper correction for multiple testing. While
recent progress has been made regarding this challenge for binary features, we
here present the first solution for continuous features. We propose an
algorithm which overcomes the combinatorial explosion of the search space of
higher-order interactions by deriving a lower bound on the p-value for each
interaction, which enables us to massively prune interactions that can never
reach significance and to thereby gain more statistical power. In our
experiments, our approach efficiently detects all significant interactions in a
variety of synthetic and real-world datasets.


  The sequence of phase transitions and the symmetry of in particular the low
temperature incommensurate and spin-Peierls phases of the quasi one-dimensional
inorganic spin-Peierls system TiOX (TiOBr and TiOCl) have been studied using
inelastic light scattering experiments. The anomalous first-order character of
the transition to the spin-Peierls phase is found to be a consequence of the
different symmetries of the incommensurate and spin-Peierls (P$2_{1}/m$)
phases.
  The pressure dependence of the lowest transition temperature strongly
suggests that magnetic interchain interactions play an important role in the
formation of the spin-Peierls and the incommensurate phases. Finally, a
comparison of Raman data on VOCl to the TiOX spectra shows that the high energy
scattering observed previously has a phononic origin.


  Constraints are placed on the spin-independent interaction cross section of
dark matter with regular matter by refining two methods. First, dark
matter--cosmic ray interactions are considered, wherein cosmic ray protons
collide with dark matter to contribute to the gamma ray sky. This constraint is
developed using the NFW and Moore dark matter density profiles and new data
from the Fermi gamma ray space telescope. Second, the Earth capture scenario is
considered, wherein particles that are captured self-annihilate at Earth's
center, thus adding to its internal heat flow. The constraint presented here is
developed based on analysis of the drift time of dark matter particles through
Earth, modeled as a core composed of iron and a mantle composed of oxygen with
linear density gradients between layers. An analysis of the cosmic ray
constraint (which rules out dark matter--regular matter interaction cross
sections greater than its value) shows that it overlaps significantly with the
Earth drift time constraint (which rules out cross sections smaller than its
value), closing the window on strongly interacting dark matter particles up to
a mass of about 10^{17} GeV when combined with other exclusions.


  We derive five-dimensional super Yang-Mills theory from mass-deformed ABJM
theory by expanding about $S^2$ for large Chern-Simons level $K$. We obtain the
Yang-Mills coupling constant $g_{YM}^2 = 4\pi^2 R/K$. If we consider
$S^3/{\mb{Z}_K}$ as a fiber bundle over $S^2$ then $R/K$ is the circumference
of the fiber. The value on the coupling constant agrees with what one gets by
compactifying M five-brane on that fiber. For this computation we take $R,K\to
\infty$ while keeping $R/K$ at a fixed finite value. We also study mass
deformed star-three-product BLG theory at K=1 and $R\to \infty$. In that limit
we obtain Lorentz covariant supersymmetry variations and gauge variations of a
non-Abelian tensor multiplet.


  We present in this paper the comparison of methods to measure parameters to
characterize a large SiPM of area of about 1 cm$^2$. We also explain the
challenges of operating it with continuous light and at room temperature. We
present a method to compensate for the voltage drop induced by emission to
continuous illumination in order to control its response stability. This is
important for applications where illumination changes with time, such as when
they are employed in cameras of gamma-ray telescopes.


  This paper investigates the dynamical generation of entanglement in
scattering systems, in particular two spin systems that interact via
rotationally-invariant scattering. The spin degrees of freedom of the in-states
are assumed to be in unentangled, pure states, as defined by the entropy of
entanglement. Because of the restriction of rotationally-symmetric
interactions, perfectly-entangling S-matrices, i.e. those that lead to a
maximally entangled out-state, only exist for a certain class of separable
in-states. Using Clebsch-Gordan coefficients for the rotation group, the
scattering phases that determine the S-matrix are determined for the case of
spin systems with $\sigma = 1/2$, 1, and 3/2.


  Recently, Keating, Linden, and Wells \cite{KLW} showed that the density of
states measure of a nearest-neighbor quantum spin glass model is approximately
Gaussian when the number of particles is large. The density of states measure
is the ensemble average of the empirical spectral measure of a random matrix;
in this paper, we use concentration of measure and entropy techniques together
with the result of \cite{KLW} to show that in fact, the empirical spectral
measure of such a random matrix is almost surely approximately Gaussian itself,
with no ensemble averaging. We also extend this result to a spherical quantum
spin glass model and to the more general coupling geometries investigated by
Erd\H{o}s and Schr\"oder.


  A logarithmic Schr\"odinger equation with time-dependent coupling to the
non-linearity is presented as a model of collisional decoherence of the
wavefunction of a quantum particle in position-space. The particular
mathematical form of the logarithmic Schr\"odinger equation has been shown to
follow from conditional wave theory, but the validity of the logarithmic
Schr\"odinger equation has not yet been investigated numerically for general
initial conditions. Using an operator-splitting approach, we solve the
non-linear equation of motion for the wavefunction numerically and compare it
it to the solution of the standard Joos-Zeh master equation for the density
matrix. We find good agreement for the time-dependent behaviour of the ensemble
widths between the two approaches, but note curious `zero-pinning' behaviour of
the logarithmic Schr\"odinger equation, whereby the zeros of the wavefunction
are not erased by continued propagation. By examining the derivation of the
logarithmic Schr\"odinger equation from conditional wave theory, we indicate
possible avenues of resolution to this zero-pinning problem.


  To a Nash function germ, we associate a zeta function similar to the one
introduced by J. Denef and F. Loeser. Our zeta function is a formal power
series with coefficients in the Grothendieck ring $\mathcal{M}$ of
$\mathcal{AS}$-sets up to $\mathbb{R}^*$-equivariant $\mathcal{AS}$-bijections
over $\mathbb{R}^*$, an analog of the Grothendieck ring constructed by G.
Guibert, F. Loeser and M. Merle. This zeta function generalizes the previous
construction of G. Fichou but thanks to its richer structure it allows us to
get a convolution formula and a Thom-Sebastiani type formula.
  We show that our zeta function is an invariant of the arc-analytic
equivalence, a version of the blow-Nash equivalence of G. Fichou. The
convolution formula allows us to obtain a partial classification of Brieskorn
polynomials up to the arc-analytic equivalence by showing that the exponents
are arc-analytic invariants.


  The fragmentation of deuterons into pions with nonzero angle emitted in the
kinematical region forbidden for free nucleon-nucleon collisions is analyzed.
The inclusive relativistic invariant spectrum of pions and the tensor analyzing
power ${\rm A}_{YY}$ are investigated within the framework of an impulse
approximation using different kinds of the deuteron wave function. The
influence of ${\rm P}$-wave contribution to the deuteron wave function is
studied, too. Our results are compared with the experimental data and other
calculations performed within both the non-relativistic and relativistic
approaches. It was found that theoretical calculations based on IA do not
provide a consistent understanding of the new data in a whole cumulative region
that can be caused by a influence of non-nucleon degrees of freedom.


  The typicality approach and the Hilbert space averaging method as its
technical manifestation are important concepts of quantum statistical
mechanics. Extensively used for expectation values we extend them in this paper
to transition probabilities. In this context we also find that the transition
probability of two random uniformly distributed states is connected to the
spectral statistics of the considered operator. Furthermore, within our
approach we are capable to consider distributions of matrix elements between
states, that are not orthogonal. We will demonstrate our quite general result
numerically for a kicked spin chain in the integrable resp. chaotic regime.


  Recently, several morphologies, each with its advantages, have been proposed
for the \textit{GelSight} high-resolution tactile sensors. However, existing
simulation methods are limited to flat-surface sensors, which prevents their
usage with the newer sensors of non-flat morphologies in Sim2Real experiments.
In this paper, we extend a previously proposed GelSight simulation method
developed for flat-surface sensors and propose a novel method for curved
sensors. In particular, we address the simulation of light rays travelling
through a curved tactile membrane in the form of geodesic paths. The method is
validated by simulating the finger-shaped GelTip sensor and comparing the
generated synthetic tactile images against the corresponding real images. Our
extensive experiments show that combining the illumination generated from the
geodesic paths, with a background image from the real sensor, produces the best
results when compared to the lighting generated by direct linear paths in the
same conditions. As the method is parameterised by the sensor mesh, it can be
applied in principle to simulate a tactile sensor of any morphology. The
proposed method not only unlocks simulating existing optical tactile sensors of
complex morphologies but also enables experimenting with sensors of novel
morphologies, before the fabrication of the real sensor. Project website:
https://danfergo.github.io/geltip-sim


  Nazarov and Sodin have shown that the number of connected components of the
nodal set of a planar Gaussian field in a ball of radius $R$, normalised by
area, converges to a constant as $R\to \infty $. This has been generalised to
excursion/level sets at arbitrary levels, implying the existence of functionals
$c_{ES}(\ell )$ and $c_{LS}(\ell )$ that encode the density of excursion/level
set components at the level $\ell $. We prove that these functionals are
continuously differentiable for a wide class of fields. This follows from a
more general result, which derives differentiability of the functionals from
the decay of the probability of `four-arm events' for the field conditioned to
have a saddle point at the origin. For some fields, including the important
special cases of the Random Plane Wave and the Bargmann-Fock field, we also
derive stochastic monotonicity of the conditioned field, which allows us to
deduce regions on which $c_{ES}(\ell )$ and $c_{LS}(\ell )$ are monotone.


  We give some general criteria of being a homeomorphism for continuous
mappings of topological manifolds, as well as criteria of being a
diffeomorphism for smooth mappings of smooth manifolds. As an illustration, we
apply these criteria to the problems arising in two- and three-dimensional grid
generation.


  The status of Charmonium production in photon-photon collisions is briefly
reviewed. I would like to mention that although the preliminary data were
obtained in experiment, the theoretical investigation is not in a compatible
status.


  The massive amount of current data has led to many different forms of data
analysis processes that aim to explore this data to uncover valuable insights.
Methodologies to guide the development of big data science projects, including
CRISP-DM and SEMMA, have been widely used in industry and academia. The data
analysis modeling phase, which involves decisions on the most appropriate
models to adopt, is at the core of these projects. However, from a software
engineering perspective, the design and automation of activities performed in
this phase are challenging. In this paper, we propose an approach to the data
analysis modeling process which involves (i) the assessment of the variability
inherent in the CRISP-DM data analysis modeling phase and the provision of
feature models that represent this variability; (ii) the definition of a
framework structural design that captures the identified variability; and (iii)
evaluation of the developed framework design in terms of the possibilities for
process automation. The proposed approach advances the state of the art by
offering a variability-aware design solution that can enhance system
flexibility, potentially leading to novel software frameworks which can
significantly improve the level of automation in data analysis modeling
process.


  For a graph $G$, we denote by $\sigma_{2}(G)$ the minimum degree sum of two
non-adjacent vertices if $G$ is non-complete; otherwise, $\sigma_{2}(G) =
+\infty$. In this paper, we prove the following two results: (i) If $s_{1},
s_{2} \ge 2$ are integers and $G$ is a non-complete graph with $\sigma_{2}(G)
\ge 2(s_{1} + s_{2} + 1) - 1$, then $G$ contains two vertex-disjoint subgraphs
$H_{1}$ and $H_{2}$ such that each $H_{i}$ is a graph of order at least
$s_{i}+1$ with $\sigma_{2}(H_{i}) \ge 2s_{i} - 1$. (ii) If $s_{1}, s_{2} \ge 2$
are integers and $G$ is a triangle-free graph of order at least $3$ with
$\sigma_{2}(G) \ge 2(s_{1} + s_{2}) - 1$, then $G$ contains two vertex-disjoint
subgraphs $H_{1}$ and $H_{2}$ such that each $H_{i}$ is a graph of order at
least $2s_{i}$ with $\sigma_{2}(H_{i}) \ge 2s_{i} - 1$. By using this result,
we also give some corollaries concerning degree conditions for the existence of
$k$ vertex-disjoint cycles.


  We solve the Percus-Yevick equation in even dimensions by reducing it to a
set of simple integro-differential equations. This work generalizes an approach
we developed previously for hard discs. We numerically obtain both the pair
correlation function and the virial coefficients for a fluid of hyper-spheres
in dimensions $d=4,6$ and 8, and find good agreement with available exact
results and Monte-Carlo simulations. This paper confirms the alternating
character of the virial series for $d \ge 6$, and provides the first evidence
for an alternating character for $d=4$. Moreover, we show that this sign
alternation is due to the existence of a branch point on the negative real
axis. It is this branch point that determines the radius of convergence of the
virial series, whose value we determine explicitly for $d=4,6,8$. Our results
complement, and are consistent with, a recent study in odd dimensions [R.D.
Rohrmann et al., J. Chem. Phys. 129, 014510 (2008)].


  Infrared (IR) microscopes measure spectral information that quantifies
molecular content to assign the identity of biomedical cells but lack the
spatial quality of optical microscopy to appreciate morphologic features. Here,
we propose a method to utilize the semantic information of cellular identity
from IR imaging with the morphologic detail of pathology images in a deep
learning-based approach to image super-resolution. Using Generative Adversarial
Networks (GANs), we enhance the spatial detail in IR imaging beyond the
diffraction limit while retaining their spectral contrast. This technique can
be rapidly integrated with modern IR microscopes to provide a framework useful
for routine pathology.


  The Legendre transformation on singular Lagrangians, e.g. Lagrangians
representing gauge theories, fails due to the presence of constraints. The
Faddeev-Jackiw technique, which offers an alternative to that of Dirac, is a
symplectic approach to calculating a Hamiltonian paired with a well-defined
initial value problem when working with a singular Lagrangian. This phase space
coordinate reduction was generalized by Barcelos-Neto and Wotzasek to simplify
its application. We present an extension of the Faddeev-Jackiw technique for
constraint reduction in gauge field theories and non-gauge field theories that
are coupled to a curved spacetime that is described by General Relativity. A
major difference from previous formulations is that we do not explicitly
construct the symplectic matrix, as that is not necessary. We find that the
technique is a useful tool that avoids some of the subtle complications of the
Dirac approach to constraints. We apply this formulation to the Ginzburg-Landau
action and provide a calculation of its Hamiltonian and Poisson brackets in a
curved spacetime.


  Internet of Things (IoT) systems have aroused enthusiasm and concerns.
Enthusiasm comes from their utilities in people daily life, and concerns may be
associated with privacy issues. By using two IoT systems as case-studies, we
examine users' privacy beliefs, concerns and attitudes. We focus on four major
dimensions: the collection of personal data, the inference of new information,
the exchange of information to third parties, and the risk-utility trade-off
posed by the features of the system. Altogether, 113 Brazilian individuals
answered a survey about such dimensions. Although their perceptions seem to be
dependent on the context, there are recurrent patterns. Our results suggest
that IoT users can be classified into unconcerned, fundamentalists and
pragmatists. Most of them exhibit a pragmatist profile and believe in privacy
as a right guaranteed by law. One of the most privacy concerning aspect is the
exchange of personal information to third parties. Individuals' perceived risk
is negatively correlated with their perceived utility in the features of the
system. We discuss practical implications of these results and suggest
heuristics to cope with privacy concerns when designing IoT systems.


  The long disputed issue of the limiting value of quasiparticle density of
states $\r(0) = \r (\e \to 0)$ in a \emph{d-}wave superconductor with
impurities (\emph{vs} its linear vanishing, $\r_0(\e) \propto |\e|/\D$, near
the nodal point $\e = 0$ in a pure system with the gap parameter $\D$) is
discussed. Using the technique of group expansions of Green functions in
complexes of interacting impurities, it is shown that finite $\r(0)$ value is
possible if the (finite) impurity perturbation $V$ is spin-dependent
(magnetic). The found value has a power law dependence on the impurity
concentration $c$: $\r(0) \propto \r_N c^{n}$, where $\r_N$ is the normal metal
density of states and $n \geq 2$ is the least number of impurities in a complex
that can localize nodal quasiparticle. This result essentially differs from the
known predictions of self-consistent approximation: $\r(0) \propto \r_N \sqrt
{c/\r_N\D}$ (for the unitary limit $V \to \infty$) or $\r(0) \propto
(\D/cV^2)\exp(-\D/cV^2\r_N)$ (for the Born limit $|V|\r_N \ll 1$). We predict
also existence of a narrow interval (mobility gap) around the Fermi energy,
where all the states are localized on proper impurity clusters, leading to
exponential suppression of low-temperature kinetics.


  The paper presents a novel view of the Dempster-Shafer belief function as a
measure of diversity in relational data bases. It is demonstrated that under
the interpretation The Dempster rule of evidence combination corresponds to the
join operator of the relational database theory. This rough-set based
interpretation is qualitative in nature and can represent a number of belief
function operators.
  The interpretation has the property that Given a definition of the belief
measure of objects in the interpretation domain we can perform operations in
this domain and the measure of the resulting object is derivable from measures
of component objects via belief operator. We demonstrated this property for
Dempster rule of combination, marginalization, Shafer's conditioning,
independent variables, Shenoy's notion of conditional independence of
variables.
  The interpretation is based on rough sets (in connection with decision
tables), but differs from previous interpretations of this type in that it
counts the diversity rather than frequencies in a decision table.


  Construction and characterization of a multichannel photodiode detector based
on commercially available components with high signal to noise of $\sim10^{6}$
and a rapid frame rate, suitable for time resolved femtosecond spectroscopy
with high repetition femtosecond sources, is presented.


  We prove a local limit theorem for nearest neighbours random walks in
stationary random environment of conductances on Z without using any of both
classic assumptions of uniform ellipticity and independence on the
conductances. Besides the central limit theorem, we use discrete differential
"Nash-type inequalities" associated with the Hausdorff's representation of the
completely decreasing sequences.


  For the case of two particles a solution of the string field theory vertex
axioms can be factorized into a standard form factor and a kinematical piece
which includes the dependence on the size of the third string. In this paper we
construct an exact solution of the kinematical axioms for AdS5xS5 which
includes all order wrapping corrections w.r.t. the size of the third string.
This solution is expressed in terms of elliptic Gamma functions and ordinary
elliptic functions. The solution is valid at any coupling and we analyze its
weak coupling, pp-wave and large L limit.


  In an era characterized by rapid societal changes and complex challenges,
institutions' traditional methods of problem-solving in the public sector are
increasingly proving inadequate. In this study, we present an innovative and
effective model for how institutions can use artificial intelligence to enable
groups of people to generate effective solutions to urgent problems more
efficiently. We describe a proven collective intelligence method, called
Smarter Crowdsourcing, which is designed to channel the collective intelligence
of those with expertise about a problem into actionable solutions through
crowdsourcing. Then we introduce Policy Synth, an innovative toolkit which
leverages AI to make the Smarter Crowdsourcing problem-solving approach both
more scalable, more effective and more efficient. Policy Synth is crafted using
a human-centric approach, recognizing that AI is a tool to enhance human
intelligence and creativity, not replace it. Based on a real-world case study
comparing the results of expert crowdsourcing alone with expert sourcing
supported by Policy Synth AI agents, we conclude that Smarter Crowdsourcing
with Policy Synth presents an effective model for integrating the collective
wisdom of human experts and the computational power of AI to enhance and scale
up public problem-solving processes. While many existing approaches view AI as
a tool to make crowdsourcing and deliberative processes better and more
efficient, Policy Synth goes a step further, recognizing that AI can also be
used to synthesize the findings from engagements together with research to
develop evidence-based solutions and policies. The study offers practical tools
and insights for institutions looking to engage communities effectively in
addressing urgent societal challenges.


  In the spacetime of a $d$-dimensional static tense brane black hole we
elaborate the mechanism by which massive scalar fields decay. The metric of a
six-dimensional black hole pierced by a topological defect is especially
interesting. It corresponds to a black hole residing on a tensional 3-brane
embedded in a six-dimensional spacetime, and this solution has gained
importance due to the planned accelerator experiments. It happened that the
intermediate asymptotic behaviour of the fields in question was determined by
an oscillatory inverse power-law. We confirm our investigations by numerical
calculations for five- and six-dimensional cases. It turned out that the
greater the brane tension is, the faster massive scalar fields decay in the
considered spacetimes.


  We define locally trivial quantum vector bundles (QVB) and QVB associated to
locally trivial quantum principal fibre bundles. There exists a differential
structure on the associated vector bundle coming from the differential
structure on the principal bundle, which allows to define connections on the
associated vector bundle associated to connections on the principal bundle.


  In this article we define a class of fractional Orlicz-Sobolev spaces on
Carnot groups and, in the spirit of the celebrated results of
Bourgain-Brezis-Mironescu and of Maz'ya-Shaposhnikova, we study the asymptotic
behavior of the Orlicz functionals when the fractional parameter goes to $1$
and $0$.


  Suppose that $K$ is a field of characteristic 0, $K_a$ is its algebraic
closure, $p$ is a prime, $q=p^r$ is a power prime. Suppose that $f(x) \in K[x]$
is a polynomial of degree $n > 4$ without multiple roots. Let us consider the
superelliptic curve $C: y^q=f(x)$ and its jacobian $J(C)$. We study the
endomorphism algebra $End^0(J(C))$ of all $K_a$-endomorphisms of $J(C)$. We
prove that $End^0(J(C))$ is "as small as possible" if the Galois group of $f$
over $K$ is either the full symmetric group $S_n$ or the alternating group
$A_n$.


  For the first time, a direct detection BOTDR is demonstrated for distributed
dynamic strain sensing incorporating double-edge technique, time-division
multiplexing technique and upconversion technique. The double edges are
realized by using the transmission curve and reflection curve of an all-fiber
Fabry-Perot interferometer (FPI). Benefiting from the low loss of the fiber at,
the time-division multiplexing technique is performed to realize the
double-edge technique by using only a single-channel FPI and only one piece of
a detector. In order to detect the weak spontaneous Brillouin backscattering
signal efficiently, a fiber-coupled upconversion detector is adopted to
upconvert the backscattering signal at 1548.1 nm to 863 nm, which is detected
by a Si-APD finally. In the experiment, dynamic strain disturbance up to
1.9m{\epsilon} over 1.5 km of polarization maintaining fiber is detected at a
sampling rate of 30 Hz. An accuracy of 30{\mu}{\epsilon} and spatial resolution
of 0.6 m is realized.


  In a recent paper, Phys. Rev E 81, 041137 (2010), the author attempts to
derive ten necessary conditions for stability of dissipative fluids and
plasmas. Assuming the validity of the local equilibrium principle, these
criteria have been obtained solely from the first and the second laws of
thermodynamics. The Onsager reciprocity relations have not been invoked and
author's results are supposed to be valid independently of the choice of the
boundary conditions. In the present paper, in agreement with the general theory
established by Glansdorff-Prigogine in 1954 and 1970, we shall show that there
is no variational principle expressing the necessary conditions for stability
of dissipative systems involving convective effects when the system is out of
the Onsager region. In particular, we shall prove that the basic equations
constituting the starting point of the analysis of the author, attempting to
derive ten necessary conditions for the stability involving
magneto-hydrodynamical effects, are incorrect and in contradiction with the
laws of the thermodynamics of irreversible processes.


  In a backdoor attack, an adversary injects corrupted data into a model's
training dataset in order to gain control over its predictions on images with a
specific attacker-defined trigger. A typical corrupted training example
requires altering both the image, by applying the trigger, and the label.
Models trained on clean images, therefore, were considered safe from backdoor
attacks. However, in some common machine learning scenarios, the training
labels are provided by potentially malicious third-parties. This includes
crowd-sourced annotation and knowledge distillation. We, hence, investigate a
fundamental question: can we launch a successful backdoor attack by only
corrupting labels? We introduce a novel approach to design label-only backdoor
attacks, which we call FLIP, and demonstrate its strengths on three datasets
(CIFAR-10, CIFAR-100, and Tiny-ImageNet) and four architectures (ResNet-32,
ResNet-18, VGG-19, and Vision Transformer). With only 2% of CIFAR-10 labels
corrupted, FLIP achieves a near-perfect attack success rate of 99.4% while
suffering only a 1.8% drop in the clean test accuracy. Our approach builds upon
the recent advances in trajectory matching, originally introduced for dataset
distillation.


  This study numerically investigates the aerodynamic forces and flow-induced
noise generated by SAE-T4, Ahmed, and Hybrid forebody shapes with a squareback
vehicle configuration using SBES-FW-H. The results show significant differences
in lift coefficients and the presence of a horseshoe vortex at the mirror, with
smaller eddies that interact with A-pillar vortices, resulting in pronounced
pressure fluctuations and noise generation on the side window for the three
configurations. Surprisingly, negligible differences in aerodynamic drag and
radiated sound are predicted despite these effects.


  We examine the outer Galactic HI disk for deviations from the b=0 plane by
constructing maps of disk surface density, mean height, and thickness. We find
that the Galactic warp is well described by a vertical offset plus two Fourier
modes of frequency 1 and 2, all of which grow with Galactocentric radius. A
perturbation theory calculation demonstrates that the tidal influence of the
Magellanic Clouds creates distortions in the dark matter halo, which combine to
produce disk warp amplitudes comparable to the observations. We also construct
a detailed map of the perturbed surface density of HI in the outer disk
demonstrating that the Galaxy is a non-axisymmetric multi-armed spiral.
Overdensities in the surface density are coincident with regions of reduced gas
thickness.


  5D orbifold has two attractive features: quasi-localized matter fields can
naturally reproduce hierarchical Yukawa coupling, while supersymmetry breaking
is inherently built in by the Scherk-Schwarz mechanism. We examine the
consequence of this quasi-localization and the Scherk-Schwarz supersymmetry
breaking in low energy flavor violating processes, under the assumption that
physics below the compactification scale is described by the minimal
supersymmetric standard model (MSSM) and find $BR(\mu \to e,\gamma)$ and
$\epsilon_K$ impose stringent constraint on flavor structure of the
Scherk-Schwarz supersymmetry breaking. Chirality measurement in lepton flavor
violating processes is crucial to deduce the surviving flavor structure.


  We study the system of a dilute gas of fermions in 3-dimensions, with
attractive interactions tuned to the unitarity point, using the
non-perturbative Restricted Path Integral Monte Carlo (R-PIMC) method. The
pairing and superfluid properties of this system are calculated at finite
temperature. The total energy at very low temperature from our results agrees
closely with that of previous ground-state Quantum Monte Carlo calculations. We
identify the temperature $T^\ast\approx 0.70\epsilon_F$ below which pairing
correlations develop, and estimate the critical temperature for the superfluid
transition $T_c\approx 0.25\epsilon_F$ from a finite size scaling analysis of
the superfluid density.


  Omnivory is defined as feeding on more than one trophic level. An example of
this is the so-called intraguild predation (IG) which includes a predator and
its prey that share a common resource. IG predation models are known to exhibit
interesting dynamics including chaos. This work considers a three-species food
web model with omnivory, where the interactions between the basal resource, the
IG prey, and the IG predator are of Lotka-Volterra type. In the absence of
predation, the basal resource follows a delayed logistic equation or popularly
known as Hutchinson's equation. Conditions for the existence, stability, and
bifurcations of all non-negative equilibrium solutions are given using the
delay time as parameter. Results are illustrated using numerical bifurcation
analysis.


  The aim of this note is to show that Poincar\'e inequalities imply
corresponding weighted versions in a quite general setting. Fractional
Poincar\'e inequalities are considered, too. The proof is short and does not
involve covering arguments.


  Deep generative models produce data according to a learned representation,
e.g. diffusion models, through a process of approximation computing possible
samples. Approximation can be understood as reconstruction and the large
datasets used to train models as sets of records in which we represent the
physical world with some data structure (photographs, audio recordings,
manuscripts). During the process of reconstruction, e.g., image frames develop
each timestep towards a textual input description. While moving forward in
time, frame sets are shaped according to learned bias and their production, we
argue here, can be considered as going back in time; not by inspiration on the
backward diffusion process but acknowledging culture is specifically marked in
the records. Futures of generative modelling, namely in film and audiovisual
arts, can benefit by dealing with diffusion systems as a process to compute the
future by inevitably being tied to the past, if acknowledging the records as to
capture fields of view at a specific time, and to correlate with our own finite
memory ideals. Models generating new data distributions can target video
production as signal processors and by developing sequences through timelines
we ourselves also go back to decade-old algorithmic and multi-track
methodologies revealing the actual predictive failure of contemporary
approaches to synthesis in moving image, both as relevant to composition and
not explanatory.


  Automated test execution scheduling is crucial in modern software development
environments, where components are frequently updated with changes that impact
their integration with hardware systems. Building test schedules, which focus
on the right tests and make optimal use of the available resources, both time
and hardware, under consideration of vast requirements on the selection of test
cases and their assignment to certain test execution machines, is a complex
optimization task. Manual solutions are time-consuming and often error-prone.
Furthermore, when software and hardware components and test scripts are
frequently added, removed or updated, static test execution scheduling is no
longer feasible and the motivation for automation taking care of dynamic
changes grows. Since 2012, our work has focused on transferring technology
based on constraint programming for automating the testing of industrial
robotic systems at ABB Robotics. After having successfully transferred
constraint satisfaction models dedicated to test case generation, we present
the results of a project called DynTest whose goal is to automate the
scheduling of test execution from a large test repository, on distinct
industrial robots. This paper reports on our experience and lessons learned for
successfully transferring constraint-based optimization models for test
execution scheduling at ABB Robotics. Our experience underlines the benefits of
a close collaboration between industry and academia for both parties.


  We present CosmoGridV1: a large set of lightcone simulations for map-level
cosmological inference with probes of large scale structure. It is designed for
cosmological parameter measurement based on Stage-III photometric surveys with
non-Gaussian statistics and machine learning. CosmoGridV1 spans the $w$CDM
model by varying $\Omega_m$, $\sigma_8$, $w_0$, $H_0$, $n_s$, $\Omega_b$, and
assumes three degenerate neutrinos with $\sum m_\nu$ = 0.06 eV. This space is
covered by 2500 grid points on a Sobol sequence. At each grid point, we run 7
simulations with PkdGrav3 and store 69 particle maps at nside=2048 up to
$z$=3.5, as well as halo catalog snapshots. The fiducial cosmology has 200
independent simulations, along with their stencil derivatives. An important
part of CosmoGridV1 is the benchmark set of 28 simulations, which include
larger boxes, higher particle counts, and higher redshift resolution of shells.
They allow for testing if new types of analyses are sensitive to choices made
in CosmoGridV1. We add baryon feedback effects on the map level, using
shell-based baryon correction model. The shells are used to create maps of weak
gravitational lensing, intrinsic alignment, and galaxy clustering, using the
UFalcon code. The main part of CosmoGridV1 are the raw particle count shells
that can be used to create full-sky maps for a given $n(z)$. We also release
projected maps for a Stage-III forecast, as well as maps used previously in
KiDS-1000 deep learning constraints with CosmoGridV1. The data is available at
www.cosmogrid.ai.


  We present parallel and sequential dense QR factorization algorithms that are
both optimal (up to polylogarithmic factors) in the amount of communication
they perform, and just as stable as Householder QR.
  We prove optimality by extending known lower bounds on communication
bandwidth for sequential and parallel matrix multiplication to provide latency
lower bounds, and show these bounds apply to the LU and QR decompositions. We
not only show that our QR algorithms attain these lower bounds (up to
polylogarithmic factors), but that existing LAPACK and ScaLAPACK algorithms
perform asymptotically more communication. We also point out recent LU
algorithms in the literature that attain at least some of these lower bounds.


  The Drinfeld twist for the opposite quasi-Hopf algebra is determined and is
shown to be related to the (second) Drinfeld twist. The twisted Drinfeld twist
is investigated. In the quasi-triangular case it is shown that the Drinfeld u
operator arises from the equivalence of the opposite quasi-Hopf algebra to the
quasi-Hopf algebra induced by twisting with the R-matrix. The Altschuler-Coste
u operator arises in a similar way and is shown to be closely related to the
Drinfeld u operator. The quasi-cocycle condition is introduced, and is shown to
play a central role in the uniqueness of twisted structures on quasi-Hopf
algebras. A generalisation of the dynamical quantum Yang-Baxter equation,
called the quasi-dynamical quantum Yang-Baxter equation is introduced.


  We investigate the evolution of rigidly and differentially rotating
protoneutron stars (PNSs) during the first twenty seconds of their life. We
solve the equations describing stationary axisymmetric configurations in
general relativity coupled to a finite temperature, relativistic equation of
state, to obtain a sequence of quasi-equilibrium configurations describing the
evolution of newly born neutron stars. Our estimates show that the scale of
variation of the angular velocity in a PNSs is of the order of 7-10 km. We
obtain the maximum rotation frequency that can be reached as the protoneutron
stars deleptonizes and cools down, as well as other relevant parameters such as
total angular momentum or |T/W|. Our study shows that imposing physical
constraints (conservation of baryonic mass and angular momentum) and choosing
reasonable thermodynamical profiles as the star evolves gives results
consistent with the energetics of more complex simulations of non-rotating
PNSs. It appears to be unlikely that PNSs formed in nearly axisymmetric core
collapse reach the critical angular velocity to undergo the bar mode
instability. They could, however, undergo secular or low |T/W| rotational
instabilities a few seconds after birth, resulting in a strong emission of
gravitational waves retarded with respect to the neutrino luminosity peak. We
also found that the geometry of strongly differentially rotating protoneutron
stars can become toroidal-like for large values of the angular velocity, before
reaching the mass shedding limit.


  We analyze heating and cooling processes in an idealized simulation of a
cool-core cluster, where momentum-driven AGN feedback balances radiative
cooling in a time-averaged sense. We find that, on average, energy dissipation
via shock waves is almost an order of magnitude higher than via turbulence.
Most of the shock waves in the simulation are very weak shocks with Mach
numbers smaller than 1.5, but the stronger shocks, although rare, dissipate
energy more effectively. We find that shock dissipation is a steep function of
radius, with most of the energy dissipated within 30 kpc, while radiative
cooling loses area less concentrated. However, adiabatic processes and mixing
(of post-shock materials and the surrounding gas) are able to redistribute the
heat throughout the core. A considerable fraction of the AGN energy also
escapes the core region. The cluster goes through cycles of AGN outbursts
accompanied by periods of enhanced precipitation and star formation, over Gyr
timescales. The cluster core is under-heated at the end of each cycle, but
over-heated at the peak of the AGN outburst. During the heating-dominant phase,
turbulent dissipation alone is often able to balance radiative cooling at every
radius but, when this is occurs, shock waves inevitably dissipate even more
energy. Our simulation explains why some clusters, such as Abell 2029, are
cooling dominated, while in some other clusters, such as Perseus, various
heating mechanisms including shock heating, turbulent dissipation and bubble
mixing can all individually balance cooling, and together, overheat the core.


  We report an angle-resolved photoemission spectroscopy investigation of the
Fermi surface and electronic band structure of BaCo$_{2}$As$_2$. Although its
quasi-nesting-free Fermi surface differs drastically from that of its
Fe-pnictide cousins, we show that the BaCo$_{2}$As$_2$ system can be used as an
approximation to the bare unoccupied band structure of the related
BaFe$_{2-x}$Co$_x$As$_2$ and Ba$_{1-x}$K$_x$Fe$_2$As$_2$ compounds. However,
our experimental results, in agreement with dynamical mean field theory
calculations, indicate that electronic correlations are much less important in
BaCo$_{2}$As$_2$ than in the ferropnictides. Our findings suggest that this
effect is due to the increased filling of the electronic 3$d$ shell in the
presence of significant Hund's exchange coupling.


  We reveal the presence of a new source of axial current due to the condensed
vacuum generated
  by the spin-spin interaction. To show this, we consider a quartic Dirac
Lagrangian containing a
  spin-spin interaction term, possibly originating from torsion in
Einstein-Cartan-like theories. We
  use a mean field approach to analyze the quantized theory. We show that the
diagonalization of the
  field Hamiltonian defines a new vacuum state, energetically favored with
respect to the free vacuum.
  Such a vacuum, which is a condensate of particle-antiparticle pairs, is
characterized by a nontrivial
  expectation value of the axial current operator. The new source of axial
current, here obtained, can
  have effects both at the atomic level and at the astrophysical-cosmological
level depending on the
  origin of the spin-spin interaction term. The condensate may affect the dark
sector of the universe
  at cosmological level and the axial current originated by the vacuum
condensate could be analyzed
  in next table top experiments on graphene


  With the increasing presence of robots in our every-day environments,
improving their social skills is of utmost importance. Nonetheless, social
robotics still faces many challenges. One bottleneck is that robotic behaviors
need to be often adapted as social norms depend strongly on the environment.
For example, a robot should navigate more carefully around patients in a
hospital compared to workers in an office. In this work, we investigate
meta-reinforcement learning (meta-RL) as a potential solution. Here, robot
behaviors are learned via reinforcement learning where a reward function needs
to be chosen so that the robot learns an appropriate behavior for a given
environment. We propose to use a variational meta-RL procedure that quickly
adapts the robots' behavior to new reward functions. As a result, given a new
environment different reward functions can be quickly evaluated and an
appropriate one selected. The procedure learns a vectorized representation for
reward functions and a meta-policy that can be conditioned on such a
representation. Given observations from a new reward function, the procedure
identifies its representation and conditions the meta-policy to it. While
investigating the procedures' capabilities, we realized that it suffers from
posterior collapse where only a subset of the dimensions in the representation
encode useful information resulting in a reduced performance. Our second
contribution, a radial basis function (RBF) layer, partially mitigates this
negative effect. The RBF layer lifts the representation to a higher dimensional
space, which is more easily exploitable for the meta-policy. We demonstrate the
interest of the RBF layer and the usage of meta-RL for social robotics on four
robotic simulation tasks.


  The Budd-Vannimenus theorem is modified to the surface of superconductors in
the Meissner state. This identity links the surface value of the electrostatic
potential to the free energy in the bulk which allows one to evaluate the
observed potential without the explicit solution of the charge profile at the
surface.


  Half-metallic ferromagnetic (HMF) materials show high spin polarization and
are therefore interesting to researchers due to their possible applications in
spintronic devices. In these materials, while one spin sub band has a finite
density of states at the Fermi level, the other sub band has a gap. Because of
their high Curie temperature and tunable electronic structure, HMF Heusler
alloys have a special importance among the HMF materials. Full Heusler alloys
with the stoichiometric composition X2YZ (where X and Y are the transition
metals and Z is a sp element) have the cubic structure with four
interpenetrating fcc sublattices. When each of these four fcc sublattices is
occupied by different atoms, a quaternary Heusler structure with different
structural symmetry is obtained. Recently, these equiatomic quaternary Heusler
alloys (EQHAs) with 1:1:1:1 stoichiometry have attracted a lot of attention due
to their superior magnetic and transport properties.


  We argue that the order of the chiral transition for N_f=2 is a sensitive
probe of the QCD vacuum, in particular of the mechanism of color confinement. A
strategy is developed to investigate the order of the transition by use of
finite size scaling analysis. An in-depth numerical investigation is performed
with staggered fermions on lattices with N_t=4 and N_s=12,16,20,24,32 and quark
masses am_q ranging from 0.01335 to 0.307036. The specific heat and a number of
susceptibilities are measured and compared with the expectations of an O(4)
second order and of a first order phase transition. A second order transition
in the O(4) and O(2) universality classes are excluded. Substantial evidence
emerges for a first order transition. A detailed comparison with previous works
is performed.


  In the present paper, we extend the Froissaron-Maximal Odderon (FMO) approach
at $t$ different from 0. Our extended FMO approach gives an excellent
description of the 3266 experimental points considered in a wide range of
energies and momentum transferred. We show that the very interesting TOTEM
results for proton-proton differential cross-section in the range 2.76-13 TeV,
together with the Tevatron data for antiproton-proton at 1.8 and 1.96 TeV give
further experimental evidence for the existence of the Odderon. One spectacular
theoretical result is the fact that the difference in the dip-bump region
between $\bar pp$ and $pp$ differential cross-sections is diminishing with
increasing energies and for very high energies (say 100 TeV), the difference
between $\bar pp$ and $pp$ in the dip-bump region is changing its sign: $pp$
becomes bigger than $\bar pp$ at $|t|$ about 1 GeV$^2$. This is a typical
Odderon effect. Another important - phenomenological - result of our approach
is that the slope in $pp$ scattering has different behavior in $t$ than the
slope in $\bar pp$ scattering. This is also a clear Odderon effect.


  In the present paper, bilinear pseudo-differential operators with symbols in
the bilinear H\"ormander class $BS^m_{0,0}$ are considered. In particular, the
boundedness of these operators on Sobolev spaces is established. Our main
result is proved by using symbolic calculus and the boundedness of those
operators with certain $S_{0,0}$-type symbols on Lebesgue spaces.


  We introduce a novel search technique that can identify trans-neptunian
objects in three to five exposures of a pointing within a single Hubble Space
Telescope orbit. The process is fast enough to allow the discovery of
candidates soon after the data are available. This allows sufficient time to
schedule follow up observations with HST within a month. We report the
discovery of 14 slow-moving objects found within 5\circ of the ecliptic in
archival data taken with the Wide Field Channel of the Advanced Camera for
Surveys. The luminosity function of these objects is consistent with previous
ground-based and space-based results. We show evidence that the size
distribution of both high and low inclination populations is similar for
objects smaller than 100 km, as expected from collisional evolution models,
while their size distribution differ for brighter objects. We suggest the two
populations formed in different parts of the protoplanetary disk and after
being dynamically mixed have collisionally evolved together. Among the objects
discovered there is an equal mass binary with an angular separation ~ 0."53.


  This paper explores the effects of numerical algorithms on global
magnetohydrodynamics (MHD) simulations of solar wind (SW) in the inner
heliosphere. To do so, we use sunRunner3D, a 3-D MHD model that employs the
boundary conditions generated by CORHEL and the PLUTO code to compute the
plasma properties of the SW with the ideal MHD approximation up to 1.1 AU in
the inner heliosphere. Mainly, we define three different combinations of
numerical algorithms based on their diffusion level. This diffusion level is
related to the way of solving the MHD equations using the finite volume
formulation, and, therefore, we set in terms of the divergence-free condition
methods, Riemann solvers, variable reconstruction schemes, limiters, and
time-steeping algorithms. According to the simulation results, we demonstrate
that sunRunner3D reproduces global features of Corotating Interaction Regions
(CIRs) observed by Earth-based spacecraft (OMNI) for a set of Carrington
rotations that cover a period that lays in the late declining phase of solar
cycle 24, independently of the numerical algorithms. Moreover, statistical
analyses between models and in-situ measurements show reasonable agreement with
the observations, and remarkably, the high diffusive method matches better with
in-situ data than low diffusive methods.


  Exact simulations of quantum circuits (QCs) are currently limited to $\sim$50
qubits because the memory and computational cost required to store the QC wave
function scale exponentially with qubit number. Therefore, developing efficient
schemes for approximate QC simulations is a current research focus. Here we
show simulations of QCs with a method inspired by density functional theory
(DFT), a widely used approach to study many-electron systems. Our calculations
can predict marginal single-qubit probabilities (SQPs) with over 90% accuracy
in several classes of QCs with universal gate sets, using memory and
computational resources linear in qubit number despite the formal exponential
cost of the SQPs. This is achieved by developing a mean-field description of
QCs and formulating optimal single- and two-qubit gate functionals $-$ analogs
of exchange-correlation functionals in DFT $-$ to evolve the SQPs without
computing the QC wave function. Current limitations and future extensions of
this formalism are discussed.


  Deep Neural Network(DNN) techniques have been prevalent in software
engineering. They are employed to faciliatate various software engineering
tasks and embedded into many software applications. However, analyzing and
understanding their behaviors is a difficult task for software engineers. In
this paper, to support software engineers in visualizing and interpreting deep
learning models, we present NeuralVis, an instance-based visualization tool for
DNN. NeuralVis is designed for: 1). visualizing the structure of DNN models,
i.e., components, layers, as well as connections; 2). visualizing the data
transformation process; 3). integrating existing adversarial attack algorithms
for test input generation; 4). comparing intermediate outputs of different
instances to guide the test input generation; To demonstrate the effectiveness
of NeuralVis, we conduct an user study involving ten participants on two
classic DNN models, i.e., LeNet and VGG-12. The result shows NeuralVis can
assist developers in identifying the critical features that determines the
prediction results. Video: https://youtu.be/hRxCovrOZFI


  We investigate the lepton flavor violation decays, $\tau \to \mu\gamma$,
$\tau \to e\gamma$ and $\mu \to e\gamma$, in the framwork of a squential fourth
generation model with a heavy fourth neutrino, $\nu'$. Using the recent
experimental bounds, we can obtain the constraints of the $4\times 4$ leptonic
mixing matrix element factors, $V_{1\nu'},V_{2\nu'}$ and $V_{3\nu'}$. We find
that LFV decays can give strigent costraints on the parameter space of the 4th
neutrino mass $m_{\nu'}$.


  MAC Net is a compositional attention network designed for Visual Question
Answering. We propose a modified MAC net architecture for Natural Language
Question Answering. Question Answering typically requires Language
Understanding and multi-step Reasoning. MAC net's unique architecture - the
separation between memory and control, facilitates data-driven iterative
reasoning. This makes it an ideal candidate for solving tasks that involve
logical reasoning. Our experiments with 20 bAbI tasks demonstrate the value of
MAC net as a data-efficient and interpretable architecture for Natural Language
Question Answering. The transparent nature of MAC net provides a highly
granular view of the reasoning steps taken by the network in answering a query.


  When the initial and transition probabilities of a finite Markov chain in
discrete time are not well known, we should perform a sensitivity analysis.
This can be done by considering as basic uncertainty models the so-called
credal sets that these probabilities are known or believed to belong to, and by
allowing the probabilities to vary over such sets. This leads to the definition
of an imprecise Markov chain. We show that the time evolution of such a system
can be studied very efficiently using so-called lower and upper expectations,
which are equivalent mathematical representations of credal sets. We also study
how the inferred credal set about the state at time n evolves as n goes to
infinity: under quite unrestrictive conditions, it converges to a uniquely
invariant credal set, regardless of the credal set given for the initial state.
This leads to a non-trivial generalisation of the classical Perron-Frobenius
Theorem to imprecise Markov chains.