  Reconstructed jets initiated from heavy quarks provide a powerful tool to
probe the properties of the quark-gluon plasma (QGP) and to explore the mass
hierarchy of jet quenching. In this article, we review the recent theoretical
progresses on heavy-flavour jets in high-energy nuclear collisions at the RHIC
and LHC. We focus on the yields and substructures of charm and bottom quark
jets with jet quenching effect, such as the nuclear modification factors,
transverse momentum imbalance, angular correlation, radial profiles,
fragmentation functions, the "dead-cone" effect, etc.


  In this paper we present in detail Newton's method and its modification,
based on the Continuous analog of Newton's method for computing periodic orbits
of the planar three-body problem. The linear system at each step of the method
is formed by solving a system of ODEs with the multiple precision Taylor series
method. We consider zero angular momentum symmetric initial configuration with
parallel velocities, bodies with equal masses and relatively short periods.
Taking candidates for the correction method with greater return proximity as
usual and correcting with the modified Newton's method, allows us to find some
new topological families that are not included in the database in [SCIENCE
CHINA Physics, Mechanics & Astronomy 60.12 (2017)]


  We study the structure of the manifold of solitary waves in a particular
three-component scalar field theoretical model in two-dimensional Minkowski
space. These solitary waves involve one, two, three, four, six or seven lumps
of energy.


  Cold gas forms a significant mass fraction of the Milky Way disk, but is its
most uncertain baryonic component. The density and distribution of cold gas is
of critical importance for Milky Way dynamics, as well as models of stellar and
galactic evolution. Previous studies have used correlations between gas and
dust to obtain high-resolution measurements of cold gas, but with large
normalization uncertainties. We present a novel approach that uses Fermi-LAT
$\gamma$-ray data to measure the total gas density, achieving a similar
precision as previous works, but with independent systematic uncertainties.
Notably, our results have sufficient precision to probe the range of results
obtained by current world-leading experiments.


  Discrete far-infrared (FIR) sources of M31 are identified in the ISO 175um
map and characterized via their FIR colours, luminosities and masses. With a
mean size of 800pc, they probably represent several clouds in chance projection
or giant cloud complexes. The 175um data point provides crucial information in
addition to the IRAS 60 and 100um data: At least two modified Planck curves
with temperatures of about 40K and 15-21K are necessary to fit the SEDs of the
knots. We distinguish between three types of knots - cold, medium, warm - in
order to recognize trends. Comparisons with radio and optical tracers show that
- statistically - the cold knots can be identified well with CO and HI radio
sources and thus might represent mainly molecular cloud complexes. The warm
knots coincide with known HII regions and supernova remnants. The medium knots
might contain a balanced mixture of molecular clouds and HII regions. The cold
knots have a considerable luminosity and their discovery raises the question of
hidden star formation. Though the optically dark dust lanes in M31 generally
match the FIR ring, surprisingly we do not find a convincing coincidence of our
knots with individual dark clouds which might therefore show mainly foreground
dust features. The ratio of FIR luminosity to dust mass, L/M, is used to
measure the energy content of the dust. The knots have a clear L/M excess over
the rest of M31, providing evidence that they are powered by star formation in
addition to the interstellar radiation field. The L/M ratio of the warm knots
is comparable to that of Galactic HII regions like M42 or NGC2024, while that
of the cold knots still reaches values like in the average Orion complex. Thus
both the warm and the cold knots are interpreted as containing large cloud
complexes with considerable ongoing star formation.


  Modern telecom systems are monitored with performance and system logs from
multiple application layers and components. Detecting anomalous events from
these logs is key to identify security breaches, resource over-utilization,
critical/fatal errors, etc. Current supervised log anomaly detection frameworks
tend to perform poorly on new types or signatures of anomalies with few or
unseen samples in the training data. In this work, we propose a
meta-learning-based log anomaly detection framework (LogAnMeta) for detecting
anomalies from sequence of log events with few samples. LoganMeta train a
hybrid few-shot classifier in an episodic manner. The experimental results
demonstrate the efficacy of our proposed method


  Since its discovery quantum teleportation has often been seen as a
manifestation, indeed the epitome, of the very paradoxical and mysterious
nature of quantum theory itself. It is commonly regarded as genuinely quantum
and essentially paradoxical. Although a common approach to teleportation
amongst physicists nowadays is a somewhat operational one, some researchers are
making an effort to deflate the above views.
  On the one hand, it was recently argued that the paradox of information
transfer taking place in teleportation is dissolved (Timpson, 2006) by
appealing the very notion of information. On the other hand, it was
demonstrated that some classical versions of teleportation retain its important
features, which hitherto were considered genuinely quantum (Cohen, 2003;
Collins&Popescu, 2002; Hardy, 1999; Mor, 2006; Spekkens, 2007).
  I will present a special version of a quantum teleportation protocol which is
in a sense split into classical and quantum steps. This description provides us
with a unified picture of teleportation in both domains. It will be explicitly
shown how classical teleportation is embedded in the quantum protocol.
Moreover, the classical step can be successfully accomplished even if the state
shared by the parties is completely disentangled [this is consistent with the
result obtained in (Wang, 2005)]. Yet, all the (apparent) paradoxical features
usually associated with quantum teleportation are clearly present in this step.
In particular, this demonstrates that entanglement cannot be ultimately
responsible and not necessary for the (paradoxical?) information transfer.
  Thus, even if one considers teleportation as mysterious, all its mysteries
are shifted from quantum domain into purely classical one.


  Designing robust algorithms capable of training accurate neural networks on
uncurated datasets from the web has been the subject of much research as it
reduces the need for time consuming human labor. The focus of many previous
research contributions has been on the detection of different types of label
noise; however, this paper proposes to improve the correction accuracy of noisy
samples once they have been detected. In many state-of-the-art contributions, a
two phase approach is adopted where the noisy samples are detected before
guessing a corrected pseudo-label in a semi-supervised fashion. The guessed
pseudo-labels are then used in the supervised objective without ensuring that
the label guess is likely to be correct. This can lead to confirmation bias,
which reduces the noise robustness. Here we propose the pseudo-loss, a simple
metric that we find to be strongly correlated with pseudo-label correctness on
noisy samples. Using the pseudo-loss, we dynamically down weight
under-confident pseudo-labels throughout training to avoid confirmation bias
and improve the network accuracy. We additionally propose to use a confidence
guided contrastive objective that learns robust representation on an
interpolated objective between class bound (supervised) for confidently
corrected samples and unsupervised representation for under-confident label
corrections. Experiments demonstrate the state-of-the-art performance of our
Pseudo-Loss Selection (PLS) algorithm on a variety of benchmark datasets
including curated data synthetically corrupted with in-distribution and
out-of-distribution noise, and two real world web noise datasets. Our
experiments are fully reproducible github.com/PaulAlbert31/SNCF


  We construct a neutrino mass model based on the flavour symmetry group
$A_4\times C_4 \times C_6 \times C_2$ which accommodates a light sterile
neutrino in the minimal extended seesaw (MES) scheme. Besides the flavour
symmetry, we introduce a $U(1)$ gauge symmetry in the sterile sector and also
impose CP symmetry. The vacuum alignments of the scalar fields in the model
spontaneously break these symmetries and lead to the construction of the
fermion mass matrices. With the help of the MES formulas, we extract the light
neutrino masses and the mixing observables. In the active neutrino sector, we
obtain the $\text{TM}_2$ mixing pattern with non-zero reactor angle and broken
$\mu$-$\tau$ reflection symmetry. We express all the active and the sterile
oscillation observables in terms of only four real model parameters. Using this
highly constrained scenario we predict $\sin^2 \theta_{23}
=0.545^{+0.003}_{-0.004}$, $\sin \delta = -0.911^{+0.006}_{-0.005}$,
$|U_{e4}|^2 = 0.029^{+0.009}_{-0.008}$, $|U_{\mu4}|^2 =
0.010^{+0.003}_{-0.003}$ and $|U_{\tau4}|^2 = 0.006^{+0.002}_{-0.002}$ which
are consistent with the current data.


  The XA region of the Cygnus Loop is a bright knot of X-ray emission on the
eastern edge of the supernova remnant resulting from the interaction of the
supernova blast wave with density enhancements at the edge of a precursor
formed cavity. To study the nature and origin of the X-ray emission we use high
spatial resolution images from Chandra. Our goal is to probe the density of
various spectral extraction regions to form a picture of the cavity wall and
characterize the interaction between this supernova and the local interstellar
medium. We find that a series of regions along the edge of the X-ray emission
appears to trace out the location of the cavity wall. The best fit plasma
models result in two temperature component equilibrium models for each region.
The low temperature components have densities that are an order of magnitude
higher than the high temperature components. The high density plasma may exist
in the cavity wall where it equilibrates rapidly and cools efficiently. The low
density plasma is interior to the enhancement and heated further by a reverse
shock from the wall. Calculations of shock velocities and timescales since
shock heating are consistent with this interpretation. Furthermore, we find a
bright knot of emission indicative of a discrete interaction of the blast wave
with a high density cloud in the cavity wall with a size scale ~0.1 pc. Aside
from this, other extractions made interior to the X-ray edge are confused by
line of sight projection of various components. Some of these regions show
evidence of detecting the cavity wall but their location makes the
interpretation difficult. In general, the softer plasmas are well fit at
temperatures kT~0.11 keV, with harder plasmas at temperatures of kT~0.27 keV.
All regions display consistent metal depletions most notably in N, O, and Ne at
an average of 0.54, 0.55, and 0.36 times solar.


  Zeroth-order optimization (ZO) algorithms have been recently used to solve
black-box or simulation-based learning and control problems, where the gradient
of the objective function cannot be easily computed but can be approximated
using the objective function values. Many existing ZO algorithms adopt
two-point feedback schemes due to their fast convergence rate compared to
one-point feedback schemes. However, two-point schemes require two evaluations
of the objective function at each iteration, which can be impractical in
applications where the data are not all available a priori, e.g., in online
optimization. In this paper, we propose a novel one-point feedback scheme that
queries the function value once at each iteration and estimates the gradient
using the residual between two consecutive points. When optimizing a
deterministic Lipschitz function, we show that the query complexity of ZO with
the proposed one-point residual feedback matches that of ZO with the existing
two-point schemes. Moreover, the query complexity of the proposed algorithm can
be improved when the objective function has Lipschitz gradient. Then, for
stochastic bandit optimization problems where only noisy objective function
values are given, we show that ZO with one-point residual feedback achieves the
same convergence rate as that of two-point scheme with uncontrollable data
samples. We demonstrate the effectiveness of the proposed one-point residual
feedback via extensive numerical experiments.


  In this paper we present an equivalent form of Granger noncausality in state
space representation. As a generalization of this result, necessary and
sufficient conditions are provided for an output process to admit a particular
minimal coordinated state space representation.


  Piebaldism usually manifests as white areas of fur, hair or skin due to the
absence of pigment-producing cells in those regions. The distribution of the
white and colored zones does not follow the classical Turing patterns. Here we
present a modeling framework for pattern formation that enables to easily
modify the relationship between three factors with different feedback
mechanisms. These factors consist of two diffusing factors and a
cell-autonomous immobile transcription factor. Globally the model allowed to
distinguishing four different situations. Two situations result in the
production of classical Turing patterns; regularly spaced spots and labyrinth
patterns. Moreover, an initial slope in the activation of the transcription
factor produces straight lines. The third situation does not lead to patterns,
but results in different homogeneous color tones. Finally, the fourth one sheds
new light on the possible mechanisms leading to the formation of piebald
patterns exemplified by the random patterns on the fur of some cow strains and
Dalmatian dogs. We demonstrate that these piebald patterns are of transient
nature, develop from random initial conditions and rely on a system's
bi-stability. The main novelty lies in our finding that the presence of a
cell-autonomous factor not only expands the range of reaction diffusion
parameters in which a pattern may arise, but also extends the pattern-forming
abilities of the reaction-diffusion equations.


  We use elementary variational arguments to prove, and improve on, gap
estimates which arise in simulating quantum circuits by adiabatic evolution.


  A Data Access Protocol has been set up to search and retrieve Planetary
Science data in general. This protocol will allow the user to select a subset
of data from an archive in a standard way, based on the IVOA Table Access
Protocol (TAP). The TAP mechanism is completed by an underlying Data Model and
reference dictionaries. This paper describes the principle of the EPN- TAP
protocol and interfaces, underlines the choices that have been made, and
discusses possible evolutions.


  Data mesh is an emerging decentralized approach to managing and generating
value from analytical enterprise data at scale. It shifts the ownership of the
data to the business domains closest to the data, promotes sharing and managing
data as autonomous products, and uses a federated and automated data governance
model. The data mesh relies on a managed data platform that offers services to
domain and governance teams to build, share, and manage data products
efficiently. However, designing and implementing a self-serve data platform is
challenging, and the platform engineers and architects must understand and
choose the appropriate design options to ensure the platform will enhance the
experience of domain and governance teams. For these reasons, this paper
proposes a catalog of architectural design decisions and their corresponding
decision options by systematically reviewing 43 industrial gray literature
articles on self-serve data platforms in data mesh. Moreover, we used
semi-structured interviews with six data engineering experts with data mesh
experience to validate, refine, and extend the findings from the literature.
Such a catalog of design decisions and options drawn from the state of practice
shall aid practitioners in building data meshes while providing a baseline for
further research on data mesh architectures.


  We consider resonances associated to the operator $-\frac{d^2}{dx^2}+V(x)$,
where $V(x)=V_+$ if $x>x_M$ and $V(x)=V_-$ if $x<-x_M$, with $V_+\not = V_-$.
We obtain asymptotics of the resonance-counting function in several regions.
Moreover, we show that in several situations, the resonances, $V_+$, and $V_-$
determine $V$ uniquely up to translation.


  Structure formation in the universe can produce high energy gamma-rays from
shock-accelerated electrons, and this process may be the origin of the
extragalactic gamma-ray background (EGRB) as well as a part of the unidentified
sources detected by EGRET in the GeV band, if about 5% of the kinetic energy of
the shock is going into electron acceleration. However, we point out that the
production of gamma-rays may be severely suppressed if the collapsing matter
has been preheated by external entropy sources at the time of gravitational
collapse, as can be inferred from the luminosity-temperature (LT) relation of
galaxy clusters and groups. We also make a rough estimate of this effect by a
simple model, showing that the EGRB flux may be suppressed by a factor of about
30. Hence structure formation is difficult to be the dominant origin of EGRB if
preheating is actually responsible for the observed anomary in the LT relation.
The detectable number of gamma-ray clusters is also reduced, but about 5-10
forming clusters should still be detectable by EGRET all sky, and this number
is similar to that of the steady and high-latitude unidentified sources in the
EGRET catalog. The future GLAST mission should detect 10^2-10^3 gamma-ray
clusters of galaxies even if the intergalactic medium has been preheated.


  The splitting of photons in the presence of an intense magnetic field has
recently found astrophysical applications in polar cap models of gamma-ray
pulsars and in magnetar scenarios for soft gamma repeaters. Numerical
computation of the polarization-dependent rates of this third order QED process
for arbitrary field strengths and energies below pair creation threshold is
difficult: thus early analyses focused on analytic developments and simpler
asymptotic forms. The recent astrophysical interest spurred the use of the
S-matrix approach by Mentzel, Berg and Wunner to determine splitting rates. In
this paper, we present numerical computations of a full proper-time expression
for the rate of splitting that was obtained by Stoneham, and is exact up to the
pair creation threshold. While the numerical results derived here are in accord
with the earlier asymptotic forms due to Adler, our computed rates still differ
by as much as factors of 3 from the S-matrix re-evaluation of Wilke and Wunner,
reflecting the extreme difficulty of generating accurate S-matrix numerics for
fields below about \teq{4.4\times 10^{13}}Gauss. We find that our proper-time
rates appear very accurate, and exceed Adler's asymptotic specializations
significantly only for photon energies just below pair threshold and for
supercritical fields, but always by less than a factor of around 2.6. We also
provide a useful analytic series expansion for the scattering amplitude valid
at low energies.


  Models of discrete space and space-time that exhibit continuum-like behavior
at large lengths could have profound implications for physics. They may tame
the infinities that arise from quantizing gravity, and dispense with the
machinery of the real numbers, which has no direct observational support. Yet
despite sophisticated attempts at formulating discrete space, researchers have
failed to construct even the simplest geometries. We investigate graphs as the
most elementary discrete models of two-dimensional space. We show that if space
is discrete, it must be disordered, by proving that all planar lattice graphs
exhibit the same taxicab metric as square grids. We give an explicit recipe for
growing disordered discrete space by sampling a Boltzmann distribution of
graphs at low temperature. We then propose three conditions which any discrete
model of Euclidean space must meet: have a Hausdorff dimension of two, support
unique straight lines and obey Pythagoras' theorem. Our model satisfies all
three, making it the first discrete model in which continuum-like behavior is
recovered at large lengths.


  In this study, beginning with the 4D asymptotically flat Kerr black hole
solution in the Boyer-Lindquist (BL) coordinate system, then by using the
technique of frame-dragging and some coordinate transformation to incorporate
the linear-order supertranslation correction, we finally obtain a 4D
asymptotically flat black hole solution with an arbitrary $a$ in the
linear-order supertranslated BL coordinate system. We can confirm that this
satisfies the Einstein equation. At the end, we calculate the angular momentum
of the spacetime we obtained in this study including the linear-order
correction. Since the supertranslation is a type of the general coordinate
transformation, which is usually defined by linear-order, the corrections we
incorporate in this study is sufficient, considering the fact that $a$ is
arbitrary. This work is interesting as a generalization of the supertranslated
spacetime to the rotating system.


  Gamma-ray bursts (GRBs) have puzzled astronomers since their accidental
discovery in the late sixties. The BATSE detector on the COMPTON-GRO satellite
has been detecting one burst per day for the last six years. Its findings have
revolutionized our ideas about the nature of these objects. They have shown
that GRBs are at cosmological distances. This idea was accepted with
difficulties at first. The recent discovery of an X-ray afterglow by the
Italian/Dutch satellite BeppoSAX has led to a detection of high red-shift
absorption lines in the optical afterglow of GRB970508 and in several other
bursts and to the identification of host galaxies to others. This has confirmed
the cosmological origin. Cosmological GRBs release $\sim 10^{51}-10^{53}$ergs
in a few seconds making them the most (electromagnetically) luminous objects in
the Universe. The simplest, most conventional, and practically inevitable,
interpretation of these observations is that GRBs result from the conversion of
the kinetic energy of ultra-relativistic particles or possibly the
electromagnetic energy of a Poynting flux to radiation in an optically thin
region. This generic "fireball" model has also been confirmed by the afterglow
observations. The "inner engine" that accelerates the relativistic flow is
hidden from direct observations. Consequently it is difficult to infer its
structure directly from current observations. Recent studies show, however,
that this ``inner engine'' is responsible for the complicated temporal
structure observed in GRBs. This temporal structure and energy considerations
indicates that the ``inner engine'' is associated with the formation of a
compact object - most likely a black hole.


  The High Performance Computing (HPC) field is witnessing a widespread
adoption of Graphics Processing Units (GPUs) as co-processors for conventional
homogeneous clusters. The adoption of prevalent Single- Program Multiple-Data
(SPMD) programming paradigm for GPU-based parallel processing brings in the
challenge of resource underutilization, with the asymmetrical
processor/co-processor distribution. In other words, under SPMD, balanced
CPU/GPU distribution is required to ensure full resource utilization. In this
paper, we propose a GPU resource virtualization approach to allow underutilized
microprocessors to effi- ciently share the GPUs. We propose an efficient GPU
sharing scenario achieved through GPU virtualization and analyze the
performance potentials through execution models. We further present the
implementation details of the virtualization infrastructure, followed by the
experimental analyses. The results demonstrate considerable performance gains
with GPU virtualization. Furthermore, the proposed solution enables full
utilization of asymmetrical resources, through efficient GPU sharing among
microprocessors, while incurring low overhead due to the added virtualization
layer.


  We consider the random walk of a particle in a two-dimensional self-affine
random potential of Hurst exponent $H=1/2$ in the presence of an external force
$F$. We present numerical results on the statistics of first-passage times that
satisfy closed backward master equations. We find that there exists a
zero-velocity phase in a finite region of the external force $0<F<F_c$, where
the dynamics follows the anomalous diffusion law $ x(t) \sim \xi(F) \
t^{\mu(F)} $. The anomalous exponent $0<\mu(F)<1$ and the correlation length
$\xi(F)$ vary continuously with $F$. In the limit of vanishing force $F \to 0$,
we measure the following power-laws : the anomalous exponent vanishes as
$\mu(F) \propto F^a$ with $a \simeq 0.6$ (instead of $a=1$ in dimension $d=1$),
and the correlation length diverges as $\xi(F) \propto F^{-\nu}$ with $\nu
\simeq 1.29$ (instead of $\nu=2$ in dimension $d=1$). Our main conclusion is
thus that the dynamics renormalizes onto an effective directed trap model,
where the traps are characterized by a typical length $\xi(F)$ along the
direction of the force, and by a typical barrier $1/\mu(F)$. The fact that
these traps are 'smaller' in linear size and in depth than in dimension $d=1$,
means that the particle uses the transverse direction to find lower barriers.


  Millisecond pulsars occur abundantly in globular clusters. They are expected
to be responsible for several spectral components in the radio through
gamma-ray waveband (e.g., involving synchrotron and inverse Compton emission),
as have been seen by Radio Telescope Effelsberg, Chandra X-ray Observatory,
Fermi Large Area Telescope, and the High Energy Stereoscopic System (H.E.S.S.)
in the case of Terzan 5 (with fewer spectral components seen for other globular
clusters). H.E.S.S. has recently performed a stacking analysis involving 15
non-detected globular clusters and obtained quite constraining average flux
upper limits above 230 GeV. We present a model that assumes millisecond pulsars
as sources of relativistic particles and predicts multi-wavelength emission
from globular clusters. We apply this model to the population of clusters
mentioned above to predict the average spectrum and compare this to the
H.E.S.S. upper limits. Such comparison allows us to test whether the model is
viable, leading to possible constraints on various average cluster parameters
within this framework.


  This paper seeks to solve the long-term transmission expansion planning
problem more effectively by reducing the solution search space and the
computational effort. The proposed methodology finds and adds cutting planes
based on structural insights about bus angle-differences along paths. Several
theorems are proposed which show the validity of these cutting planes onto the
underlying mathematical formulations. The path-based bus angle-difference
constraints, which tighten the relaxed feasible region, are used in combination
with branch-and-bound to find lower bounds on the optimal investment of the
transmission expansion planning problem. This work also creates an algorithm
that automates the process of finding and applying the discussed valid
inequalities, resulting in significantly reduced testing and computation time.
The algorithm is implemented in Python, using the solver CPLEX to add
constraints and solve the transmission expansion problem. This paper uses two
different-sized systems to illustrate the effectiveness of the proposed
framework: a modified IEEE 118-bus system and the Polish 2383-bus system.


  Radial velocities of 2768 fundamental mode RR Lyrae stars (RRLs) toward the
Southern Galactic bulge are presented, spanning the southern bulge from -8 < l
< +8 and -3 < b <-6. Distances derived from the pulsation properties of the
RRLs are combined with Gaia proper motions to give constraints on the orbital
motions of 1389 RRLs. The majority (~75%) of the bulge RRLs have orbits
consistent with these stars being permanently bound to <3.5 kpc from the
Galactic Center, similar to the bar. However, unlike the bulge giants, the RRLs
exhibit slower rotation and a higher velocity dispersion. The higher velocity
dispersion arises almost exclusively from halo interlopers passing through the
inner Galaxy. We present 82 stars with space velocities > 500 km/s and find
that the majority of these high-velocity stars are halo interlopers; it is
unclear if a sub-sample of these stars with similar space velocities have a
common origin. Once the 25% of the sample represented by halo interlopers is
cleaned, we can clearly discern two populations of bulge RRLs in the inner
Galaxy. One population of RRLs is not as tightly bound to the Galaxy (but is
still confined to the inner ~3.5 kpc), and is both spatially and kinematically
consistent with the barred bulge. The second population is more centrally
concentrated and does not trace the bar. One possible interpretation is that
this population was born prior to bar formation, as its spatial location,
kinematics and pulsation properties suggest, possibly from an accretion event
at high redshift.


  Manually annotating nuclei from the gigapixel Hematoxylin and Eosin
(H&E)-stained Whole Slide Images (WSIs) is a laborious and costly task, meaning
automated algorithms for cell nuclei instance segmentation and classification
could alleviate the workload of pathologists and clinical researchers and at
the same time facilitate the automatic extraction of clinically interpretable
features. But due to high intra- and inter-class variability of nuclei
morphological and chromatic features, as well as H&E-stains susceptibility to
artefacts, state-of-the-art algorithms cannot correctly detect and classify
instances with the necessary performance. In this work, we hypothesise context
and attention inductive biases in artificial neural networks (ANNs) could
increase the generalization of algorithms for cell nuclei instance segmentation
and classification. We conduct a thorough survey on context and attention
methods for cell nuclei instance segmentation and classification from
H&E-stained microscopy imaging, while providing a comprehensive discussion of
the challenges being tackled with context and attention. Besides, we illustrate
some limitations of current approaches and present ideas for future research.
As a case study, we extend both a general instance segmentation and
classification method (Mask-RCNN) and a tailored cell nuclei instance
segmentation and classification model (HoVer-Net) with context- and
attention-based mechanisms, and do a comparative analysis on a multi-centre
colon nuclei identification and counting dataset. Although pathologists rely on
context at multiple levels while paying attention to specific Regions of
Interest (RoIs) when analysing and annotating WSIs, our findings suggest
translating that domain knowledge into algorithm design is no trivial task, but
to fully exploit these mechanisms, the scientific understanding of these
methods should be addressed.


  Memory or transistor devices based on electron's spin rather than its charge
degree of freedom offer certain distinct advantages and comprise a cornerstone
of spintronics. Recent years have witnessed the emergence of a new field,
valleytronics, which seeks to exploit electron's valley index rather than its
spin. An important component in this quest would be the ability to control the
valley index in a convenient fashion. Here we show that the valley polarization
can be switched from zero to one by a small reduction in density, simply tuned
by a gate bias, in a two-dimensional electron system. This phenomenon arises
fundamentally as a result of electron-electron interaction in an itinerant,
dilute electron system. Essentially, the kinetic energy favors an equal
distribution of electrons over the available valleys, whereas the interaction
between electrons prefers single-valley occupancy below a critical density. The
gate-bias-tuned transition we observe is accompanied by a sudden, two-fold
change in sample resistance, making the phenomenon of interest for potential
valleytronic transistor device applications. Our observation constitutes a
quintessential demonstration of valleytronics in a very simple experiment.


  We discuss a recent computation of Compton scattering on the deuteron at
photon energies of order the pion mass. An interaction kernel is computed in
baryon chiral perturbation theory and sewn to phenomenological deuteron wave
functions. As an appetizer, we consider a computation of the pion-deuteron
scattering length using this method.


  We show that the space $\mathcal{R}^{\mathrm{pRc}}(W_g^{2n})$ of metrics with
positive Ricci curvature on the manifold $W^{2n}_g := \sharp^g (S^n \times
S^n)$ has nontrivial rational homology if $n \not \equiv 3 \pmod 4$ and $g$ are
both sufficiently large. The same argument applies to
$\mathcal{R}^{\mathrm{pRc}}(W_g^{2n} \sharp N)$ provided that $N$ is spin and
$W_g^{2n} \sharp N$ admits a Ricci positive metric.


  This paper studies the problem of learning message propagation strategies for
graph neural networks (GNNs). One of the challenges for graph neural networks
is that of defining the propagation strategy. For instance, the choices of
propagation steps are often specialized to a single graph and are not
personalized to different nodes. To compensate for this, in this paper, we
present learning to propagate, a general learning framework that not only
learns the GNN parameters for prediction but more importantly, can explicitly
learn the interpretable and personalized propagate strategies for different
nodes and various types of graphs. We introduce the optimal propagation steps
as latent variables to help find the maximum-likelihood estimation of the GNN
parameters in a variational Expectation-Maximization (VEM) framework. Extensive
experiments on various types of graph benchmarks demonstrate that our proposed
framework can significantly achieve better performance compared with the
state-of-the-art methods, and can effectively learn personalized and
interpretable propagate strategies of messages in GNNs.


  A mathematical framework for optimal bilinear control of nonlinear
Schr\"odinger equations of Gross-Pitaevskii type arising in the description of
Bose-Einstein condensates is presented. The obtained results generalize earlier
efforts found in the literature in several aspects. In particular, the cost
induced by the physical work load over the control process is taken into
account rather then often used $L^2$- or $H^1$-norms for the cost of the
control action. Well-posedness of the problem and existence of an optimal
control is proven. In addition, the first order optimality system is rigorously
derived. Also a numerical solution method is proposed, which is based on a
Newton type iteration, and used to solve several coherent quantum control
problems.


  We present a photometric analysis of star and star cluster (SC) formation in
a high-resolution simulation of a dwarf galaxy starburst that allows the
formation of individual stars to be followed. Previous work demonstrated that
the properties of the SCs formed in the simulation are in good agreement with
observations. In this paper, we create mock spectral energy distributions and
broad-band photometric images using the radiative transfer code SKIRT 9. We
test several observational star formation rate (SFR) tracers and find that $24$
$\mu$m, total infrared and H$\alpha$ trace the underlying SFR during the
(post)starburst phase, while UV tracers yield a more accurate picture of star
formation during quiescent phases prior to and after the merger. We then place
the simulated galaxy at distances of $10$ and $50$ Mpc and use aperture
photometry at Hubble Space Telescope resolution to analyse the simulated SC
population. During the starburst phase, a hierarchically forming set of SCs
leads inaccurate source separation because of crowding. This results in
estimated SC mass function slopes that are up to $\sim0.3$ shallower than the
true slope of $\sim-1.9$ to $-2$ found for the bound clusters identified from
the particle data in the simulation. The masses of the largest clusters are
overestimated by a factor of up to $2.9$ due to unresolved clusters within the
apertures. The aperture-based analysis also produces a relation between cluster
formation efficiency and SFR surface density that is slightly flatter than that
recovered from bound clusters. The differences are strongest in quiescent SF
environments.


  Some time ago we extended our monogenity investigations and calculations of
generators of power integral bases to the relative case. Up to now we
considered (usually totally real) extensions of complex quartic fields. In the
present paper we consider power integral bases in relative extensions of
totally real fields. Totally complex quartic extensions of totally real number
fields seems to be the most simple case, that we detail here. As we shall see,
even in this case we have to overcome several unexpected difficulties, which we
can, however solve by properly (but not trivially) adjusting standard methods.
We demonstrate our general algorithm on an explicit example. We describe how
the general methods for solving relative index form equations in quartic
relative extensions are modified in this case. As a byproduct we show that
relative Thue equations in totally complex extensions of totally real fields
can only have small solutions, and we construct a special method for the
enumeration of small solutions of special unit equations. These statements can
be applied to other diophantine problems, as well.


  We investigate the emergence of complex dynamics in networks with
heavy-tailed connectivity by developing a non-Hermitian random matrix theory.
We uncover the existence of an extended critical regime of spatially
multifractal fluctuations between the quiescent and active phases. This
multifractal critical phase combines features of localization and
delocalization and differs from the edge of chaos in classical networks by the
appearance of universal hallmarks of Anderson criticality over an extended
region in phase space. We show that the rich nonlinear response properties of
the extended critical regime can account for a variety of neural dynamics such
as the diversity of timescales, providing a computational advantage for
persistent classification in a reservoir setting.


  We report on the white dwarf cooling sequence of the old globular cluster NGC
6752, which is chemically complex and hosts a blue horizontal branch. This is
one of the last globular cluster white dwarf (WD) cooling sequences accessible
to imaging by the Hubble Space Telescope. Our photometry and completeness tests
show that we have reached the peak of the luminosity function of the WD cooling
sequence, at a magnitude m_F606W=29.4+/-0.1, which is consistent with a formal
age of ~14Gyr. This age is also consistent with the age from fits to the
main-sequence turnoff (13-14Gyr), reinforcing our conclusion that we observe
the expected accumulation of white dwarfs along the cooling sequence.


  Internet is a complex network composed by several networks: the Autonomous
Systems, each one designed to transport information efficiently. Routing
protocols aim to find paths between nodes whenever it is possible (i.e., the
network is not partitioned), or to find paths verifying specific constraints
(e.g., a certain QoS is required). As connectivity is a measure related to both
of them (partitions and selected paths) this work provides a formal lower bound
to it based on core-decomposition, under certain conditions, and low complexity
algorithms to find it. We apply them to analyze maps obtained from the
prominent Internet mapping projects, using the LaNet-vi open-source software
for its visualization.


  We present a spin-induced none-geodesic effect of Dirac wave packets in a
static uniform gravitational field. Our approach is based on the
Foldy-Wouthuysen transformation of Dirac equation in a curved spacetime, which
predicts the gravitational spin-orbit coupling. Due to this coupling, we find
that the dynamics of the free-fall Dirac wave packets with opposite spin
polarization will yield the transverse splitting in the direction perpendicular
to spin orientation and gravity, which is known as the gravitational spin Hall
effect. Even in a static uniform gravitational field, such effect suggests that
the weak equivalence principle is violated for quantum particles.


  This paper is an introduction to the hyperbolic geometry of noncommutative
polyballs B_n of bounded linear operators on Hilbert spaces. We use the theory
of free pluriharmonic functions on polyballs and noncommutative Poisson kernels
on tensor products of full Fock spaces to define hyperbolic type metrics on
B_n, study their properties, and obtain hyperbolic versions of Schwarz-Pick
lemma for free holomorphic functions on polyballs. As a consequence, the
polyballs can be viewed as noncommutative hyperbolic spaces. When specialized
to the operatorial polydisk D_k, our hyperbolic metric is complete and
invariant under the group of all free holomorphic automorphisms of D_k, and the
topology induced on D_k is the usual operator norm topology.


  We aim at accelerating super-resolution (SR) networks on large images
(2K-8K). The large images are usually decomposed into small sub-images in
practical usages. Based on this processing, we found that different image
regions have different restoration difficulties and can be processed by
networks with different capacities. Intuitively, smooth areas are easier to
super-solve than complex textures. To utilize this property, we can adopt
appropriate SR networks to process different sub-images after the
decomposition. On this basis, we propose a new solution pipeline -- ClassSR
that combines classification and SR in a unified framework. In particular, it
first uses a Class-Module to classify the sub-images into different classes
according to restoration difficulties, then applies an SR-Module to perform SR
for different classes. The Class-Module is a conventional classification
network, while the SR-Module is a network container that consists of the
to-be-accelerated SR network and its simplified versions. We further introduce
a new classification method with two losses -- Class-Loss and Average-Loss to
produce the classification results. After joint training, a majority of
sub-images will pass through smaller networks, thus the computational cost can
be significantly reduced. Experiments show that our ClassSR can help most
existing methods (e.g., FSRCNN, CARN, SRResNet, RCAN) save up to 50% FLOPs on
DIV8K datasets. This general framework can also be applied in other low-level
vision tasks.


  In recent years there has been a great deal of focus on a globe-spanning
quantum network, including linked satellites for applications ranging from
quantum key distribution to distributed sensors and clocks. In many of these
schemes, relativistic transformations may have deleterious effects on the
purity of the distributed entangled pairs. This becomes particularly important
for the application of distributed clocks. In this paper, we have developed a
Lorentz invariant entanglement distribution protocol that completely removes
the effects due to the relative motions of the satellites.


  We examine the physical properties and evolutionary stages of a sample of 17
clumps within 8 Infrared Dark Clouds (IRDCs) by combining existing infrared,
millimeter, and radio data with new Bolocam Galactic Plane Survey (BGPS) 1.1 mm
data, VLA radio continuum data, and HHT dense gas (HCO+ and N2H+) spectroscopic
data. We combine literature studies of star formation tracers and dust
temperatures within IRDCs with our search for ultra-compact (UC) HII regions to
discuss a possible evolutionary sequence for IRDC clumps. In addition, we
perform an analysis of mass tracers in IRDCs and find that 8 micron extinction
masses and 1.1 mm Bolocam Galactic Plane Survey (BGPS) masses are complementary
mass tracers in IRDCs except for the most active clumps (notably those
containing UCHII regions), for which both mass tracers suffer biases. We find
that the measured virial masses in IRDC clumps are uniformly higher than the
measured dust continuum masses on the scale of ~1 pc. We use 13CO, HCO+, and
N2H+ to study the molecular gas properties of IRDCs and do not see any evidence
of chemical differentiation between hot and cold clumps on the scale of ~1 pc.
However, both HCO+ and N2H+ are brighter in active clumps, due to an increase
in temperature and/or density. We report the identification of four UCHII
regions embedded within IRDC clumps and find that UCHII regions are associated
with bright (>1 Jy) 24 micron point sources, and that the brightest UCHII
regions are associated with "diffuse red clumps" (an extended enhancement at 8
micron). The broad stages of the discussed evolutionary sequence (from a
quiescent clump to an embedded HII region) are supported by literature dust
temperature estimates; however, no sequential nature can be inferred between
the individual star formation tracers.


  We introduce and theoretically demonstrate a quantum metamaterial made of
dense ultracold neutral atoms loaded into an inherently defect-free artificial
crystal of light, immune to well-known critical chal- lenges inevitable in
conventional solid-state platforms. We demonstrate an all-optical control on
ultrafast time scales over the photonic topological transition of the
isofrequency contour from an open to close topology at the same frequency. This
atomic lattice quantum metamaterial enables a dynamic manipula- tion of the
decay rate of a probe quantum emitter by more than an order of magnitude. This
proposal may lead to practically lossless, tunable and
topologically-reconfigurable quantum metamaterials, for single- or
few-photon-level applications as varied as quantum sensing, quantum information
processing, and quantum simulations using metamaterials.


  Modelling dust formation in single stars evolving through the carbon-star
stage of the asymptotic giant branch (AGB) reproduces well the mid-infrared
colours and magnitudes of most of the C-rich sources in the Large Magellanic
Cloud (LMC), apart from a small subset of extremely red objects (EROs). The
analysis of EROs spectral energy distribution suggests the presence of large
quantities of dust, which demand gas densities in the outflow significantly
higher than expected from theoretical modelling. We propose that binary
interaction mechanisms that involve common envelope (CE) evolution could be a
possible explanation for these peculiar stars; the CE phase is favoured by the
rapid growth of the stellar radius occurring after C$/$O overcomes unity. Our
modelling of the dust provides results consistent with the observations for
mass-loss rates $\dot M \sim 5\times 10^{-4}~\dot M/$yr, a lower limit to the
rapid loss of the envelope experienced in the CE phase. We propose that EROs
could possibly hide binaries of orbital periods $\sim$days and are likely to be
responsible for a large fraction of the dust production rate in galaxies.


  We provide exact and asymptotic formulae for the number of unrestricted,
respectively indecomposable, $d$-dimensional matrices where the sum of all
matrix entries with one coordinate fixed equals 2.


  Mobile and wearable interfaces and interaction paradigms are highly
constrained by the available screen real estate, and the computational and
power resources. Although there exist many ways of displaying information to
mobile users, inputting data to a mobile device is, usually, limited to a
conventional touch based interaction, that distracts users from their ongoing
activities. Furthermore, emerging applications, like augmented, mixed and
virtual reality (AR/MR/VR), require new types of input methods in order to
interact with complex virtual worlds, challenging the traditional techniques of
Human-Computer Interaction (HCI). Leveraging of Natural User Interfaces (NUIs),
as a paradigm of using natural intuitive actions to interact with computing
systems, is one of many ways to meet these challenges in mobile computing and
its modern applications. Brain-Machine Interfaces that enable thought-only
hands-free interaction, Myoelectric input methods that track body gestures and
gaze-tracking input interfaces - are the examples of NUIs applicable to mobile
and wearable interactions. The wide adoption of wearable devices and the
penetration of mobile technologies, alongside with the growing market of
AR/MR/VR, motivates the exploration and implementation of new interaction
paradigms. The concurrent development of bio-signal acquisition techniques and
accompanying ecosystems offers a useful toolbox to address open challenges. In
this survey, we present state-of-the-art bio-signal acquisition methods,
summarize and evaluate recent developments in the area of NUIs and outline
potential application in mobile scenarios. The survey will provide a bottoms-up
overview starting from (i) underlying biological aspects and signal acquisition
techniques, (ii) portable NUI hardware solutions, (iii) NUI-enabled
applications, as well as (iv) research challenges and open problems.


  We prove that irreducible symmetric domains are uniquely determined by the
homotopy equivalence classes of their Shilov boundaries.


  We review some aspects of string tachyon dynamics with special emphasis on
effective actions


  Solar power becomes one of the most promising renewable energy resources in
recent years. However, the weather is continuously changing, and this causes a
discontinuity of energy generation. PV Power forecasting is a suitable solution
to handle sudden disjointedness on energy generation by providing fast
dispatching to grid electricity. These methods present a key insight into
matchmaking grid electricity and photovoltaic plants. Bootstrap aggregation
Ensemble method(Bagging) is classified as one of the most useful machine
learning models which are applicable to supervised learning regression tasks.
Following this regard, this paper proposes a state-of-art method based on
bagging and this method works perfectly for PV power forecasting. The latter
had powerful capabilities of tracking the behavior of stochastic problems with
good accuracy with the aid of feature importance information. This approach
comes to optimize bias/variance using feature weighting vector. Thus, this
paper is devoted to present various feature importance techniques for
Photovoltaic forecasting parameters. This technique consists of improving the
aforementioned ensemble model via contributing the knowledge expertise obtained
from features analysis to be directly transformed into the Ensemble model. The
proposed model is tested on PV power prediction. Therefore, the benchmarked
technique shows an improvement in accuracy in terms of RMSE to 5%.


  We analyse the temporal evolution of the inclination component of the
magnetic field vector for the penumbral area of 25 isolated sunspots. Compared
to previous works, the use of data from the HMI instrument aboard the SDO
observatory facilitates the study of very long time series ($\approx$1 week),
compared to previous works, with a good spatial and temporal resolution. We
used the wavelet technique and we found some filamentary-shaped events with
large wavelet power. Their distribution of periods is broad, ranging from the
lower limit for this study of 48 minutes up to 63 hours. An interesting
property of these events is that they do not appear homogeneously all around
the penumbra but they seem to concentrate at particular locations. The
cross-comparison of these wavelet maps with AIA data shows that the regions
where these events appear are visually related to the coronal loops that
connect the outer penumbra to one or more neighbouring opposite polarity flux
patches.


  Weakly supervised visual grounding aims to predict the region in an image
that corresponds to a specific linguistic query, where the mapping between the
target object and query is unknown in the training stage. The state-of-the-art
method uses a vision language pre-training model to acquire heatmaps from
Grad-CAM, which matches every query word with an image region, and uses the
combined heatmap to rank the region proposals. In this paper, we propose two
simple but efficient methods for improving this approach. First, we propose a
target-aware cropping approach to encourage the model to learn both object and
scene level semantic representations. Second, we apply dependency parsing to
extract words related to the target object, and then put emphasis on these
words in the heatmap combination. Our method surpasses the previous SOTA
methods on RefCOCO, RefCOCO+, and RefCOCOg by a notable margin.


  We investigate the problem of the rate of convergence to equilibrium for
ergodic stochastic differential equations driven by fractional Brownian motion
with Hurst parameter $H\in (1/3,1)$ and multiplicative noise component
$\sigma$. When $\sigma$ is constant and for every $H\in (0,1)$, it was proved
in [19] that, under some mean-reverting assumptions, such a process converges
to its equilibrium at a rate of order $t^{-\alpha}$ where $\alpha \in (0,1)$
(depending on $H$). In [11], this result has been extended to the
multiplicative case when $H\textgreater{}1/2$. In this paper, we obtain these
types of results in the rough setting $H\in (1/3,1/2)$. Once again, we retrieve
the rate orders of the additive setting. Our methods also extend the
multiplicative results of [11] by deleting the gradient assumption on the noise
coefficient $\sigma$. The main theorems include some existence and uniqueness
results for the invariant distribution.


  We developed a 3D end-effector type of upper limb assistive robot, named as
Assistive Robotic Arm Extender (ARAE), that provides transparency movement and
adaptive arm support control to achieve home-based therapy and training in the
real environment. The proposed system composes five degrees of freedom,
including three active motors and two passive joints at the end-effector
module. The core structure of the system is based on a parallel mechanism. The
kinematic and dynamic modeling are illustrated in detail. The proposed adaptive
arm support control framework calculates the compensated force based on the
estimated human arm posture in 3D space. It firstly estimates human arm joint
angles using two proposed methods: fixed torso and sagittal plane models
without using external sensors such as IMUs, magnetic sensors, or depth
cameras. The experiments were carried out to evaluate the performance of the
two proposed angle estimation methods. Then, the estimated human joint angles
were input into the human upper limb dynamics model to derive the required
support force generated by the robot. The muscular activities were measured to
evaluate the effects of the proposed framework. The obvious reduction of
muscular activities was exhibited when participants were tested with the ARAE
under an adaptive arm gravity compensation control framework. The overall
results suggest that the ARAE system, when combined with the proposed control
framework, has the potential to offer adaptive arm support. This integration
could enable effective training with Activities of Daily Living (ADLs) and
interaction with real environments.


  We present a method to calculate the pion screening mass in 2+1-flavor
lattice QCD to $\mathcal{O}(\mu^2_\ell)$, where $\mu_\ell$ is the isoscalar
chemical potential. Our approach is based on the expression for the free theory
pion screening correlator for massless quarks. We use the Taylor expansion
method to calculate the screening correlator to $\mathcal{O}(\mu^4_\ell)$. We
then extract the $\mathcal{O}(\mu^2_\ell)$ Taylor coefficient of the screening
mass from the Taylor coefficients of the correlator, for two temperatures in
the range 2 - 3 GeV. Our calculations were done using the Highly Improved
Staggered Quark action, and the strange and light quark masses were set
respectively to their physical and nearly physical values, corresponding to
meson masses $M_{\bar{s}s}=686$ MeV and $M_\pi=160$ MeV.


  Virtual flow metering (VFM) is a cost-effective and non-intrusive technology
for inferring multiphase flow rates in petroleum assets. Inferences about flow
rates are fundamental to decision support systems that operators extensively
rely on. Data-driven VFM, where mechanistic models are replaced with machine
learning models, has recently gained attention due to its promise of lower
maintenance costs. While excellent performances in small sample studies have
been reported in the literature, there is still considerable doubt about the
robustness of data-driven VFM. In this paper, we propose a new multi-task
learning (MTL) architecture for data-driven VFM. Our method differs from
previous methods in that it enables learning across oil and gas wells. We study
the method by modeling 55 wells from four petroleum assets and compare the
results with two single-task baseline models. Our findings show that MTL
improves robustness over single-task methods, without sacrificing performance.
MTL yields a 25-50% error reduction on average for the assets where single-task
architectures are struggling.


  In this work we present Ludwig, a flexible, extensible and easy to use
toolbox which allows users to train deep learning models and use them for
obtaining predictions without writing code. Ludwig implements a novel approach
to deep learning model building based on two main abstractions: data types and
declarative configuration files. The data type abstraction allows for easier
code and sub-model reuse, and the standardized interfaces imposed by this
abstraction allow for encapsulation and make the code easy to extend.
Declarative model definition configuration files enable inexperienced users to
obtain effective models and increase the productivity of expert users.
Alongside these two innovations, Ludwig introduces a general modularized deep
learning architecture called Encoder-Combiner-Decoder that can be instantiated
to perform a vast amount of machine learning tasks. These innovations make it
possible for engineers, scientists from other fields and, in general, a much
broader audience to adopt deep learning models for their tasks, concretely
helping in its democratization.


  Let G/H be a hyperbolic space over R C or H, and let K be a maximal compact
subgroup of G. Let D denote a certain explicit invariant differential operator,
such that the non-cuspidal discrete series belong to the kernel of D. For any
L^2-Schwartz function f on G/H, we prove that the Abel transform A(Df) of Df is
a Schwartz function. This is an extension of a result established in [2] for
K-finite and K\cap H-invariant functions.


  We present an effective Hamiltonian based real-space approach for studying
the weak-coupling Bardeen-Cooper-Schrieffer (BCS) to the strong-coupling
Bose-Einstein condensate (BEC) crossover in the two-dimensional attractive
Hubbard model at finite temperatures. We introduce and justify an effective
classical Hamiltonian to describe the thermal fluctuations of the relevant
auxiliary fields. Our results for $T_c$ and phase diagrams compare very well
with those obtained from more sophisticated and {\it cpu}-intensive numerical
methods. We demonstrate that the method works in the presence of disorder and
is useful for a real-space description of the effect of disorder on
superconductivity. From a combined analysis of the superconducting order
parameter, the distribution of auxiliary fields and the quasiparticle density
of states, we identify the regions of metallic, insulating, superconducting and
pseudogapped behavior. Our finding of the importance of phase fluctuations for
the pseudogap behavior is consistent with the conclusions drawn from recent
experiments on NbN superconductors. The method can be generalized to study
superconductors with non-trivial order parameter symmetries by identifying the
relevant auxiliary variables.


  We introduced a generalized Maxwell-Higgs model in a $(3+1)$ isotropic
spacetime, and we found their stationary solutions using the BPS approach in
curved spacetime. In order to investigate the compact-like vortices, we assume
a particular choice for the generalization term. The model is controlled by a
potential driven by a single real parameter that can be used to change the
vortex solutions profile as they approach their bound values. Resembling some
flat spacetime vortex solutions, our model tends to compress the vortices when
the parameter $l$ increases. Through numerical analysis, we also show the
energy behavior and the magnetic field of the model.


  Over the past few years there has been growing concern among authorities over
crimes committed worldwide. In Brazil it is no different. High crime rates have
encouraged government authorities involved in public safety to identify
solutions to minimize crimes. In this context, one way to plan and manage
security is in the division of neighborhoods in ISA (Integrated Security
Areas). Each ISA has a neighborhood conglomerates taking into account their
geolocation. From this it becomes possible to maximize security management and
combat crime. Based on that, one of the main points that generate great
discussion at the governmental level is the choice of a certain integrated
security area for the installation of a certain police battalion. This choice
involves multiple decision makers since several hierarchies are involved. Thus,
this paper aims to identify the best ISA to deploy a police battalion using
group decision techniques and tools. For this work the Group Decision Support
System (GDSS) called GRoUp Support (GRUS) was used from two main Vote
techniques: Condorcet and Borda. With this it was possible to identify the best
ISA taking into account the pre-established criteria.


  This work investigates the theory behind a thought experiment that intends to
measure momentum and angular momentum of matter exposed to `isotropic radiative
noise'. Radiative momentum has been a controversial subject for decades. The
momentum of isotropic noise is intuitively expected to be zero. We formulate
the general features of the isotropic noise such as equipartition of energy and
the vanishing of integrated Poynting vector. We demonstrate that in
bi-anisotropic materials, a finite radiative momentum persists that performs
work on the matter when its parameters change slowly. We find that Faraday
rotation in the scattering of the radiative noise induces an angular momentum
along the applied external magnetic field. Also, a Poynting vector starts
circulating around the matter, raising the question whether it really describes
the energy flow. These effects are small and hard to measure in any real
experiment. Yet, they are surprising predictions of the classical, macroscopic
Maxwell equations, and make contact with the outcome of recent QED calculations
done for the quantum vacuum.


  Spectral-temporal shaping of quantum light has important applications in
quantum communications and photonic quantum information processing.
Electro-optic temporal lenses have recently been recognized as a tool for
noise-free, efficient spectral bandwidth manipulation of single-photon
wavepackets. However, standard electro-optic time lenses based on single-tone
modulation exhibit limited bandwidth manipulation due to material limitations
on phase modulation amplitude. Here we numerically investigate the use of
complex electro-optic temporal phase modulation patterns for bandwidth
compression of light over multiple orders of magnitude and show the feasibility
of their use in photonic interfaces for quantum network applications.


  The experimental study of gravity became much more active in the late 1950s,
a change pronounced enough be termed the birth, or naissance, of experimental
gravity physics. I present a review of developments in this subject since 1915,
through the broad range of new approaches that commenced in the late 1950s, and
up to the transition of experimental gravity physics to what might be termed a
normal and accepted part of physical science in the late 1960s. This review
shows the importance of advances in technology, here as in all branches of
natural science. The role of contingency is illustrated by Robert Dicke's
decision in the mid-1950s to change directions in mid-career, to lead a
research group dedicated to the experimental study of gravity. The review also
shows the power of nonempirical evidence. Some in the 1950s felt that general
relativity theory is so logically sound as to be scarcely worth the testing.
But Dicke and others argued that a poorly tested theory is only that, and that
other nonempirical arguments, based on Mach's Principle and Dirac's Large
Numbers hypothesis, suggested it would be worth looking for a better theory of
gravity. I conclude by offering lessons from this history, some peculiar to the
study of gravity physics during the naissance, some of more general relevance.
The central lesson, which is familiar but not always well advertised, is that
physical theories can be empirically established, sometimes with surprising
results.


  The European Extremely Large Telescope will see first lights by the end of
2024. With a diameter of almost 40 meters, it will be the biggest optical
telescope ever built from the ground. The ELT will open a brand new window in a
sensitivity / spatial angular resolution parameter space. To take the full
benefit of the scientific potential of this giant, all the instruments will be
equipped with Adaptive Optics (AO), providing the sharpest images. This paper
provides a quick overview of the AO capabilities of the future instruments to
be deployed at the ELT, highlight some of the expected performance and describe
a couple of technical challenges that are still to tackle for an optimal
scientific use. This paper has been presented at the "Societe Francaise
d'Astronomie et Astrophysique" symposium in Bordeaux 2018, it is then naturally
biased toward the French contribution for the ELT.


  We predict the electromagnetic field driven nucleation of nanoparticles that
provide plasmonic oscillations in resonance with the field frequency. The
oscillations assume a phase that maximizes the particle polarization and energy
gain due to nucleation. We derive closed form expressions for the corresponding
nucleation barrier and particle shape vs. field frequency and strength, metal
plasma frequency, conductivity, and the host dielectric permittivity. We show
that the plasmonic polarization allows for nucleation of particles that would
not be stable in zero field.


  I discuss the saturation regime of QCD: the weak-coupling regime that
describes large parton densities inside hadrons and the resulting high-energy
behavior of scattering amplitudes. I briefly review past successes in the
context of deep inelastic scattering at HERA and forward particle production at
RHIC, and I present some recent progresses that significantly improved our
understanding of high-energy scattering in QCD.


  We consider pure continuous variable entanglement with non-equal correlations
between orthogonal quadratures. We introduce a simple protocol which equates
these correlations and in the process transforms the entanglement onto a state
with the minimum allowed number of photons. As an example we show that our
protocol transforms, through unitary local operations, a single squeezed beam
split on a beam splitter into the same entanglement that is produced when two
squeezed beams are mixed orthogonally. We demonstrate that this technique can
in principle facilitate perfect teleportation utilising only one squeezed beam.


  We deal with finite dimensional linear and nonlinear control systems. If the
system is linear and autonomous and satisfies the classical normality
assumption, we improve the well known result on the strict convexity of the
reachable set from the origin by giving a polynomial estimate. The result is
based on a careful analysis of the switching function. We extend this result to
nonautonomous linear systems, provided the time dependent system is not too far
from the autonomous system obtained by taking the time to be 0 in the dynamics.
Using a linearization approach, we prove a bang-bang principle, valid in
dimensions 2 and 3 for a class of nonlinear systems, affine and symmetric with
respect to the control. Moreover we show that, for two dimensional systems, the
reachable set from the origin satisfies the same polynomial strict convexity
property as for the linearized dynamics, provided the nonlinearity is small
enough. Finally, under the same assumptions we show that the epigraph of the
minimum time function has positive reach, hence proving the first result of
this type in a nonlinear setting. In all the above results, we require that the
linearization at the origin be normal. We provide examples showing the
sharpness of our assumptions.


  A search is presented for $\tau$ slepton pairs produced in proton-proton
collisions at a center-of-mass energy of 13 TeV. The search is carried out in
events containing two $\tau$ leptons in the final state, on the assumption that
each $\tau$ slepton decays primarily to a $\tau$ lepton and a neutralino.
Events are considered in which each $\tau$ lepton decays to one or more hadrons
and a neutrino, or in which one of the $\tau$ leptons decays instead to an
electron or a muon and two neutrinos. The data, collected with the CMS detector
in 2016 and 2017, correspond to an integrated luminosity of 77.2 fb$^{-1}$. The
observed data are consistent with the standard model background expectation.
The results are used to set 95% confidence level upper limits on the cross
section for $\tau$ slepton pair production in various models for $\tau$ slepton
masses between 90 and 200 GeV and neutralino masses of 1, 10, and 20 GeV. In
the case of purely left-handed $\tau$ slepton production and decay to a $\tau$
lepton and a neutralino with a mass of 1 GeV, the strongest limit is obtained
for a $\tau$ slepton mass of 125 GeV at a factor of 1.14 larger than the
theoretical cross section.


  Consider Bernoulli bond percolation on a locally finite, connected graph $G$
and let $p_{\mathrm{cut}}$ be the threshold corresponding to a "first-moment
method" lower bound. Kahn (\textit{Electron.\ Comm.\ Probab.\ Volume 8,
184-187.} (2003)) constructed a counter-example to Lyons' conjecture of
$p_{\mathrm{cut}}=p_c$ and proposed a modification. Here we give a positive
answer to Kahn's modified question. The key observation is that in Kahn's
modification, the new expectation quantity also appears in the differential
inequality of one-arm events. This links the question to a lemma of
Duminil-Copin and Tassion (\textit{Comm. Math. Phys. Volume 343, 725-745.}
(2016)). We also study some applications for Bernoulli percolation on periodic
trees.


  We study the problem of deciding whether a crease pattern can be folded by
simple folds (folding along one line at a time) under the infinite all-layers
model introduced by [Akitaya et al., 2017], in which each simple fold is
defined by an infinite line and must fold all layers of paper that intersect
this line. This model is motivated by folding in manufacturing such as
sheet-metal bending. We improve on [Arkin et al., 2004] by giving a
deterministic $O(n)$-time algorithm to decide simple foldability of 1D crease
patterns in the all-layers model. Then we extend this 1D result to 2D, showing
that simple foldability in this model can be decided in linear time for
unassigned axis-aligned orthogonal crease patterns on axis-aligned 2D
orthogonal paper. On the other hand, we show that simple foldability is
strongly NP-complete if a subset of the creases have a mountain-valley
assignment, even for an axis-aligned rectangle of paper.


  We show that vortices of Yang-Mills-Higgs model in $R^{2}$ space can be
regarded as instantons of Yang-Mills model in $R^{2}\times Z_{2}$ space. For
this, we construct the noncommutative $Z_{2}$ space by explicitly fixing the
$Z_{2}$ coordinates and then show, by using the $Z_{2}$ coordinates, that BPS
equation for the vortices can be considered as a self-dual equation. We also
propose the possibility to rewrite the BPS equations for vortices as ADHM
equations through the use of self-dual equation.


  A five dimensional space without invariance under local Lorentz
transformations is studied, and the transformations under which the theory is
invariant are introduced. We show that the Lorentz force is included in the
ensuing equations of motion. The four dimensional Einstein and Maxwell
equations emerge from the field equations while the homogeneous Maxwell
equations naturally appear in the work. A corresponding quantum theory is
introduced. The spectrum of a free particle and the hydrogen atom are
recovered. Quantum structure of Schwarzschild spaces are also studied.


  We have monitored the return to quiescence of novae previously observed in
outburst as supersoft X-ray sources, with optical photometry of the
intermediate polar (IP) V4743 Sgr and candidate IP V2491 Cyg, and optical
spectroscopy of these two and seven other systems. Our sample includes
classical and recurrent novae, short period (few hours), intermediate period
(1-2 days) and long period (symbiotic) binaries. The light curves of V4743 Sgr
and V2491 Cyg present clear periodic modulations. For V4743 Sgr, the modulation
occurs with the beat of the rotational and orbital periods. If the period
measured for V2491 Cyg is also the beat of these two periods, the orbital one
should be almost 17 hours. The recurrent nova T Pyx already shows fragmentation
of the nebular shell less than 3 years after the outburst. While this nova
still had strong [OIII] at this post-outburst epoch, these lines had already
faded after 3 to 7 years in all the others. We did not find any difference in
the ratio of equivalent widths of high ionization/excitation lines to that of
the Hbeta line in novae with short and long orbital period, indicating that
irradiation does not trigger high mass transfer rate from secondaries with
small orbital separation. An important difference between the spectra of RS Oph
and V3890 Sgr and those of many symbiotic persistent supersoft sources is the
absence of forbidden coronal lines. With the X-rays turn-off, we interpret this
as an indication that mass transfer in symbiotics recurrent novae is
intermittent.


  A novel analysis of the gauge theory of the local Lorentz group is
implemented both in flat and in curved space-time, and the resulting dynamics
is analyzed in view of the geometrical interpretation of the gauge potential.
The Yang-Mills picture of local Lorentz transformations is first approached in
a second-order formalism. For the Lagrangian approach to reproduce the second
Cartan structure equation as soon as the Lorentz gauge connections are
identified with the contortion tensor, an interaction term between the Lorentz
gauge fields and the spin connections has to be postulated. The full picture
involving gravity, torsion and spinors is described by a coupled set of field
equations, which allows one to interpret both gravitational spin connections
and matter spin density as the source term for the Yang-Mills equations. The
contortion tensor acquires a propagating character, because of its non-Abelian
feature, and the pure contact interaction is restored in the limit of vanishing
Lorentz connections.


  The leading semiclassical estimates of the electromagnetic Casimir stresses
on a spherical and a cylindrical metallic shell are within 1% of the field
theoretical values. The electromagnetic Casimir energy for both geometries is
given by two decoupled massless scalars that satisfy conformally covariant
boundary conditions. Surface contributions vanish for smooth metallic
boundaries and the finite electromagnetic Casimir energy in leading
semiclassical approximation is due to quadratic fluctuations about periodic
rays in the interior of the cavity only. Semiclassically the non-vanishing
Casimir energy of a metallic cylindrical shell is almost entirely due to
Fresnel diffraction.


  Given a valued field $(K,v)$ and its completion $(\widehat{K},v)$, we study
the set of all possible extensions of $v$ to $\widehat{K}(X)$. We show that any
such extension is closely connected with the underlying subextension
$(K(X)|K,v)$. The connections between these extensions are studied via minimal
pairs, key polynomials, pseudo-Cauchy sequences and implicit constant fields.
As a consequence, we obtain strong ramification theoretic properties of
$(\widehat{K},v)$. We also give necessary and sufficient conditions for
$(K(X),v)$ to be dense in $(\widehat{K}(X),v)$.


  In many musical traditions, the melody line is of primary significance in a
piece. Human listeners can readily distinguish melodies from accompaniment;
however, making this distinction given only the written score -- i.e. without
listening to the music performed -- can be a difficult task. Solving this task
is of great importance for both Music Information Retrieval and musicological
applications. In this paper, we propose an automated approach to identifying
the most salient melody line in a symbolic score. The backbone of the method
consists of a convolutional neural network (CNN) estimating the probability
that each note in the score (more precisely: each pixel in a piano roll
encoding of the score) belongs to the melody line. We train and evaluate the
method on various datasets, using manual annotations where available and solo
instrument parts where not. We also propose a method to inspect the CNN and to
analyze the influence exerted by notes on the prediction of other notes; this
method can be applied whenever the output of a neural network has the same size
as the input.


  We establish that the quadratic non-linear Schr\"odinger equation $$ iu_t +
u_{xx} = u^2$$ where $u: \R \times \R \to \C$, is locally well-posed in
$H^s(\R)$ when $s \geq -1$ and ill-posed when $s < -1$. Previous work of Kenig,
Ponce and Vega had established local well-posedness for $s > -3/4$. The local
well-posedness is achieved by an iteration using a modification of the standard
$X^{s,b}$ spaces. The ill-posedness uses an abstract and general argument
relying on the high-to-low frequency cascade present in the non-linearity, and
a computation of the first non-linear iterate.


  Atkinson [2] found a sequence of three-dimensional hyperbolic polyhedra whose
dihedral angles are $\pi /3$. In this paper, we construct another sequence of
such polyhedra. We also determine the volumes of some of these polyhedra.


  (2+1)-dimensional Georgi-Glashow model, else called the Polyakov model, is
explored at nonzero temperatures and in the regime when the Higgs boson is not
infinitely heavy. The finiteness of the Higgs-boson mass leads to the
appearance of the upper bound on the parameter of the weak-coupling
approximation, necessary to maintain the stochasticity of the Higgs vacuum. The
modification of the finite-temperature behavior of the model emerging due to
the introduction of massless quarks is also discussed.


  The General Randi\'c index $R_\alpha$ of a simple graph $G$ is defined as \[
R_\alpha(G)=\sum_{v_{i}\sim v_{j}} (\delta_{i}\delta_{j})^\alpha, \] where
$\delta_i$ denotes the degree of the vertex $v_i$. Rodr\'iguez-Vel\'azquez and
Tom\'as-Andreu [MATCH Commun. Math. Comput. Chem. 74 (1) (2015) 145--160]
obtained closed formulae for the Randi\'c index $R_{-1/2}$ of Sierpi\'nski-type
polymeric networks, where the base graph is a complete graph, a triangle-free
regular graph or a bipartite semiregular graph. In the present article we
obtain closed formulae for the general Randi\'c index $R_{\alpha}$ of
Sierpi\'nski-type polymeric networks, where the base graph is arbitrary.


  The local, uncorrelated multiplicative noises driving a second-order, purely
noise-induced, ordering phase transition (NIPT) were assumed to be Gaussian and
white in the model of [Phys. Rev. Lett. \textbf{73}, 3395 (1994)]. The
potential scientific and technological interest of this phenomenon calls for a
study of the effects of the noises' statistics and spectrum. This task is
facilitated if these noises are dynamically generated by means of stochastic
differential equations (SDE) driven by white noises. One such case is that of
Ornstein--Uhlenbeck noises which are stationary, with Gaussian pdf and a
variance reduced by the self-correlation time (\tau), and whose effect on the
NIPT phase diagram has been studied some time ago. Another such case is when
the stationary pdf is a (colored) Tsallis' (q)--\emph{Gaussian} which, being a
\emph{fat-tail} distribution for (q>1) and a \emph{compact-support} one for
(q<1), allows for a controlled exploration of the effects of the departure from
Gaussian statistics. As done before with stochastic resonance and other
phenomena, we now exploit this tool to study--within a simple mean-field
approximation and with an emphasis on the \emph{order parameter} and the
``\emph{susceptibility}''--the combined effect on NIPT of the noises'
statistics and spectrum. Even for relatively small (\tau), it is shown that
whereas fat-tail noise distributions ((q>1)) counteract the effect of
self-correlation, compact-support ones ((q<1)) enhance it. Also, an interesting
effect on the susceptibility is seen in the last case.


  Reservoir computing, using nonlinear dynamical systems, offers a
cost-effective alternative to neural networks for complex tasks involving
processing of sequential data, time series modeling, and system identification.
Echo state networks (ESNs), a type of reservoir computer, mirror neural
networks but simplify training. They apply fixed, random linear transformations
to the internal state, followed by nonlinear changes. This process, guided by
input signals and linear regression, adapts the system to match target
characteristics, reducing computational demands. A potential drawback of ESNs
is that the fixed reservoir may not offer the complexity needed for specific
problems. While directly altering (training) the internal ESN would reintroduce
the computational burden, an indirect modification can be achieved by
redirecting some output as input. This feedback can influence the internal
reservoir state, yielding ESNs with enhanced complexity suitable for broader
challenges. In this paper, we demonstrate that by feeding some component of the
reservoir state back into the network through the input, we can drastically
improve upon the performance of a given ESN. We rigorously prove that, for any
given ESN, feedback will almost always improve the accuracy of the output. For
a set of three tasks, each representing different problem classes, we find that
with feedback the average error measures are reduced by $30\%-60\%$.
Remarkably, feedback provides at least an equivalent performance boost to
doubling the initial number of computational nodes, a computationally expensive
and technologically challenging alternative. These results demonstrate the
broad applicability and substantial usefulness of this feedback scheme.


  We introduce a category of Kuranishi presentations, whose objects are a
variant of the Kuranishi structures introduced by Fukaya and Ono, and which can
be seen as a refinement of the version studied by Pardon. We then formulate the
notion of virtual chains categorically as a natural transformation between two
functors from this category to the category of chain complexes; we call such a
datum 'a theory of virtual counts'. To show that this definition carries
non-trivial content, we then construct a multicategory whose objects are
Kuranishi flow categories, and show that a theory of virtual counts determines
a multifunctor to the multicategory of chain complexes. We then implement this
construction in the setting of Hamiltonian Floer theory, borrowing from some
joint work with Groman and Varolgunes, yielding a construction of Hamiltonian
Floer groups (and operations on them) as an output of this machine. We plan to
provide a similar account for Lagrangian Floer theory in subsequent joint work.


  We extend a Poincar\'{e}-type inequality for functions with large zero-sets
by Jiang and Lin to fractional Sobolev spaces. As a consequence, we obtain a
Hausdorff dimension estimate on the size of zero sets for fractional Sobolev
functions whose inverse is integrable. Also, for a suboptimal Hausdorff
dimension estimate, we give a completely elementary proof based on a pointwise
Poincar\'{e}-style inequality.


  Third-generation (3G) gravitational-wave (GW) detectors will be able to
observe binary-black-hole mergers (BBHs) up to redshift of $\sim 30$. This
gives unprecedented access to the formation and evolution of BBHs throughout
cosmic history. In this paper we consider three sub-populations of BBHs
originating from the different evolutionary channels: isolated formation in
galactic fields, dynamical formation in globular clusters and mergers of black
holes formed from Population III (Pop III) stars at very high redshift. Using
input from populations synthesis analyses, we created two months of simulated
data of a network of 3G detectors made of two Cosmic Explorers and an Einstein
Telescope, consisting of $\sim16000$ field and cluster BBHs as well as
$\sim400$ Pop III BBHs. First, we show how one can use non-parametric models to
infer the existence and characteristic of a primary and secondary peak in the
merger rate distribution. In particular, the location and the height of the
secondary peak around $z\approx 12$, arising from the merger of Pop III
remnants, can be constrained at $\mathcal{O}(10\%)$ level. Then we perform a
modeled analysis, using phenomenological templates for the merger rates of the
three sub-population, and extract the branching ratios and the characteristic
parameters of the merger rate densities of the individual formation channels.
With this modeled method, the uncertainty on the measurement of the fraction of
Pop III BBHs can be improved to $\lesssim 10\%$, while the ratio between field
and cluster BBHs can be measured with an uncertainty of $\sim 50\%$.


  A harmonic self-homeomorphism of a disk does not increase the area of any
concentric disk.


  The K\"ahler cone of a compact K\"ahler manifold carries a natural Riemannian
metric, given by the intersection product of its cohomology ring. We give
cohomological expressions for the Levi-Civita connection and curvature tensor
of this metric, and determine when the metric is complete.


  Global sensitivity analysis is now established as a powerful approach for
determining the key random input parameters that drive the uncertainty of model
output predictions. Yet the classical computation of the so-called Sobol'
indices is based on Monte Carlo simulation, which is not affordable when
computationally expensive models are used, as it is the case in most
applications in engineering and applied sciences. In this respect metamodels
such as polynomial chaos expansions (PCE) and Gaussian processes (GP) have
received tremendous attention in the last few years, as they allow one to
replace the original, taxing model by a surrogate which is built from an
experimental design of limited size. Then the surrogate can be used to compute
the sensitivity indices in negligible time. In this chapter an introduction to
each technique is given, with an emphasis on their strengths and limitations in
the context of global sensitivity analysis. In particular, Sobol' (resp. total
Sobol') indices can be computed analytically from the PCE coefficients. In
contrast, confidence intervals on sensitivity indices can be derived
straightforwardly from the properties of GPs. The performance of the two
techniques is finally compared on three well-known analytical benchmarks
(Ishigami, G-Sobol and Morris functions) as well as on a realistic engineering
application (deflection of a truss structure).


  Insufficient labeled training datasets is one of the bottlenecks of 3D hand
pose estimation from monocular RGB images. Synthetic datasets have a large
number of images with precise annotations, but the obvious difference with
real-world datasets impacts the generalization. Little work has been done to
bridge the gap between two domains over their wide difference. In this paper,
we propose a domain adaptation method called Adaptive Wasserstein Hourglass (AW
Hourglass) for weakly-supervised 3D hand pose estimation, which aims to
distinguish the difference and explore the common characteristics (e.g. hand
structure) of synthetic and real-world datasets. Learning the common
characteristics helps the network focus on pose-related information. The
similarity of the characteristics makes it easier to enforce domain-invariant
constraints. During training, based on the relation between these common
characteristics and 3D pose learned from fully-annotated synthetic datasets, it
is beneficial for the network to restore the 3D pose of weakly labeled
real-world datasets with the aid of 2D annotations and depth images. While in
testing, the network predicts the 3D pose with the input of RGB.


  A common question when studying a class of context-free grammars is whether
equivalence is decidable within this class. We answer this question positively
for the class of Clark-congruential grammars, which are of interest to
grammatical inference. We also consider the problem of checking whether a given
CFG is Clark-congruential, and show that it is decidable given that the CFG is
a DCFG.


  This paper deals with the discrete system being the finite-difference
approximation of the Sturm-Liouville problem with frozen argument. The inverse
problem theory is developed for this discrete system. We describe the two
principal cases: degenerate and non-degenerate. For these two cases,
appropriate inverse problems statements are provided, uniqueness theorems are
proved, and reconstruction algorithms are obtained. Moreover, the relationship
between the eigenvalues of the continuous problem and its finite-difference
approximation is investigated. We obtain the "correction terms" for
approximation of the discrete problem eigenvalues by using the eigenvalues of
the continuous problem. Relying on these results, we develop a numerical
algorithm for recovering the potential of the Sturm-Liouville operator with
frozen argument from a finite set of eigenvalues. The effectiveness of this
algorithm is illustrated by numerical examples.


  In this work the specific heat of a two-dimensional Hubbard model, suitable
to discuss high-$T_c$ superconductors (HTSC), is studied taking into account
hopping to first ($t$) and second ($t_2$) nearest neighbors. Experimental
results for the specific heat of HTSC's, for instance, the YBCO and LSCO,
indicate a close relation between the pseudogap and the specific heat. In the
present work, we investigate the specific heat by the Green's function method
within a $n$-pole approximation. The specific heat is calculated on the
pseudogap and on the superconducting regions. In the present scenario, the
pseudogap emerges when the antiferromagnetic (AF) fluctuations become
sufficiently strong. The specific heat jump coefficient $\Delta \gamma$
decreases when the total occupation per site ($n_T$) reaches a given value.
Such behavior of $\Delta \gamma$ indicates the presence of a pseudogap in the
regime of high occupation.


  In the preparation of Caf\'e Latte, spectacular layer formation can occur
between the expresso shot in a glass of milk and the milk itself. Xue et al.
(Nat. Commun., vol. 8, 2017, pp. 1-6) showed that the injection velocity of
expresso determines the depth of coffee-milk mixture. After a while when a
stable stratification forms in the mixture, the layering process can be
modelled as a double diffusive convection system with a stably-stratified
coffee-milk mixture cooled from the side. More specifically, we perform
(two-dimensional) direct numerical simulations of laterally cooled double
diffusive convection for a wide parameter range, where the convective flow is
driven by a lateral temperature gradient while stabilized by a vertical
concentration gradient. When the thermal driving force dominates over the
stabilizing force, the flow behaves like vertical convection in which a
large-scale circulation develops. However, with increasing strength of the
stabilizing force, a meta-stable layered regime emerges. Initially, several
vertically-stacked convection rolls develop, and these well-mixed layers are
separated by sharp interfaces with large concentration gradients. The initial
thickness of these emerging layers can be estimated by balancing the work
exerted by thermal driving and the required potential energy to bring fluid out
of its equilibrium position in the stably stratified fluid. In the layered
regime, we further observe successive layer merging, and eventually only a
single convection roll remains. We elucidate the following merging mechanism:
As weakened circulation leads to accumulation of hot fluid adjacent to the hot
sidewall, larger buoyancy forces associated with hotter fluid eventually break
the layer interface. Then two layers merge into a larger layer, and circulation
establishes again within the merged structure.


  Using the unification of the chiral SU(3) model and QCD sum rules, we deduce
the in-medium properties of $K^\pm_1$ meson. Within the chiral SU(3) model,
medium modified gluon and quark condensates are evaluated through their
interactions with the scalar fields ($\sigma$, $\zeta$, $\delta$, and $\chi$).
These condensates are further used as input in the Borel transformed equations
of QCD sum rules to evaluate the in-medium mass of strange $K^\pm_1$ meson. The
in-medium property of the above meson can be used to study the restoration of
chiral symmetry in nuclear matter.


  We prove general Dwork-type congruences for Hasse--Witt matrices attached to
tuples of Laurent polynomials. We apply this result to establishing arithmetic
and $p$-adic analytic properties of functions originating from polynomial
solutions modulo $p^s$ of Knizhnik--Zamolodchikov (KZ) equations, solutions
which come as coefficients of master polynomials and whose coefficients are
integers. As an application we show that the $p$-adic KZ connection associated
with the family of hyperelliptic curves $y^2=(t-z_1)\dots (t-z_{2g+1})$ has an
invariant subbundle of rank $g$. Notice that the corresponding complex KZ
connection has no nontrivial subbundles due to the irreducibility of its
monodromy representation.


  Although we have witnessed great success of pre-trained models in natural
language processing (NLP) and computer vision (CV), limited progress has been
made for general time series analysis. Unlike NLP and CV where a unified model
can be used to perform different tasks, specially designed approach still
dominates in each time series analysis task such as classification, anomaly
detection, forecasting, and few-shot learning. The main challenge that blocks
the development of pre-trained model for time series analysis is the lack of a
large amount of data for training. In this work, we address this challenge by
leveraging language or CV models, pre-trained from billions of tokens, for time
series analysis. Specifically, we refrain from altering the self-attention and
feedforward layers of the residual blocks in the pre-trained language or image
model. This model, known as the Frozen Pretrained Transformer (FPT), is
evaluated through fine-tuning on all major types of tasks involving time
series. Our results demonstrate that pre-trained models on natural language or
images can lead to a comparable or state-of-the-art performance in all main
time series analysis tasks, as illustrated in Figure 1. We also found both
theoretically and empirically that the self-attention module behaviors
similarly to principle component analysis (PCA), an observation that helps
explains how transformer bridges the domain gap and a crucial step towards
understanding the universality of a pre-trained transformer.The code is
publicly available at https://github.com/DAMO-DI-ML/One_Fits_All.


  Panel studies typically suffer from attrition, which reduces sample size and
can result in biased inferences. It is impossible to know whether or not the
attrition causes bias from the observed panel data alone. Refreshment samples -
new, randomly sampled respondents given the questionnaire at the same time as a
subsequent wave of the panel - offer information that can be used to diagnose
and adjust for bias due to attrition. We review and bolster the case for the
use of refreshment samples in panel studies. We include examples of both a
fully Bayesian approach for analyzing the concatenated panel and refreshment
data, and a multiple imputation approach for analyzing only the original panel.
For the latter, we document a positive bias in the usual multiple imputation
variance estimator. We present models appropriate for three waves and two
refreshment samples, including nonterminal attrition. We illustrate the
three-wave analysis using the 2007-2008 Associated Press-Yahoo! News Election
Poll.


  In the Internet era, online social media emerged as the main tool for sharing
opinions and information among individuals. In this work we study an adaptive
model of a social network where directed links connect users with similar
tastes, and over which information propagates through social recommendation.
Agent-based simulations of two different artificial settings for modeling user
tastes are compared with patterns seen in real data, suggesting that users
differing in their scope of interests is a more realistic assumption than users
differing only in their particular interests. We further introduce an extensive
set of similarity metrics based on users' past assessments, and evaluate their
use in the given social recommendation model with both artificial simulations
and real data. Superior recommendation performance is observed for similarity
metrics that give preference to users with small scope---who thus act as
selective filters in social recommendation.