  We study the generation of baryon asymmetry through the flavored resonant
leptogenesis in the $U(1)_X$ extension of the Standard Model. Being a
generalization of the $U(1)_{B\text{-}L}$, $U(1)_X$ is an ultraviolet-complete
model of the right-handed neutrinos (RHNs), whose CP violating
out-of-equilibrium decays lead to the generation of baryon asymmetry via
leptogenesis. We can also explain the neutrino masses via the seesaw mechanism
in this model. We consider three different cases for different $U(1)_X$ charges
of the scalar particle responsible for $U(1)_X$ breaking at TeV-scale. These
include the popular $U(1)_{B\text{-}L}$ and $U(1)_{R}$ models, as well as a
$U(1)_C$ model which maximizes the collider signal. We numerically solve the
flavored Boltzmann transport equations to calculate the total baryon asymmetry.
We show that all three cases considered here can naturally explain the observed
baryon asymmetry of the Universe in a large portion of the available parameter
space, while satisfying the neutrino oscillation data. We find that the
$U(1)_C$ case offers successful leptogenesis in a larger portion of the
parameter space as compared to $U(1)_{B\text{-}L}$ and $U(1)_{R}$. We also
perform a comparative study between the flavored and unflavored leptogenesis
parameter space. Finally, we also study the collider prospects for all these
scenarios using the lepton number violating signal of $pp\to \ell^\pm
\ell^\pm+$jets mediated by the $Z'$ boson associated with $U(1)_X$. We find
that HL-LHC may be able to probe a small portion of the relevant parameter
space having successful leptogenesis, if neutrinos have normal mass ordering,
while a $\sqrt s=100$ TeV future collider can access a much larger region of
the parameter space, thereby offering an opportunity to test resonant
leptogenesis in the $U(1)_X$ model.


  The Federal Communications Commission (FCC) has allocated the 6 GHz band
(5.925 - 7.125 GHz) for unlicensed, shared use in the US. Incumbents in the
band are protected via Low Power Indoor (LPI) rules that do not require the use
of an Automatic Frequency Control (AFC) mechanism and Standard Power (SP) rules
which do. As the deployment of Wi-Fi 6E APs implementing LPI rules have been
increasing, there is limited research examining the real-world interference
potential of dense LPI deployments to fixed links, which remains a concern for
incumbents. We have conducted a first-of-its-kind extensive measurement
campaign of a dense indoor Wi-Fi 6E network at the University of Michigan,
which includes walking, driving, and drone measurements to assess outdoor
beacon Received Signal Strength Indicator (RSSI), building entry loss (BEL),
channel utilization, and appropriate enabling signal level for a proposed
client-to-client (C2C) mode in 6 GHz. Our detailed measurements under various
conditions show median outdoor RSSI between -75 dBm and -85 dBm, BEL between 12
dB and 16 dB through double-pane low-emission windows, and only 5% of indoor
Basic Service Set Identifiers (BSSIDs) observed outdoors. Our overall
conclusion is that the probability of interference to incumbent fixed links is
low, but more research is required to determine the appropriate signal level
for the C2C enabling signal.


  This article is a survey about applications of bi-quantization theory in Lie
theory. We focus on a conjecture of M. Duflo. Most of the applications are
coming from our article with Alberto Cattaneo and some extensions are relating
discussions with my student. The end of the article is completely new. We prove
that the conjecture E=1 implies the Kashiwara-Vergne conjecture. Our
deformation is non geometric but uses a polynomial deformation of the
coefficients.


  We consider discretization of the 'geometric cover problem' in the plane:
Given a set $P$ of $n$ points in the plane and a compact planar object $T_0$,
find a minimum cardinality collection of planar translates of $T_0$ such that
the union of the translates in the collection contains all the points in $P$.
We show that the geometric cover problem can be converted to a form of the
geometric set cover, which has a given finite-size collection of translates
rather than the infinite continuous solution space of the former. We propose a
reduced finite solution space that consists of distinct canonical translates
and present polynomial algorithms to find the reduce solution space for disks,
convex/non-convex polygons (including holes), and planar objects consisting of
finite Jordan curves.


  We present chemical abundance analysis of a sample of 15 red giant branch
(RGB) stars of the Globular Cluster NGC~1851 distributed along the two RGBs of
the (v, v-y) CMD. We determined abundances for C+N+O, Na, $\alpha$, iron-peak,
and s-elements. We found that the two RGB populations significantly differ in
their light (N,O,Na) and s-element content. On the other hand, they do not show
any significant difference in their $\alpha$ and iron-peak element content.
More importantly, the two RGB populations do not show any significant
difference in their total C+N+O content. Our results do not support previous
hypotheses suggesting that the origin of the two RGBs and the two subgiant
branches of the cluster is related to a different content of either $\alpha$
(including Ca) or iron-peak elements, or C+N+O abundance, due to a second
generation polluted by SNeII.


  We have investigated negative-parity uudd\bar{s} pentaquarks by employing a
quark model with the meson exchange and the effective gluon exchange as qq and
q\bar{q} interactions. The system for the five quarks is dynamically solved;
the effects of qq and q\bar{q} correlations on the wave function are taken into
account.
  It is found that the masses of the pentaquarks are reasonably low. The
lowest-mass state is found to be TJ^P=0{1/2}^-, and the next lowest one is
0{3/2}^-. The former is reported to have a large width. We argue the observed
narrow peak corresponds to the latter state. It is still necessary to introduce
an extra attraction to reduce the mass further by 140 -- 280 MeV to reproduce
the observed \Theta^+ mass. Since their level splitting is found to be less
than 80 MeV, the lower level will not become a bound state below the NK
threshold even after such an attraction is introduced.
  It is also found that the relative distance of two quarks with the attractive
interaction is found to be by about 1.2 -- 1.3 times closer than that of the
repulsive one. The two-body correlation seems important in the pentaquark
systems.


  In $L_2({\mathbb R}^3;{\mathbb C}^3)$, we consider a selfadjoint operator
${\mathcal L}_\varepsilon$, $\varepsilon >0$, given by the differential
expression $\mu_0^{-1/2}\operatorname{curl} \eta(\mathbf{x}/\varepsilon)^{-1}
\operatorname{curl} \mu_0^{-1/2} - \mu_0^{1/2}\nabla
\nu(\mathbf{x}/\varepsilon) \operatorname{div} \mu_0^{1/2}$, where $\mu_0$ is a
constant positive matrix, a matrix-valued function $\eta(\mathbf{x})$ and a
real-valued function $\nu(\mathbf{x})$ are periodic with respect to some
lattice, positive definite and bounded. We study the behavior of the
operator-valued functions $\cos (\tau {\mathcal L}_\varepsilon^{1/2})$ and
${\mathcal L}_\varepsilon^{-1/2} \sin (\tau {\mathcal L}_\varepsilon^{1/2})$
for $\tau \in {\mathbb R}$ and small $\varepsilon$. It is shown that these
operators converge to the corresponding operator-valued functions of the
operator ${\mathcal L}^0$ in the norm of operators acting from the Sobolev
space $H^s$ (with a suitable $s$) to $L_2$. Here ${\mathcal L}^0$ is the
effective operator with constant coefficients. Also, an approximation with
corrector in the $(H^s \to H^1)$-norm for the operator ${\mathcal
L}_\varepsilon^{-1/2} \sin (\tau {\mathcal L}_\varepsilon^{1/2})$ is obtained.
We prove error estimates and study the sharpness of the results regarding the
type of the operator norm and regarding the dependence of the estimates on
$\tau$. The results are applied to homogenization of the Cauchy problem for the
nonstationary Maxwell system in the case where the magnetic permeability is
equal to $\mu_0$, and the dielectric permittivity is given by the matrix
$\eta(\mathbf{x}/\varepsilon)$.


  It is well accepted that image segmentation can benefit from utilizing
multilevel cues. The paper focuses on utilizing the FCNN-based dense semantic
predictions in the bottom-up image segmentation, arguing to take semantic cues
into account from the very beginning. By this we can avoid merging regions of
similar appearance but distinct semantic categories as possible. The semantic
inefficiency problem is handled. We also propose a straightforward way to use
the contour cues to suppress the noise in multilevel cues, thus to improve the
segmentation robustness. The evaluation on the BSDS500 shows that we obtain the
competitive region and boundary performance. Furthermore, since all individual
regions can be assigned with appropriate semantic labels during the
computation, we are capable of extracting the adjusted semantic segmentations.
The experiment on Pascal VOC 2012 shows our improvement to the original
semantic segmentations which derives directly from the dense predictions.


  Understanding genetic variation, e.g., through mutations, in organisms is
crucial to unravel their effects on the environment and human health. A
fundamental characterization can be obtained by solving the haplotype assembly
problem, which yields the variation across multiple copies of chromosomes.
Variations among fast evolving viruses that lead to different strains (called
quasispecies) are also deciphered with similar approaches. In both these cases,
high-throughput sequencing technologies that provide oversampled mixtures of
large noisy fragments (reads) of genomes, are used to infer constituent
components (haplotypes or quasispecies). The problem is harder for polyploid
species where there are more than two copies of chromosomes. State-of-the-art
neural approaches to solve this NP-hard problem do not adequately model
relations among the reads that are important for deconvolving the input signal.
We address this problem by developing a new method, called NeurHap, that
combines graph representation learning with combinatorial optimization. Our
experiments demonstrate substantially better performance of NeurHap in real and
synthetic datasets compared to competing approaches.


  It is known that an automorphism of $F_g$, the oriented closed surface of
genus $g$, is extendable over the 4-sphere $S^4$ if and only if it has a
bounding invariant spin structure \cite{WsWz}.
  We show that each automorphism of $F_g$ has an invariant spin structure, and
obtain a stably extendable result: Each automorphism of $F_g$ is extendable
over $S^4$ after a connected sum with the identity map on the torus. Then each
automorphism of an oriented once punctured surface is extendable over $S^4$.
  For each $g\neq 4$, we construct a periodic map on $F_g$ that is not
extendable over $S^4$, and we prove that every periodic map on $F_4$ is
extendable over $S^4$, which answer a question in \cite{WsWz}.
  We illustrate for an automorphism $f$ of $F_g$, how to find its invariant
spin structures, bounding or not; and once $f$ has a bounding invariant spin
structure, how to construct an embedding $F_g\hookrightarrow S^4$ so that $f$
is extendable with respect to this embedding.


  In the paper we introduce a notion of the Bruck-Reilly $\lambda$-polycyclic
extension of a monoid $S$ with a homomorphism $\theta$ which is an analogue of
the Bruck-Reilly extension of a monoid $S$. We describe idempotens of the
semigroup $\left(\mathscr{P}_{\lambda}(\theta,S),*\right)$ and Green's
relations on $\left(\mathscr{P}_{\lambda}(\theta,S),*\right)$. It is proved
that $\left(\mathscr{P}_{\lambda}(\theta,S),*\right)$ is a $0$-simple semigroup
for any semigroup $S$. We find necessary and sufficient conditions on a monoid
$S$ and a homomorphism $\theta$ under which the semigroup
$\left(\mathscr{P}_{\lambda}(\theta,S),*\right)$ is regular, inverse,
$0$-bisimple, combinatorial, congruence free, or inverse 0-E-unitary. Also we
study topologizations of the semigroup
$\left(\mathscr{P}_{\lambda}(\theta,S),*\right)$.


  We study the temporal linear instability of channel flow subject to a
tensorial slip boundary condition that models the slip effect induced by
microgroove-type super-hydrophobic surfaces. The microgrooves are not
necessarily aligned with the driving pressure gradient. Pralits et al. Phys.
Rev. Fluids 2, 013901 (2017) investigated the same problem and reported that a
proper tilt angle of the microgrooves about the driving pressure gradient can
reduce the critical Reynolds number and that the flow with a single
superhydrophobic wall is much more unstable/less stable than that with two
superhydrophobic walls. In contrast, we show that the lowest critical Reynolds
number is always realized with two superhydrophobic walls, and we obtain
critical Reynolds numbers significantly lower than the reported. Besides, we
show that the critical Reynolds number can be further reduced by increasing the
anisotropy in the slip length. As the tilt angle changes, there appears to be a
strong correlation between the strength of the instability and the magnitude of
the cross-flow component of the base flow incurred by the tilt angle. In case
the tilt angles of the microgrooves differ on the two walls, the critical
Reynolds number increases as the difference in the tilt angles increases, i.e.
two superhydrophobic walls with parallel microgrooves give the lowest critical
Reynolds number. The results are informative for designing the microgroove-type
wall texture to introduce instability at low Reynolds number channel flow,
which may be of interest for enhancing mixing or heat transfer in small flow
systems where turbulence cannot be triggered.


  We perform an analysis of photometric redshifts estimated by using a
non-representative training sets in magnitude space. We use the ANNz2 and GPz
algorithms to estimate the photometric redshift both in simulations as well as
in real data from the Sloan Digital Sky Survey (DR12). We show that for the
representative case, the results obtained by using both algorithms have the
same quality, either using magnitudes or colours as input. In order to reduce
the errors when estimating the redshifts with a non-representative training
set, we perform the training in colour space. We estimate the quality of our
results by using a mock catalogue which is split samples cuts in the $r$-band
between $19.4< r< 20.8$. We obtain slightly better results with GPz on single
point z-phot estimates in the complete training set case, however the
photometric redshifts estimated with ANNz2 algorithm allows us to obtain mildly
better results in deeper $r$-band cuts when estimating the full redshift
distribution of the sample in the incomplete training set case. By using a
cumulative distribution function and a Monte-Carlo process, we manage to define
a photometric estimator which fits well the spectroscopic distribution of
galaxies in the mock testing set, but with a larger scatter. To complete this
work, we perform an analysis of the impact on the detection of clusters via
density of galaxies in a field by using the photometric redshifts obtained with
a non-representative training set.


  In this paper, we present rotational and self-similar solutions for the
compressible Euler equations in R^3 using the separation method. These
solutions partly complement Yuen's irrotational and elliptic solutions in R^3
[Commun. Nonlinear Sci. Numer. Simul. 17 (2012), 4524-4528] as well as
rotational and radial solutions in R^2 [Commun. Nonlinear Sci. Numer. Simul. 19
(2014), 2172-2180]. A newly deduced Emden dynamical system is obtained. Some
blowup phenomena and global existences of the responding solutions can be
determined. The 3D rotational solutions provide concrete reference examples for
vortices in computational fluid dynamics.


  In this paper, we establish the continuous dependence for the non-resistive
MHD equations in Sobolev spaces. Our obtained result fills considerably the
recent result [C. Fefferman, D. McCormick, J. Robinson and J. Rodrigo, Higher
order commutator estimates and local existence for the non-resistive MHD
equations and related models, J. Funct. Anal. 267 (2014), 1035--1056] for the
continuous dependence on initial data. We also show that this result is
optimal. More precisely, we proved that the data-to-solution map is not uniform
continuous dependence in Sobolev spaces.


  We present a wind tunnel experiment on a large smooth circular cylinder
mounted between walls. The Reynolds number range of interest is [800 000 : 2
170 000]. This low supercritical regime is often encountered in wind
engineering applications, especially the excitation of flexible circular
structures by vortex shedding. Main measurements are the unsteady wall pressure
distributions around the cylinder by means of synchronized pressure taps with
high resolution in time and space. By using the bi-orthogonal decomposition of
this set of signals, twin Strouhal numbers are detected which correspond to the
second and the fourth terms of the decomposition. These terms are found to
produce the unsteady lift on the cylinder with a main contribution of the
second one for which the associated Strouhal number is close to the one usually
found at subcritical Reynolds numbers.


  The aim of two-dimensional line spectral estimation is to super-resolve the
spectral point sources of the signal from time samples. In many associated
applications such as radar and sonar, due to cut-off and saturation regions in
electronic devices, some of the numbers of samples are corrupted by spiky
noise. To overcome this problem, we present a new convex program to
simultaneously estimate spectral point sources and spiky noise in two
dimensions. To prove uniqueness of the solution, it is sufficient to show that
a dual certificate exists. Construction of the dual certificate imposes a mild
condition on the separation of the spectral point sources. Also, the number of
spikes and detectable sparse sources are shown to be a logarithmic function of
the number of time samples. Simulation results confirm the conclusions of our
general theory.


  We study a discrete-time finite-horizon two-players nonzero-sum stopping game
where the filtration of Player 1 is richer than the filtration of Player 2. A
major difficulty which is caused by the information asymmetry is that Player 2
may not know whether Player 1 has already stopped the game or not. Furthermore,
the classical backward-induction approach is not applicable in the current
setup. This is because when the informed player decides not to stop, he reveals
information to the uninformed player and hence the decision of the uninformed
player at time $t$ may not be determined by the play after time $t$, but also
on the play before time $t$. In the current work we initially show that the
expected utility of Player 2 will remain the same even if he knows whether
Player 1 has already stopped. Then, this result is applied in order to prove
that, under appropriate conditions, a recursive construction in the style of
Hamad\'ene and Zhang (2010) converges to a pure-strategy Nash equilibrium.


  Given a tetrahedral mesh and objective functionals measuring the mesh quality
which take into account the shape, size, and orientation of the mesh elements,
our aim is to improve the mesh quality as much as possible. In this paper, we
combine the moving mesh smoothing, based on the integration of an ordinary
differential equation coming from a given functional, with the lazy flip
technique, a reversible edge removal algorithm to modify the mesh connectivity.
Moreover, we utilize radial basis function (RBF) surface reconstruction to
improve tetrahedral meshes with curved boundary surfaces. Numerical tests show
that the combination of these techniques into a mesh improvement framework
achieves results which are comparable and even better than the previously
reported ones.


  Pyroxenes with the general formula $AM$Si$_2$O$_6$ ($A$ = mono- or divalent
metal, $M$ = di- or trivalent metal) are shown to be a new class of
multiferroic materials. In particular, we have found so far that
NaFeSi$_2$O$_6$ becomes ferroelectric in a magnetically ordered state below
$\simeq 6$ K. Similarly, magnetically driven ferroelectricity is also detected
in the Li homologues, LiFeSi$_2$O$_6$ ($T_C \simeq 18$ K) and LiCrSi$_2$O$_6$
($T_C \simeq 11$ K). In all these monoclinic systems the electric polarization
can be strongly modified by magnetic fields. Measurements of magnetic
susceptibility, pyroelectric current and dielectric constants (and their
dependence on magnetic field) are performed using a natural crystal of aegirine
(NaFeSi$_2$O$_6$) and synthetic crystals of LiFeSi$_2$O$_6$ and LiCrSi$_2$O$_6$
grown from melt solution. For NaFeSi$_2$O$_6$ a temperature versus magnetic
field phase diagram for NaFeSi$_2$O$_6$ is proposed. Exchange constants are
computed on the basis of {\it ab initio} band structure calculations. The
possibility of a spiral magnetic structure caused by frustration as origin of
the multiferroic behaviour is discussed. We propose that other pyroxenes may
also be multiferroic, and that the versatility of this family offers an
exceptional opportunity to study general conditions for and mechanisms of
magnetically driven ferroelectricity.


  The matrix elements of the multi-channel Jost matrices are written in such a
way that their dependencies on all possible odd powers of channel momenta are
factorized explicitly. As a result the branching of the Riemann energy surface
at all the channel thresholds is represented in them via exact analytic
expressions. The remaining single-valued functions of the energy are expanded
in the Taylor series near an arbitrary point on the real axis. Using the thus
obtained Jost matrices, the $S$-matrix is constructed and then the scattering
cross section is calculated, which therefore depends on the Taylor expansion
coefficients. These coefficients are considered as the adjustable parameters
that are optimized to fit a given set of experimental data. After finding the
coefficients, the resonances are located as zeros of the Jost matrix
determinant at complex energies. Within this approach the $S$-matrix has proper
analytic structure. This enables us not only to locate multi-channel resonances
but also to reproduce their partial widths as well as the scattering cross
section in the channels for which the data are not available.


  We describe how to convert the heuristic search algorithm A* into an anytime
algorithm that finds a sequence of improved solutions and eventually converges
to an optimal solution. The approach we adopt uses weighted heuristic search to
find an approximate solution quickly, and then continues the weighted search to
find improved solutions as well as to improve a bound on the suboptimality of
the current solution. When the time available to solve a search problem is
limited or uncertain, this creates an anytime heuristic search algorithm that
allows a flexible tradeoff between search time and solution quality. We analyze
the properties of the resulting Anytime A* algorithm, and consider its
performance in three domains; sliding-tile puzzles, STRIPS planning, and
multiple sequence alignment. To illustrate the generality of this approach, we
also describe how to transform the memory-efficient search algorithm Recursive
Best-First Search (RBFS) into an anytime algorithm.


  The interaction between molecules is commonly ignored in surface-enhanced
Raman scattering (SERS). Under this assumption, the total SERS signal is
described as the sum of the individual contributions of each molecule treated
independently. We adopt here an optomechanical description of SERS within a
cavity quantum electrodynamics framework to study how collective effects emerge
from the quantum correlations of distinct molecules. We derive analytical
expressions for identical molecules and implement numerical simulations to
analyze two types of collective phenomena: (i) a decrease of the laser
intensity threshold to observe strong non-linearities as the number of
molecules increases, within intense illumination, and (ii) identification of
superradiance in the SERS signal, namely a quadratic scaling with the number of
molecules. The laser intensity required to observe the latter in the
anti-Stokes scattering is relatively moderate, which makes it particularly
accessible to experiments. Our results also show that collective phenomena can
survive in the presence of moderate homogeneous and inhomogeneous broadening.


  In this note, we give an elementary proof that the Hausdorff dimension of the
Rauzy gasket is at most 1.7407, improving upon results of Avila et al. and
Fougeron.


  The number of sudden spin-ups in radio pulsars known as pulsar glitches has
increased over the years. Though a consensus has not been reached with regards
to the actual cause of the phenomenon, the electromagnetic braking torque on
the crust quantified via the magnitude of pulsar spin frequency first
derivative, $ \dot{\nu} $ is a key factor in mechanisms put across toward the
understanding of the underlying principles involved. The glitch size has been
used to establish a quantity used to constrain the mean possible change in
pulsar spin frequency $ (\nu) $ per year due to a glitch known as the `glitch
activity'. Traditionally, the glitch activity parameter $ A_{g} $ is calculated
from the cumulative glitch sizes in a pulsar at a certain observational time
span. In this analysis, we test the possibility of of quantifying the $ A_{g} $
with the pulsars main spin frequency derivatives (i.e. $ \dot{\nu} $ and
$\ddot{\nu} $). In this approach, the ratio of the frequency derivatives, i.e.
$ |\ddot{\nu}|/\dot{\nu}^{2} $ is seen to constrains the glitch activity in
radio pulsars. The glitch size is found to be independent of the magnitude of
the ratio, however, based on the recorded glitch events, the lower end of $
|\ddot{\nu}|/\dot{\nu}^{2} $ distribution appear to have more glitches. The
minimum inter-glitch time interval in the ensemble of pulsars scale with the
ratio as $t_{g} \sim 3.35(|\ddot{\nu}|/\dot{\nu}^{2})^{0.23} $. The $ A_{g} $
quantified in this analysis supports the idea of neutron star inner-crust
superfluid being the reservoir of momentum transferred during glitches. It
suggests that the moment of inertia of the inner-crust to be at most 10 % of
the entire neutron star moment of inertia.


  Electric-field control of magnetism provides a promising route towards
ultralow power information storage and sensor technologies. The effects of
magneto-ionic motion have so far been prominently featured in the direct
modification of interface chemical and physical characteristics. Here we
demonstrate magnetoelectric coupling moderated by voltage-driven oxygen
migration beyond the interface limit in relatively thick AlOx/GdOx/Co (15 nm)
films. Oxygen migration and its ramifications on the Co magnetization are
quantitatively mapped with polarized neutron reflectometry under thermal and
electro-thermal conditionings. The depth-resolved profiles uniquely identify
interfacial and bulk behaviors and a semi-reversible suppression and recovery
of the magnetization. Magnetometry measurements show that the conditioning
changes the microstructure so as to disrupt long-range ferromagnetic ordering,
resulting in an additional magnetically soft phase. X-ray spectroscopy confirms
electric field induced changes in the Co oxidation state but not in the Gd,
suggesting that the GdOx transmits oxygen but does not source or sink it. These
results together provide crucial insight into controlling magnetic
heterostructures via magneto-ionic motion, not only at the interface, but also
throughout the bulk of the films.


  This paper investigates the influence of directed networks on decentralized
stochastic non-convex optimization associated with column-stochastic mixing
matrices. Surprisingly, we find that the canonical spectral gap, a widely used
metric in undirected networks, is insufficient to characterize the impact of
directed topology on decentralized algorithms. To overcome this limitation, we
introduce a novel metric termed equilibrium skewness. This metric, together
with the spectral gap, accurately and comprehensively captures the influence of
column-stochastic mixing matrices on decentralized stochastic algorithms. With
these two metrics, we clarify, for the first time, how the directed network
topology influences the performance of prevalent algorithms such as Push-Sum
and Push-Diging. Furthermore, we establish the first lower bound of the
convergence rate for decentralized stochastic non-convex algorithms over
directed networks. Since existing algorithms cannot match our lower bound, we
further propose the MG-Push-Diging algorithm, which integrates Push-Diging with
a multi-round gossip technique. MG-Push-Diging attains our lower bound up to
logarithmic factors, demonstrating its near-optimal performance and the
tightness of the lower bound. Numerical experiments verify our theoretical
results.


  Physical Unclonable Functions (PUFs) are modern solutions for cheap and
secure key storage. The security level strongly depends on a PUF's
unpredictability, which is impaired if certain bits of the PUF response tend
towards the same value on all devices. The expectation for the probability of 1
at some position in the response, the Bit-Alias, is a state-of-the-art metric
in this regard. However, the confidence interval of the Bit-Alias is never
considered, which can lead to an overestimation of a PUF's unpredictability.
Moreover, no tool is available to verify if the Bit-Alias is within given
limits. This work adapts a method for the calculation of confidence intervals
to Bit-Alias. It further proposes a statistical hypothesis test to verify if a
PUF design meets given specifications on Bit-Alias or bit-wise entropy.
Application to several published PUF designs demonstrates the methods'
capabilities. The results prove the need for a high number of samples when the
unpredictability of PUFs is tested. The proposed methods are publicly available
and should improve the design and evaluation of PUFs in the future.


  We suggest a model of a multi-agent society of decision makers taking
decisions being based on two criteria, one is the utility of the prospects and
the other is the attractiveness of the considered prospects. The model is the
generalization of quantum decision theory, developed earlier for single
decision makers realizing one-step decisions, in two principal aspects. First,
several decision makers are considered simultaneously, who interact with each
other through information exchange. Second, a multistep procedure is treated,
when the agents exchange information many times. Several decision makers
exchanging information and forming their judgement, using quantum rules, form a
kind of a quantum information network, where collective decisions develop in
time as a result of information exchange. In addition to characterizing
collective decisions that arise in human societies, such networks can describe
dynamical processes occurring in artificial quantum intelligence composed of
several parts or in a cluster of quantum computers. The practical usage of the
theory is illustrated on the dynamic disjunction effect for which three
quantitative predictions are made: (i) the probabilistic behavior of decision
makers at the initial stage of the process is described; (ii) the decrease of
the difference between the initial prospect probabilities and the related
utility factors is proved; (iii) the existence of a common consensus after
multiple exchange of information is predicted. The predicted numerical values
are in very good agreement with empirical data.


  Investigating the behavior of amorphous solids under various external loading
conditions continues to be an intriguing area of research with significant
practical implications. In this study, we demonstrate the utilization of
self-motility as a means to anneal glasses and use that as a means to fine-tune
the failure mode of the system under uniaxial tensile deformation. We begin by
highlighting the annealing effects of activity and draw parallels with other
well-known mechanical annealing processes, such as oscillatory shearing (both
uni- and multi-directional). Furthermore, we explore the annealing effects in
the presence of open boundaries, observing enhanced surface relaxations due to
activity. By implementing various activity-induced annealing protocols, we
successfully induce a transition in the failure mode from ductile to brittle.
This is demonstrated via performing tensile tests on the glass samples
resulting from the active-annealing process. The intricate effects of geometry
on the formation of shear bands are also examined. We find that samples having
an aspect ratio greater than one fail via shear band formation, owing to their
formation angle of $45\degree$ from the strain axis. In conclusion, we
introduce a novel method for producing well-annealed glasses in silico and
establish a correspondence between sheared and actively driven glasses.


  Photons in blackbody radiation have non-zero interactions due to their
couplings to virtual electron-positron pairs in the vacuum. For temperatures
much less than the electron mass $m$ these effects can be described by an
effective theory incorporating the Uehling and Euler-Heisenberg interactions as
dominant terms. By a redefinition of the electromagnetic field, the Uehling
term is shown not to contribute. The Stefan-Boltzmann energy is then modified
by a term proportional with $T^8/m^4$ in agreement with the semi-classical
result of Barton. The same effects give a speed of light smaller than one at
non-zero temperatures as has also recently been derived using full QED.


  The existence of a global martingale solution to a cross-diffusion system
with multiplicative Wiener noise in a bounded domain with no-flux boundary
conditions is shown. The model describes the dynamics of population densities
of different species due to segregation cross-diffusion effects. The diffusion
matrix is generally neither symmetric nor positive semidefinite. This
difficulty is overcome by exploiting the Rao entropy structure. The existence
proof uses a stochastic Galerkin method, uniform estimates from the Rao entropy
inequality, and the Skorokhod--Jakubowski theorem. Furthermore, an exponential
equilibration result is proved for sufficiently small Lipschitz constants of
the noise by using the relative Rao entropy. Numerical tests illustrate the
behavior of solutions in one space dimension for two and three population
species.


  We study a model of directed percolation (DP) with immunization, i.e. with
different probabilities for the first infection and subsequent infections. The
immunization effect leads to an additional non-Markovian term in the
corresponding field theoretical action. We consider immunization as a small
perturbation around the DP fixed point in d<6, where the non-Markovian term is
relevant. The immunization causes the system to be driven away from the
neighbourhood of the DP critical point. In order to investigate the dynamical
critical behaviour of the model, we consider the limits of low and high first
infection rate, while the second infection rate remains constant at the DP
critical value. Scaling arguments are applied to obtain an expression for the
survival probability in both limits. The corresponding exponents are written in
terms of the critical exponents for ordinary DP and DP with a wall. We find
that the survival probability does not obey a power law behaviour, decaying
instead as a stretched exponential in the low first infection probability limit
and to a constant in the high first infection probability limit. The
theoretical predictions are confirmed by optimized numerical simulations in 1+1
dimensions.


  We develop some calculation schemes to determine dynamics of a wide class of
integrable quantum-optical models using their symmetry adapted reformulation in
terms of polynomial Lie algebras $su_{pd}(2)$. These schemes, based on
"diagonal" representations of model evolution operators (via diagonalizing
Hamiltonians with the help of the $su_{pd}(2)$ defining relations), are
implemented in the form adapted for numerical calculations. Their efficiency is
demonstrated on the example of the second-harmonic-generation model.


  The Friedman Universe with the remnant stochastic scalar field in the hybrid
inflation model is examined. It is shown that the small effective cosmological
constant appears which increases the cosmological expansion.


  We report density functional calculations of the electronic structure, Fermi
surface, phonon spectrum, magnetism and electron-phonon coupling for the
superconducting phase FeSe, as well as the related compounds FeS and FeTe. We
find that the Fermi surface structure of these compounds is very similar to
that of the Fe-As based superconductors, with cylindrical electron sections at
the zone corner, cylindrical hole surface sections, and depending on the
compound, other small hole sections at the zone center. As in the Fe-As based
materials, these surfaces are separated by a 2D nesting vector at
($\pi$,$\pi$). The density of states, nesting and Fermi surface size increase
going from FeSe to FeTe. Both FeSe and FeTe show spin density wave ground
states, while FeS is close to an instability. In a scenario where
superconductivity is mediated by spin fluctuations at the SDW nesting vector,
the strongest superconductor in this series would be doped FeTe.


  We present an analytic solution of type IIB supergravity plus D7-branes,
describing the addition of any number of flavors to the Klebanov-Strassler
background. The dual field theory and its self-similar RG flow, described by a
cascade of Seiberg dualities also in the presence of flavors, are discussed.
The solution indicates that the dual gauge theory enjoys a duality wall in the
UV. We stress the correspondence between Seiberg duality on the field theory
side, and large gauge transformations on the RR and NSNS potentials on the
gravity side. This contribution is mostly based on arxiv:0706.1238.


  A new shooting string holographic model of jet quenching of light quarks in
strongly coupled plasmas is presented to overcome the phenomenological
incompatibilities of previous falling string holographic scenarios that emerged
when confronted with the recent LHC data. This model is based on strings with
finite momentum endpoints that start close to the horizon and lose energy as
they approach the boundary. This framework is applied to compute the nuclear
modification factor RAA of light hadrons at RHIC and LHC, showing that this
model improves greatly the comparison with the recent light hadron suppression
data. The effects of the Gauss-Bonnet quadratic curvature corrections to the
AdS5 geometry further improve the agreement with the data.


  Mobile navigation apps are among the most used mobile applications and are
often used as a baseline to evaluate new mobile navigation technologies in
field studies. As field studies often introduce external factors that are hard
to control for, we investigate how pedestrian navigation methods can be
evaluated in virtual reality (VR). We present a study comparing navigation
methods in real life (RL) and VR to evaluate if VR environments are a viable
alternative to RL environments when it comes to testing these. In a series of
studies, participants navigated a real and a virtual environment using a paper
map and a navigation app on a smartphone. We measured the differences in
navigation performance, task load and spatial knowledge acquisition between RL
and VR. From these we formulate guidelines for the improvement of pedestrian
navigation systems in VR like improved legibility for small screen devices. We
furthermore discuss appropriate low-cost and low-space VR-locomotion techniques
and discuss more controllable locomotion techniques.


  We propose a reduction procedure for symplectic connections with symmetry.
This is applied to coadjoint orbits whose isotropy is reductive.


  We consider stability analysis of constrained switching linear systems in
which the dynamics is unknown and whose switching signal is constrained by an
automaton. We propose a data-driven Lyapunov framework for providing
probabilistic stability guarantees based on data harvested from observations of
the system. By generalizing previous results on arbitrary switching linear
systems, we show that, by sampling a finite number of observations, we are able
to construct an approximate Lyapunov function for the underlying system.
Moreover, we show that the entropy of the language accepted by the automaton
allows to bound the number of samples needed in order to reach some
pre-specified accuracy.


  A generalized trial wave function termed as the "multi-D1 Ansatz" has been
developed to study the ground state of the spin-boson model with simultaneous
diagonal and off-diagonal coupling in the sub-Ohmic regime. Ground-state
properties including the energy and the spin polarization are investigated, and
the results are consistent with those from the exact diagonalization and
density matrix renormalization group approaches for the cases involving two
oscillators and two baths described by a continuous spectral density function.
Breakdown of the rotational and parity symmetries along the continuous quantum
phase transition separating the localized phase from the critical phase has
been uncovered. Moreover, the phase boundary is determined accurately with the
corresponding symmetry parameters of the rotational and parity symmetries. A
critical value of the spectral exponent s* = 0.49(1) is predicted in the weak
coupling limit, which is in agreement with the mean-field prediction of 1/2,
but much smaller than the earlier literature estimate of 0.75(1).


  In this letter we discuss new soft theorems for the Goldstone boson
amplitudes with non-vanishing soft limits. The standard argument is that the
non-linearly realized shift symmetry leads to the vanishing of scattering
amplitudes in the soft limit, known as the Alder zero. This statement involves
certain assumptions of the absence of cubic vertices and the absence of linear
terms in the transformations of fields. For theories which fail to satisfy
these conditions, we derive a new soft theorem which involves certain linear
combinations of lower point amplitudes, generalizing the Adler zero statement.
We provide an explicit example of $SU(N)/SU(N-1)$ sigma model which was also
recently studied in the context of $U(1)$ fibrated models. The soft theorem can
be then used as an input into the modified soft recursion relations for the
reconstruction of all tree-level amplitudes.


  In this paper we report two new super Li-rich K giants: KIC 2305930 and KIC
12645107 with Li abundances exceeding that of ISM (A(Li) $\geq$ 3.2 dex).
Importantly, both the giants have been classified as core He-burning red clump
stars based on asteroseismic data from Kepler mission. Also, both the stars are
found to be low mass (M $\approx$ 1.0 M$_{\odot}$) which, together with an
evidence of their evolutionary status of being red clump imply that the stars
have gone through both the luminosity bump and He-flash during their RGB
evolution. Stars' large Li abundance and evolutionary phase suggest that Li
enrichment occurred very recently probably at the tip of RGB either during
He-flash, an immediate preceding event on RGB, or by some kind of external
event such as merger of RGB star with white dwarf. The findings will provide
critical constraints to theoretical models for understanding of Li enhancement
origin in RGB stars.


  One of the signatures of superconductivity is the formation of the Abrikosov
vortex lattice in type-II superconductors in the presence of an external
magnetic field. Here, we study the near-field radiative heat transfer between a
spherical nanoparticle and a nearby planar substrate, both made of
optimally-doped YBa$_2$Cu$_3$O$_{7-\delta}$. We show that the heat flux
displays a periodic spatial pattern congruent with the material optical
response modulated by the Abrikosov lattice. Our results enrich the toolbox of
methods available to study non-conventional superconductivity.


  Many modern and growing cities are facing declines in public transport usage,
with few efficient methods to explain why. In this article, we show that urban
mobility patterns and transport mode choices can be derived from cellphone call
detail records coupled with public transport data recorded from smart cards.
Specifically, we present new data mining approaches to determine the spatial
and temporal variability of public and private transportation usage and
transport mode preferences across Singapore. Our results, which were validated
by Singapore's quadriennial Household Interview Travel Survey (HITS), revealed
that there are 3.5 (HITS: 3.5 million) million and 4.3 (HITS: 4.4 million)
million inter-district passengers by public and private transport,
respectively. Along with classifying which transportation connections are weak
or underserved, the analysis shows that the mode share of public transport use
increases from 38 percent in the morning to 44 percent around mid-day and 52
percent in the evening.


  We consider different cosmological aspects of Heterotic M-theory. In
particular we look at the dynamical behaviour of the two relevant moduli in the
theory, namely the length of the eleventh segment (pi rho) and the volume of
the internal six manifold (V) in models where supersymmetry is broken by
multiple gaugino condensation. We look at different ways to stabilise these
moduli, namely racetrack scenarios with or without non-perturbative corrections
to the Kahler potential. The existence of different flat directions in the
scalar potential, and the way in which they can be partially lifted, is
discussed as well as their possible role in constructing a viable model of
inflation. Some other implications such as the status of the moduli problem
within these models are also studied.


  Video denoising refers to the problem of removing "noise" from a video
sequence. Here the term "noise" is used in a broad sense to refer to any
corruption or outlier or interference that is not the quantity of interest. In
this work, we develop a novel approach to video denoising that is based on the
idea that many noisy or corrupted videos can be split into three parts - the
"low-rank layer", the "sparse layer", and a small residual (which is small and
bounded). We show, using extensive experiments, that our denoising approach
outperforms the state-of-the-art denoising algorithms.


  Within D=5 N=2 gauged supergravity coupled to hypermultiplets we derive
consistency conditions for BPS domain walls with constant negative curvature on
the wall. For such wall solutions to exist, the covariant derivative of the
projector, governing the constraint on the Killing spinor, has to be non-zero
and proportional to the cosmological constant on the domain walls. We also
prove that in this case solutions of the Killing spinor equations are solutions
of the equations of motion. We present explicit, analytically solved examples
of such domain walls, employing the universal hypermultiplet fields. These
examples involve the running of two scalar fields and the space-time in the
transverse direction that is cut off at a critical distance, governed by the
magnitude of the negative cosmological constant on the wall.


  Due to electron-nucleus weak interaction, atomic bound states with different
parities turn out to be mixed. We discuss a prospect for measuring the mixing
parameter between the nearly degenerate metastable states 1s_{1/2} 2s_{1/2} : J
= 0 and 1s_{1/2} 2p_{1/2} : J = 0 in heliumlike Uranium. Our analysis is based
on the polarization properties of the photons emitted in the two-photon decays
of such states.


  A recently developed lattice method for large numbers of strongly interacting
nonrelativistic fermions exhibits a heavy tail in the distributions of
correlators for large Euclidean time {\tau} and large number of fermions N,
which only allows the measurement of ground state energies for a limited number
of fermions using standard techniques. In such cases, it is suggested that
measuring the log of the correlator is more efficient, and a cumulant expansion
of this quantity can be exactly related to the correlation function. The
cumulant expansion technique allows us to determine the ground state energies
of up to 66 unpolarized unitary fermions on lattices as large as
72$\times$14^3, and up to 70 unpolarized unitary fermions trapped in a harmonic
potential on lattices as large as 72$\times$64^3. We have also improved our
lattice action with a Galilean invariant form for the four-fermion interaction,
which results in predictive volume scaling of the lowest energy of three
fermions in a periodic box and in good agreement of our results for N \leq 6
trapped unitary fermions with those from other benchmark calculations.


  In image processing, one of the most challenging tasks is to render an
image's semantic meaning using a variety of artistic approaches. Existing
techniques for arbitrary style transfer (AST) frequently experience
mode-collapse, over-stylization, or under-stylization due to a disparity
between the style and content images. We propose a novel framework called
D$^2$Styler (Discrete Diffusion Styler) that leverages the discrete
representational capability of VQ-GANs and the advantages of discrete
diffusion, including stable training and avoidance of mode collapse. Our method
uses Adaptive Instance Normalization (AdaIN) features as a context guide for
the reverse diffusion process. This makes it easy to move features from the
style image to the content image without bias. The proposed method
substantially enhances the visual quality of style-transferred images, allowing
the combination of content and style in a visually appealing manner. We take
style images from the WikiArt dataset and content images from the COCO dataset.
Experimental results demonstrate that D$^2$Styler produces high-quality
style-transferred images and outperforms twelve existing methods on nearly all
the metrics. The qualitative results and ablation studies provide further
insights into the efficacy of our technique. The code is available at
https://github.com/Onkarsus13/D2Styler.


  We present results for the kaon mixing parameter $B_K$ obtained using
improved staggered fermions on a much enlarged set of MILC asqtad lattices.
Compared to our previous publication, which was based largely on a single
ensemble at each of the three lattice spacings $a\approx 0.09\;$fm, $0.06\;$fm
and $0.045\;$fm, we have added seven new fine and four new superfine ensembles,
with a range of values of the light and strange sea-quark masses. We have also
increased the number of measurements on one of the original ensembles. This
allows us to do controlled extrapolations in the light and strange sea-quark
masses, which we do simultaneously with the continuum extrapolation. This
reduces the extrapolation error and improves the reliability of our error
estimates. Our final result is $\hat{B}_K = 0.7379 \pm 0.0047 (\text{stat}) \pm
0.0365 (\text{sys})$.


  The extragalactic background light (EBL) is all the electromagnetic energy
released by resolved and unresolved extragalactic sources since the
recombination era. Its intensity and spectral shape provide information about
the evolution of galaxies throughout cosmic history. Since direct observations
of the EBL are very difficult to perform, the study of the interaction between
the low energy EBL photons and high energy photons from distant extragalactic
sources becomes relevant to constrain the EBL intensity. The main goal of this
study is to investigate the opacity of the EBL to gamma rays by observing a
sample of active galaxies nuclei (AGN) with the High Altitude Water Cherenkov
(HAWC) Gamma-Ray Observatory. Current gamma-ray observations up to 20 TeV
performed by Imaging Atmospheric Cherenkov Telescopes (IACTs) have constrained
the EBL intensity in the 0.1-50 $\mu$m region. HAWC, which monitors the
gamma-ray sky in the 100 GeV to 100 TeV energy range, will be able to detect
about 10 AGN with the first year of HAWC data based on the extrapolation of
steady-state spectra from the GeV band to TeV, and thus constrain the EBL in
the poorly-measured 1-100 $\mu$m range.


  A square-free monomial ideal $I$ is called an {\it $f$-ideal}, if both
$\delta_{\mathcal{F}}(I)$ and $\delta_{\mathcal{N}}(I)$ have the same
$f$-vector, where $\delta_{\mathcal{F}}(I)$ ($\delta_{\mathcal{N}}(I)$,
respectively) is the facet (Stanley-Reisner, respectively) complex related to
$I$. In this paper, we introduce the concepts of perfect set containing $k$ and
perfect set without $k$. We study the $(n, d)^{th}$ perfect sets and show that
$V(n, d) \neq \emptyset$ for $d \geq 2$ and $n \geq d+2$. Then we give some
algorithms to construct $(n, d)^{th}$ $f$-ideals and show an upper bound for
the $(n, d)^{th}$ perfect number.


  A new maximum likelihood estimation approach for blind channel equalization,
using variational autoencoders (VAEs), is introduced. Significant and
consistent improvements in the error rate of the reconstructed symbols,
compared to constant modulus equalizers, are demonstrated. In fact, for the
channels that were examined, the performance of the new VAE blind channel
equalizer was close to the performance of a nonblind adaptive linear minimum
mean square error equalizer. The new equalization method enables a
significantly lower latency channel acquisition compared to the constant
modulus algorithm (CMA). The VAE uses a convolutional neural network with two
layers and a very small number of free parameters. Although the computational
complexity of the new equalizer is higher compared to CMA, it is still
reasonable, and the number of free parameters to estimate is small.


  Errors are prevalent in time series data, such as GPS trajectories or sensor
readings. Existing methods focus more on anomaly detection but not on repairing
the detected anomalies. By simply filtering out the dirty data via anomaly
detection, applications could still be unreliable over the incomplete time
series. Instead of simply discarding anomalies, we propose to (iteratively)
repair them in time series data, by creatively bonding the beauty of temporal
nature in anomaly detection with the widely considered minimum change principle
in data repairing. Our major contributions include: (1) a novel framework of
iterative minimum repairing (IMR) over time series data, (2) explicit analysis
on convergence of the proposed iterative minimum repairing, and (3) efficient
estimation of parameters in each iteration. Remarkably, with incremental
computation, we reduce the complexity of parameter estimation from O(n) to
O(1). Experiments on real datasets demonstrate the superiority of our proposal
compared to the state-of-the-art approaches. In particular, we show that (the
proposed) repairing indeed improves the time series classification application.


  We study the boundary regularity of almost minimal and quasiminimal sets that
satisfy sliding boundary conditions. The competitors of a set $E$ are defined
as $F = \varphi_1(E)$, where $\{ \varphi_t \}$ is a one parameter family of
continuous mappings defined on $E$, and that preserve a given collection of
boundary pieces. We generalize known interior regularity results, and in
particular we show that the quasiminimal sets are locally Ahlfors-regular,
rectifiable, and some times uniformly rectifiable, that our classes are stable
under limits, and that for almost minimal sets the density of Hausdorff measure
in balls centered on the boundary is almost nondecreasing.


  We present an improved measurement of the $\eta'$ meson energy spectrum in
$\Upsilon(1S)$ decays, using 1.2 fb^{-1} of data taken at the $\Upsilon(1S)$
center-of-mass energy with the CLEO III detector. We compare our results with
models of the $\eta'$ gluonic form factor that have been suggested to explain
the unexpectedly large $B\to \eta 'X_s$ rate. Models based on perturbative QCD
fail to fit the data for large $\eta '$ energies, and thus an explanation
outside the realm of the Standard Model or an improved understanding of
non-perturbative QCD effects may be needed to account for this large rate.


  We prove a large deviation principle for a sequence of point processes
defined by Gibbs probability measures on a Polish space. This is obtained as a
consequence of a more general Laplace principle for the non-normalized Gibbs
measures. We consider three main applications: Conditional Gibbs measures on
compact spaces, Coulomb gases on compact Riemannian manifolds and the usual
Gibbs measures in the Euclidean space. Finally, we study the generalization of
Fekete points and prove a deterministic version of the Laplace principle known
as $\Gamma$-convergence. The approach is partly inspired by the works of Dupuis
and co-authors. It is remarkably natural and general compared to the usual
strategies for singular Gibbs measures.


  There are situations in which an agent should receive rewards only after
having accomplished a series of previous tasks, that is, rewards are
non-Markovian. One natural and quite general way to represent history-dependent
rewards is via a Mealy machine, a finite state automaton that produces output
sequences from input sequences. In our formal setting, we consider a Markov
decision process (MDP) that models the dynamics of the environment in which the
agent evolves and a Mealy machine synchronized with this MDP to formalize the
non-Markovian reward function. While the MDP is known by the agent, the reward
function is unknown to the agent and must be learned.
  Our approach to overcome this challenge is to use Angluin's $L^*$ active
learning algorithm to learn a Mealy machine representing the underlying
non-Markovian reward machine (MRM). Formal methods are used to determine the
optimal strategy for answering so-called membership queries posed by $L^*$.
  Moreover, we prove that the expected reward achieved will eventually be at
least as much as a given, reasonable value provided by a domain expert. We
evaluate our framework on three problems. The results show that using $L^*$ to
learn an MRM in a non-Markovian reward decision process is effective.


  We report on a systematic study of Hall effect using high quality single
crystals of type-II Weyl semimetal WTe2 with the applied magnetic field B//c.
The residual resistivity ratio of 1330 and the large magnetoresistance of
1.5\times10^6 % in 9 T at 2 K, being in the highest class in the literature,
attest to their high quality. Based on a simple two-band model, the densities
(n_e and n_h) and mobilities (\mu_e and \mu_h) for electron and hole carriers
have been uniquely determined combining both Hall- and electrical-resistivity
data. The difference between ne and nh is ~1% at 2 K, indicating that the
system is in an almost compensated condition. The negative Hall resistivity
growing rapidly below ~20 K is due to a rapidly increasing \mu_h/\mu_e
approaching one. Below 3 K in a low field region, we found the Hall resistivity
becomes positive, reflecting that \mu_h/\mu_e finally exceeds one in this
region. These anomalous behaviors of the carrier densities and mobilities might
be associated with the existence of a Lifshitz transition and/or the spin
texture on the Fermi surface.


  We describe a map-based model which reproduces many of the behaviors seen in
partial differential equations (PDE's). Like PDE's, we show that this model can
support an infinite number of stationary solutions, traveling solutions,
breathing solutions, and elastically colliding solutions. Unlike PDE's, the
model can be applied with minimal computational machinery, and few sources of
numerical error. Moreover, this model clarifies possible mechanisms by which
various coherent solutions are maintained in the face of dispersion.


  We prove that the extended Poincare group in (1+1) dimensions is
non-nilpotent solvable exponential, and therefore that it belongs to type I. We
determine its first and second cohomology groups in order to work out a
classification of the two-dimensional relativistic elementary systems.
Moreover, all irreducible unitary representations of the extended Poincare
group are constructed by the orbit method. The most physically interesting
class of irreducible representations corresponds to the anomaly-free
relativistic particle in (1+1) dimensions, which cannot be fully quantized.
However, we show that the corresponding coadjoint orbit of the extended
Poincare group determines a covariant maximal polynomial quantization by
unbounded operators, which is enough to ensure that the associated quantum
dynamical problem can be consistently solved, thus providing a physical
interpretation for this particular class of representations.


  This work outlines a fast, high-precision time-domain solver for scalar,
electromagnetic and gravitational perturbations on hyperboloidal foliations of
Kerr space-times. Time-domain Teukolsky equation solvers have typically used
explicit methods, which numerically violate Noether symmetries and are
Courant-limited. These restrictions can limit the performance of explicit
schemes when simulating long-time extreme mass ratio inspirals, expected to
appear in LISA band for 2-5 years. We thus explore symmetric (exponential,
Pad\'e or Hermite) integrators, which are unconditionally stable and known to
preserve certain Noether symmetries and phase-space volume. For linear
hyperbolic equations, these implicit integrators can be cast in explicit form,
making them well-suited for long-time evolution of black hole perturbations.
The 1+1 modal Teukolsky equation is discretized in space using polynomial
collocation methods and reduced to a linear system of ordinary differential
equations, coupled via mode-coupling arrays and discretized (matrix)
differential operators. We use a matricization technique to cast the
mode-coupled system in a form amenable to a method-of-lines framework, which
simplifies numerical implementation and enables efficient parallelization on
CPU and GPU architectures. We test our numerical code by studying late-time
tails of Kerr spacetime perturbations in the sub-extremal and extremal cases.


  It is a classical theorem that the free product of ordered groups is
orderable. In this note we show that, using a method of G. Bergman, an ordering
of the free product can be constructed in a functorial manner, in the category
of ordered groups and order-preserving homomorphisms. With this functor
interpreted as a tensor product this category becomes a tensor (or monoidal)
category. Moreover, if $O(G)$ denotes the space of orderings of the group $G$
with the natural topology, then for fixed groups $F$ and $G$ our construction
can be considered a function $O(F) \times O(G) \to O(F * G)$. We show that this
function is continuous and injective. Similar results hold for left-ordered
groups.


  We report 2868 new multi-object spectroscopic measurements of galaxy
redshifts in an area roughly 12\deg$\times$ 6\deg (right ascension $\times$
declination) centered on the Shapley Supercluster (SSC). These correspond to
2627 different galaxies. Including other measurements reported in the
literature, the total number of galaxies with measured redshifts in a
19\deg$\times$ 16\deg area centered on the supercluster now reaches 5090. Of
these, 2949 lie in the redshift range 9,000 - 18,000 km s$^{-1}$, which we
tentatively identify with the SSC. This unprecedented sample allows a quite
detailed qualitative morphological study of the SSC. Based on the
three-dimensional distribution of galaxies and clusters of galaxies, we
identify several sub-condensations of the supercluster, as well as walls and
filaments connecting them. We also find another supercluster in the background,
at redshift $\sim$ 23,000 km s$^{-1}$.


  The ability of nonlinear dynamical systems to process incoming information is
a key problem of many fundamental and applied sciences. Information processing
by computation with attractors (steady states, limit cycles and strange
attractors) has been a subject of many publications. In this paper we discuss a
new direction in information dynamics based on neurophysiological experiments
that can be applied for the explanation and prediction of many phenomena in
living biological systems and for the design of new paradigms in neural
computation. This new concept is the Winnerless Competition (WLC) principle.
The main point of this principle is the transformation of the incoming identity
or spatial inputs into identity-temporal output based on the intrinsic
switching dynamics of the neural system. In the presence of stimuli the
sequence of the switching, whose geometrical image in the phase space is a
heteroclinic contour, uniquely depends on the incoming information. The key
problem in the realization of the WLC principle is the robustness against noise
and, simultaneously, the sensitivity of the switching to the incoming input. In
this paper we prove two theorems about the stability of the sequential
switching and give several examples of WLC networks that illustrate the
coexistence of sensitivity and robustness.


  We are interested in a fast solver for linear systems obtained by
discretizing the Stokes problem with multi-patch Isogeometric Analysis. We use
Dual-Primal Isogeometric Tearing and Interconnecting (IETI-DP) methods. In
resent years, IETI-DPand related methods have been studied extensively, mainly
for the Poisson problem. For the Stokes equations, several challenges arise
since the corresponding system is not positive definite, but has saddle point
structure. Moreover, the Stokes equations with Dirichlet boundary conditions
have a null-space, consisting of the constant pressure modes. This poses a
challenge when considering the scaled Dirichlet preconditioner. We test out two
different scaled Dirichlet preconditioners with different choices of primal
degrees of freedom. The tests are performed on rather simple domains (the unit
square and a quarter annulus) and a more complicated domain (a Yeti-footprint).


  Let $G$ be a group which is the semidirect product of a normal subgroup $N$
and some subgroup $T$. Let $I^n(G)$, $n\ge 1$, denote the powers of the
augmentation ideal $I(G)$ of the group ring $\Z(G)$. Using homological methods
the groups $Q_n(G,H) = I^{n-1}(G)I(H)/I^{n}(G)I(H)$, $H=G,N,T$, are
functorially expressed in terms of enveloping algebras of certain Lie rings
associated with $N$ and $T$, in the following cases: for $n\le 4$ and arbitrary
$G,N,T$ (except from one direct summand of $Q_4(G,N)$), and for all $n\ge 2$ if
certain filtration quotients of $N$ and $T$ are torsionfree.


  In this study, we conduct three-dimensional hydrodynamic simulations
systematically to investigate the flow patterns behind the accretion shock
waves that are commonly formed in the post-bounce phase of core-collapse
supernovae. Adding small perturbations to spherically symmetric, steady,
shocked accretion flows, we compute the subsequent evolutions to find what flow
pattern emerges as a consequence of hydrodynamical instabilities such as
convection and standing accretion shock instability (SASI) for different
neutrino luminosities and mass accretion rates. Depending on these two
controlling parameters, various flow patterns are indeed realized. We classify
them into three basic patterns and two intermediate ones; the former includes
sloshing motion (SL), spiral motion (SP) and multiple buoyant bubble formation
(BB); the latter consists of spiral motion with buoyant-bubble formation (SPB)
and spiral motion with pulsationally changing rotational velocities (SPP).
Although the post-shock flow is highly chaotic, there is a clear trend in the
pattern realization. The sloshing and spiral motions tend to be dominant for
high accretion rates and low neutrino luminosities, and multiple buoyant
bubbles prevail for low accretion rates and high neutrino luminosities. It is
interesting that the dominant pattern is not always identical between the
semi-nonlinear and nonlinear phases near the critical luminosity; the
intermediate cases are realized in the latter case. Running several simulations
with different random perturbations, we confirm that the realization of flow
pattern is robust in most cases.


  Magnetic and vibrational excitations in SrCu$_2$(BO$_3$)$_2$ are studied
using Raman spectroscopy at hydrostatic pressures up to 34 kbar and
temperatures down to 2.6 K. The frequency of a particular optical phonon, the
so-called pantograph mode, shows a very strong anomalous temperature dependence
below about 40 K. We link the magnitude of the effect to the magnetic exchange
energy on the dimer bonds in the Sutherland-Shastry spin lattice in this
material. The corresponding dimer spin correlations are quantitatively
estimated and found to be strongly pressure dependent. At around P$_2\sim$22
kbar they switch from antiferromagnetic to being predominantly ferromagnetic.


  It is shown that the values of the ratios $\mathcal{B}(D^0\to
a_0(980)^+\pi^-)/\mathcal{B} (D^0\to a_0(980)^-\pi^+ )$ and $\mathcal{B}(D^+\to
a_0(980)^+\pi^0)/\mathcal{B} (D^+\to a_0(980)^0 \pi^+)$, recently measured by
the BESIII Collaboration, are naturally explained in the four-quark model of
the $a_0(980) $ resonance.


  The broad-band X-ray observations of the youngest known galactic supernova
remnant, G1.9+0.3, provide unique information about the particle acceleration
at the early stages of evolution of supernova remnants. Based on the publicly
available X-ray data obtained with the Chandra and NuSTAR satellites over two
decades in energy, we derived the energy distribution of relativistic electrons
under the assumption that detected X-rays are of entirely synchrotron origin.
The acceleration of electrons was found to be an order of magnitude slower than
the maximum rate provided by the shock acceleration in the nominal Bohm
diffusion regime. We discuss the implications of this result in the context of
contribution of SNRs to the Galactic Cosmic Rays at PeV energies.


  360-degrees digitalization of three-dimensional (3D) solids using a projected
light-strip is a well established technique. These profilometers project a
light-strip over the solid under analysis while the solid is rotated a full
revolution. Then a computer program typically extracts the centroid of this
light-strip, and by triangulation one obtains the shape of the solid. Here
instead of using intensity-based strip centroid estimation, we propose to use
Fourier phase-demodulation. This 360-degrees profilometer first constructs a
carrier-frequency fringe-pattern by closely adding individual light-strip
images. Secondly this high-density fringe-pattern is phase-demodulated using
the standard Fourier technique.


  A complete set of inequivalent realizations of three- and four-dimensional
real unsolvable Lie algebras in vector fields on a space of an arbitrary
(finite) number of variables is obtained.


  In this paper, we shall study existence of weak solutions to complex Hessian
equations. With appropriate assumptions, it is possible to obtain weak
solutions in pluripotential sense.


  Federated Learning (FL) has emerged as a promising solution for
privacy-enhancement and latency minimization in various real-world
applications, such as transportation, communications, and healthcare. FL
endeavors to bring Machine Learning (ML) down to the edge by harnessing data
from million of devices and IoT sensors, thus enabling rapid responses to
dynamic environments and yielding highly personalized results. However, the
increased amount of sensors across diverse applications poses challenges in
terms of communication and resource allocation, hindering the participation of
all devices in the federated process and prompting the need for effective FL
client selection. To address this issue, we propose Cellular Automaton-based
Client Selection (CA-CS), a novel client selection algorithm, which leverages
Cellular Automata (CA) as models to effectively capture spatio-temporal changes
in a fast-evolving environment. CA-CS considers the computational resources and
communication capacity of each participating client, while also accounting for
inter-client interactions between neighbors during the client selection
process, enabling intelligent client selection for online FL processes on data
streams that closely resemble real-world scenarios. In this paper, we present a
thorough evaluation of the proposed CA-CS algorithm using MNIST and CIFAR-10
datasets, while making a direct comparison against a uniformly random client
selection scheme. Our results demonstrate that CA-CS achieves comparable
accuracy to the random selection approach, while effectively avoiding
high-latency clients.


  Four-point super-conformal blocks for the N = 1 Neveu-Schwarz algebra are
defined in terms of power series of the even super-projective invariant.
Coefficients of these expansions are represented both as sums over poles in the
"intermediate" conformal weight and as sums over poles in the central charge of
the algebra. The residua of these poles are calculated in both cases. Closed
recurrence relations for the block coefficients are derived.


  Data clustering involves identifying latent similarities within a dataset and
organizing them into clusters or groups. The outcomes of various clustering
algorithms differ as they are susceptible to the intrinsic characteristics of
the original dataset, including noise and dimensionality. The effectiveness of
such clustering procedures directly impacts the homogeneity of clusters,
underscoring the significance of evaluating algorithmic outcomes. Consequently,
the assessment of clustering quality presents a significant and complex
endeavor. A pivotal aspect affecting clustering validation is the cluster
validity metric, which aids in determining the optimal number of clusters. The
main goal of this study is to comprehensively review and explain the
mathematical operation of internal and external cluster validity indices, but
not all, to categorize these indices and to brainstorm suggestions for future
advancement of clustering validation research. In addition, we review and
evaluate the performance of internal and external clustering validation indices
on the most common clustering algorithms, such as the evolutionary clustering
algorithm star (ECA*). Finally, we suggest a classification framework for
examining the functionality of both internal and external clustering validation
measures regarding their ideal values, user-friendliness, responsiveness to
input data, and appropriateness across various fields. This classification aids
researchers in selecting the appropriate clustering validation measure to suit
their specific requirements.


  In this paper we determine the number and typical structure of sets of
integers with bounded doubling. In particular, improving recent results of
Green and Morris, and of Mazur, we show that the following holds for every
fixed $\lambda > 2$ and every $k \geqslant (\log n)^4$: if $\omega \to \infty$
as $n \to \infty$ (arbitrarily slowly), then almost all sets $A \subset [n]$
with $|A| = k$ and $|A + A| \leqslant \lambda k$ are contained in an arithmetic
progression of length $\lambda k/2 + \omega$.


  We analyze the long-time large-distance asymptotics of the longitudinal
correlation functions of the Heisenberg-Ising chain in the easy-axis regime. We
show that in this regime the leading asymptotics of the dynamical two-point
functions is entirely determined by the two-spinon contribution to their form
factor expansion. Its explicit form is obtained from a saddle-point analysis of
the corresponding double integral. It describes the propagation of a wave front
with velocity $v_{c_1}$ which is found to be the maximal possible group
velocity. Like in wave propagation in dispersive media the wave front is
preceded by a precursor running ahead with velocity $v_{c_2}$. As a special
case we obtain the explicit form of the asymptotics of the auto-correlation
function.


  The nuclear transition between the ground and the low-energy isomeric state
in the ^{229}Th nucleus is of interest due to its high sensitivity to a
hypothetical temporal variation of the fundamental constants and a possibility
to build a very precise nuclear clock, but precise knowledge of the nuclear
clock transition frequency is required. In this work we estimate the
probability of an electronic bridge process in ^{229}Th^35+, allowing to
determine the nuclear transition frequency and reduce its uncertainty. Using
configuration interaction methods we calculated energies of the low-lying
states of Th^35+ and determined their uncertainties.


  Given a collection of images, humans are able to discover landmarks by
modeling the shared geometric structure across instances. This idea of
geometric equivariance has been widely used for the unsupervised discovery of
object landmark representations. In this paper, we develop a simple and
effective approach by combining instance-discriminative and
spatially-discriminative contrastive learning. We show that when a deep network
is trained to be invariant to geometric and photometric transformations,
representations emerge from its intermediate layers that are highly predictive
of object landmarks. Stacking these across layers in a "hypercolumn" and
projecting them using spatially-contrastive learning further improves their
performance on matching and few-shot landmark regression tasks. We also present
a unified view of existing equivariant and invariant representation learning
approaches through the lens of contrastive learning, shedding light on the
nature of invariances learned. Experiments on standard benchmarks for landmark
learning, as well as a new challenging one we propose, show that the proposed
approach surpasses prior state-of-the-art.


  Gravitational wave (GW) source counts have been recently shown to be able to
test how gravitational radiation propagates with the distance from the source.
Here, we extend this formalism to cosmological scales, i.e. the high redshift
regime, and we discuss the complications of applying this methodology to high
redshift sources. We also allow for models with compactified extra dimensions
like in the Kaluza-Klein model. Furthermore, we also consider the case of
intermediate redshifts, i.e. $0<z\lesssim1$, where we show it is possible to
find an analytical approximation for the source counts $\frac{dN}{d(S/N)}$.
This can be done in terms of cosmological parameters, such as the matter
density $\Omega_{m,0}$ of the cosmological constant model or the cosmographic
parameters for a general dark energy model. Our analysis is as general as
possible, but it depends on two important factors: a source model for the black
hole binary mergers and the GW source to galaxy bias. This methodology also
allows us to obtain the higher order corrections of the source counts in terms
of the signal-to-noise $S/N$. We then forecast the sensitivity of future
observations in constraining GW physics but also the underlying cosmology by
simulating sources distributed over a finite range of signal-to-noise with a
number of sources ranging from 10 to 500 sources as expected from future
detectors. We find that with 500 events it will be possible to provide
constraints on the matter density parameter at present $\Omega_{m,0}$ on the
order of a few percent and with the precision growing fast with the number of
events. In the case of extra dimensions we find that depending on the
degeneracies of the model, with 500 events it may be possible to provide
stringent limits on the existence of the extra dimensions if the aforementioned
degeneracies can be broken.


  This paper addresses the problem of estimating the depth map of a scene given
a single RGB image. We propose a fully convolutional architecture, encompassing
residual learning, to model the ambiguous mapping between monocular images and
depth maps. In order to improve the output resolution, we present a novel way
to efficiently learn feature map up-sampling within the network. For
optimization, we introduce the reverse Huber loss that is particularly suited
for the task at hand and driven by the value distributions commonly present in
depth maps. Our model is composed of a single architecture that is trained
end-to-end and does not rely on post-processing techniques, such as CRFs or
other additional refinement steps. As a result, it runs in real-time on images
or videos. In the evaluation, we show that the proposed model contains fewer
parameters and requires fewer training data than the current state of the art,
while outperforming all approaches on depth estimation. Code and models are
publicly available.


  A prescription is presented for a new and practical correlation coefficient,
$\phi_K$, based on several refinements to Pearson's hypothesis test of
independence of two variables. The combined features of $\phi_K$ form an
advantage over existing coefficients. First, it works consistently between
categorical, ordinal and interval variables. Second, it captures non-linear
dependency. Third, it reverts to the Pearson correlation coefficient in case of
a bi-variate normal input distribution. These are useful features when studying
the correlation between variables with mixed types. Particular emphasis is paid
to the proper evaluation of statistical significance of correlations and to the
interpretation of variable relationships in a contingency table, in particular
in case of low statistics samples and significant dependencies. Three practical
applications are discussed. The presented algorithms are easy to use and
available through a public Python library.


  This paper reviews the potential of a high luminosity e+e- linear collider
(LC) in the precision study of the Higgs boson profile. The complementarity of
the linear collider data with that from the LHC is also discussed together with
a comparison with a muon collider.


  We study how phonon structure manifests itself in the electronic density of
states of graphene. A procedure for extracting the value of the electron-phonon
renormalization $\lambda$ is developed. In addition, we identify direct phonon
structures. With increasing doping, these structures, along with $\lambda$,
grow in amplitude and no longer display particle-hole symmetry.


  Bernoulli random walks, a simple avalanche model, and a special branching
process are essesntially identical. The identity gives alternative insights
into the properties of these basic model sytems.


  The observation and electrical manipulation of infrared surface plasmons in
graphene have triggered a search for similar photonic capabilities in other
atomically thin materials that enable electrical modulation of light at visible
and near-infrared frequencies, as well as strong interaction with optical
quantum emitters. Here, we present a simple analytical description of the
optical response of such kinds of structures, which we exploit to investigate
their application to light modulation and quantum optics. Specifically, we show
that plasmons in one-atom-thick noble-metal layers can be used both to produce
complete tunable optical absorption and to reach the strong-coupling regime in
the interaction with neighboring quantum emitters. Our methods are applicable
to any plasmon-supporting thin materials, and in particular, we provide
parameters that allow us to readily calculate the response of silver, gold, and
graphene islands. Besides their interest for nanoscale electro-optics, the
present study emphasizes the great potential of these structures for the design
of quantum nanophotonics devices.


  We review the search for the glass transition in water in its various
amorphous forms, and highlight the paradoxes that the search has produced.
Focussing on the glassy form of water obtained by hyperquenching, we examine
its reported calorimetric properties in the light of new findings for bulk
glassformers vitrified by hyperquenching. Like the hyperquenched bulk
glassformers, hyperquenched water exhibits a large relaxation exotherm on
reheating, as the high enthalpy quenched form relaxes to a lower enthalpy
state. When the scanning temperature is scaled by the glass transition
temperature, the bulk glassformers show a common pattern in this relaxation
exotherm. However, when the relaxation exotherm for hyperquenched water is
scaled onto the plot for the bulk glassformers using the commonly accepted
glass transition temperature for water, 136K, its behavior appears completely
different. If we require that the behavior of water be comparable to the other
cases, except for crystallizing before the glass transition (like the majority
of hyperquenched metallic glasses), then the glass transition for water must be
reset to a value of 165-170K.


  The presence of Multiple Stellar Populations (MSPs) in Galactic Globular
Clusters (GCs) is a poorly understood phenomenon. By probing different spectral
ranges that are affected by different absorption lines using the multi-band
photometric survey S-PLUS, we study four GCs -- NGC 104, NGC 288, NGC 3201 and
NGC 7089 -- that span a wide range in metallicities. With the combination of
broad and narrow-band photometry in 12 different filters from 3485A (u) to
9114A (z), we identified MSPs along the rectified red-giant branch in
colour-magnitude diagrams (CMDs) and separated them using a K-means clustering
algorithm. Additionally, we take advantage of the large Field of View of the
S-PLUS detector to investigate radial trends in our sample. We report on six
colour combinations that can be used to successfully identify two stellar
populations in all studied clusters and show that they can be characterized as
Na-rich and Na-poor. For both NGC 288 and NGC 7089, their radial profiles show
a clear concentration of 2P. This directly supports the formation theories that
propose an enrichment of the intra-cluster medium and subsequent star formation
in the more dense central regions. However, in the case of NGC 3201, the trend
is reversed. The 1P is more centrally concentrated, in direct contradiction
with previous literature studies. NGC 104 shows a well-mixed population. We
also constructed radial profiles up to 1 half-light radius of the clusters with
HST data to highlight that radial differences are lost in the inner regions of
the GCs and that wide-field studies are essential when studying this.


  Data quality and data cleaning are context dependent activities. Starting
from this observation, in previous work a context model for the assessment of
the quality of a database instance was proposed. In that framework, the context
takes the form of a possibly virtual database or data integration system into
which a database instance under quality assessment is mapped, for additional
analysis and processing, enabling quality assessment. In this work we extend
contexts with dimensions, and by doing so, we make possible a multidimensional
assessment of data quality assessment. Multidimensional contexts are
represented as ontologies written in Datalog+-. We use this language for
representing dimensional constraints, and dimensional rules, and also for doing
query answering based on dimensional navigation, which becomes an important
auxiliary activity in the assessment of data. We show ideas and mechanisms by
means of examples.


  With computers to handle more and more complicated things in variable
environments, it becomes an urgent requirement that the artificial intelligence
has the ability of automatic judging and deciding according to numerous
specific conditions so as to deal with the complicated and variable cases. ANNs
inspired by brain is a good candidate. However, most of current numeric ANNs
are not good at representing logical relations because these models still try
to represent logical relations in the form of ratio based on functional
approximation. On the other hand, researchers have been trying to design novel
neural network models to make neural network model represent logical relations.
In this work, a novel neural network model specified for representing logical
relations is proposed and applied. New neurons and multiple kinds of links are
defined. Inhibitory links are introduced besides exciting links. Different from
current numeric ANNs, one end of an inhibitory link connects an exciting link
rather than a neuron. Inhibitory links inhibit the connected exciting links
conditionally to make this neural network model represent logical relations
correctly. This model can simulate the operations of Boolean logic gates, and
construct complex logical relations with the advantages of simpler neural
network structures than recent works in this area. This work provides some
ideas to make neural networks represent logical relations more directly and
efficiently, and the model could be used as the complement to current numeric
ANN to deal with logical issues and expand the application areas of ANN.


  We describe nef vector bundles on a projective space with first Chern class
three and second Chern class eight over an algebraically closed field of
characteristic zero by giving them a minimal resolution in terms of a full
strong exceptional collection of line bundles.


  Community affiliation of a node plays an important role in determining its
contextual position in the network, which may raise privacy concerns when a
sensitive node wants to hide its identity in a network. Oftentimes, a target
community seeks to protect itself from adversaries so that its constituent
members remain hidden inside the network. The current study focuses on hiding
such sensitive communities so that the community affiliation of the targeted
nodes can be concealed. This leads to the problem of community deception which
investigates the avenues of minimally rewiring nodes in a network so that a
given target community maximally hides from a community detection algorithm. We
formalize the problem of community deception and introduce NEURAL, a novel
method that greedily optimizes a node-centric objective function to determine
the rewiring strategy. Theoretical settings pose a restriction on the number of
strategies that can be employed to optimize the objective function, which in
turn reduces the overhead of choosing the best strategy from multiple options.
We also show that our objective function is submodular and monotone. When
tested on both synthetic and 7 real-world networks, NEURAL is able to deceive 6
widely used community detection algorithms. We benchmark its performance with
respect to 4 state-of-the-art methods on 4 evaluation metrics. Additionally,
our qualitative analysis of 3 other attributed real-world networks reveals that
NEURAL, quite strikingly, captures important meta-information about edges that
otherwise could not be inferred by observing only their topological structures.


  We prove some "universality" results for topological dynamical systems. In
particular, we show that for any continuous self-map $T$ of a perfect Polish
space, one can find a dense, $T$-invariant set homeomorphic to the Baire space
${\mathbb N}^{\mathbb N}$; that there exists a bounded linear operator $U:
\ell_1 \rightarrow \ell_1$ such that any linear operator $T$ from a separable
Banach space into itself with $\Vert T\Vert\leq 1$ is a linear factor of $U$;
and that given any $\sigma$-compact family ${\mathcal F}$ of continuous
self-maps of a compact metric space, there is a continuous self-map
$U_{\mathcal F}$ of ${\mathbb N}^{\mathbb N}$ such that each $T\in {\mathcal
F}$ is a factor of $U_{\mathcal F}$.


  We present results from the multi-wavelength study of XMMU J1230.3+1339 at z
~ 1. We analyze deep multi-band wide-field images from the Large Binocular
Telescope, multi-object spectroscopy observations from VLT, as well as
space-based serendipitous observations, from the GALEX and Chandra X-ray
observatories. We apply a Bayesian photometric redshift code to derive the
redshifts using the FUV, NUV and the deep U, B, V, r, i, z data. We achieve an
accuracy of $\triangle z/(1+z)$ = 0.07 (0.04) and the fraction of catastrophic
outliers is $\eta$ = 13 (0)%, when using all (secure) spectroscopic data,
respectively. The i - z against z colour-magnitude relation of the photo-z
members shows a tight red-sequence with a zero point of 0.935 mag, and slope
equal to -0.027. We observe evidence for a truncation at the faint end of the
red-cluster-sequence and the Butcher-Oemler effect, finding a fraction of blue
galaxies $f_b \approx$ 0.5. Further we conduct a weak lensing analysis of the
deep 26' $\times$ 26' r-band LBC image. The observed shear is fitted with a
Single-Isothermal-Sphere and a Navarro-Frenk-White model to obtain the velocity
dispersion and the model parameters, respectively. Our best fit values are, for
the velocity dispersion $\sigma_{SIS}$ = 1308 $\pm$ 284, concentration
parameter c = 4.0$^{+14}_{-2}$ and scale radius r$_{s}$ = 345$^{+50}_{-57}$
kpc. Combining our mass estimates from the kinematic, X-ray and weak lensing
analyses we obtain a total cluster mass of $M^{tot}_{200}$ = (4.56 $\pm$ 2.3)
$\times$ 10$^{14}$ M$_{\sun}$. This study demonstrates the feasibility of
ground based weak lensing measurements of galaxy clusters up to z ~ 1.


  The electro-diffusion model, which arises in electrohydrodynamics, is a
coupling between the Nernst-Planck-Poisson system and the incompressible
Navier-Stokes equations. For the generally smooth doping profile, the
quasineutral limit (zero-Debye-length limit) is justified rigorously in Sobolev
norm uniformly in time. The proof is based on the elaborate energy analysis and
the key point is to establish the uniform estimates with respect to the scaled
Debye length.


  Waveform design for interference cancellation in next-generation wireless
systems, which includes precoding and modulation, aims to achieve orthogonality
among data signals/symbols across all Degrees of Freedom (DoF). Conventional
methods struggle with non-stationary channel states due to high mobility,
density, and time-varying multipath propagation. In this article, we review the
HOGMT-Precoding and MEM modulations for non-stationary channels. We also
discuss practical challenges and future directions.


  The solitons of the SO(3) gauged Skyrme model with no pion-mass potential
were studied in Refs. {nl,jmp}. Here, the effects of the inclusion of this
potential are studied. In contrast with the (ungauged) Skyrme model, where the
effect of this potential on the solitons is marginal, here it turns out to be
decisive, resulting in very different dependence of the energy as a function of
the Skyrme coupling constant.


  We propose a novel approach for modeling semantic contextual relationships in
videos. This graph-based model enables the learning and propagation of
higher-level spatial-temporal contexts to facilitate the semantic labeling of
local regions. We introduce an exemplar-based nonparametric view of contextual
cues, where the inherent relationships implied by object hypotheses are encoded
on a similarity graph of regions. Contextual relationships learning and
propagation are performed to estimate the pairwise contexts between all pairs
of unlabeled local regions. Our algorithm integrates the learned contexts into
a Conditional Random Field (CRF) in the form of pairwise potentials and infers
the per-region semantic labels. We evaluate our approach on the challenging
YouTube-Objects dataset which shows that the proposed contextual relationship
model outperforms the state-of-the-art methods.


  The rapid development of large language models has revolutionized code
intelligence in software development. However, the predominance of
closed-source models has restricted extensive research and development. To
address this, we introduce the DeepSeek-Coder series, a range of open-source
code models with sizes from 1.3B to 33B, trained from scratch on 2 trillion
tokens. These models are pre-trained on a high-quality project-level code
corpus and employ a fill-in-the-blank task with a 16K window to enhance code
generation and infilling. Our extensive evaluations demonstrate that
DeepSeek-Coder not only achieves state-of-the-art performance among open-source
code models across multiple benchmarks but also surpasses existing
closed-source models like Codex and GPT-3.5. Furthermore, DeepSeek-Coder models
are under a permissive license that allows for both research and unrestricted
commercial use.


  Fine-tuning pretrained models is a common practice in domain generalization
(DG) tasks. However, fine-tuning is usually computationally expensive due to
the ever-growing size of pretrained models. More importantly, it may cause
over-fitting on source domain and compromise their generalization ability as
shown in recent works. Generally, pretrained models possess some level of
generalization ability and can achieve decent performance regarding specific
domains and samples. However, the generalization performance of pretrained
models could vary significantly over different test domains even samples, which
raises challenges for us to best leverage pretrained models in DG tasks. In
this paper, we propose a novel domain generalization paradigm to better
leverage various pretrained models, named specialized ensemble learning for
domain generalization (SEDGE). It first trains a linear label space adapter
upon fixed pretrained models, which transforms the outputs of the pretrained
model to the label space of the target domain. Then, an ensemble network aware
of model specialty is proposed to dynamically dispatch proper pretrained models
to predict each test sample. Experimental studies on several benchmarks show
that SEDGE achieves significant performance improvements comparing to strong
baselines including state-of-the-art method in DG tasks and reduces the
trainable parameters by ~99% and the training time by ~99.5%.


  The inhomogeneous Muskat problem models the dynamics of an interface between
two fluids of differing characteristics inside a non-uniform porous medium. We
consider the case of a porous media with a permeability jump across a
horizontal boundary away from an interface between two fluids of different
viscosities and densities. For initial data of explicit medium size, depending
on the characteristics of the fluids and porous media, we will prove the global
existence and uniqueness of a solution which is instantly analytic and decays
in time to the flat interface.


  We give a characterization of closed, simply connected, rationally elliptic
6-manifolds in terms of their rational cohomology rings and a partial
classification of their real cohomology rings. We classify rational, real and
complex homotopy types of closed, simply connected, rationally elliptic
7-manifolds. We give partial results in dimensions 8 and 9.