  By using the coproduct and evaluation map for the affine Yangian and the
Miura map for non-rectangular $W$-algebras, we construct a homomorphism from
the affine Yangian associated with $\widehat{\mathfrak{sl}}(n)$ to the
universal enveloping algebra of a non-rectangular $W$-algebra of type $A$,
which is an affine analogue of the one given in De Sole-Kac-Valeri. As a
consequence, we find that the coproduct for the affine Yangian is compatible
with some of the parabolic induction for non-rectangular $W$-algebras via this
homomorphism. We also show that the image of this homomorphism is contained in
the affine coset of the $W$-algebra in the special case that the $W$-algebra is
associated with a nilpotent element of type $(1^{m-n},2^n)$.


  The formation environment of stars in massive stellar clusters is similar to
the environment of stars forming in galaxies at a redshift of 1 - 3, at the
peak star formation rate density of the Universe. As massive clusters are still
forming at the present day at a fraction of the distance to high-redshift
galaxies they offer an opportunity to understand the processes controlling star
formation and feedback in conditions similar to those in which most stars in
the Universe formed. Here we describe a system of massive clusters and their
progenitor gas clouds in the centre of the Milky Way, and outline how detailed
observations of this system may be able to: (i) help answer some of the
fundamental open questions in star formation and (ii) quantify how stellar
feedback couples to the surrounding interstellar medium in this high-pressure,
high-redshift analogue environment.


  We study the properties of a generalized metallic, a generalized product and
a generalized complex structure induced on the generalized tangent bundle of
$M$ by a metallic Riemannian structure $(J,g)$ on $M$, providing conditions for
their integrability with respect to a suitable connection. Moreover, by using
methods of generalized geometry, we lift $(J,g)$ to metallic Riemannian
structures on the tangent and cotangent bundles of $M$, underlying the
relations between them.


  The ALICE detector at the LHC studies $\pi^0$ and $\eta$ meson production by
two complementary methods, using electromagnetic calorimeters and the central
tracking system for converted photons. Production spectra of $\pi^0$ and $\eta$
mesons were measured with ALICE in pp collisions at LHC energies at
mid-rapidity in a wide transverse momentum range. The spectrum and the nuclear
modification factor $R_{AA}$ of $\pi^0$ measured in Pb-Pb collisions at
different centralities, show a clear pattern of strong suppression in a hot QCD
medium with respect to pp collisions.


  Within the framework of the local limit of nonlocal gravity (NLG), we
investigate a class of Bianchi type I spatially homogeneous but anisotropic
cosmological models. The modified field equations are presented in this case
and some special solutions are discussed in detail. This modified gravity
theory contains a susceptibility function S(x) such that general relativity
(GR) is recovered for S = 0. In the modified anisotropic cosmological models,
we explore the contribution of S(t) and its temporal derivative to the local
anisotropic cosmic acceleration. The implications of our results for
observational cosmology are briefly discussed.


  We study the possibility of using the entire probability distribution
function (PDF) of the aperture mass Map and its related cumulative probability
distribution function (CPDF) to obtain meaningful constraints on cosmological
parameters. Deriving completely analytic expressions for the associated
covariance matrices, we construct the Fisher matrix and use it to estimate the
accuracy with which various cosmological parameters can be recovered from
future surveys using such statistics. This formalism also includes the effect
of various noises such as intrinsic ellipticity distribution of galaxies and
finite survey size. The estimation errors are then compared with the ones
derived from low order moments of the PDF (variance and skewness) to check how
efficiently the high Map tail can be used to constrain cosmological parameters
such as Omega_m, sigma_8 and dark energy equation of state w_de. We find that
for future surveys such as JDEM the full PDF does not bring significant
tightening of constraints on cosmology beyond what is already achievable by the
joint use of second and third order moments.


  Let $\alpha\in (0,2)$ and consider the operator $$L f(x) =\int
[f(x+h)-f(x)-1_{(|h|\leq 1)} \nabla f(x)\cdot h] \frac{A(x,h)}{|h|^{d+\alpha}}
dh, $$ where the $\nabla f(x)\cdot h$ term is omitted if $\alpha<1$. We
consider the martingale problem corresponding to the operator $L$ and under
mild conditions on the function $A$ prove that there exists a unique solution.


  The bucketed PCA neural network (PCA-NN) with transforms is developed here in
an effort to benchmark deep neural networks (DNN's), for problems on supervised
classification. Most classical PCA models apply PCA to the entire training data
set to establish a reductive representation and then employ non-network tools
such as high-order polynomial classifiers. In contrast, the bucketed PCA-NN
applies PCA to individual buckets which are constructed in two consecutive
phases, as well as retains a genuine architecture of a neural network. This
facilitates a fair apple-to-apple comparison to DNN's, esp. to reveal that a
major chunk of accuracy achieved by many impressive DNN's could possibly be
explained by the bucketed PCA-NN (e.g., 96% out of 98% for the MNIST data set
as an example). Compared with most DNN's, the three building blocks of the
bucketed PCA-NN are easier to comprehend conceptually - PCA, transforms, and
bucketing for error correction. Furthermore, unlike the somewhat quasi-random
neurons ubiquitously observed in DNN's, the PCA neurons resemble or mirror the
input signals and are more straightforward to decipher as a result.


  Thermal power flow (TPF) is an important task for various control purposes in
4 Th generation district heating grids with multiple decentral heat sources and
meshed grid structures. Computing the TPF, i.e., determining the grid state
consisting of temperatures, pressures, and mass flows for given supply and
demand values, is classically done by solving the nonlinear heat grid
equations, but can be sped up by orders of magnitude using learned models such
as neural networks. We propose a novel, efficient scheme to generate a
sufficiently large training data set covering relevant supply and demand
values. Instead of sampling supply and demand values, our approach generates
training examples from a proxy distribution over generator and consumer mass
flows, omitting the iterations needed for solving the heat grid equations. The
exact, but slightly different, training examples can be weighted to represent
the original training distribution. We show with simulations for typical grid
structures that the new approach can reduce training set generation times by
two orders of magnitude compared to sampling supply and demand values directly,
without loss of relevance for the training samples. Moreover, learning TPF with
a training data set is shown to outperform sample-free, physics-aware training
approaches significantly.


  In this paper we study the Painlev\'e analysis for two models of chemotaxis.
We find that in some cases the reductions of these models in terms of
travelling wave variable allow exact analytical solutions.


  A lattice version of quantum nonlinear Schrodinger (NLS) equation is
considered, which has significantly simple form and fullfils most of the
criteria desirable for such lattice variants of field models. Unlike most of
the known lattice NLS, the present model belongs to a class which does not
exhibit the usual symmetry properties. However this lack of symmetry itself
seems to be responsible for the remarkable simplification of the relevant
objects in the theory, such as the Lax operator, the
  Hamiltonian and other commuting conserved quantities as well as their
spectrum.
  The model allows exact quantum solution through algebraic Bethe ansatz and
also a straightforward and natural generalisation to the vector case, giving
thus a new exact lattice version of the vector NLS model. A deformation
representing a new quantum integrable system involving Tamm-Dancoff like
$q$-boson operators is constructed.


  Over the last decade, systems of individually-controlled neutral atoms,
interacting with each other when excited to Rydberg states, have emerged as a
promising platform for quantum simulation of many-body problems, in particular
spin systems. Here, we review the techniques underlying quantum gas microscopes
and arrays of optical tweezers used in these experiments, explain how the
different types of interactions between Rydberg atoms allow a natural mapping
onto various quantum spin models, and describe recent results that were
obtained with this platform to study quantum many-body physics.


  We consider the ground state of supermembrane on the maximally supersymmetric
pp-wave background by using the quantum-mechanical procedure of de
Wit-Hoppe-Nicolai. In the pp-wave case the ground state has non-trivial
structure even in the zero-mode Hamiltonian, which is identical with that of
superparticles on the pp-wave and resembles supersymmetric harmonic
oscillators. The supergravity multiplet in the flat case is splitting with a
certain energy difference. We explicitly construct the unique supersymmetric
ground-state wave function of the zero-mode Hamiltonian, which is obviously
normalizable. Moreover, we discuss the nonzero-mode Hamiltonian and construct
an example for the ground-state wave function with a truncation of the
variables. This special solution seems non-normalizable but its L^2-norm can be
represented by an asymptotic series.


  We propose a new analytic approach to study the phase diagram of random
heteropolymers, based on the cavity method. For copolymers we analyze the
nature and phenomenology of the glass transition as a function of sequence
correlations. Depending on these correlations, we find that two different
scenarios for the glass transition can occur. We show that, beside the much
studied possibility of an abrupt freezing transition at low temperature, the
system can exhibit, upon cooling, a first transition to a soft glass phase with
fully broken replica symmetry and a continuously growing degree of freezing as
the temperature is lowered.


  As data structures and mathematical objects used for complex systems
modeling, hypergraphs sit nicely poised between on the one hand the world of
network models, and on the other that of higher-order mathematical abstractions
from algebra, lattice theory, and topology. They are able to represent complex
systems interactions more faithfully than graphs and networks, while also being
some of the simplest classes of systems representing topological structures as
collections of multidimensional objects connected in a particular pattern. In
this paper we discuss the role of (undirected) hypergraphs in the science of
complex networks, and provide a mathematical overview of the core concepts
needed for hypernetwork modeling, including duality and the relationship to
bicolored graphs, quantitative adjacency and incidence, the nature of walks in
hypergraphs, and available topological relationships and properties. We close
with a brief discussion of two example applications: biomedical databases for
disease analysis, and domain-name system (DNS) analysis of cyber data.


  We measure electronic conductance through single conjugated molecules bonded
to Au metal electrodes with direct Au-C covalent bonds using the scanning
tunneling microscope based break-junction technique. We start with molecules
terminated with trimethyltin end groups that cleave off in situ resulting in
formation of a direct covalent sigma bond between the carbon backbone and the
gold metal electrodes. The molecular carbon backbone used in this study consist
of a conjugated pi-system that has one terminal methylene group on each end,
which bonds to the electrodes, achieving large electronic coupling of the
electrodes to the pi-system. The junctions formed with the prototypical example
of 1,4-dimethylenebenzene show a conductance approaching one conductance
quantum (G0 = 2e2/h). Junctions formed with methylene terminated oligophenyls
with two to four phenyl units show a hundred-fold increase in conductance
compared with junctions formed with amine-linked oligophenyls. The conduction
mechanism for these longer oligophenyls is tunneling as they exhibit an
exponential dependence of conductance with oligomer length. In addition,
density functional theory based calculations for the Au-xylylene-Au junction
show near-resonant transmission with a cross-over to tunneling for the longer
oligomers.


  This paper establishes the existence of a unique nonnegative continuous
viscosity solution to the HJB equation associated with a Markovian
linear-quadratic control problems with singular terminal state constraint and
possibly unbounded cost coefficients. The existence result is based on a novel
comparison principle for semi-continuous viscosity sub- and supersolutions for
PDEs with singular terminal value. Continuity of the viscosity solution is
enough to carry out the verification argument.


  We describe all affine maps from a Riemannian manifold to a metric space and
all possible image spaces.


  Update: A time-independent $n\times n$ PT-symmetric (and symmetric)
Hamiltonian is diagonalizable since it has all distinct real eigenvalues and
the resulting diagonal matrix is a real symmetric matrix. The diagonalization
results an isometry so there shouldn't be any issue with unitarity and
unfortunately this very elementary mathematical fact somehow did not draw the
authors' attention. However, PT-symmetric quantum mechanics is not out of
trouble. For time-dependent PT-symmetric (and symmetric) Hamiltonians (even
$2\times 2$ ones) the authors observed that there is a violation of unitarity.
Moreover, the first named author showed in his recent article arXiv:1312.7738
that PT-symmetric quantum mechanics is indeed a certain kind of Hermitian
quantum mechanics and that in order for time-evolution to be unitary with
respect to $J$-inner product (one that gives rise to a Hilbert space structure
on the space of state functions), the potential energy operator $V(x)$ must be
real. This means that those complex PT-symmetric Hamiltonians that have been
studied by physicists are unfortunately unphysical. The first named author
discussed in a subsequent article arXiv:1401.5149 that while finite-state
PT-symmetric quantum mechanics with time-independent Hamiltonians is not
physically any different from Hermitian quantum mechanics, PT-symmetric quantum
mechanics exhibits a distinctive symmetry from that of Hermitian quantum
mechanics.


  In this article we review the problem of reaction annihilation $A+A
\rightarrow \emptyset$ on a real lattice in one dimension, where $A$ particles
move ballistically in one direction with a discrete set of possible velocities.
We first discuss the case of pure ballistic annihilation, that is a model in
which each particle moves simultaneously at constant speed. We then review
ballistic annihilation with superimposed diffusion in one dimension. This model
consists of diffusing particles each of which diffuses with a fixed bias, which
can be either positive or negative with probability $1/2$, and annihilate upon
contact. When the initial concentration of left and right moving particles is
same, the concentration $c(t)$ decays as $t^{-1/2}$ with time, for pure
ballistic annihilation. However when the diffusion is superimposed decay is
faster and the concentration $c(t) \sim t^{-3/4}$. We also discuss the
nearest-neighbor distance distribution as well as crossover behavior.


  Pandemic(epidemic) modeling, aiming at disease spreading analysis, has always
been a popular research topic especially following the outbreak of COVID-19 in
2019. Some representative models including SIR-based deep learning prediction
models have shown satisfactory performance. However, one major drawback for
them is that they fall short in their long-term predictive ability. Although
graph convolutional networks (GCN) also perform well, their edge
representations do not contain complete information and it can lead to biases.
Another drawback is that they usually use input features which they are unable
to predict. Hence, those models are unable to predict further future. We
propose a model that can propagate predictions further into the future and it
has better edge representations. In particular, we model the pandemic as a
spatial-temporal graph whose edges represent the transition of infections and
are learned by our model. We use a two-stream framework that contains GCN and
recursive structures (GRU) with an attention mechanism. Our model enables
mobility analysis that provides an effective toolbox for public health
researchers and policy makers to predict how different lock-down strategies
that actively control mobility can influence the spread of pandemics.
Experiments show that our model outperforms others in its long-term predictive
power. Moreover, we simulate the effects of certain policies and predict their
impacts on infection control.


  For a family of weight functions, $h_\kappa$, invariant under a finite
reflection group on $\RR^d$, analysis related to the Dunkl transform is carried
out for the weighted $L^p$ spaces. Making use of the generalized translation
operator and the weighted convolution, we study the summability of the inverse
Dunkl transform, including as examples the Poisson integrals and the
Bochner-Riesz means. We also define a maximal function and use it to prove the
almost everywhere convergence.


  We prove a strong dichotomy result for countably-infinite oriented graphs;
that is, we prove that for all countably-infinite oriented graphs $G$, either
(i) there is a countably-infinite tournament $K$ such that $G\not\subseteq K$,
or (ii) every countably-infinite tournament contains a \emph{spanning} copy of
$G$. Furthermore, we are able to give a concise characterization of such
oriented graphs. Our characterization becomes even simpler in the case of
transitive acyclic oriented graphs (i.e. strict partial orders).
  For uncountable oriented graphs, we are able to extend the dichotomy result
mentioned above to all regular cardinals $\kappa$; however, we are only able to
provide a concise characterization in the case when $\kappa=\aleph_1$.


  The X-ray-flash XRF030723 was detected by the HETE satellite and rapidly
disseminated, allowing for an optical transient to be detected ~1 day after the
burst. We discuss observations in the optical with Magellan, which confirmed
the fade of the optical transient. In a 2-epoch ToO observation with Chandra,
we discovered a fading X-ray source spatially coincident with the optical
transient. We present spectral fits to the X-ray data. We also discuss the
possibility that the source underwent a rebrightening in the X-rays, as was
observed in the optical. We find that the significance of a possible
rebrightening is very low (~1 sigma).


  We report Raman light-scattering and optical conductivity measurements on a
single crystal of La(1.775)Sr(0.225)NiO(4) which exhibits incommensurate
charge-stripe order. The extra phonon peaks induced by stripe order can be
understood in terms of the energies of phonons that occur at the charge-order
wave vector, Q_c. A strong Fano antiresonance for a Ni-O bond-stretching mode
provides clear evidence for finite dynamical conductivity within the charge
stripes.


  In this article we prove that a semialgebraic map is a branched covering if
and only if its associated spectral map is a branched covering. In addition,
such spectral map has a neat behavior with respect to the branching locus, the
ramification set and the ramification index. A crucial fact to prove the
preceding result is the characterization of the prime ideals whose fibers under
the previous spectral map are singletons.


  Our investigation reveals a groundbreaking discovery of a negative inverse
orbital Hall effect (IOHE) in Ge thin films. We employed the innovative orbital
pumping technique where spin-orbital coupled current is injected into Ge films
using YIG/Pt(2)/Ge($t_{Ge}$) and YIG/W(2)/Ge($t_{Ge}$) heterostructures.
Through comprehensive analysis, we observe significant reductions in the
signals generated by coherent (RF-driven) and incoherent (thermal-driven)
spin-orbital pumping techniques. These reductions are attributed to the
presence of a remarkable strong negative IOHE in Ge, showing its magnitude
comparable to the spin-to-charge signal in Pt. Our findings reveal that
although the spin-to-charge conversion in Ge is negligible, the
orbital-to-charge conversion exhibits large magnitude. Our results are
innovative and pioneering in the investigation of negative IOHE by the
injection of spin-orbital currents.


  Semi-infinite forms on the moduli spaces of genus-zero Riemann surfaces with
punctures and local coordinates are introduced. A partial operad for
semi-infinite forms is constructed. Using semi-infinite forms and motivated by
a partial suboperad of the partial operad for semi-infinite forms, topological
vertex partial operads of type $k<0$ and strong topological vertex partial
operads of type $k<0$ are constructed. It is proved that the category of
(locally-)grading-restricted (strong) topological vertex operator algebras of
type $k<0$ and the category of (weakly) meromorphic ${\Bbb Z}\times {\Bbb
Z}$-graded algebras over the (strong) topological vertex partial operad of type
k are isomorphic. As an application of this isomorphism theorem, the following
conjecture of Lian-Zuckerman and Kimura-Voronov-Zuckerman is proved: A strong
topological vertex operator algebra gives a homotopy Gerstenhaber algebra.
These results hold in particular for the tensor product of the moonshine module
vertex operator algebra, the vertex algebra constructed {from} a rank 2 Lorentz
lattice and the ghost vertex operator algebra, studied in detail first by Lian
and Zuckerman.


  The Bolocam Galactic Plane Survey (BGPS) is a 1.1 mm continuum survey of
dense clumps of dust throughout the Galaxy covering 170 square degrees. We
present spectroscopic observations using the Heinrich Hertz Submillimeter
Telescope of the dense gas tracers, HCO+ and N2H+ 3-2, for all 6194 sources in
the Bolocam Galactic Plane Survey v1.0.1 catalog between 7.5 <= l <= 194
degrees. This is the largest targeted spectroscopic survey of dense molecular
gas in the Milky Way to date. We find unique velocities for 3126 (50.5%) of the
BGPS v1.0.1 sources observed. Strong N2H+ 3-2 emission (T_{mb} > 0.5 K) without
HCO+ 3-2 emission does not occur in this catalog. We characterize the
properties of the dense molecular gas emission toward the entire sample. HCO+
is very sub-thermally populated and the 3-2 transitions are optically thick
toward most BGPS clumps. The median observed line width is 3.3 km/s consistent
with supersonic turbulence within BGPS clumps. We find strong correlations
between dense molecular gas integrated intensities and 1.1 mm peak flux and the
gas kinetic temperature derived from previously published NH3 observations.
These intensity correlations are driven by the sensitivity of the 3-2
transitions to excitation conditions rather than by variations in molecular
column density or abundance. We identify a subset of 113 sources with stronger
N2H+ than HCO+ integrated intensity, but we find no correlations between the
N2H+ / HCO+ ratio and 1.1 mm continuum flux density, gas kinetic temperature,
or line width. Self-absorbed profiles are rare (1.3%).


  We study an intrinsic Lagrange spectrum of the unit circle $|z|=1$ in the
complex plane with respect to the Eisensteinian field $\mathbb{Q}(\sqrt{-3})$.
We prove that the minimum of the Lagrange spectrum is $2$ and that its smallest
accumulation point is $4/\sqrt{3}$. In addition, we characterize the set of all
values in the spectrum between $2$ and $4/\sqrt3$.


  The spatial range for feedback from star formation varies from molecular
cloud disruption on parsec scales to supershells and disk blowout on kiloparsec
scales. The relative amounts of energy and momentum given to these scales is
important for understanding the termination of star formation in any one region
and the origin of interstellar turbulence and disk stability in galaxies as a
whole. Here we measure for eleven THINGS galaxies the excess kinetic energy,
velocity dispersion and surface density of HI gas associated with regions of
excess star formation, where the excess is determined from the difference
between the observed local value and the azimuthal average. We find small
decreases in the excess kinetic energy and velocity dispersion in regions of
excess star formation rate density, suggesting that most of the feedback energy
does not go into local HI motion. Most likely it disrupts molecular clouds and
dissipates rapidly at high gas density. Some could also be distributed over
larger regions, filling in spaces between the peaks of star formation and
contributing to other energy sources from self-gravity and spiral arm shocks.


  I give the explicit formula for the (set-theoretical) system of Resultants of
m+1 homogeneous polynomials in n+1 variables.


  The literature of heavy tails (typically) starts with a random walk and finds
mechanisms that lead to fat tails under aggregation. We follow the inverse
route and show how starting with fat tails we get to thin-tails when deriving
the probability distribution of the response to a random variable. We introduce
a general dose-response curve and argue that the left and right-boundedness or
saturation of the response in natural things leads to thin-tails, even when the
"underlying" random variable at the source of the exposure is fat-tailed.


  Domain phases in ultrathin Fe/Ni/W(110) films with perpendicular anisotropy
have been studied using the ac magnetic susceptibility. Dynamics on time scales
of minutes to hours were probed by quenching the system from high temperature
to the stripe phase region, and varying the constant rate of temperature
increase as the susceptibility traces were measured. The entire susceptibility
peak is observed to relax slowly along the temperature axis, with the peak
temperature increasing as the rate of heating is decreased. This is precisely
opposite to what would happen if this slow relaxation was driven by changes in
the domain density within the stripe phase. The data are instead consistent
with a simple model for the removal of a significant density of pattern defects
and curvature trapped in the quench from high temperature. A quantitative
analysis confirms that the relaxation dynamics are consistent with the
mesoscopic rearrangement of domains required to remove pattern defects, and
that the experiment constitutes a "dynamical" observation of the phase
transition from a high temperature, positionally disordered phase to the low
temperature, ordered stripe phase.


  This paper is framed within a large project devoted to studying the presence
of circumstellar material around main sequence stars, and looking for
exocometary events. The work concentrates on HR 10 (A2 IV/V), known for its
conspicuous variability in the circumstellar narrow absorption features of Ca
II K and other lines, so far interpreted as $\beta$ Pic-like phenomena, within
the falling evaporating body scenario. The main goal of this paper is to carry
out a thorough study of HR 10 to find the origin of the observed variability,
determine the nature of the star, its absolute parameters, and evolutionary
status. Interferometric near-infrared (NIR) observations, multi-epoch
high-resolution optical spectra spanning a time baseline of more than 32 years,
and optical and NIR photometry, together with theoretical modelling, were used
to tackle the above objectives. Our results reveal that HR 10 is a binary. The
narrow circumstellar absorption features superimposed on the photospheric Ca II
K lines -- and lines of other species -- can be decomposed into two or more
components, the two deep ones tracing the radial velocity of the individual
stars, which implies that their origin cannot be ascribed to transient
exocometary events, their variability being fully explained by the binarity of
the object. There does not appear to be transient events associated with
potential exocomets. Each individual star holds its own circumstellar shell and
there are no traces of a circumbinary envelope. The combined use of the
interferometric and radial velocity data leads to a complete spectrometric and
orbital solution for the binary, the main parameters being: an orbital period
of 747.6 days, eccentricities of the orbits around the centre of mass 0.25 (HR
10-A), 0.21 (HR 10-B) and a mass ratio of q=M$_{\rm B}$/M$_{\rm A}$=0.72-0.84.
The stars are slightly off the main sequence, the binary being $\sim530$ Myr
old.


  We present results of the search for the super-symmetric partner of the top
quark, the stop quark, decaying to a b-quark and chargino with the subsequent
chargino decay into a neutralino, lepton and neutrino. Using the data sample
corresponding to 2.7 inv fb of integrated luminosity, collected with the CDF
Detector of the Tevatron collider, we reconstruct the stop mass of candidate
events and set 95% C. L. upper limits on masses of the stop quark, chargino and
neutralino and the branching ratio of chargino to neutralino+lepton+neutrino.


  We report the detection and observed characteristics of giant pulses from the
Crab Nebula pulsar (B0531+21) in four frequency bands covering 20-84 MHz using
the recently-completed Long Wavelength Array Station 1 (LWA1) radio telescope.
In 10 hours of observations distributed over a 72-day period in Fall of 2012,
33 giant pulses having peak flux densities between 400 Jy and 2000 Jy were
detected. Twenty-two of these pulses were detected simultaneously in channels
of 16 MHz bandwidth centered at 44 MHz, 60 MHz, and 76 MHz, including one pulse
which was also detected in a channel centered at 28 MHz. We quantify statistics
of pulse amplitude and pulse shape characteristics, including pulse broadening.
Amplitude statistics are consistent with expectations based on extrapolations
from previous work at higher and lower frequencies. Pulse broadening is found
to be relatively high, but not significantly greater than expected. We present
procedures that have been found to be effective for observing giant pulses in
this frequency range.


  We put forward a conjecture about an universal asymptotical behaviour of the
symbol of the Dirichlet-to-Neumann operator (considered as a pseudodifferential
operator) in the 2D exterior problem for the Hemholtz equation. The conjecture
is motivated by simple explicit examples and backed by numerical calculations,
even in the case of a non-convex obstacle. It implies (at a physical level of
rigor) Kirhhoff's approximation.


  Access to granular demand data is essential for the net zero transition; it
allows for accurate profiling and active demand management as our reliance on
variable renewable generation increases. However, public release of this data
is often impossible due to privacy concerns. Good quality synthetic data can
circumnavigate this issue. Despite significant research on generating synthetic
smart meter data, there is still insufficient work on creating a consistent
evaluation framework. In this paper, we investigate how common frameworks used
by other industries leveraging synthetic data, can be applied to synthetic
smart meter data, such as fidelity, utility and privacy. We also recommend
specific metrics to ensure that defining aspects of smart meter data are
preserved and test the extent to which privacy can be protected using
differential privacy. We show that standard privacy attack methods like
reconstruction or membership inference attacks are inadequate for assessing
privacy risks of smart meter datasets. We propose an improved method by
injecting training data with implausible outliers, then launching privacy
attacks directly on these outliers. The choice of $\epsilon$ (a metric of
privacy loss) significantly impacts privacy risk, highlighting the necessity of
performing these explicit privacy tests when making trade-offs between fidelity
and privacy.


  Certain exact relations among transverse momentum dependent parton
distribution functions due to QCD equations of motion turn into approximate
ones upon the neglect of pure twist-3 terms. On the basis of available data
from HERMES we test the practical usefulness of one such
``Wandzura-Wilczek-type approximation'', namely of that connecting
h_{1L}^{\perp(1)a}(x) to h_L^a(x), and discuss how it can be further tested by
future CLAS and COMPASS data.


  In the age of cloud computing, cloud users with limited storage can outsource
their data to remote servers. These servers, in lieu of monetary benefits,
offer retrievability of their clients' data at any point of time. Secure cloud
storage protocols enable a client to check integrity of outsourced data. In
this work, we explore the possibility of constructing a secure cloud storage
for dynamic data by leveraging the algorithms involved in secure network
coding. We show that some of the secure network coding schemes can be used to
construct efficient secure cloud storage protocols for dynamic data, and we
construct such a protocol (DSCS I) based on a secure network coding protocol.
To the best of our knowledge, DSCS I is the first secure cloud storage protocol
for dynamic data constructed using secure network coding techniques which is
secure in the standard model. Although generic dynamic data support arbitrary
insertions, deletions and modifications, append-only data find numerous
applications in the real world. We construct another secure cloud storage
protocol (DSCS II) specific to append-only data -- that overcomes some
limitations of DSCS I. Finally, we provide prototype implementations for DSCS I
and DSCS II in order to evaluate their performance.


  This proceeding describes the experimental results presented at the TOP2014
conference on searches for physics beyond the standard model in final states
containing top quarks by the ATLAS and CMS experiments at the LHC. The searches
presented were done in the context of a wide range of theoretical models except
those related to Supersymmetry. Results presented use about 20 fb$^{-1}$ of
data taken with 8 TeV proton-proton collisions from the LHC. No significant
excesses beyond the standard model are observed, therefore limits on potential
new signals are set.


  This article presents results on the concentration properties of the
smoothing and filtering distributions of some partially observed chaotic
dynamical systems. We show that, rather surprisingly, for the geometric model
of the Lorenz equations, as well as some other chaotic dynamical systems, the
smoothing and filtering distributions do not concentrate around the true
position of the signal, as the number of observations tends to infinity.
Instead, under various assumptions on the observation noise, we show that the
expected value of the diameter of the support of the smoothing and filtering
distributions remains lower bounded by a constant times the standard deviation
of the noise, independently of the number of observations. Conversely, under
rather general conditions, the diameter of the support of the smoothing and
filtering distributions are upper bounded by a constant times the standard
deviation of the noise. To some extent, applications to the three dimensional
Lorenz 63' model and to the Lorenz 96' model of arbitrarily large dimension are
considered.


  In this paper we develop a method for clustering belief functions based on
attracting and conflicting metalevel evidence. Such clustering is done when the
belief functions concern multiple events, and all belief functions are mixed
up. The clustering process is used as the means for separating the belief
functions into subsets that should be handled independently. While the
conflicting metalevel evidence is generated internally from pairwise conflicts
of all belief functions, the attracting metalevel evidence is assumed given by
some external source.


  A new Monte Carlo algorithm for 2-dimensional spin glasses is presented. The
use of clusters makes possible global updates and leads to a gain in speed of
several orders of magnitude. As an example, we study the 2-dimensional +/-J
Edwards-Anderson model. The new algorithm allows us to equilibrate systems of
size 100^2 down to temperature T = 0.1. Our main result is that the correlation
length diverges as an exponential and not as a power law as T -> Tc = 0.


  Low-Light Image Enhancement (LLIE) has advanced with the surge in phone
photography demand, yet many existing methods neglect compression, a crucial
concern for resource-constrained phone photography. Most LLIE methods overlook
this, hindering their effectiveness. In this study, we investigate the effects
of JPEG compression on low-light images and reveal substantial information loss
caused by JPEG due to widespread low pixel values in dark areas. Hence, we
propose the Compression-Aware Pre-trained Transformer (CAPformer), employing a
novel pre-training strategy to learn lossless information from uncompressed
low-light images. Additionally, the proposed Brightness-Guided Self-Attention
(BGSA) mechanism enhances rational information gathering. Experiments
demonstrate the superiority of our approach in mitigating compression effects
on LLIE, showcasing its potential for improving LLIE in resource-constrained
scenarios.


  We generalize the notion of a Lie algebroid over infinite jet bundle by
replacing the variational anchor with an N-tuple of differential operators
whose images in the Lie algebra of evolutionary vector fields of the jet space
are subject to collective commutation closure. The linear space of such
operators becomes an algebra with bi-differential structural constants, of
which we study the canonical structure. In particular, we show that these
constants incorporate bi-differential analogues of Christoffel symbols.


  The method of maximum entropy is quite a powerful tool to solve the
generalized moment problem, which consists of determining the probability
density of a random variable X from the knowledge of the expected values of a
few functions of the variable. In actual practice, such expected values are
determined from empirical samples, leaving open the question of the dependence
of the solution upon the sample. It is the purpose of this note to take a few
steps towards the analysis of such dependence.


  We provide a general overview of the velocity-dependent one-scale model for
cosmic string evolution and discuss two further extensions to it. We introduce
and justify a new ansatz for the momentum parameter $k$, and also incorporate
the effect of radiation backreaction. We thus discuss the evolution of the
basic large-scale features of cosmic string networks in all relevant
cosmological scenarios, concentrating in particular on the `scaling' solutions
relevant for each case. In a companion paper, we show, by comparing with
numerical simulations, that this model provides an accurate description of the
large-scale features of cosmic string networks.


  For N-mode light described by the Wigner function of generic Gaussian form
the photon distribution function is obtained explicitly and expressed in terms
of Hermite polynomial of $2N$ variables with equal pairs of indices.The mean
values and dispersions of photon numbers are obtained for this generic mixed
state.Generating function for photon distribution is obtained explicitly. The
expression for $N$-mode photon distribution function for squeezed photon number
states in terms of Hermite polynomials of $2N$ variables and for squeezed
coherent states in terms of Hermite polynomials of $N$ variables is discussed.


  Deep neural network models for image segmentation can be a powerful tool for
the automation of motor claims handling processes in the insurance industry. A
crucial aspect is the reliability of the model outputs when facing adverse
conditions, such as low quality photos taken by claimants to document damages.
We explore the use of a meta-classification model to empirically assess the
precision of segments predicted by a model trained for the semantic
segmentation of car body parts. Different sets of features correlated with the
quality of a segment are compared, and an AUROC score of 0.915 is achieved for
distinguishing between high- and low-quality segments. By removing low-quality
segments, the average mIoU of the segmentation output is improved by 16
percentage points and the number of wrongly predicted segments is reduced by
77%.


  We consider a class of branching-selection particle systems on $\R$ similar
to the one considered by E. Brunet and B. Derrida in their 1997 paper "Shift in
the velocity of a front due to a cutoff". Based on numerical simulations and
heuristic arguments, Brunet and Derrida showed that, as the population size $N$
of the particle system goes to infinity, the asymptotic velocity of the system
converges to a limiting value at the unexpectedly slow rate $(\log N)^{-2}$. In
this paper, we give a rigorous mathematical proof of this fact, for the class
of particle systems we consider. The proof makes use of ideas and results by R.
Pemantle, and by N. Gantert, Y. Hu and Z. Shi, and relies on a comparison of
the particle system with a family of $N$ independent branching random walks
killed below a linear space-time barrier.


  We consider an isomorphism between the idempotent convexity based on the
maximum and the addition operations and the idempotent measure convexity on the
maximum and the multiplication operations. We use this isomorphism to
investigate topological properties of the barycenter map related to the maximum
and the multiplication operations.


  Individual human decision-makers may benefit from different forms of support
to improve decision outcomes, but when each form of support will yield better
outcomes? In this work, we posit that personalizing access to decision support
tools can be an effective mechanism for instantiating the appropriate use of AI
assistance. Specifically, we propose the general problem of learning a decision
support policy that, for a given input, chooses which form of support to
provide to decision-makers for whom we initially have no prior information. We
develop $\texttt{Modiste}$, an interactive tool to learn personalized decision
support policies. $\texttt{Modiste}$ leverages stochastic contextual bandit
techniques to personalize a decision support policy for each decision-maker and
supports extensions to the multi-objective setting to account for auxiliary
objectives like the cost of support. We find that personalized policies
outperform offline policies, and, in the cost-aware setting, reduce the
incurred cost with minimal degradation to performance. Our experiments include
various realistic forms of support (e.g., expert consensus and predictions from
a large language model) on vision and language tasks. Our human subject
experiments validate our computational experiments, demonstrating that
personalization can yield benefits in practice for real users, who interact
with $\texttt{Modiste}$.


  In this paper we study q-Bernoulli numbers and polynomials related to
q-Stirling numbers. From thsese studying we investigate some interesting
q-stirling numbers' identities related to q-Bernoulli numbers.


  Partially observable Markov decision processes (POMDPs) provide a modeling
framework for autonomous decision making under uncertainty and imperfect
sensing, e.g. robot manipulation and self-driving cars. However, optimal
control of POMDPs is notoriously intractable. This paper considers the
quantitative problem of synthesizing sub-optimal stochastic finite state
controllers (sFSCs) for POMDPs such that the probability of satisfying a set of
high-level specifications in terms of linear temporal logic (LTL) formulae is
maximized. We begin by casting the latter problem into an optimization and use
relaxations based on the Poisson equation and McCormick envelopes. Then, we
propose an stochastic bounded policy iteration algorithm, leading to a
controlled growth in sFSC size and an any time algorithm, where the performance
of the controller improves with successive iterations, but can be stopped by
the user based on time or memory considerations. We illustrate the proposed
method by a robot navigation case study.


  We consider a wireless network with multiple single-antenna repeaters that
amplify and instantaneously re-transmit the signals they receive to improve the
channel rank and system coverage. Due to the positive feedback formed by
inter-repeater interference, stability could become a critical issue. We
investigate the problem of determining the maximum amplification gain that the
repeaters can use without breaking the system stability. Specifically, we
obtain a bound by using the Gershgorin disc theorem, which reveals that the
maximum amplification gain is restricted by the sum of channel amplitude gains.
We show by case studies the usefulness of the so-obtained bound and provide
insights on how the repeaters should be deployed.


  This paper develops an averaging technique based on the combination of the
eigenfunction expansion method and the collaboration method to investigate the
multiple scattering effect of the SH wave propagation in a porous medium. The
semi-analytical averaging technique is conducted using Monto Carlo method to
understand the macroscopic dispersion and attenuation phenomena of the stress
wave propagation in a porous solid caused by the multiple scattering effects.
The averaging technique is verified by finite element analysis. Finally, a
simple homogenized elastic model with damping is proposed to describe the
macroscopic dispersion and attenuation effects of SH waves in porous media.


  A common approach to prediction and planning in partially observable domains
is to use recurrent neural networks (RNNs), which ideally develop and maintain
a latent memory about hidden, task-relevant factors. We hypothesize that many
of these hidden factors in the physical world are constant over time, changing
only sparsely. To study this hypothesis, we propose Gated $L_0$ Regularized
Dynamics (GateL0RD), a novel recurrent architecture that incorporates the
inductive bias to maintain stable, sparsely changing latent states. The bias is
implemented by means of a novel internal gating function and a penalty on the
$L_0$ norm of latent state changes. We demonstrate that GateL0RD can compete
with or outperform state-of-the-art RNNs in a variety of partially observable
prediction and control tasks. GateL0RD tends to encode the underlying
generative factors of the environment, ignores spurious temporal dependencies,
and generalizes better, improving sampling efficiency and overall performance
in model-based planning and reinforcement learning tasks. Moreover, we show
that the developing latent states can be easily interpreted, which is a step
towards better explainability in RNNs.


  We give, for the first time, self-consistent large-$N$ analytical solutions
of inhomogeneous condensates in the quantum ${\mathbb C}P^{N-1}$ model in the
large-$N$ limit. We find a map from a set of gap equations of the ${\mathbb
C}P^{N-1}$ model to those of the Gross-Neveu (GN) model (or the gap equation
and the Bogoliubov-de Gennes equation), which enables us to find the
self-consistent solutions. We find that the Higgs field of the ${\mathbb
C}P^{N-1}$ model is given as a zero mode of solutions of the GN model, and
consequently only topologically nontrivial solutions of the GN model yield
nontrivial solutions of the ${\mathbb C}P^{N-1}$ model. A stable single soliton
is constructed from an anti-kink of the GN model and has a broken (Higgs) phase
inside its core,in which ${\mathbb C}P^{N-1}$ modes are localized,with a
symmetric (confining) phase outside. We further find a stable periodic soliton
lattice constructed from a real kink crystal in the GN model,while the
Ablowitz-Kaup-Newell-Segur hierarchy yields multiple solitons at arbitrary
separations.


  We develop an analogy between right-angled Artin groups and mapping class
groups through the geometry of their actions on the extension graph and the
curve graph respectively. The central result in this paper is the fact that
each right-angled Artin group acts acylindrically on its extension graph. From
this result we are able to develop a Nielsen--Thurston classification for
elements in the right-angled Artin group. Our analogy spans both the algebra
regarding subgroups of right-angled Artin groups and mapping class groups, as
well as the geometry of the extension graph and the curve graph. On the
geometric side, we establish an analogue of Masur and Minsky's Bounded Geodesic
Image Theorem and their distance formula.


  Atomically thin MoS2 layer is a direct bandgap semiconductor exhibiting
strong electron-hole interaction due to the extreme quantum confinement and
reduced screening of Coulomb interactions, which results in the formation of
stable excitons at room temperature. Therefore, various excitonic properties of
MoS2 monolayer are extremely important in determining the strength of light
matter interactions including their radiative recombination lifetime and
optoelectronic response. In this paper, we report a comprehensive study of the
underlying annihilation mechanism of various types of exciton in MoS2 monolayer
using the transient absorption spectroscopy. We rigorously demonstrate that the
F\"orster-type resonance energy transfer is the main annihilation mechanism of
A- and B-excitons while multistep diffusion process is responsible for
C-exciton annihilation, which is supported by critical scientific evidence.


  Blind people can now use maps located at Mapy.cz, thanks to the long-standing
joint efforts of the ELSA Center at the Czech Technical University in Prague,
the Teiresias Center at Masaryk University, and the company Seznam.cz.
Conventional map underlays are automatically adjusted so that they could be
read through touch after being printed on microcapsule paper, which opens a
whole new perspective in the use of tactile maps. Users may select an area of
their choice in the Czech Republic (only within its boundaries, for the time
being) and also the production of tactile maps, including the preparation of
the map underlays, takes no more than several minutes.


  Dense, star-forming gas is believed to form at the stagnation points of
large-scale ISM flows, but observational examples of this process in action are
rare. We here present a giant molecular cloud (GMC) sandwiched between two
colliding Milky Way supershells, which we argue shows strong evidence of having
formed from material accumulated at the collision zone. Combining 12CO, 13CO
and C18O(J=1-0) data with new high-resolution, 3D hydrodynamical simulations of
colliding supershells, we discuss the origin and nature of the GMC
(G288.5+1.5), favoring a scenario in which the cloud was partially seeded by
pre-existing denser material, but assembled into its current form by the action
of the shells. This assembly includes the production of some new molecular gas.
The GMC is well interpreted as non-self-gravitating, despite its high mass (MH2
~ 1.7 x 10^5 Msol), and is likely pressure confined by the colliding flows,
implying that self-gravity was not a necessary ingredient for its formation.
Much of the molecular gas is relatively diffuse, and the cloud as a whole shows
little evidence of star formation activity, supporting a scenario in which it
is young and recently formed. Drip-like formations along its lower edge may be
explained by fluid dynamical instabilities in the cooled gas.


  The main aim of this paper is to recall the notion of the Gelfand-Tsetlin
bases (GT bases for short) and to use it for an explicit construction of
orthogonal bases for the spaces of spherical monogenics (i.e., homogeneous
solutions of the Dirac or the generalized Cauchy-Riemann equation,
respectively) in dimension 3. In the paper, using the GT construction, we
obtain explicit orthogonal bases for spherical monogenics in dimension 3 having
the Appell property and we compare them with those constructed by the first and
the second author recently (by a direct analytic approach).


  Recently, a myriad of conditional image generation and editing models have
been developed to serve different downstream tasks, including text-to-image
generation, text-guided image editing, subject-driven image generation,
control-guided image generation, etc. However, we observe huge inconsistencies
in experimental conditions: datasets, inference, and evaluation metrics -
render fair comparisons difficult. This paper proposes ImagenHub, which is a
one-stop library to standardize the inference and evaluation of all the
conditional image generation models. Firstly, we define seven prominent tasks
and curate high-quality evaluation datasets for them. Secondly, we built a
unified inference pipeline to ensure fair comparison. Thirdly, we design two
human evaluation scores, i.e. Semantic Consistency and Perceptual Quality,
along with comprehensive guidelines to evaluate generated images. We train
expert raters to evaluate the model outputs based on the proposed metrics. Our
human evaluation achieves a high inter-worker agreement of Krippendorff's alpha
on 76% models with a value higher than 0.4. We comprehensively evaluated a
total of around 30 models and observed three key takeaways: (1) the existing
models' performance is generally unsatisfying except for Text-guided Image
Generation and Subject-driven Image Generation, with 74% models achieving an
overall score lower than 0.5. (2) we examined the claims from published papers
and found 83% of them hold with a few exceptions. (3) None of the existing
automatic metrics has a Spearman's correlation higher than 0.2 except
subject-driven image generation. Moving forward, we will continue our efforts
to evaluate newly published models and update our leaderboard to keep track of
the progress in conditional image generation.


  We consider the problem of making a networked system contracting by designing
minimal effort local controllers. Our method combines a hierarchical
contraction characterization and a matrix-balancing approach to stabilizing a
Metzler matrix via minimal diagonal perturbations. We demonstrate our approach
by designing local controllers that render contractive a network of
FitzHugh-Nagumo neurons with a general topology of interactions.


  Long-distance quantum communication requires quantum repeaters to overcome
photon loss in optical fibers. Here we demonstrate a repeater node with two
memory atoms in an optical cavity. Both atoms are individually and repeatedly
entangled with photons that are distributed until each communication partner
has independently received one of them. An atomic Bell-state measurement
followed by classical communication serves to establish a key. We demonstrate
scaling advantage of the key rate, increase the effective attenuation length by
a factor of two, and beat the error-rate threshold of 11\% for unconditionally
secure communication, the corner stones for repeater-based quantum networks.


  In this article, we extend the compactness theorems proved by Sprouse and
Hwang-Lee to a weighted manifold under the assumption that the weighted Ricci
curvature is bounded below in terms of its weight function. With the help of
the $\varepsilon$-range, we treat the case that the effective dimension is at
most $1$ in addition to the case that the effective dimension is at least the
dimension of the manifold. To show these theorems, we extend the segment
inequality of Cheeger-Colding to a weighted manifold.


  The blind deconvolution problem amounts to reconstructing both a signal and a
filter from the convolution of these two. It constitutes a prominent topic in
mathematical and engineering literature. In this work, we analyze a sparse
version of the problem: The filter $h\in \mathbb{R}^\mu$ is assumed to be
$s$-sparse, and the signal $b \in \mathbb{R}^n$ is taken to be $\sigma$-sparse,
both supports being unknown. We observe a convolution between the filter and a
linear transformation of the signal. Motivated by practically important
multi-user communication applications, we derive a recovery guarantee for the
simultaneous demixing and deconvolution setting. We achieve efficient recovery
by relaxing the problem to a hierarchical sparse recovery for which we can
build on a flexible framework. At the same time, for this we pay the price of
some sub-optimal guarantees compared to the number of free parameters of the
problem. The signal model we consider is sufficiently general to capture many
applications in a number of engineering fields. Despite their practical
importance, we provide first rigorous performance guarantees for efficient and
simple algorithms for the bi-sparse and generalized demixing setting. We
complement our analytical results by presenting results of numerical
simulations. We find evidence that the sub-optimal scaling $s^2\sigma
\log(\mu)\log(n)$ of our derived sufficient condition is likely overly
pessimistic and that the observed performance is better described by a scaling
proportional to $ s\sigma$ up to log-factors.


  The motion of an air-fluid interface through an irregularly coated capillary
is studied by analysing the Lucas-Washburn equation with a random capillary
force. The pinning probability goes from zero to a maximum value, as the
interface slows down. Under a critical velocity, the distribution of waiting
times $\tau$ displays a power-law tail $\sim \tau^{-2}$ which corresponds to a
strongly intermittent dynamics, also observed in experiments. We elaborate a
procedure to predict quantities of experimental interest, such as the average
interface trajectory and the distribution of pinning lengths.


  We investigate the effect of new physics interacting with both Dark Matter
(DM) and neutrinos at DM direct detection experiments. Working within a
simplified model formalism, we consider vector and scalar mediators to
determine the scattering of DM as well as the modified scattering of solar
neutrinos off nuclei. Using existing data from LUX as well as the expected
sensitivity of LUX-ZEPLIN and DARWIN, we set limits on the couplings of the
mediators to quarks, neutrinos and DM. Given the current limits, we also assess
the true DM discovery potential of direct detection experiments under the
presence of exotic neutrino interactions. In the case of a vector mediator, we
show that the DM discovery reach of future experiments is affected for DM
masses $m_\chi \lesssim 10$ GeV or DM scattering cross sections $\sigma_\chi
\lesssim 10^{-47}$ cm$^2$. On the other hand, a scalar mediator will not affect
the discovery reach appreciably.


  Twisted atomic bilayers are emerging platforms for manipulating chiral
light-matter interaction at the extreme nanoscale, due to their inherent
magnetoelectric responses induced by the finite twist angle and quantum
interlayer coupling between the atomic layers. Recent studies have reported the
direct correspondence between twisted atomic bilayers and chiral metasurfaces,
which features a chiral surface conductivity, in addition to the electric and
magnetic surface conductivities. However, far-field chiral optics in light of
these consitututive conductivities remains unexplored. Within the framework of
the full Maxwell equations, we find that the chiral surface conductivity can be
exploited to realize perfect polarization transformation between linearly
polarized light. Remarkably, such an exotic chiral phenomenon can occur either
for the reflected or transmitted light.


  We present a method for simulating the production and decay of particles in
the sextet representation of $SU(3)_C$ including the simulation of QCD
radiation. First results from the Monte Carlo simulation of sextet diquark
production at the LHC including both resonant and pair production are
presented. We include limits on resonant diquark production from recent ATLAS
results and perform the first simulation studies of the less model dependent
pair production mechanism.


  Probabilistic graphical modeling (PGM) provides a framework for formulating
an interpretable generative process of data and expressing uncertainty about
unknowns, but it lacks flexibility. Deep learning (DL) is an alternative
framework for learning from data that has achieved great empirical success in
recent years. DL offers great flexibility, but it lacks the interpretability
and calibration of PGM. This thesis develops deep probabilistic graphical
modeling (DPGM.) DPGM consists in leveraging DL to make PGM more flexible. DPGM
brings about new methods for learning from data that exhibit the advantages of
both PGM and DL.
  We use DL within PGM to build flexible models endowed with an interpretable
latent structure. One model class we develop extends exponential family PCA
using neural networks to improve predictive performance while enforcing the
interpretability of the latent factors. Another model class we introduce
enables accounting for long-term dependencies when modeling sequential data,
which is a challenge when using purely DL or PGM approaches. Finally, DPGM
successfully solves several outstanding problems of probabilistic topic models,
a widely used family of models in PGM.
  DPGM also brings about new algorithms for learning with complex data. We
develop reweighted expectation maximization, an algorithm that unifies several
existing maximum likelihood-based algorithms for learning models parameterized
by neural networks. This unifying view is made possible using expectation
maximization, a canonical inference algorithm in PGM. We also develop
entropy-regularized adversarial learning, a learning paradigm that deviates
from the traditional maximum likelihood approach used in PGM. From the DL
perspective, entropy-regularized adversarial learning provides a solution to
the long-standing mode collapse problem of generative adversarial networks, a
widely used DL approach.


  This work applies the modular reduction technique to the Coxeter group of
rank 4 having a star diagram with labels 5, 3, and $k = 3, 4, 5, \text{ or }
6$. As moduli, we use the primes in the quadratic integer ring
$\mathbb{Z}[\tau]$, where $\tau = \frac{1 + \sqrt{5}}{2}$, the golden ratio. We
prove that each reduced group is a C-group, regardless of the prime used in the
reduction. We also classify each reduced group as a reflection group over a
finite field, whenever applicable.


  The pinning phenomena of the domain wall in the presence of exchange bias is
studied analytically. The analytic solution of the domain wall spin
configuration is presented. Unlike the traditional solution which is symmetric,
our new solution could exhibit the asymmetry of the domain wall spin profile.
Using the solution, the domain wall position, its width, its stability, and the
depinning field are discussed analytically.


  Active replication is commonly built on top of the atomic broadcast
primitive. Passive replication, which has been recently used in the popular
ZooKeeper coordination system, can be naturally built on top of the
primary-order atomic broadcast primitive. Passive replication differs from
active replication in that it requires processes to cross a barrier before they
become primaries and start broadcasting messages. In this paper, we propose a
barrier function tau that explains and encapsulates the differences between
existing primary-order atomic broadcast algorithms, namely semi-passive
replication and Zookeeper atomic broadcast (Zab), as well as the differences
between Paxos and Zab. We also show that implementing primary-order atomic
broadcast on top of a generic consensus primitive and tau inherently results in
higher time complexity than atomic broadcast, as witnessed by existing
algorithms. We overcome this problem by presenting an alternative,
primary-order atomic broadcast implementation that builds on top of a generic
consensus primitive and uses consensus itself to form a barrier. This algorithm
is modular and matches the time complexity of existing tau-based algorithms.


  The measurements of the cosmic microwave background (CMB) have played a
significant role in understanding the nature of dark energy. In this article,
we investigate the dynamics of the dark energy equation of state, utilizing
high-precision CMB data from multiple experiments. We begin by examining the
Chevallier-Polarski-Linder (CPL) parametrization, a commonly used and
recognized framework for describing the dark energy equation of state. We then
explore the general Exponential parametrization, which incorporates CPL as its
first-order approximation, and extensions of this parametrization that
incorporate nonlinear terms. We constrain these scenarios using CMB data from
various missions, including the Planck 2018 legacy release, the Wilkinson
Microwave Anisotropy Probe (WMAP), the Atacama Cosmology Telescope (ACT), and
the South Pole Telescope (SPT), as well as combinations with low-redshift
cosmological probes such as Baryon Acoustic Oscillations (BAO) and the Pantheon
sample. While the $\Lambda$CDM cosmology remains consistent within the 68\%
confidence level, we observe that the extensions of the CPL parametrization are
indistinguishable for Planck data. However, for ACT and SPT data, the inclusion
of additional terms begins to reveal a peak in $w_{\rm a, DE}$ that was
previously unconstrained.


  The aim of this paper is to give a mathematical justification of cloaking due
to anomalous localized resonance (CALR). We consider the dielectric problem
with a source term in a structure with a layer of plasmonic material. Using
layer potentials and symmetrization techniques, we give a necessary and
sufficient condition on the fixed source term for electromagnetic power
dissipation to blow up as the loss parameter of the plasmonic material goes to
zero. This condition is written in terms of the Newtonian potential of the
source term. In the case of concentric disks, we make the condition even more
explicit. Using the condition, we are able to show that for any source
supported outside a critical radius CALR does not take place, and for sources
located inside the critical radius satisfying certain conditions CALR does take
place as the loss parameter goes to zero.


  In a world of aging infrastructure, structural health monitoring (SHM)
emerges as a major step towards resilient and sustainable societies. The
current advancements in machine learning and sensor technology have made SHM a
more promising damage detection method than the traditional non-destructive
testing methods. SHM using unsupervised learning methods offers an attractive
alternative to the more commonly used supervised learning since it only
requires data of the structure in normal conditions for the training process.
The density-based novelty detection method provides a statistical element to
the damage detection process but it relies heavily on the accuracy of the
estimated probability density function (PDF). In this study, a novel
unsupervised learning approach for SHM is proposed. It is based on the Kernel
Density Maximum Entropy method by leveraging Bayesian optimization for
hyperparameter tuning and also by extending the method into the multivariate
space by the use of independent components analysis. The proposed approach is
evaluated on a numerically simulated three-story reinforced concrete moment
frame, where 94% of accuracy is achieved in structural damage detection.


  We present some theoretical results on the lattice vibrations that are
necessary for a concise derivation of the Debye-Waller factor in the harmonic
approximation. First we obtain an expression for displacement of an atom in a
crystal lattice from its equilibrium position. Then we show that an atomic
displacement has the Gaussian distribution. Finally, we obtain the
computational formula for the Debye-Waller factor in the Debye model.


  Spatially inhomogeneous electronic states are expected to be key ingredients
for the emergence of superconducting phases in quantum materials hosting
charge-density-waves (CDWs). Prototypical materials are transition-metal
dichalcogenides (TMDCs) and among them, 1$T$-TiSe$_2$ exhibiting intertwined
CDW and superconducting states under Cu intercalation, pressure or electrical
gating. Although it has been recently proposed that the emergence of
superconductivity relates to CDW fluctuations and the development of spatial
inhomogeneities in the CDW order, the fundamental mechanism underlying such a
phase separation (PS) is still missing. Using angle-resolved photoemission
spectroscopy and variable-temperature scanning tunneling microscopy, we report
on the phase diagram of the CDW in 1$T$-TiSe$_2$ as a function of Ti
self-doping, an overlooked degree of freedom inducing CDW texturing. We find an
intrinsic tendency towards electronic PS in the vicinity of Fermi surface (FS)
"hot spots", i.e. locations with band crossings close to, but not at the Fermi
level. We therefore demonstrate an intimate relationship between the FS
topology and the emergence of spatially textured electronic phases which is
expected to be generalizable to many doped CDW compounds.


  When x-rays penetrate soft matter, their phase changes more rapidly than
their amplitude. In- terference effects visible with high brightness sources
creates higher contrast, edge enhanced images. When the object is piecewise
smooth (made of big blocks of a few components), such higher con- trast
datasets have a sparse solution. We apply basis pursuit solvers to improve SNR,
remove ring artifacts, reduce the number of views and radiation dose from phase
contrast datasets collected at the Hard X-Ray Micro Tomography Beamline at the
Advanced Light Source. We report a GPU code for the most computationally
intensive task, the gridding and inverse gridding algorithm (non uniform
sampled Fourier transform).


  We consider Brans-Dicke cosmology with cosmological constant with negative w
parameter and an arbitrary (in general non-vanishing) scale factor at the Big
Bang. The field equations describe the flat universe, current observational
values for Hubble constant H and deceleration parameter q play a role of
initial conditions. In order to solve field equations we adopt the approach of
Uehara and Kim (1982). They considered only positive values of w, hence our
solution is a new one and has not been described in their paper. Our main
result is that unlike the standard cosmology the scale factor may not vanish in
it's evolution back in time. In other words our model displays a cosmological
bounce and avoids the initial singularity. The formula for the scale factor,
leading to the bounce, is only valid for the dust-filled universe and hence
cannot be adequate for the hot stage in the early universe when the bounce
happens. So, the results of this paper are only qualitative in nature and must
be used to obtain initial values for the hot stage of the unverse when one
considers the evolution back in time.


  The gravitational wave signals from coalescing Supermassive Black Hole
Binaries are prime targets for the Laser Interferometer Space Antenna (LISA).
With optimal data processing techniques, the LISA observatory should be able to
detect black hole mergers anywhere in the Universe. The challenge is to find
ways to dig the signals out of a combination of instrument noise and the large
foreground from stellar mass binaries in our own galaxy. The standard procedure
of matched filtering against a grid of templates can be computationally
prohibitive, especially when the black holes are spinning or the mass ratio is
large. Here we develop an alternative approach based on Metropolis-Hastings
sampling and simulated annealing that is orders of magnitude cheaper than a
grid search. We demonstrate our approach on simulated LISA data streams that
contain the signals from binary systems of Schwarzschild Black Holes, embedded
in instrument noise and a foreground containing 26 million galactic binaries.
The search algorithm is able to accurately recover the 9 parameters that
describe the black hole binary without first having to remove any of the bright
foreground sources, even when the black hole system has low signal-to-noise.


  This paper considers an unmanned vehicle-robot pickup and delivery system, in
which a self-driving vehicle carrying multiple unmanned robots in the form of
the mother ship travels from a depot to a number of stations distributed in a
neighborhood to perform multiple pickup and delivery services. First of all, we
present it as a Multi-modal Vehicle Routing Problem (MMVRP) with time
constraints, which are typical service requirements for grocery and food
delivery in practice. We then formulate it as a Mixed Integer Quadratically
Con-strained Program (MIQCP) model to determine the optimal integrated routing
plan (vehicle routing and robot routing) to minimize the total weighted
tardiness of all services. Finally, a small-size and a medium-size problem
instance are solved using the Gurobi solver in Python to demonstrate the
validity and the performance of the proposed MIQCP model.


  A possible way to calculate particle spectra as a function of position in
pulsar wind nebulae is to solve a Fokker-Planck transport equation. This paper
presents numerical solutions to the transport equation with the processes of
convection, diffusion, adiabatic losses, and synchrotron radiation included. In
the first part of the paper the steady-state version of the transport equation
is solved as a function of position and energy. This is done to distinguish the
various effects of the aforementioned processes on the solutions to the
transport equation. The second part of the paper deals with a time-dependent
solution to the transport equation, specifically taking into account the effect
of a moving outer boundary. The paper highlights the fact that diffusion can
play a significant role in reducing the amount of synchrotron losses, leading
to a modification in the expected particle spectra. These modified spectra can
explain the change in the photon index of the synchrotron emission as a
function of position. The solutions presented in this paper are not limited to
pulsar wind nebulae, but can be applied to any similar central source system,
e.g. globular clusters.


  We study the link between the X-ray emission in radio-quiet AGNs and the
accretion rate on the central Supermassive Black-Hole (SMBH) using a
well-defined and statistically complete sample of 70 type1 AGNs extracted from
the XMM-Newton Bright Serendipitous survey (XBS). To this end, we search and
quantify the statistical correlations between the main parameters that
characterize the X-ray emission (i.e. the X-ray spectral slope and the X-ray
loudness), and the accretion rate, both absolute and relative to the Eddington
limit (Eddington ratio). Here, we summarize and discuss the main statistical
correlations found and their possible implications on current disk-corona
models.


  Coronal jets represent important manifestations of ubiquitous solar
transients, which may be the source of significant mass and energy input to the
upper solar atmosphere and the solar wind. While the energy involved in a
jet-like event is smaller than that of "nominal" solar flares and coronal mass
ejections (CMEs), jets share many common properties with these phenomena, in
particular, the explosive magnetically driven dynamics. Studies of jets could,
therefore, provide critical insight for understanding the larger, more complex
drivers of the solar activity. On the other side of the size-spectrum, the
study of jets could also supply important clues on the physics of transients
close or at the limit of the current spatial resolution such as spicules.
Furthermore, jet phenomena may hint to basic process for heating the corona and
accelerating the solar wind; consequently their study gives us the opportunity
to attack a broad range of solar-heliospheric problems.


  PYTHIA is a general-purpose event generator for multiparticle production in
high-energy physics. After a general introduction and program news survey, some
areas of recent physics progress are considered: the matching to matrix
elements, especially for W/Z production; charm and bottom hadronization;
multiple interactions; and interconnection effects. The report concludes with
some words on the future, specifically the ongoing transition to C++.


  This paper provides a theoretical foundation for some common formulations of
inverse problems in wave propagation, based on hyperbolic systems of linear
integro-differential equations with bounded and measurable coefficients. The
coefficients of these time-dependent partial differential equations respresent
parametrically the spatially varying mechanical properties of materials. Rocks,
manufactured materials, and other wave propagation environments often exhibit
spatial heterogeneity in mechanical properties at a wide variety of scales, and
coefficient functions representing these properties must mimic this
heterogeneity. We show how to choose domains (classes of nonsmooth coefficient
functions) and data definitions (traces of weak solutions) so that optimization
formulations of inverse wave problems satisfy some of the prerequisites for
application of Newton's method and its relatives. These results follow from the
properties of a class of abstract first-order evolution systems, of which
various physical wave systems appear as concrete instances. Finite speed of
propagation for linear waves with bounded, measurable mechanical parameter
fields is one of the by-products of this theory.


  Proposed special requirements exist for the rheological properties and time
varying characteristics of 3D printing cementitious materials (3DPC). In this
study, high dispersion polycarboxylate superplasticizer (HD PC) and fluidity
retaining polycarboxylate superplasticizer (FR PC) were used to control the
rheological behaviors of 3DPC. The correlation of the time interval, the time
varying characteristics of the rheological properties and the interlayer
bonding strength were investigated. The results indicated that FR PC improved
the fluidity retention ability and thixotropy of the fresh pastes. The
thixotropic hysteresis loop area and reflocculation rate (Rthix) of FR PC were
101.9% and 80.4% higher than those of HD PC, respectively. Furthermore, the FR
PC polymer has a positive effect on the interlayer bonding and may reduce the
negative effect caused by extending the time interval. During the time interval
of 20 s to 30 min, the interlayer bonding strength with FR PC decreases by
14.1%, while that with HD PC decreases by 50.0%.


  Disk galaxies at high redshift have been predicted to maintain high gas
surface densities due to continuous feeding by intense cold streams leading to
violent gravitational instability, transient features and giant clumps.
Gravitational torques between the perturbations drive angular momentum out and
mass in, and the inflow provides the energy for keeping strong turbulence. We
use analytic estimates of the inflow for a self-regulated unstable disk at a
Toomre stability parameter Q~1, and isolated galaxy simulations capable of
resolving the nuclear inflow down to the central parsec. We predict an average
inflow rate ~10 Msun/yr through the disk of a 10^11 Msun galaxy, with
conditions representative of z~2 stream-fed disks. The inflow rate scales with
disk mass and (1+z)^{3/2}. It includes clump migration and inflow of the
smoother component, valid even if clumps disrupt. This inflow grows the bulge,
while only a fraction ~ 10^-3 of it needs to accrete onto a central black hole
(BH), in order to obey the observed BH-bulge relation. A galaxy of 10^11 Msun
at z~2 is expected to host a BH of ~10^8 Msun, accreting on average with
moderate sub-Eddington luminosity L_X ~ 10^42-43 erg/s, accompanied by brighter
episodes when dense clumps coalesce. We note that in rare massive galaxies at
z~6, the same process may feed 10^9 Msun BH at the Eddington rate. High central
gas column densities can severely obscure AGN in high-redshift disks, possibly
hindering their detection in deep X-ray surveys.


  Cavity-enhanced direct frequency comb spectroscopy (CE-DFCS) has demonstrated
powerful potential for trace gas detection based on its unique combination of
high bandwidth, rapid data acquisition, high sensitivity, and high resolution,
which is unavailable with conventional systems. However, previous
demonstrations have been limited to proof-of-principle experiments or studies
of fundamental laboratory science. Here we present the development of CE-DFCS
towards an industrial application -- measuring impurities in arsine, an
important process gas used in III-V semiconductor compound manufacturing. A
strongly absorbing background gas with an extremely complex, congested, and
broadband spectrum renders trace detection exceptionally difficult, but the
capabilities of CE-DFCS overcome this challenge and make it possible to
identify and quantify multiple spectral lines associated with water impurities.
Further, frequency combs allow easy access to new spectral regions via
efficient nonlinear optical processes. Here, we demonstrate detection of
multiple potential impurities across 1.75-1.95 um (5710-5130 cm-1) with
single-channel detection sensitivities of ~1 x 10-7 cm-1 Hz-1/2 in nitrogen and
identify water doped in arsine with a sensitivity of ~1 x 10-6 cm-1 Hz-1/2.


  We demonstrate that rapidity separated di-hadron spectra in high multiplicity
proton-proton collisions at the LHC can be quantitatively described by a
combination of BFKL and saturation dynamics. Based on these results, we predict
the systematics of di-hadron spectra in proton-nucleus collisions at the LHC.


  Air-gapped systems are isolated from the Internet due to the sensitive
information they handle. This paper presents COVID-bit, a new COVert channel
attack that leaks sensitive information over the air from highly isolated
systems. The information emanates from the air-gapped computer over the air to
a distance of 2m and more and can be picked up by a nearby insider or spy with
a mobile phone or laptop. Malware on an air-gapped computer can generate radio
waves by executing crafted code on the target system. The malicious code
exploits the dynamic power consumption of modern computers and manipulates the
momentary loads on CPU cores. This technique allows the malware to control the
computer's internal utilization and generate low-frequency electromagnetic
radiation in the 0 - 60 kHz band. Sensitive information (e.g., files,
encryption keys, biometric data, and keylogging) can be modulated over the
emanated signals and received by a nearby mobile phone at a max speed of 1000
bits/sec. We show that a smartphone or laptop with a small \$1 antenna carried
by a malicious insider or visitor can be used as a covert receiver. Notably,
the attack is highly evasive since it executes from an ordinary user-level
process, does not require root privileges, and is effective even within a
Virtual Machine (VM). We discuss the attack model and provide technical
details. We implement air-gap transmission of texts and files, and present
signal generation and data modulation. We test the covert channel and show
evaluation results. Finally, we present a set of countermeasures to this
air-gap attack.


  Given an orientation-preserving diffeomorphism of the interval [0;1],
consider the uniform norm of the differential of its n-th iteration. We get a
function of n called the growth sequence. Its asymptotic behaviour is an
interesting invariant which naturally appears both in geometry of the
diffeomorphisms groups and in smooth dynamics. Our main result is the following
Gap Theorem: the growth rate of this sequence is either exponential, or at most
quadratic with n. Further, we construct diffeomorphisms with quite an irregular
behaviour of the growth sequence. This construction easily extends to arbitrary
manifolds.


  The cosmological significance of the neutralino sector is studied for a class
of models in which electroweak symmetry breaking is seeded by a gauge singlet.
Extensive use is made of the renormalisation group equations to significantly
reduce the parameter space, by deriving analytic expressions for all the
supersymmetry-breaking couplings in terms of the universal gaugino mass
$m_{1/2}$, the universal scalar mass $m_0$ and the coupling $A$. The
composition of the LSP is determined exactly below the W mass, no
approximations are made for sfermion masses, and all particle exchanges are
considered in calculating the annihilation cross-section; the relic abundance
is then obtained by an analytic approximation. We find that in these models,
stable neutralinos may make a significant contribution to the dark matter in
the universe.


  In cavity-based axion dark matter detectors, quantum noise remains a primary
barrier to achieving the scan rate necessary for a comprehensive search of
axion parameter space. Here we introduce a method of scan rate enhancement in
which an axion-sensitive cavity is coupled to an auxiliary resonant circuit
through simultaneous two-mode squeezing (entangling) and state swapping
interactions. We show analytically that when combined, these interactions can
amplify an axion signal before it becomes polluted by vacuum noise introduced
by measurement. This internal amplification yields a wider bandwidth of axion
sensitivity, increasing the rate at which the detector can search through
frequency space. With interaction rates predicted by circuit simulations of
this system, we show that this technique can increase the scan rate up to
15-fold relative to the scan rate of a detector limited by vacuum noise.


  Oxygen optimal distribution is one of the most important energy management
problems in the modern iron and steel industry. Normally, the supply of the
energy generation system is determined by the energy demand of manufacturing
processes. However, the balance between supply and demand fluctuates frequently
due to the uncertainty arising in manufacturing processes. In this paper, we
developed an oxygen optimal distribution model considering uncertain demands
and proposed a two-stage robust optimization (TSRO) with a budget-based
uncertainty set that protects the initial distribution decisions with low
conservatism. The main goal of the TSRO model is to make wait-and-see decisions
maximizing production profits and make here-and-now decisions minimizing
operational stability and surplus/shortage penalty. To represent the
uncertainty set of energy demands, we developed a Gaussian process (GP)-based
time series model to forecast the energy demands of continuous processes and a
capacity-constrained scheduling model to generate multi-scenario energy demands
of discrete processes. We carried out extensive computational studies on TSRO
and its components using well-synthetic instances from historical data. The
results of model validation and analysis are promising and demonstrate our
approach is adapted to solve industrial cases under uncertainty.


  Forecasting a time series from multivariate predictors constitutes a
challenging problem, especially using model-free approaches. Most techniques,
such as nearest-neighbor prediction, quickly suffer from the curse of
dimensionality and overfitting for more than a few predictors which has limited
their application mostly to the univariate case. Therefore, selection
strategies are needed that harness the available information as efficiently as
possible. Since often the right combination of predictors matters, ideally all
subsets of possible predictors should be tested for their predictive power, but
the exponentially growing number of combinations makes such an approach
computationally prohibitive. Here a prediction scheme that overcomes this
strong limitation is introduced utilizing a causal pre-selection step which
drastically reduces the number of possible predictors to the most predictive
set of causal drivers making a globally optimal search scheme tractable. The
information-theoretic optimality is derived and practical selection criteria
are discussed. As demonstrated for multivariate nonlinear stochastic delay
processes, the optimal scheme can even be less computationally expensive than
commonly used sub-optimal schemes like forward selection. The method suggests a
general framework to apply the optimal model-free approach to select variables
and subsequently fit a model to further improve a prediction or learn
statistical dependencies. The performance of this framework is illustrated on a
climatological index of El Ni\~no Southern Oscillation.


  The task of chemical reaction predictions (CRPs) plays a pivotal role in
advancing drug discovery and material science. However, its effectiveness is
constrained by the vast and uncertain chemical reaction space and challenges in
capturing reaction selectivity, particularly due to existing methods'
limitations in exploiting the data's inherent knowledge. To address these
challenges, we introduce a data-curated self-feedback knowledge elicitation
approach. This method starts from iterative optimization of molecular
representations and facilitates the extraction of knowledge on chemical
reaction types (RTs). Then, we employ adaptive prompt learning to infuse the
prior knowledge into the large language model (LLM). As a result, we achieve
significant enhancements: a 14.2% increase in retrosynthesis prediction
accuracy, a 74.2% rise in reagent prediction accuracy, and an expansion in the
model's capability for handling multi-task chemical reactions. This research
offers a novel paradigm for knowledge elicitation in scientific research and
showcases the untapped potential of LLMs in CRPs.


  We use the cosmo-OWLS suite of cosmological hydrodynamical simulations, which
includes different galactic feedback models, to predict the cross-correlation
signal between weak gravitational lensing and the thermal Sunyaev-Zeldovich
(tSZ) $y$-parameter. The predictions are compared to the recent detection
reported by van Waerbeke and collaborators. The simulations reproduce the weak
lensing-tSZ cross-correlation, $\xi_{y\kappa}(\theta)$, well. The uncertainty
arising from different possible feedback models appears to be important on
small scales only ($\theta \lesssim 10$ arcmin), while the amplitude of the
correlation on all scales is sensitive to cosmological parameters that control
the growth rate of structure (such as $\sigma_8$, $\Omega_m$ and $\Omega_b$).
This study confirms our previous claim (in Ma et al.) that a significant
proportion of the signal originates from the diffuse gas component in low-mass
($M_{\rm{halo}} \lesssim 10^{14} M_{\odot}$) clusters as well as from the
region beyond the virial radius. We estimate that approximately 20$\%$ of the
detected signal comes from low-mass clusters, which corresponds to about 30$\%$
of the baryon density of the Universe. The simulations also suggest that more
than half of the baryons in the Universe are in the form of diffuse gas outside
halos ($\gtrsim 5$ times the virial radius) which is not hot or dense enough to
produce a significant tSZ signal or be observed by X-ray experiments. Finally,
we show that future high-resolution tSZ-lensing cross-correlation observations
will serve as a powerful tool for discriminating between different galactic
feedback models.


  The current mainstream approach to train natural language systems is to
expose them to large amounts of text. This passive learning is problematic if
we are interested in developing interactive machines, such as conversational
agents. We propose a framework for language learning that relies on multi-agent
communication. We study this learning in the context of referential games. In
these games, a sender and a receiver see a pair of images. The sender is told
one of them is the target and is allowed to send a message from a fixed,
arbitrary vocabulary to the receiver. The receiver must rely on this message to
identify the target. Thus, the agents develop their own language interactively
out of the need to communicate. We show that two networks with simple
configurations are able to learn to coordinate in the referential game. We
further explore how to make changes to the game environment to cause the "word
meanings" induced in the game to better reflect intuitive semantic properties
of the images. In addition, we present a simple strategy for grounding the
agents' code into natural language. Both of these are necessary steps towards
developing machines that are able to communicate with humans productively.


  We study the guided modes in the wire medium slab taking into account both
the nonlocality and losses in the structure. We show that due to the fact that
the wire medium is an extremeley spatially dispersive metamaterial, the effect
of nonlocality plays a critical role since it results in coupling between the
otherwise orthogonal guided modes. We observe both the effects of strong and
weak coupling, depending on the level of losses in the system.


  We show that a straightforward application of the predicted relation
connecting the absolute K magnitude of fundamental RR Lyrae variables to their
period and metal content, together with current evolutionary predictions on the
mass and luminosity of Horizontal Branch stars, supply a distance estimate to
the prototype star RR Lyr in close agreement with the recent parallax
determination by HST (Benedict et al. 2002), largely unaffected by the
interstellar extinction correction. Adopting <A_V>=0.12+-0.10 mag as a safe
estimate of the extinction correction to RR Lyr, we obtain a "pulsation"
parallax of 3.858+-0.131 mas which agrees quite well, and with a smaller formal
error, with the HST measurement (3.82+-0.20 mas) and with the weighted average
of HST, Hipparcos, and ground-based determinations (3.87+-0.19 mas). This
result suggests that near-infrared magnitudes and pulsation models could
constrain the distance to well-studied RR Lyrae stars, both in the field and in
globular clusters, with an accuracy better than current direct trigonometric
measurements.


  Quantum teleportation is investigated between Alice who is far from the
horizon and Bob locates near the event horizon of a Schwarzschild black hole.
The results show that the fidelity of the teleportation is reduced in this
curved space-time. However, high fidelity can still be reached outside a
massive black hole.


  ChatGPT disrupted the application of machine-learning methods and drastically
reduced the usage barrier. Chatbots are now widely used in a lot of different
situations. They provide advice, assist in writing source code, or assess and
summarize information from various sources. However, their scope is not only
limited to aiding humans; they can also be used to take on tasks like
negotiating or bargaining. To understand the implications of Chatbot usage on
bargaining situations, we conduct a laboratory experiment with the ultimatum
game. In the ultimatum game, two human players interact: The receiver decides
on accepting or rejecting a monetary offer from the proposer. To shed light on
the new bargaining situation, we let ChatGPT provide an offer to a human
player. In the novel design, we vary the wealth of the receivers. Our results
indicate that humans have the same beliefs about other humans and chatbots.
However, our results contradict these beliefs in an important point: Humans
favor poor receivers as correctly anticipated by the humans, but ChatGPT favors
rich receivers which the humans did not expect to happen. These results imply
that ChatGPT's answers are not aligned with those of humans and that humans do
not anticipate this difference.


  We briefly review the Whitham hierarchies and their applications to
integrable systems of the Seiberg-Witten type. The simplest example of the N=2
supersymmetric SU(2) pure gauge theory is considered in detail and the
corresponding Whitham solutions are found explicitely.


  We present a tracking algorithm to maintain the communication link between a
base station (BS) and a mobile station (MS) in a millimeter wave (mmWave)
communication system, where antenna arrays are used for beamforming in both the
BS and MS. Downlink transmission is considered, and the tracking is performed
at the MS as it moves relative to the BS. Specifically, we consider the case
that the MS rotates quickly due to hand movement. The algorithm estimates the
angle of arrival (AoA) by using variations in the radiation pattern of the beam
as a function of this angle. Numerical results show that the algorithm achieves
accurate beam alignment when the MS rotates in a wide range of angular speeds.
For example, the algorithm can support angular speeds up to 800 degrees per
second when tracking updates are available every 10 ms.


  Simulation plays a crucial role in the development of autonomous vehicles
(AVs) due to the potential risks associated with real-world testing. Although
significant progress has been made in the visual aspects of simulators,
generating complex behavior among agents remains a formidable challenge. It is
not only imperative to ensure realism in the scenarios generated but also
essential to incorporate preferences and conditions to facilitate controllable
generation for AV training and evaluation. Traditional methods, mainly relying
on memorizing the distribution of training datasets, often fall short in
generating unseen scenarios. Inspired by the success of retrieval augmented
generation in large language models, we present RealGen, a novel
retrieval-based in-context learning framework for traffic scenario generation.
RealGen synthesizes new scenarios by combining behaviors from multiple
retrieved examples in a gradient-free way, which may originate from templates
or tagged scenarios. This in-context learning framework endows versatile
generative capabilities, including the ability to edit scenarios, compose
various behaviors, and produce critical scenarios. Evaluations show that
RealGen offers considerable flexibility and controllability, marking a new
direction in the field of controllable traffic scenario generation. Check our
project website for more information: https://realgen.github.io.