  The problem of coverage control, i.e., of coordinating multiple agents to
optimally cover an area, arises in various applications. However, coverage
applications face two major challenges: (1) dealing with nonlinear dynamics
while respecting system and safety critical constraints, and (2) performing the
task in an initially unknown environment. We solve the coverage problem by
using a hierarchical framework, in which references are calculated at a central
server and passed to the agents' local model predictive control (MPC) tracking
schemes. Furthermore, to ensure that the environment is actively explored by
the agents a probabilistic exploration-exploitation trade-off is deployed. In
addition, we derive a control framework that avoids the hierarchical structure
by integrating the reference optimization in the MPC formulation. Active
learning is then performed drawing inspiration from Upper Confidence Bound
(UCB) approaches. For all developed control architectures, we guarantee
closed-loop constraint satisfaction and convergence to an optimal
configuration. Furthermore, all methods are tested and compared on hardware
using a miniature car platform.


  The kaon electromagnetic (e.m.) form factor is reviewed considering a
light-front constituent quark model. In this approach, it is discussed the
relevance of the quark-antiquark pair terms for the full covariance of the e.m.
current. It is also verified, by considering a QCD dynamical model, that a good
agreement with experimental data can be obtained for the kaon weak decay
constant once a probability of about 80% of the valence component is taken into
account.


  This study presents comparative CP sensitivities for various sets of water
Cerenkov and liquid argon detectors combined with various running scenarios
associated with DAEdALUS and LBNE neutrino beams at DUSEL. LBNE-only running
scenarios show fairly small differences in sensitivity for the various detector
combinations. On the other hand, the DAEdALUS-only and DAEdALUS-plus-LBNE
running gives significantly better sensitivity for a detector combination that
includes at least 200 kt of Gd-doped water Cerenkov detector, exceeding the
sensitivity of a Project-X 10 year run. A 300 kt Gd-doped water Cerenkov
detector yields the best sensitivity for combined running.


  We have modeled the layer-by-layer assembly process of flexible
polyelectrolytes on flat surfaces. The multilayer has a three-zone structure.
An exponential growth is found for the first several layers, followed by a
linear growth for subsequent layers evolving towards a steady state. While
adjacent layers are highly interpenetrating, stratification can be seen for
every four or more layers. The effects of surface charge density, bulk salt
concentration, and solvent quality on the thickness and internal structure of
the multilayer are also studied. Our results agree with experimental findings.


  Let X and Y be compact, simply connected and locally connected subsets of
R^2, and let f : X -> Y be a homeomorphism isotopic to the identity on X.
Generalizing Brouwer's plane translation theorem for self-maps of the plane, we
prove that f has no recurrent (in particular, no periodic) points if it has no
fixed points.


  Recently, large language models (LLMs), particularly GPT-4, have demonstrated
significant capabilities in various planning and reasoning tasks
\cite{cheng2023gpt4,bubeck2023sparks}. Motivated by these advancements, there
has been a surge of interest among researchers to harness the capabilities of
GPT-4 for the automated design of quantitative factors that do not overlap with
existing factor libraries, with an aspiration to achieve alpha returns
\cite{webpagequant}. In contrast to these work, this study aims to examine the
fidelity of GPT-4's comprehension of classic trading theories and its
proficiency in applying its code interpreter abilities to real-world trading
data analysis. Such an exploration is instrumental in discerning whether the
underlying logic GPT-4 employs for trading is intrinsically reliable.
Furthermore, given the acknowledged interpretative latitude inherent in most
trading theories, we seek to distill more precise methodologies of deploying
these theories from GPT-4's analytical process, potentially offering invaluable
insights to human traders.
  To achieve this objective, we selected daily candlestick (K-line) data from
specific periods for certain assets, such as the Shanghai Stock Index. Through
meticulous prompt engineering, we guided GPT-4 to analyze the technical
structures embedded within this data, based on specific theories like the
Elliott Wave Theory. We then subjected its analytical output to manual
evaluation, assessing its interpretative depth and accuracy vis-\`a-vis these
trading theories from multiple dimensions. The results and findings from this
study could pave the way for a synergistic amalgamation of human expertise and
AI-driven insights in the realm of trading.


  We calculate inclusive dijet production cross section in high energy hadron
collisions within the BFKL resummation formalism for the QCD Pomeron. Unlike
the previous calculations with the Pomeron developing only between tagging
jets, we include also the Pomerons which are adjacent to the hadrons. With
these adjacent Pomerons we define a new object --- the BFKL structure function
of hadron --- which enables one to calculate the inclusive dijet production for
any rapidity intervals. We present predictions for the K-factor and the
azimuthal angle decorrelation in the inclusive dijet production for
Fermilab-Tevatron and CERN-LHC energies.


  This study focuses attention on interstitial microwave hyperthermia, which is
a popular medical procedure for treatment of pathological human tissues
containing tumors. In the paper, the 2D finite element analysis is used to
compare the models of the coaxial antenna with one, two and three air slots.
The implemented models are based on the wave equation in the TM mode coupled
with the Pennes equation under a transient state condition. Moreover, the model
takes into account the thermo-electrical properties of human tissue for the
antenna operating frequency of 2.45 GHz. Simulation results for different
tissues and various multi-slot antenna configurations have been presented. The
comparative analysis for single-, double-and triple-slot coaxial antennas shown
in this article does not have equivalents in the current literature in the
field of hyperthermia.


  The $J/\psi$ decay angular distributions have been measured in inelastic
photoproduction in $e p$ collisions with the ZEUS detector at HERA, using an
integrated luminosity of 468 $pb^{-1}$. The range in photon-proton
centre-of-mass energy, $W$, was 50 $< W <$ 180 $GeV$. The $J/\psi$ mesons were
identified through their decay into muon pairs. The polar and azimuthal angles
of the $\mu^+$ were measured in the $J/\psi$ rest frame and compared to
theoretical predictions at leading and next-to-leading order in QCD.


  In this paper, we develop statistical inference techniques for the unknown
coefficient functions and single-index parameters in single-index
varying-coefficient models. We first estimate the nonparametric component via
the local linear fitting, then construct an estimated empirical likelihood
ratio function and hence obtain a maximum empirical likelihood estimator for
the parametric component. Our estimator for parametric component is
asymptotically efficient, and the estimator of nonparametric component has an
optimal convergence rate. Our results provide ways to construct the confidence
region for the involved unknown parameter. We also develop an adjusted
empirical likelihood ratio for constructing the confidence regions of
parameters of interest. A simulation study is conducted to evaluate the finite
sample behaviors of the proposed methods.


  A Hadron Blind Detector (HBD) has been developed, constructed and
successfully operated within the PHENIX detector at RHIC. The HBD is a
Cherenkov detector operated with pure CF4. It has a 50 cm long radiator
directly coupled in a window- less configuration to a readout element
consisting of a triple GEM stack, with a CsI photocathode evaporated on the top
surface of the top GEM and pad readout at the bottom of the stack. This paper
gives a comprehensive account of the construction, operation and in-beam
performance of the detector.


  Inducing and controlling electrostatic barriers in two-dimensional (2D)
quantum materials has shown extraordinary promise to enable control of charge
carriers, and is key for the realization of nanoscale electronic and
optoelectronic devices1-10. Because of their atomically thin nature, the 2D
materials have a congenital advantage to construct the thinnest possible p-n
junctions1,3,7,9,10. To realize the ultimate functional unit for future
nanoscale devices, creating atomically wide electrostatic barriers embedded in
2D materials is desired and remains an extremely challenge. Here we report the
creation and manipulation of atomically wide electrostatic barriers embedded in
graphene WSe2 heterostructures. By using a STM tip, we demonstrate the ability
to generate a one-dimensional (1D) atomically wide boundary between 1T-WSe2
domains and continuously tune positions of the boundary because of
ferroelasticity of the 1T-WSe2. Our experiment indicates that the 1D boundary
introduces atomically wide electrostatic barriers in graphene above it. Then
the 1D electrostatic barrier changes a single graphene WSe2 heterostructure
quantum dot from a relativistic artificial atom to a relativistic artificial
molecule.


  We present symbol tuning - finetuning language models on in-context
input-label pairs where natural language labels (e.g., "positive/negative
sentiment") are replaced with arbitrary symbols (e.g., "foo/bar"). Symbol
tuning leverages the intuition that when a model cannot use instructions or
natural language labels to figure out a task, it must instead do so by learning
the input-label mappings.
  We experiment with symbol tuning across Flan-PaLM models up to 540B
parameters and observe benefits across various settings. First, symbol tuning
boosts performance on unseen in-context learning tasks and is much more robust
to underspecified prompts, such as those without instructions or without
natural language labels. Second, symbol-tuned models are much stronger at
algorithmic reasoning tasks, with up to 18.2% better performance on the List
Functions benchmark and up to 15.3% better performance on the Simple Turing
Concepts benchmark. Finally, symbol-tuned models show large improvements in
following flipped-labels presented in-context, meaning that they are more
capable of using in-context information to override prior semantic knowledge.


  Large language models based on self-attention mechanisms have achieved
astonishing performances not only in natural language itself, but also in a
variety of tasks of different nature. However, regarding processing language,
our human brain may not operate using the same principle. Then, a debate is
established on the connection between brain computation and artificial
self-supervision adopted in large language models. One of most influential
hypothesis in brain computation is the predictive coding framework, which
proposes to minimize the prediction error by local learning. However, the role
of predictive coding and the associated credit assignment in language
processing remains unknown. Here, we propose a mean-field learning model within
the predictive coding framework, assuming that the synaptic weight of each
connection follows a spike and slab distribution, and only the distribution,
rather than specific weights, is trained. This meta predictive learning is
successfully validated on classifying handwritten digits where pixels are input
to the network in sequence, and moreover on the toy and real language corpus.
Our model reveals that most of the connections become deterministic after
learning, while the output connections have a higher level of variability. The
performance of the resulting network ensemble changes continuously with data
load, further improving with more training data, in analogy with the emergent
behavior of large language models. Therefore, our model provides a starting
point to investigate the connection among brain computation, next-token
prediction and general intelligence.


  We present a Chandra study of the metal distribution in the X-ray bright
compact group of galaxies HCG 62. We find that the diffuse X-ray emission is
peaked at the core of the central galaxy NGC 4778, and is dominated by the
contribution of the hot gas. The diffuse emission is roughly symmetric within
\simeq0.25^{\prime}, which is straddled by double-sided X-ray cavities aligned
in the northeast-southwest direction. By mapping the emission hardness ratio
distributions and by performing the 2-dimensional spectral analysis, we
identify a remarkable high-abundance arc region at about 2^{\prime}
(33.6h_{70}^{-1} kpc) from the X-ray peak that spans over a vast region from
south to northwest, a part of which roughly coinciding with the outer edge of
the southwest X-ray cavity. The measured average abundance in this arc is
higher than that in its neighboring regions by a factor of about 2, and the
abundance ratios therein are nicely consistent with the dominance of the SN Ia
yields. We estimate that the mass of iron contained in the arc is >3\times
10^{6}h_{70}^{-2.5} solar mass, which accounts for >3% of the iron synthesized
in the galaxy. The high-abundance arc could have been formed by the AGN
activities. However, it is also possible that the arc was formed in a recent
merger as is implied by the recent optical kinematic study (Spavone et al.
2006), which implies that mergers may be as important as AGN activities in
metal redistributions in early-type galaxies and their associated groups or
clusters.


  We construct, for the first time, a Bemfica-Disconzi-Noronha-Kovtun (BDNK)
theory for linear stochastic fluctuations, which is proved to be mathematically
consistent, causal, and covariantly stable. The Martin-Siggia-Rose action is
shown to be bilocal in most cases, and the noise is not white. The presence of
nonhydrodynamic modes induces long-range correlations in the primary fluid
variables (temperature, chemical potential, and flow velocity). However,
correlators of conserved densities remain localized in space, and coincide with
those calculated within fluctuating Isreal-Stewart theory. We show that, in
some cases, there is a nonlocal change of variables that maps the
Israel-Stewart action into the BDNK action.


  Let $S$ be a Dedekind scheme, $X$ a connected $S$-scheme locally of finite
type and $x\in X(S)$ a section. The aim of the present paper is to establish
the existence of the fundamental group scheme of $X$, when $X$ has reduced
fibers or when $X$ is normal. We also prove the existence of a group scheme,
that we will call the quasi-finite fundamental group scheme of $X$ at $x$,
which classifies all the quasi-finite torsors over $X$, pointed over $x$. We
define Galois torsors, which play in this context a role similar to the one of
Galois covers in the theory of \'etale fundamental group.


  Borrowing from the transformer models that revolutionized the field of
natural language processing, self-supervised feature learning for visual tasks
has also seen state-of-the-art success using these extremely deep, isotropic
networks. However, the typical AI researcher does not have the resources to
evaluate, let alone train, a model with several billion parameters and
quadratic self-attention activations. To facilitate further research, it is
necessary to understand the features of these huge transformer models that can
be adequately studied by the typical researcher. One interesting characteristic
of these transformer models is that they remove most of the inductive biases
present in classical convolutional networks. In this work, we analyze the
effect of these and more inductive biases on small to moderately-sized
isotropic networks used for unsupervised visual feature learning and show that
their removal is not always ideal.


  We study the Single-Parton-Scattering (SPS) production of double quarkonia
(J/psi+J/psi, J/psi+Upsilon, and Upsilon+Upsilon) in pp and pp(bar) collisions
at the LHC and the Tevatron as measured by the CMS, ATLAS, LHCb, and D0
experiments in the Colour-Evaporation Model (CEM), based on the
quark-hadron-duality, including Next-to-Leading Order (NLO) QCD corrections up
to alpha_s^5. To do so, we also perform the first true NLO --up to alpha_s^4--
study of the p_T-differential cross section for single-quarkonium production.
This allows us to fix the non-perturbative CEM parameters at NLO accuracy in
the region where quarkonium-pair data are measured. Our results show that the
CEM at NLO in general significantly undershoots these experimental data and, in
view of the other existing SPS studies, confirm the need for Double Parton
Scattering (DPS) to account for the data. Our NLO study of single-quarkonium
production at mid and large p_T also confirms the difficulty of the approach to
account for the measured p_T spectra; this is reminiscent of the impossibility
to fit single-quarkonium data with the sole 3S18 NRQCD contribution from gluon
fragmentation. We stress that the discrepancy occurs in a kinematical region
where the new features of the improved CEM are not relevant.


  Let G be a right-angled Artin group. We use geometric methods to compute a
presentation of the subgroup H of Aut(G) consisting of the automorphisms that
send each generator to a conjugate of itself. This generalizes a result of
McCool on basis-conjugating automorphisms of free groups.


  There are few robust solutions to the doublet-triplet splitting problem in
supersymmetric GUT theories. One of the more promising solutions is the Higgs
as pseudo-Goldstone boson mechanism. In its minimal implementation, such a
solution places an additional restriction on the parameter space of the minimal
supersymmetric standard model. A testable consequence of this constraint is an
equation for $\tan \beta$. We present this restriction and study its solutions
in order to constrain the allowed parameter space. Thus the assumptions on the
GUT scale Higgs sector should yield testable predictions for weak scale
physics. If the SUSY parameters are measured then it should be possible to
check the predictions, yielding insight into GUT scale physics.


  Alternative theories of gravity predict up to six distinct polarization modes
for gravitational waves. Strong gravitational lensing of gravitational waves
allows us to probe the polarization content of these signals by effectively
increasing the number of observations from the same astrophysical source. The
lensing time delays due to the multiple observed lensed images combined with
the rotation of the Earth allows for effective non-collocated interferometers
to be defined with respect to the source location and hence probe the
alternative polarization amplitudes with more observations. To measure these
amplitudes, we jointly fit the image observations to a single gravitational
wave signal model that takes into account the image magnifications, time
delays, and polarization mode amplitudes. We show that for certain systems, we
can make a measurement of the relative mode amplitudes for lensed events with
two detectable images.


  Evaluating and comparing text-to-image models is a challenging problem.
Significant advances in the field have recently been made, piquing interest of
various industrial sectors. As a consequence, a gold standard in the field
should cover a variety of tasks and application contexts. In this paper a novel
evaluation approach is experimented, on the basis of: (i) a curated data set,
made by high-quality royalty-free image-text pairs, divided into ten
categories; (ii) a quantitative metric, the CLIP-score, (iii) a human
evaluation task to distinguish, for a given text, the real and the generated
images. The proposed method has been applied to the most recent models, i.e.,
DALLE2, Latent Diffusion, Stable Diffusion, GLIDE and Craiyon. Early
experimental results show that the accuracy of the human judgement is fully
coherent with the CLIP-score. The dataset has been made available to the
public.


  Intuitively speaking, a bipartite graph is mirror if it can be drawn in the
Cartesian plane in such a way that, the vertices of one stable are points in
x=0, the vertices of the other stable set are points in x=1, the edges are
straight line segments joining adjacent vertices and the resulting
configuration is symmetric with respect to the line x=1/2. The concept of
mirror bipartite graph appears naturally when studying certain types of
products of graphs as for instance the Kronecker product. Motivated by this
fact, we study mirror bipartite graphs from the point of view of their degree
sequences and of their degree sets. We characterize the sequences of degrees of
mirror bipartite graphs. We also show that from a given set P of positive
integers, we can construct a bipartite graph of order 2max P, which is mirror.
Furthermore, very little is known for the degree sequences of graphs with loops
attached, when the number of loops attached is limited. We show in this paper
that mirror bipartite graphs constitute a powerful tool to study the degree
sequences of these graphs when the number of loops attached at each vertex is
at most $1$.


  Let $\Gamma$ be a finite graph and let $A(\Gamma)$ be the corresponding
right-angled Artin group. From an arbitrary basis $\mathcal B$ of
$H^1(A(\Gamma),\mathbb F)$ over an arbitrary field, we construct a natural
graph $\Gamma_{\mathcal B}$ from the cup product, called the \emph{cohomology
basis graph}. We show that $\Gamma_{\mathcal B}$ always contains $\Gamma$ as a
subgraph. This provides an effective way to reconstruct the defining graph
$\Gamma$ from the cohomology of $A(\Gamma)$, to characterize the planarity of
the defining graph from the algebra of $A(\Gamma)$, and to recover many other
natural graph-theoretic invariants. We also investigate the behavior of the
cohomology basis graph under passage to elementary subminors, and show that it
is not well-behaved under edge contraction.


  We consider interconnected nonlinear systems with external inputs, where each
of the subsystems is assumed to be input-to-state stable (ISS). Sufficient
conditions of small gain type are provided guaranteeing that the
interconnection is ISS with respect to the external input. To this end we
extend recently obtained small gain theorems to a more general type of
interconnections. The small gain theorem provided here is applicable to
situations where the ISS conditions are formulated differently for each
subsystem and are either given in the maximization or the summation sense.
Furthermore it is shown that the conditions are compatible in the sense that it
is always possible to transform sum formulations to maximum formulations
without destroying a given small gain condition. An example shows the
advantages of our results in comparison with the known ones.


  The slight deformations observed upon transmission of a light pulse through a
short length of a silica glass nano-filament offer the possibility of
determining the momentum of light inside the filament. Using precise numerical
calculations that take into account not only the electromagnetic momentum
inside and outside the filament, but also the Lorentz force exerted by a light
pulse in its entire path through the nano-waveguide, we conclude that the net
effect of a short pulse exiting the nano-filament should be a pull force on the
end face of the filament.


  In this paper, for a single-input multiple-output (SIMO) system aided by a
passive reconfigurable intelligent surface (RIS), the joint transmission
accomplished by the single transmit antenna and the RIS with multiple
controllable reflective elements is considered. Relying on a general capacity
upper bound derived by a maximum-trace argument, we respectively characterize
the capacity of such \rev{a} channel in the low-SNR or the rank-one regimes, in
which the optimal configuration of the RIS is proved to be beamforming with
carefully-chosen phase shifts. To exploit the potential of modulating extra
information on the RIS, based on the QR decomposition, successive interference
cancellation, and a strategy named \textit{partially beamforming and partially
information-carrying}, we propose a novel transceiver architecture with only a
single RF front end at the transmitter, by which the considered channel can be
regarded as a concatenation of a vector Gaussian channel and several
phase-modulated channels. Especially, we investigate a class of vector Gaussian
channels with a hypersphere input support constraint, and not only generalize
the existing result to arbitrary-dimensional real spaces but also present its
high-order capacity asymptotics, by which both capacities of
hypersphere-constrained channels and achievable rates of the proposed
transceiver with two different signaling schemes can be well-approximated.
Information-theoretic analyses show that the transceiver architecture designed
for the SIMO channel has a boosted multiplexing gain, rather than one for the
conventionally-used optimized beamforming scheme.Numerical results verify our
derived asymptotics and show notable superiority of the proposed transceiver.


  We study a class of random polymers, introduced by Sina\u{i}, which are
related to persistence probabilities in integrated simple random walk bridges.
We find the precise asymptotics of these probabilities, and describe their
combinatorics, using limit theory for infinitely divisible distributions, and
the number-theoretic subset counting formulas of von Sterneck from the early
1900s. Our results sharpen estimates by Aurzada, Dereich and Lifshits, and
respond to a conjecture of Caravenna and Deuschel, which arose in their study
of the pinning/wetting models, for random linear chains exhibiting entropic
repulsion. Our key combinatorial result is an analogue of Sparre Andersen's
classical formula.


  We study the model selection problem in conditional average treatment effect
(CATE) prediction. Unlike previous works on this topic, we focus on preserving
the rank order of the performance of candidate CATE predictors to enable
accurate and stable model selection. To this end, we analyze the model
performance ranking problem and formulate guidelines to obtain a better
evaluation metric. We then propose a novel metric that can identify the ranking
of the performance of CATE predictors with high confidence. Empirical
evaluations demonstrate that our metric outperforms existing metrics in both
model selection and hyperparameter tuning tasks.


  One of the hallmarks of molecular dynamics in deeply supercooled liquids is
the non-exponential character of the relaxation functions. It has been a long
standing issue if 'universal' features govern the lineshape of glassy dynamics
independent of any particular molecular structure or interaction. In the paper,
we elucidate this matter by giving a comprehensive comparison of the spectral
shape of depolarized light scattering and dielectric data of deeply supercooled
liquids. The light scattering spectra of very different systems, e.g. hydrogen
bonding and van der Waals liquids but also ionic systems, almost perfectly
superimpose and show a generic lineshape of the structural relaxation,
approximately following a high frequency power law $\omega^{-1/2}$ . However,
the dielectric loss peak shows a more individual shape. In systems with low
dipole moment generic behavior is also observed in the dielectric spectra,
while in strongly dipolar liquids additional crosscorrelation contributions
mask the generic structural relaxation.


  In this article, the following controlled convective Brinkman-Forchheimer
extended Darcy (CBFeD) system is considered in a $d$-dimensional torus:
  \begin{align*}
  \frac{\partial\boldsymbol{y}}{\partial t}-\mu
\Delta\boldsymbol{y}+(\boldsymbol{y}\cdot\nabla)\boldsymbol{y}+\alpha\boldsymbol{y}+\beta\vert
\boldsymbol{y}\vert ^{r-1}\boldsymbol{y}+\gamma\vert \boldsymbol{y}\vert
^{q-1}\boldsymbol{y}+\nabla p=\boldsymbol{g}+\boldsymbol{u},\
\nabla\cdot\boldsymbol{y}=0,
  \end{align*}
  where $d\in\{2,3\}$, $\mu,\alpha,\beta>0$, $\gamma\in\mathbb{R}$,
$r,q\in[1,\infty)$ with $r>q\geq 1$. We prove the exponential stabilization of
CBFeD system by finite- and infinite-dimensional feedback controllers. The
solvability of the controlled problem is achieved by using the abstract theory
of $m$-accretive operators and density arguments. As an application of the
above solvability result, by using infinite-dimensional feedback controllers,
we demonstrate exponential stability results such that the solution preserves
an invariance condition for a given closed and convex set. By utilizing the
unique continuation property of controllability for finite-dimensional systems,
we construct a finite-dimensional feedback controller which exponentially
stabilizes CBFeD system locally, where the control is localized in a smaller
subdomain. Furthermore, we establish the local exponential stability of CBFeD
system via proportional controllers.


  Gaussian processes (GPs) are a powerful tool for probabilistic inference over
functions. They have been applied to both regression and non-linear
dimensionality reduction, and offer desirable properties such as uncertainty
estimates, robustness to over-fitting, and principled ways for tuning
hyper-parameters. However the scalability of these models to big datasets
remains an active topic of research. We introduce a novel re-parametrisation of
variational inference for sparse GP regression and latent variable models that
allows for an efficient distributed algorithm. This is done by exploiting the
decoupling of the data given the inducing points to re-formulate the evidence
lower bound in a Map-Reduce setting. We show that the inference scales well
with data and computational resources, while preserving a balanced distribution
of the load among the nodes. We further demonstrate the utility in scaling
Gaussian processes to big data. We show that GP performance improves with
increasing amounts of data in regression (on flight data with 2 million
records) and latent variable modelling (on MNIST). The results show that GPs
perform better than many common models often used for big data.


  The Savitzky-Golay FIR digital filter is based on a least-squares polynomial
fit to a hypothetical sample of equally spaced data. This gives the filter the
ability to preserve moments of features like peaks in the input. Descriptions
of the filter typically consider the case where equal weights are implicitly
applied to the residuals of the fit. In a largely overlooked paper Turton
showed that weighting the residuals with a triangular function significantly
improves the frequency response of the filter in the stopband. The
Savitzky-Golay filter is commonly referred to as a smoothing filter. This paper
uses a particular measure of smoothness to show that a quadratic residual
weighting function optimizes the smoothness of the filter output for a given
sample size and degree of the fitting polynomial. This weighting function can
provide substantially better smoothness than that with a constant weighting
function.


  We answer a question of Piotr Minc by proving that there is no compact
metrizable space whose set of components contains a unique topological copy of
every metrizable compactification of a ray (i.e. a half-open interval) with an
arc (i.e. closed bounded interval) as the remainder. To this end we use the
concept of Borel reductions coming from Invariant descriptive set theory. It
follows as a corollary that there is no compact metrizable space such that
every continuum is homeomorphic to exactly one component of this space.


  We show that for any positive integer $n$ there exists a constant $C(n)>0$
such that any $n$-generated group $G$, which acts by isometries on a
$\delta$-hyperbolic space (with $\delta>0$), is either free or has a nontrivial
element with translation length at most $\delta C(n)$.


  We show how to perfectly transfer, without state initialization and remote
collaboration, arbitrary functions in interacting boson lattices. We describe a
possible implementation of state transfer through bosonic atoms trapped in
optical lattices or polaritons in on-chip coupled cavities. Significantly, a
family of Hamiltonians, both linear and nonlinear, is found which are related
to the Bose-Hubbard model and that enable the perfect transfer of arbitrary
functions. It is shown that the state transfer between two sites in
two-dimensional lattices can result in quantum interference due to the
different numbers of intermediate sites in different paths. The signature
factor in nuclear physics can be useful to characterize this quantum
interference.


  We consider a new matrix model based on the simply connected compact
exceptional Lie group E6. A matrix Chern-Simons theory is directly derived from
the invariant on E6. It is stated that the similar argument as Smolin which
derives an effective action of the matrix string type can also be held in our
model. An important difference is that our model has twice as many degrees of
freedom as Smolin's model has. One way to introduce the cosmological term is
the compactification on directions. It is of great interest that the properties
of the product space $\Vec{\mathfrak{J}^c} \times \Vec{\mathcal{G}}$, in which
the degrees of freedom of our model live, are very similar to those of the
physical Hilbert space.


  We construct quasi-Hopf algebras associated with a semisimple Lie algebra, a
complex curve and a rational differential. This generalizes our previous joint
work with V. Rubtsov (Israel J. Math. (1999) and q-alg/9608005).


  We prove uniqueness and existence of the weak solutions of Euler equations
with helical symmetry, with initial vorticity in $L^{\infty}$ under "no
vorticity stretching" geometric constraint. Our article follows the argument of
the seminal work of Yudovich. We adjust the argument to resolve the
difficulties which are specific to the helical symmetry.


  We present aperture masking interferometry (AMI) observations of the star HIP
65426 at $3.8\,\rm{\mu m}$ as a part of the \textit{JWST} Direct Imaging Early
Release Science (ERS) program obtained using the Near Infrared Imager and
Slitless Spectrograph (NIRISS) instrument. This mode provides access to very
small inner working angles (even separations slightly below the Michelson limit
of ${}0.5\lambda/D$ for an interferometer), which are inaccessible with the
classical inner working angles of the \textit{JWST} coronagraphs. When combined
with \textit{JWST}'s unprecedented infrared sensitivity, this mode has the
potential to probe a new portion of parameter space across a wide array of
astronomical observations. Using this mode, we are able to achieve a contrast
of $\Delta m_{F380M}{\sim }7.8$\,mag relative to the host star at a separation
of ${\sim}0.07\arcsec$ but detect no additional companions interior to the
known companion HIP\,65426\,b. Our observations thus rule out companions more
massive than $10{-}12\,\rm{M\textsubscript{Jup}}$ at separations
${\sim}10{-}20\,\rm{au}$ from HIP\,65426, a region out of reach of ground or
space-based coronagraphic imaging. These observations confirm that the AMI mode
on \textit{JWST} is sensitive to planetary mass companions orbiting at the
water frost line, even for more distant stars at $\sim$100\,pc. This result
will allow the planning and successful execution of future observations to
probe the inner regions of nearby stellar systems, opening essentially
unexplored parameter space.


  Critical infrastructures form a technological skeleton of our world by
providing us with water, food, electricity, gas, transportation, communication,
banking, and finance. Moreover, as urban population increases, the role of
infrastructures become more vital. In this paper, we adopt a network
perspective and discuss the ever growing need for fundamental interdisciplinary
study of critical infrastructure networks, efficient methods for estimating
their reliability, and cost-effective strategies for enhancing their
resiliency. We also highlight some of the main challenges arising on this way,
including cascading failures, feedback loops, and cross-sector
interdependencies.


  We develop a procedure to determine the portion of exact Hartree-Fock
exchange interaction contained in a hybrid density functional to treat the
range of electronic correlation governing the physics of a system as a function
of a thermodynamical parameter. This includes systems that depend on physical
parameters accessible to experiment (i.e., temperature, pressure, composition,
etc.) or those composed of two or more materials such as heterostructures and
interfaces. This approach is applied to LaMnO$_3$ where for the first time we
are able to simulate the high temperature insulator-to-metal transition (IMT)
and observe a half-metallic orbital disorder ferromagnetic state using density
functional theory. In particular, we show that the softening of the
\textit{Q$_2$} Jahn-Teller mode plays a central role in driving the IMT. These
findings are likely to motivate the investigation of heterostructures and bulk
materials that contain a range of electronic correlation in similar material
systems.


  Acrylic acid (AA) is a monomer commonly employed in emulsion polymerization
to provide electrostatic colloidal stability and improve specific film
performance. The addition of AA not only modifies the kinetics of the
polymerization, but also it takes part in the interaction between colloidal
particles, which has a strong influence on their packing and consequent latex
film properties. In this contribution a theoretical modeling of the latex film
formation is presented and compared to experimental results: water vapor
permeability and latex film capacitance are studied as a function of AA
content. It has been shown that water uptake is mainly affected by film
morphology which in turn is defined by intercolloidal interaction and drying
rate.


  The case of gamma-ray absorption due to photon-photon pair production of jet
photons in the external photon environment like accretion disk and broad-line
region radiation field of gamma-ray loud active galactic nuclei (AGN) that
exhibit strong emission lines is considered. I demonstrate that this ''local
opacity'', if detected, will almost unavoidably be redshift-dependent in the
sub-TeV range. This introduces non-negligible biases, and complicates
approaches for studying the evolution of the extragalactic background light
with contemporary GeV instruments like e.g. the Gamma-ray Large Area Space
Telescope (GLAST), etc., where the gamma-ray horizon is probed by means of
statistical analysis of absorption features (e.g. Fazio-Stecker relation, etc.)
in AGN spectra at various redshifts. It particularly applies to strong-line
quasars where external photon fields are potentially involved in gamma-ray
production.


  We extend the recently developed Izergin-Korepin analysis on the
wavefunctions of the $U_q(sl_2)$ six-vertex model to the reflecting boundary
conditions. Based on the Izergin-Korepin analysis, we determine the exact forms
of the symmetric functions which represent the wavefunctions and its dual.
Comparison of the symmetric functions with the coordinate Bethe ansatz
wavefunctions for the open XXZ chain by Alcaraz-Barber-Batchelor-Baxter-Quispel
is also made. As an application, we derive algebraic identities for the
symmetric functions by combining the results with the determinant formula of
the domain wall boundary partition function of the six-vertex model with
reflecting end.


  We investigate nonequilibrium properties of the single impurity Anderson
model by means of the functional renormalization group (fRG) within Keldysh
formalism. We present how the level broadening Gamma/2 can be used as flow
parameter for the fRG. This choice preserves important aspects of the Fermi
liquid behaviour that the model exhibits in case of particle-hole symmetry. An
approximation scheme for the Keldysh fRG is developed which accounts for the
frequency dependence of the two-particle vertex in a way similar but not
equivalent to a recently published approximation to the equilibrium Matsubara
fRG. Our method turns out to be a flexible tool for the study of weak to
intermediate on-site interactions U <= 3 Gamma. In equilibrium we find
excellent agreement with NRG results for the linear conductance at finite gate
voltage, magnetic field, and temperature. In nonequilibrium, our results for
the current agree well with TD-DMRG. For the nonlinear conductance as function
of the bias voltage, we propose reliable results at finite magnetic field and
finite temperature. Furthermore, we demonstrate the exponentially small scale
of the Kondo temperature to appear in the second order derivative of the
self-energy. We show that the approximation is, however, not able to reproduce
the scaling of the effective mass at large interactions.


  We present minute cadence photometry of 31732 point sources observed in one 3
$deg^{2}$ DECam pointing centred at RA = 09:03:02 and Dec = -04:35:00 over
eight consecutive half-nights. We use these data to search for eclipse-like
events consistent with a planetary transit of a white dwarf and other sources
of stellar variability within the field. We do not find any significant
evidence for minute-long transits around our targets, hence we rule out
planetary transits around ~370 white dwarfs that should be present in this
field. Additionally, we identify 49 variables, including 40 new systems. These
include 23 detached or contact stellar binaries, 1 eclipsing white dwarf + M
dwarf binary, 16 $\delta$ Scuti, three RR Lyrae, and two ZZ Ceti pulsators.
Results from the remaining two fields in our survey will allow us to place more
stringent constraints on the frequency of planets orbiting white dwarfs in the
habitable zone.


  Associated to any hypergraph is a toric ideal encoding the algebraic
relations among its edges. We study these ideals and the combinatorics of their
minimal generators, and derive general degree bounds for both uniform and
non-uniform hypergraphs in terms of balanced hypergraph bicolorings,
separators, and splitting sets. In turn, this provides complexity bounds for
algebraic statistical models associated to hypergraphs. As two main
applications, we recover a well-known complexity result for Markov bases of
arbitrary 3-way tables, and we show that the defining ideal of the tangential
variety is generated by quadratics and cubics in cumulant coordinates.


  VPSolver is a vector packing solver based on an arc-flow formulation with
graph compression. In this paper, we present the algorithm introduced in
VPSolver 3.0.0 for building compressed arc-flow models for the multiple-choice
vector packing problem.


  We discuss a connection between the Dirac equation for an electron and the
Dirac type tensor equation with ${\rm U}(1)$ gauge symmetry.


  We present the 2-D photometric decomposition of the Virgo galaxy IC3328. The
analysis of the global light distribution of this morphologically classified
nucleated dwarf elliptical galaxy (dE1,N) reveals a tightly wound, bi-symmetric
spiral structure with a diameter of 4.5 kpc, precisely centered on the nucleus
of the dwarf. The amplitude of the spiral is only three percent of the dwarf's
surface brightness making it the faintest and smallest spiral ever found in a
galaxy. In terms of pitch angle and arm winding the spiral is similar to the
intermediate-type galaxy M51, but it lacks the dust and prominent HII regions
which signal the presence of gas. The visual evidence of a spiral pattern in an
early-type dwarf galaxy reopens the question on whether these dwarfs are
genuine rotationally supported or anisotropic stellar systems. In the case of
IC3328, we argue for a nearly face-on disk (dS0) galaxy with an estimated
maximum rotation velocity of v_c,max = 55kms-1. The faintness of the spiral and
the small motions within it, suggests that we could be seeing swing-amplified
noise. The other possibility is a tidal origin, caused by the near passage of a
small companion.


  This paper presents an efficient finite element iterative method for solving
a nonuniform size-modified Poisson-Nernst-Planck ion channel (SMPNPIC) model,
along with a SMPNPIC program package that works for an ion channel protein with
a three-dimensional crystallographic structure and an ionic solvent with
multiple ionic species. In particular, the SMPNPIC model is constructed and
then reformulated by novel mathematical techniques so that each iteration of
the method only involves linear boundary value problems and nonlinear algebraic
systems, circumventing the numerical difficulties caused by the strong
nonlinearities, strong asymmetries, and strong differential equation coupling
of the SMPNPIC model. To further improve the method's efficiency, an efficient
modified Newton iterative method is adapted to the numerical solution of each
related nonlinear algebraic system. Numerical results for a voltage-dependent
anion channel (VDAC) and a mixture solution of four ionic species demonstrate
the method's convergence, the package's high performance, and the importance of
considering nonuniform ion size effects. They also partially validate the
SMPNPIC model by the anion selectivity property of VDAC.


  Organic solar cells (OSCs) based on non-fullerene acceptors can show high
charge generation yields despite near-zero donor-acceptor energy offsets to
drive charge separation and overcome the mutual Coulomb attraction between
electron and hole. Here we use time-resolved optical spectroscopy to show that
free charges in these systems are generated by thermally activated dissociation
of interfacial charge-transfer excitons (CTEs) that occurs over hundreds of
picoseconds at room temperature, three orders of magnitude slower than
comparable fullerene-based systems. Upon free electron-hole encounters at later
times, CTEs and emissive excitons are regenerated, thus setting up an
equilibrium between excitons, CTEs and free charges. This endothermic charge
separation process enables these systems to operate close to
quasi-thermodynamic equilibrium conditions with no requirement for energy
offsets to drive charge separation and achieve greatly suppressed non-radiative
recombination.


  Voice Recognition (VR) facilitates a human interaction with the machine. VR
may be used to replace the manual task of pushing buttons on a wireless
telephone keypad. This is particularly useful when the hands of the user are
busy with other activities like driving a car.
  However, the VRS system has several limitations. The VRS requires lot of
training and customization in order to be effectively used by individual users
as each individual falls into different voice patterns. Besides the voice
interface is complex and is not as reliable as the keyboard or mouse. This
article illustrates some interesting inventions on using sound and voice in
Graphical User Interfaces.


  Gravitational waves (GWs) induced by scalar curvature fluctuations are an
important source of the cosmological GW background and a crucial counterpart of
the primordial black hole scenario. However, doubts have been cast on the
theoretically predicted induced GW spectrum due to its seeming gauge
dependence. In this paper, we shed light on the gauge dependence issue of the
induced GW spectrum in general cosmological backgrounds. First, inspired by the
Hamiltonian formalism we provide very simple formulas for the tensor modes at
second order in cosmological perturbation theory. We also emphasize the
difference between observable and gauge invariant variables. Second, we argue
that the Newton (or shear-free) gauge is suitable for both the calculation of
induced GWs and the physical interpretation. We then show that, most notably,
the induced GW spectrum is invariant under a set of reasonable gauge
transformations, i.e. physically well behaved on small scales, once the source
term has become inactive. This includes the commonly used flat, constant Hubble
and synchronous gauges but excludes the comoving slicing gauge. We also show
that a particular solution of the GW equation in a dust dominated universe
while the source term is active can be gauged away by a small change of gauge.


  We describe an essential improvement of our recent algorithm for computing
cohomology of Lie (super)algebra based on partition of the whole cochain
complex into minimal subcomplexes. We replace the arithmetic of rational
numbers or integers by a much cheaper arithmetic of a modular field and use the
inequality between the dimensions of cohomology H over any modular field F_p =
Z/pZ and over Q: dim H(F_p) >= dim H(Q). With this inequality we can, by
computing over arbitrary F_p, quickly find the (usually, rare) subcomplexes for
which dim H(F_p) > 0 and then carry out the full computation over Q within
these subcomplexes. We also present the results of application of the
corresponding C program to the Lie superalgebra of special vector fields
preserving an "odd-symplectic" structure on the (2|2)-dimensional
supermanifold. For this algebra, we found some new basis elements of the
cohomology in the trivial module.


  By decomposing the image formation process into a sequential application of
denoising autoencoders, diffusion models (DMs) achieve state-of-the-art
synthesis results on image data and beyond. Additionally, their formulation
allows for a guiding mechanism to control the image generation process without
retraining. However, since these models typically operate directly in pixel
space, optimization of powerful DMs often consumes hundreds of GPU days and
inference is expensive due to sequential evaluations. To enable DM training on
limited computational resources while retaining their quality and flexibility,
we apply them in the latent space of powerful pretrained autoencoders. In
contrast to previous work, training diffusion models on such a representation
allows for the first time to reach a near-optimal point between complexity
reduction and detail preservation, greatly boosting visual fidelity. By
introducing cross-attention layers into the model architecture, we turn
diffusion models into powerful and flexible generators for general conditioning
inputs such as text or bounding boxes and high-resolution synthesis becomes
possible in a convolutional manner. Our latent diffusion models (LDMs) achieve
a new state of the art for image inpainting and highly competitive performance
on various tasks, including unconditional image generation, semantic scene
synthesis, and super-resolution, while significantly reducing computational
requirements compared to pixel-based DMs. Code is available at
https://github.com/CompVis/latent-diffusion .


  We present one inherent shortcoming of the LDA+U method in respect of its
self-interaction correction of the LDA. By reexamining the mean-field
approximation on the Hubbard energy in the Hartree-Fock form, we have derived a
new expression for the ``double-counting'' energy which led to a more
reasonable self-interaction correction in an alternative LDA+U scheme. For the
bulk Gd metal, the new scheme resulted in electronic properties more consistent
with experiments in comparison to the LDA and the existing LDA+U schemes.


  An $n$-length binary word is $q$-decreasing, $q\geq 1$, if every of its
length maximal factor of the form $0^a1^b$ satisfies $a=0$ or $q\cdot a > b$.We
show constructively that these words are in bijection with binary words having
no occurrences of $1^{q+1}$, and thus they are enumerated by the
$(q+1)$-generalized Fibonacci numbers. We give some enumerative results and
reveal similarities between $q$-decreasing words and binary words having no
occurrences of $1^{q+1}$ in terms of frequency of $1$ bit. In the second part
of our paper, we provide an efficient exhaustive generating algorithm for
$q$-decreasing words in lexicographic order, for any $q\geq 1$, show the
existence of 3-Gray codes and explain how a generating algorithm for these Gray
codes can be obtained. Moreover, we give the construction of a more restrictive
1-Gray code for $1$-decreasing words, which in particular settles a conjecture
stated recently in the context of interconnection networks by E\u{g}ecio\u{g}lu
and Ir\v{s}i\v{c}.


  Collective behavior is one of the most intriguing aspects of the hydrodynamic
approach to electronic transport. Here we provide a consistent, unified
calculation of the dispersion relations of the hydrodynamic collective modes in
graphene. Taking into account viscous effects, we show that the hydrodynamic
sound mode in graphene becomes overdamped at sufficiently large momentum
scales. Extending the linearized theory beyond the hydrodynamic regime, we
connect the diffusive hydrodynamic charge density fluctuations with plasmons.


  Confronting the pandemic of COVID-19, is nowadays one of the most prominent
challenges of the human species. A key factor in slowing down the virus
propagation is the rapid diagnosis and isolation of infected patients. The
standard method for COVID-19 identification, the Reverse transcription
polymerase chain reaction method, is time-consuming and in short supply due to
the pandemic. Thus, researchers have been looking for alternative screening
methods and deep learning applied to chest X-rays of patients has been showing
promising results. Despite their success, the computational cost of these
methods remains high, which imposes difficulties to their accessibility and
availability. Thus, the main goal of this work is to propose an accurate yet
efficient method in terms of memory and processing time for the problem of
COVID-19 screening in chest X-rays. Methods: To achieve the defined objective
we exploit and extend the EfficientNet family of deep artificial neural
networks which are known for their high accuracy and low footprints in other
applications. We also exploit the underlying taxonomy of the problem with a
hierarchical classifier. A dataset of 13,569 X-ray images divided into healthy,
non-COVID-19 pneumonia, and COVID-19 patients is used to train the proposed
approaches and other 5 competing architectures. Finally, 231 images of the
three classes were used to assess the quality of the methods. Results: The
results show that the proposed approach was able to produce a high-quality
model, with an overall accuracy of 93.9%, COVID-19, sensitivity of 96.8% and
positive prediction of 100%, while having from 5 to 30 times fewer parameters
than other than the other tested architectures. Larger and more heterogeneous
databases are still needed for validation before claiming that deep learning
can assist physicians in the task of detecting COVID-19 in X-ray images.


  The ultraviolet and optical background forms a baseline for any observation
of the sky. It includes emission lines and scattered light from the atmosphere;
resonant scattering from the Lyman lines of interplanetary hydrogen and the
scattering of sunlight from Solar System dust (zodiacal light); scattering of
starlight from interstellar dust (DGL) with emission from molecular hydrogen
fluorescence or from line emission in selected areas; and an extragalactic
component seen most easily at high Galactic latitudes. We will discuss the
different components of the diffuse radiation field in the UV and the optical.
We close with a hope that there will be new observations from missions near the
edge of the Solar System.


  Federated learning (FL) is an emerging technique aiming at improving
communication efficiency in distributed networks, where many clients often
request to transmit their calculated parameters to an FL server simultaneously.
However, in wireless networks, the above mechanism may lead to prolonged
transmission time due to unreliable wireless transmission and limited
bandwidth. This paper proposes a communication scheme to minimize the uplink
transmission time for FL in wireless networks. The proposed approach consists
of two major elements, namely a modified Ring All-reduce (MRAR) architecture
that integrates D2D wireless communications to facilitate the communication
process in FL, and a modified Ant Colony Optimization algorithm to identify the
optimal composition of the MRAR architecture. Numerical results show that our
proposed approach is robust and can significantly reduce the transmission time
compared to the conventional star topology. Notably, the reduction in uplink
transmission time compared to baseline policies can be substantial in scenarios
applicable to large-scale FL, where client devices are densely distributed.


  Casini et al raise the issue that the entanglement entropy in gauge theories
is ambiguous because its definition depends on the choice of the boundary
between two regions.; even a small change in the boundary could annihilate the
otherwise finite topological entanglement entropy between two regions. In this
article, we first show that the topological entanglement entropy in the Kitaev
model which is not a true gauge theory, is free of ambiguity. Then, we give a
physical interpretation, from the perspectives of what can be measured in an
experiement, to the purported ambiguity of true gauge theories, where the
topological entanglement arises as redundancy in counting the degrees of
freedom along the boundary separating two regions. We generalize these
discussions to non-Abelian gauge theories.


  The problem how to speed up the convolution computations in Deep Neural
Networks is widely investigated in recent years. The Winograd convolution
algorithm is a common used method that significantly reduces time consumption.
However, it suffers from a problem with numerical accuracy particularly for
lower precisions. In this paper we present the application of base change
technique for quantized Winograd-aware training model. We show that we can
train the $8$ bit quantized network to nearly the same accuracy (up to 0.5%
loss) for tested network (Resnet18) and dataset (CIFAR10) as for quantized
direct convolution with few additional operations in pre/post transformations.
Keeping Hadamard product on $9$ bits allow us to obtain the same accuracy as
for direct convolution.


  We propose a simple scaling theory describing the variation of the mean first
passage time (MFPT) $\tau(N,M)$ of a regular block copolymer of chain length
$N$ and block size $M$ which is dragged through a selective liquid-liquid
interface by an external field $B$. The theory predicts a non-Arrhenian $\tau$
vs. $B$ relationship which depends strongly on the size of the blocks, $M$, and
rather weakly on the total polymer length, $N$. The overall behavior is
strongly influenced by the degree of selectivity between the two solvents
$\chi$.
  The variation of $\tau(N,M)$ with $N$ and $M$ in the regimes of weak and
strong selectivity of the interface is also studied by means of computer
simulations using a dynamic Monte Carlo coarse-grained model. Good qualitative
agreement with theoretical predictions is found. The MFPT distribution is found
to be well described by a $\Gamma$ - distribution. Transition dynamics of ring-
and telechelic polymers is also examined and compared to that of the linear
chains.
  The strong sensitivity of the ``capture'' time $\tau(N,M)$ with respect to
block length $M$ suggests a possible application as a new type of
chromatography designed to separate and purify complex mixtures with different
block sizes of the individual macromolecules.


  We study the impact on mechanisms for facility location of moving from one
dimension to two (or more) dimensions and Euclidean or Manhattan distances. We
consider three fundamental axiomatic properties: anonymity which is a basic
fairness property, Pareto optimality which is one of the most important
efficiency properties, and strategy proofness which ensures agents do not have
an incentive to mis-report. We also consider how well such mechanisms can
approximate the optimal welfare. Our results are somewhat negative. Moving from
one dimension to two (or more) dimensions often makes these axiomatic
properties more difficult to achieve. For example, with two facilities in
Euclidean space or with just a single facility in Manhattan space, no mechanism
is anonymous, Pareto optimal and strategy proof. By contrast, mechanisms on the
line exist with all three properties.We also show that approximation ratios may
increase when moving to two (or more) dimensions. All our impossibility results
are minimal. If we drop one of the three axioms (anonymity, Pareto optimality
or strategy proofness) multiple mechanisms satisfy the other two axioms.


  Gluon distributions of colliding hadrons saturate as a result of the
non-linear evolution equations of QCD. As a consequence there exists the so
called saturation momentum, which is related to the gluon density per unit
rapidity per transverse area. When saturation momentum is the only scale for
physical processes, different observables exhibit geometrical scaling (GS). We
show a number of examples of GS and its violation in different reactions.


  The D0 Collaboration has studied for the first time the properties of
hadron-collider jets reconstructed with a successive-combination algorithm
based on relative transverse momenta ($k_{\perp}$) of energy clusters. Using
the standard value D = 1.0 of the jet-separation parameter in the $k_{\perp}$
algorithm, we find that the $p_T$ of such jets is higher than the $E_T$ of
matched jets reconstructed with cones of radius R = 0.7, by about 5 (8) GeV at
$p_T \approx 90$ (240) GeV. To examine internal jet structure, the $k_{\perp}$
algorithm is applied within D = 0.5 jets to resolve any subjets. The
multiplicity of subjets in jet samples at $\sqrt{s} = 1800$ GeV and 630 GeV is
extracted separately for gluons ($M_g$) and quarks ($M_q$), and the ratio of
average subjet multiplicities in gluon and quark jets is measured as $(M_{g} -
1) / (M_{q} - 1) = 1.84 \pm 0.15 (stat.) \pm ^{0.22}_{0.18} (sys.)$. This ratio
is in agreement with the expectations from the HERWIG Monte Carlo event
generator and a resummation calculation, and with observations in $e^+e^-$
annihilations, and is close to the naive prediction for the ratio of color
charges of $C_A/C_F = 9/4 = 2.25$.


  We combine ideas of Scott and Swarup on good position for almost invariant
subsets of a group with ideas of Sageev on constructing cubings from such sets.
We construct cubings which are more canonical than in Sageev's original
construction. We also show that almost invariant sets can be chosen to be in
very good position.


  We treat a turbulent velocity field as a message in the same way as a book or
a picture. All messages can be described by their entropy per symbol $h$,
defined as in Shannon's theory of communication. In a turbulent flow, as the
Reynolds number $Re$ increases, more correlated degrees of freedom are excited
and participate in the turbulent cascade. Experiments in a turbulent soap film
suggest that the spatial entropy density $h$ is a decreasing function of $Re$,
namely $h \propto -\log Re$ + const. In the logistic map, also analyzed here,
increasing the control parameter $r$ increases $h$. A modified logistic map
with additional coupling to past iterations suggests the significance of
correlations.


  ASR error correction is an interesting option for post processing speech
recognition system outputs. These error correction models are usually trained
in a supervised fashion using the decoding results of a target ASR system. This
approach can be computationally intensive and the model is tuned to a specific
ASR system. Recently generative large language models (LLMs) have been applied
to a wide range of natural language processing tasks, as they can operate in a
zero-shot or few shot fashion. In this paper we investigate using ChatGPT, a
generative LLM, for ASR error correction. Based on the ASR N-best output, we
propose both unconstrained and constrained, where a member of the N-best list
is selected, approaches. Additionally, zero and 1-shot settings are evaluated.
Experiments show that this generative LLM approach can yield performance gains
for two different state-of-the-art ASR architectures, transducer and
attention-encoder-decoder based, and multiple test sets.


  For a large class of repulsive interaction models, the Mayer cluster
integrals can be transformed into a tridiagonal real symmetric matrix $R_{mn}$,
whose elements converge to two constants. This allows for an effective
extrapolation of the equation of state for these models. Due to a nearby
(nonphysical) singularity on the negative real z axis, standard methods (e.g.
Pad\`e approximants based on the cluster integrals expansion) fail to capture
the behavior of these models near the ordering transition, and, in particular,
do not detect the critical point. A recent work (Eisenberg and Baram, PNAS {\bf
104}, 5755 (2007)) has shown that the critical exponents $\sigma$ and
$\sigma'$, characterizing the singularity of the density as a function of the
activity, can be exactly calculated if the decay of the $R$ matrix elements to
their asymptotic constant follows a $1/n^2$ law. Here we employ renormalization
arguments to extend this result and analyze cases for which the asymptotic
approach of the $R$ matrix elements towards their limiting value is of a more
general form. The relevant asymptotic correction terms (in RG sense) are
identified and we then provide a corrected exact formula for the critical
exponents. We identify the limits of usage of the formula, and demonstrate one
physical model which is beyond its range of validity. The new formula is
validated numerically and then applied to analyze a number of concrete physical
models.


  The variation of single-particle spectral functions with doping is studied
numerically within the t-J model. It is shown that corresponding self energies
change from local ones at the intermediate doping to strongly nonlocal ones for
a weakly doped antiferromagnet. The nonlocality shows up most clearly in the
pseudogap emerging in the density of states, due to the onset of short-range
antiferromagnetic correlations.


  We present a comment on "Spin-Momentum-Locked Edge Mode for Topological
Vortex Lasing, Phys. Rev. Lett. vol. 125, 013903 (2020)"(hereafter the
Letter).In the Letter, Yang et al. reported on an elegant topological vortex
laser and proposed that the near-field spin and OAM of the topological edge
mode lasing have a one-to-one far-field radiation correspondence. The
near-field information is based on frequency dispersions of the topological
edge modes, without supporting measurements and/or computer simulations.
Unfortunately, their frequency dispersions shown in Fig. 1(c) (see also Fig. S6
and Eqs. (5.3) and (5.4) in Supplemental Material) are wrong. As the result,
the mode assignment of the main mode |-2,+> investigated in the Letter is
mistaken, which should be |2,+>. This spoils the one-to-one correspondence
claimed in the Letter.


  We study Efimov physics for three identical bosons interacting via a pairwise
square-well potential, analyze the validity of the separable approximation as a
function of the interaction strength, and investigate what is needed to improve
this approximation. We find separable approximations to be accurate for
potentials with just one (nearly) bound dimer state. For potentials with more
bound or almost bound dimer states, these states need to be included for an
accurate determination of the Efimov spectrum and the corresponding three-body
observables. We also show that a separable approximation is insufficient to
accurately compute the trimer states for energies larger than the finite-range
energy even when the two-body T matrix is highly separable in this energy
regime. Additionally, we have analyzed three distinct expansion methods for the
full potential that give exact results and thus improve on the separable
approximation. With these methods, we demonstrate the necessity to include
higher partial-wave components of the off-shell two-body T matrix in the
three-body calculations. Moreover, we analyze the behavior of the Efimov states
near the atom-dimer threshold and observe the formation of non-Efimovian trimer
states as the potential depth is increased. Our results can help to elaborate
simpler theoretical models that are capable of reproducing the correct
three-body physics in atomic systems.


  The problem considered is the non-preemptive scheduling of independent jobs
that consume a resource (which is non-renewable and replenished regularly) on
parallel uniformly related machines. The input defines the speed of machines,
size of jobs, the quantity of resource required by the jobs, the replenished
quantities, and replenishment dates of the resource. Every job can start
processing only after the required quantity of the resource is allocated to the
job. The objective function is the minimization of the convex combination of
the makespan and an objective that is equivalent to the $l_p$-norm of the
vector of loads of the machines. We present an EPTAS for this problem. Prior to
our work only a PTAS was known in this non-renewable resource settings and this
PTAS was only for the special case of our problem of makespan minimization on
identical machines.


  We present the exact solution of two-body motion in (1+1) dimensional dilaton
gravity by solving the constraint equations in the canonical formalism. The
determining equation of the Hamiltonian is derived in a transcendental form and
the Hamiltonian is expressed for the system of two identical particles in terms
of the Lambert $W$ function. The $W$ function has two real branches which join
smoothly onto each other and the Hamiltonian on the principal branch reduces to
the Newtonian limit for small coupling constant. On the other branch the
Hamiltonian yields a new set of motions which can not be understood as
relativistically correcting the Newtonian motion. The explicit trajectory in
the phase space $(r, p)$ is illustrated for various values of the energy. The
analysis is extended to the case of unequal masses. The full expression of
metric tensor is given and the consistency between the solution of the metric
and the equations of motion is rigorously proved.


  We explore some of the rich structure of the polarization observables
recently developed for processes like $\gamma N\to\pi\pi N$ and $\gamma N\to K
\bar{K} N$ in the framework of a specific model for the latter process.
Emphasis is placed on observables that may be accesible at existing facilities
in the near future. The sensitivity of the observables to the details of the
model indicate that they will be a very useful tool in differentiating between
different models for reactions like these. In the framework of a model for
$\gamma N\to K \bar{K} N$, we examine the sensitivity of the observables to
coupling constants of the $\phi$, to the properties of the $\Lambda(1405)$, and
to the existence of the $\Theta^+$.


  We demonstrate how dispersive atom number measurements during evaporative
cooling can be used for enhanced determination of the parameter dependence of
the transition to a Bose-Einstein condensate (BEC). In this way shot-to-shot
fluctuations in initial conditions are detected and the information extracted
per experimental realization is increased. We furthermore calibrate in-situ
images from dispersive probing of a BEC with corresponding absorption images in
time-of-flight. This allows for the determination of the transition point in a
single experimental realization by applying multiple dispersive measurements.
Finally, we explore the continuous probing of several consecutive phase
transition crossings using the periodic addition of a focused "dimple"
potential.


  This study addresses the task of Unknown Sense Detection in English and
Swedish. The primary objective of this task is to determine whether the meaning
of a particular word usage is documented in a dictionary or not. For this
purpose, sense entries are compared with word usages from modern and historical
corpora using a pre-trained Word-in-Context embedder that allows us to model
this task in a few-shot scenario. Additionally, we use human annotations to
adapt and evaluate our models. Compared to a random sample from a corpus, our
model is able to considerably increase the detected number of word usages with
non-recorded senses.


  We investigate the connection between galactic outflows and star formation
using two independent data sets covering a sample of 22 galaxies between $1
\lesssim z \lesssim 1.5$. The HST WFC3/G141 grism provides low spectral
resolution, high spatial resolution spectroscopy yielding H$\alpha$ emission
line maps from which we measure the spatial extent and strength of star
formation. In the rest-frame near-UV, Keck/DEIMOS observes Fe II and Mg II
interstellar absorption lines, which provide constraints on the intensity and
velocity of the outflows. We compare outflow properties from individual and
composite spectra with the star formation rate (SFR) and SFR surface density
(SigmaSFR), as well as the stellar mass and specific star formation rate
(sSFR). The Fe II and Mg II equivalent widths (EWs) increase with both SFR and
SigmaSFR at $\gtrsim 3\sigma$ significance, while the composite spectra show
larger Fe II EWs and outflow velocities in galaxies with higher SFR, SigmaSFR,
and sSFR. Absorption line profiles of the composite spectra further indicate
that the differences between subsamples are driven by outflows rather than the
ISM. While these results are consistent with those of previous studies, the use
of H$\alpha$ images makes them the most direct test of the relationship between
star formation and outflows at $z>1$ to date. Future facilities such as the
James Webb Space Telescope and the upcoming Extremely Large Telescopes will
extend these direct, H$\alpha$-based studies to lower masses and star formation
rates, probing galactic feedback across orders of magnitude in galaxy
properties and augmenting the correlations we find here.


  We use the framework of asymptotic safety above the Planck scale to constrain
the parameter space of simple models of new physics that can accommodate the
measured value of the anomalous magnetic moment of the muon and the relic
density of dark matter. We couple parametrically to the trans-Planckian quantum
physics a set of SU(2)$\times$U(1) invariant extensions of the Standard Model,
each comprising an inert scalar field and one pair of colorless fermions that
communicate to the muons through Yukawa-type interactions. The presence of an
interactive UV fixed point in the system of gauge and Yukawa couplings imposes
a set of boundary conditions at the Planck scale, which allow one to derive
unique phenomenological predictions in each case and distinguish the different
representations of the gauge group from one another. We apply to the models
constraints from the $h\to \mu\mu$ signal strength at ATLAS and CMS, direct LHC
searches for electroweak production with leptons and missing energy in the
final state, and the dark matter relic density. We find that they further
restrict the available parameter space.


  On the basis of the universal seesaw mass matrix model, which is a promising
model of the unified description of the quark and lepton mass matrices, the
behaviors of the gauge coupling constants and intermediate energy scales in the
SO(10)_L \times SO(10)_R model are investigated related to the neutrino mass
generation scenarios. The non-SUSY model cannot give favorable values of the
intermediate energy scales to explain the smallness of the neutrino masses,
while the SUSY model can give the plausible values if the number n_\phi of the
weak doublet Higgs scalars is n_\phi \geq 3.


  Incomplete sky analysis of cosmic microwave background (CMB) polarization
spectra poses a major problem of leakage between $E$- and $B$-modes. We present
a machine learning approach to remove this $E$-to-$B$ leakage using a
convolutional neural network (CNN) in presence of detector noise. The CNN
predicts the full sky $E$- and $B$-modes spectra for multipoles $2 \leq \ell
\leq 384$ from the partial sky spectra for $N_{\rm{side}} = 256$. We use
tensor-to-scalar ratio $r=0.001$ to simulate the CMB polarization maps. We
train our CNN using $10^5$ full sky target spectra and an equal number of noise
contaminated partial sky spectra obtained from the simulated maps. The CNN
works well for two masks covering the sky area of $\sim 80\%$ and $\sim 10\%$
respectively after training separately for each mask. For the assumed
theoretical $E$- and $B$-modes spectra, predicted full sky $E$- and $B$-modes
spectra agree well with the corresponding target spectra and their means agree
with theoretical spectra. The CNN preserves the cosmic variances at each
multipole, effectively removes correlations of the partial sky $E$- and
$B$-modes spectra, and retains the entire statistical properties of the targets
avoiding the problem of so-called $E$-to-$B$ leakage for the chosen theoretical
model.


  The study of interactive proofs in the context of distributed network
computing is a novel topic, recently introduced by Kol, Oshman, and Saxena
[PODC 2018]. In the spirit of sequential interactive proofs theory, we study
the power of distributed interactive proofs. This is achieved via a series of
results establishing trade-offs between various parameters impacting the power
of interactive proofs, including the number of interactions, the certificate
size, the communication complexity, and the form of randomness used. Our
results also connect distributed interactive proofs with the established field
of distributed verification. In general, our results contribute to providing
structure to the landscape of distributed interactive proofs.


  Motivated by a recent work of us [1], we reconstruct the different
f(T)-gravity models corresponding to a set of dark energy scenarios containing
the polytropic, the standard Chaplygin, the generalized Chaplygin and the
modified Chaplygin gas models. We also derive the equation of state parameter
of the selected f(T)-gravity models and obtain the necessary conditions for
crossing the phantom-divide line.


  The use of wide-field multi-object fiber-input spectrographs for large
redshift surveys introduces the possibility of variations in the observed
signal-to-noise ratio across the survey area due to errors in positioning the
fibers with respect to the target image positions, leading to
position-dependent errors in the survey catalog. This paper brings together a
comprehensive description of the sources of fiber-to-image position errors in
different instrument designs, and quantifies their effects on the efficiency
with which signal is recorded. For point sources, a function relating a
fractional efficiency and an equivalent aperture correction to the
fiber-to-image position error, the fiber diameter and the image size is plotted
for typical values of fiber and image sizes found in current instruments. The
tools required by observers to maximize the efficiency of fiber-spectrographic
surveys are discussed.


  We have developed two radiation-hard ASICs for optical data transmission in
the ATLAS pixel detector at the LHC at CERN: a driver chip for a Vertical
Cavity Surface Emitting Laser (VCSEL) diode for 80 Mbit/s data transmission
from the detector, and a Bi-Phase Mark decoder chip to recover the control data
and 40 MHz clock received optically by a PIN diode. We have successfully
implemented both ASICs in 0.25 um CMOS technology using enclosed layout
transistors and guard rings for increased radiation hardness. We present
results from prototype circuits and from irradiation studies with 24 GeV
protons up to 57 Mrad (1.9 x 10e15 p/cm2).


  This work represents an extension of the single pion production model
proposed by D. Rein. The model consists of resonant pion production and
nonresonant background contributions coming from three Born diagrams in the
helicity basis. The new work includes lepton mass effects, and nonresonant
interaction is described by five diagrams based on a nonlinear {\sigma} model.
This work provides a full kinematic description of single pion production in
the neutrino-nucleon interactions, including resonant and nonresonant
interactions in the helicity basis, in order to study the interference effect.


  For high resolution scene mapping and object recognition, optical
technologies such as cameras and LiDAR are the sensors of choice. However, for
robust future vehicle autonomy and driver assistance in adverse weather
conditions, improvements in automotive radar technology, and the development of
algorithms and machine learning for robust mapping and recognition are
essential. In this paper, we describe a methodology based on deep neural
networks to recognise objects in 300GHz radar images, investigating robustness
to changes in range, orientation and different receivers in a laboratory
environment. As the training data is limited, we have also investigated the
effects of transfer learning. As a necessary first step before road trials, we
have also considered detection and classification in multiple object scenes.


  Temperature and velocity-dependent $^1$S$_0$ pairing gaps, chemical
potentials and entrainment matrix in dense homogeneous neutron-proton
superfluid mixtures constituting the outer core of neutron stars, are
determined fully self-consistently by solving numerically the time-dependent
Hartree-Fock-Bogoliubov equations over the whole range of temperatures and flow
velocities for which superfluidity can exist. Calculations have been made for
$npe\mu$ in beta-equilibrium using the Brussels-Montreal functional BSk24. The
accuracy of various approximations is assessed and the physical meaning of the
different velocities and momentum densities appearing in the theory is
clarified. Together with the unified equation of state published earlier, the
present results provide consistent microscopic inputs for modeling superfluid
neutron-star cores.


  We investigate tensionless (or null) bosonic string theory with a Kalb-Ramond
background turned on. In analogy with the tensile case, we find that the
Kalb-Ramond field has a non-trivial effect on the spectrum only when the theory
is compactified on an (\left(S^1\right)^{\otimes d}) background with (d\geq 2).
We discuss the effect of this background field on the tensionless spectrum
constructed on three known consistent null string vacua. We elucidate further
on the intriguing fate of duality symmetries in these classes of string
theories when the background field is turned on.


  There has been a considerable amount of research aimed at automating the
documentation of accessibility in the built environment. Yet so far, there has
been no fully automatic system that has been shown to reliably document surface
quality barriers in the built environment in real-time. This is a mixed problem
of HCI and applied machine learning, requiring the careful use of applied
machine learning to address the real-world concern of practical documentation.
To address this challenge, we offer a framework for designing applied machine
learning approaches aimed at documenting the (in)accessibility of the built
environment. This framework is designed to take into account the real-world
picture, recognizing that the design of any accessibility documentation system
has to take into account a range of factors that are not usually considered in
machine learning research. We then apply this framework in a case study,
illustrating an approach which can obtain a f-ratio of 0.952 in the best-case
scenario.


  In SuperCool Inflation (SCI), a technically natural and thermal effect gives
a graceful exit to old inflation. The Universe starts off hot and trapped in a
false vacuum. The Universe supercools and inflates solving the horizon and
flatness problems. The inflaton couples to a set of QCD like fermions. When the
fermions' non-Abelian gauge group freezes, the Yukawa terms generate a tadpole
for the inflaton, which removes the barrier. Inflation ends, and the Universe
rapidly reheats. The thermal effect is technically natural in the same way that
the QCD scale is technically natural. In fact, Witten used a similar mechanism
to drive the Electro-Weak (EW) phase transition; critically, no scalar field
drives inflation, which allows SCI to avoid eternal inflation and the measure
problem. SCI also works at scales, which can be probed in the lab, and could be
connected to EW symmetry breaking. Finally, we introduce a light spectator
field to generate density perturbations, which match the CMB. The light field
does not affect the inflationary dynamics and can potentially generate
non-Gaussianities and isocurvature perturbations observable with Planck.


  Log analysis is one of the main techniques that engineers use for
troubleshooting large-scale software systems. Over the years, many supervised,
semi-supervised, and unsupervised log analysis methods have been proposed to
detect system anomalies by analyzing system logs. Among these, semi-supervised
methods have garnered increasing attention as they strike a balance between
relaxed labeled data requirements and optimal detection performance,
contrasting with their supervised and unsupervised counterparts. However,
existing semi-supervised methods overlook the potential bias introduced by
highly frequent log messages on the learned normal patterns, which leads to
their less than satisfactory performance. In this study, we propose LogSD, a
novel semi-supervised self-supervised learning approach. LogSD employs a
dual-network architecture and incorporates a frequency-based masking scheme, a
global-to-local reconstruction paradigm and three self-supervised learning
tasks. These features enable LogSD to focus more on relatively infrequent log
messages, thereby effectively learning less biased and more discriminative
patterns from historical normal data. This emphasis ultimately leads to
improved anomaly detection performance. Extensive experiments have been
conducted on three commonly-used datasets and the results show that LogSD
significantly outperforms eight state-of-the-art benchmark methods.


  The eROSITA X-ray telescope, launched in 2019, is predicted to observe
roughly 100,000 galaxy clusters. Follow-up observations of these clusters from
Chandra, for example, will be needed to resolve outstanding questions about
galaxy cluster physics. Deep Chandra cluster observations are expensive and
follow-up of every eROSITA cluster is infeasible, therefore, objects chosen for
follow-up must be chosen with care. To address this, we have developed an
algorithm for predicting longer duration, background-free observations based on
mock eROSITA observations. We make use of the hydrodynamic cosmological
simulation Magneticum, have simulated eROSITA instrument conditions using
SIXTE, and have applied a novel convolutional neural network to output a deep
Chandra-like "super observation" of each cluster in our simulation sample. Any
follow-up merit assessment tool should be designed with a specific use case in
mind; our model produces observations that accurately and precisely reproduce
the cluster morphology, which is a critical ingredient for determining cluster
dynamical state and core type. Our model will advance our understanding of
galaxy clusters by improving follow-up selection and demonstrates that
image-to-image deep learning algorithms are a viable method for simulating
realistic follow-up observations.


  We study a weighted asymmetry in the azimuthal distribution of photon-jet
pairs produced in the process p^\uparrow p --> \gamma jet X with a transversely
polarized proton. We focus on the contribution of the Sivers effect only,
considering experimental configurations accessible at RHIC-BNL. We show that
predictions for the asymmetry, obtained in terms of gluonic-pole cross sections
calculable in perturbative QCD, can be tested and clearly discriminated from
those based on a generalized parton model, involving standard partonic cross
sections. Experimental measurements of the asymmetry will therefore test our
present understanding of single-spin asymmetries.


  Algorithmic Gaussianization is a phenomenon that can arise when using
randomized sketching or sampling methods to produce smaller representations of
large datasets: For certain tasks, these sketched representations have been
observed to exhibit many robust performance characteristics that are known to
occur when a data sample comes from a sub-gaussian random design, which is a
powerful statistical model of data distributions. However, this phenomenon has
only been studied for specific tasks and metrics, or by relying on
computationally expensive methods. We address this by providing an algorithmic
framework for gaussianizing data distributions via averaging, proving that it
is possible to efficiently construct data sketches that are nearly
indistinguishable (in terms of total variation distance) from sub-gaussian
random designs. In particular, relying on a recently introduced sketching
technique called Leverage Score Sparsified (LESS) embeddings, we show that one
can construct an $n\times d$ sketch of an $N\times d$ matrix $A$, where $n\ll
N$, that is nearly indistinguishable from a sub-gaussian design, in time
$O(\text{nnz}(A)\log N + nd^2)$, where $\text{nnz}(A)$ is the number of
non-zero entries in $A$. As a consequence, strong statistical guarantees and
precise asymptotics available for the estimators produced from sub-gaussian
designs (e.g., for least squares and Lasso regression, covariance estimation,
low-rank approximation, etc.) can be straightforwardly adapted to our sketching
framework. We illustrate this with a new approximation guarantee for sketched
least squares, among other examples.


  Continuing the study of boundary BCFW recursion relation of tree level
amplitudes initiated in \cite{Feng:2009ei}, we consider boundary contributions
coming from fermion pair deformation. We present the general strategy for these
boundary contributions and demonstrate calculations using two examples, i.e,
the standard QCD and deformed QCD with anomalous magnetic momentum coupling. As
a by-product, we have extended BCFW recursion relation to off-shell gluon
current, where because off-shell gluon current is not gauge invariant, a new
feature must be cooperated.


  This paper outlines two approaches for mathematical, simulation, modeling,
and analysis of hypothetical creatures, in particular, the dragons of HBO's
television series Game of Thrones (GOT). Our first approach, the forward model,
utilizes quasi-empirical observations of various features of GOT dragons. We
then mathematically derive the growth rate, other dimensions, energy
consumption, etc. In the backward model, we use projected energy consumption by
given ecological impact to model an expected dragon in terms of physical
features. We compare and contrast both models to examine the plausibility of a
real-world existence for our titular dragons and provide brief analyses of
potential impacts on ecology.


  We evaluate the W-exchange diagram within the framework of light cone sum
rules (LCSR), taking $B_d^0 \to D_s^-K^+$ as an example. This decay mode,
though proceeding via the W-exchange diagram only and therefore expected to be
highly suppressed, has the branching ratio $3.2 \times 10^{-5}$. We estimate
the W-exchange amplitude within LCSR, including soft gluon corrections, to
twist-3 accuracy. The calculation naturally brings out the features which
suggest that such an amplitude is expected to be small in cases when both the
final state mesons are light, without relying on any kind of an assumption.
With minor changes, it is also possible to have a rough estimate of the
exchange/annihilation type contributions to other processes like $B \to \pi
\pi,~\pi K, KK$. We find that though it appears as if the sum rule method
yields a fair agreement with the observed value, a careful analysis of
individual terms shows that the method, in its present form, is inadequate to
capture the correct physical answer for the case of heavy final states.


  Thermodynamic properties of a tetrameric bond-alternating Heisenberg spin
chain with ferromagnetic-ferromagnetic-antiferromagnetic-antiferromagnetic
exchange interactions are studied using the transfer-matrix renormalization
group and compared to experimental measurements. The temperature dependence of
the uniform susceptibility exhibits typical ferrimagnetic features. Both the
uniform and staggered magnetic susceptibilities diverge in the limit $T\to 0$,
indicating that the ground state has both ferromagnetic and antiferromagnetic
long-range orders. A double-peak structure appears in the temperature
dependence of the specific heat. Our numerical calculation gives a good account
for the temperature and field dependence of the susceptibility, the
magnetization, and the specific heat for Cu(3-Clpy)$_{2}$(N$_{3}$)$_{2}$
(3-Clpy=3-Chloroyridine).


  We present a measure of entanglement that can be computed effectively for any
mixed state of an arbitrary bipartite system. We show that it does not increase
under local manipulations of the system, and use it to obtain a bound on the
teleportation capacity and on the distillable entanglement of mixed states.


  This paper intends to explain Venezuela's country spread behavior through the
Neural Networks analysis of a monthly economic activity general index of
economic indicators constructed by the Central Bank of Venezuela, a measure of
the shocks affecting country risk of emerging markets and the U.S. short term
interest rate. The use of non parametric methods allowed the finding of non
linear relationship between these inputs and the country risk. The networks
performance was evaluated using the method of excess predictability.


  We explore simple methods for adapting a trained multi-task UNet which
predicts canopy cover and height to a new geographic setting using remotely
sensed data without the need of training a domain-adaptive classifier and
extensive fine-tuning. Extending previous research, we followed a selective
alignment process to identify similar images in the two geographical domains
and then tested an array of data-based unsupervised domain adaptation
approaches in a zero-shot setting as well as with a small amount of
fine-tuning. We find that the selective aligned data-based image matching
methods produce promising results in a zero-shot setting, and even more so with
a small amount of fine-tuning. These methods outperform both an untransformed
baseline and a popular data-based image-to-image translation model. The best
performing methods were pixel distribution adaptation and fourier domain
adaptation on the canopy cover and height tasks respectively.


  A representation field for a non-maximal order H in a central simple algebra
A is a subfield of the spinor class field of maximal orders which determines
the set of spinor genera of maximal orders representing H. In our previous work
we have proved the existence of the representation field for several important
families of suborders, like commutative orders, while we have also found
examples where the representation field fails to exist. In this article, we
prove that the representation field is defined for any order H of rank 7 or
lower. The same technique yields the existence of representation fields for any
order in an algebra whose semi-simple reduction is commutative. We also
construct a rank-8 order whose representation field is not defined.


  Within nonlocal elastic interaction between an adsorbed atom and matrix atoms
and in consideration of the mirror image forces, a dispersion law of elastic
surface acoustic waves is found to the long-wavelength approximation depending
on the concentration of adsorbed atoms and the deformation potential of the
adatom. The energy width of the surface acoustic mode is calculated depending
on the concentration of adsorbed atoms.


  In the framework of a multi-phase transport model, fluctuation and
correlation of the azimuthal anisotropies in $^{12}$C+$^{197}$Au collisions at
$\sqrt{S_{NN}}$ = 200 GeV are explored. Properties of the initial eccentricity
and final harmonic flow fluctuation resulted from $^{12}$C+$^{197}$Au
collisions with Woods-Saxon configuration and triangular $\alpha$-clustering
configurations of $^{12}$C are investigated via scaled variance, skewness and
kurtosis. Comparisons are made between results from $\alpha$-clustered
configurations and Woods-Saxon configuration. The triangular flow fluctuation
is found to have particular sensitivity in distinguishing triangular
$\alpha$-clustering structure of $^{12}$C. Furthermore, correlations between
initial eccentricities and final flow harmonics are studied with strong
multiplicity dependence observed for the correlation functions.


  Active M-dwarfs are known to produce bursty radio emission, and
multi-wavelength studies have shown that Solar-like magnetic activity occurs in
these stars. However, coherent bursts from active M-dwarfs have often been
difficult to interpret in the Solar activity paradigm. We present Australian
Square Array Pathfinder (ASKAP) observations of UV Ceti at a central frequency
of 888 MHz. We detect several periodic, coherent pulses occurring over a
timescale consistent with the rotational period of UV Ceti. The properties of
the pulsed emission show that they originate from the electron cyclotron maser
instability, in a cavity $\sim7$ orders of magnitude less dense than the mean
coronal density at the estimated source altitude. These results confirm that
auroral activity can occur in active M-dwarfs, suggesting that these stars mark
the beginning of the transition from Solar-like to auroral magnetospheric
behaviour. These results demonstrate the capabilities of ASKAP for detecting
polarized, coherent bursts from active stars and other systems.