  We investigate the bifurcation structure of stationary states in a dipolar
Bose-Einstein condensate located in an external PT-symmetric potential. The
imaginary part of this external potential allows for the effective description
of in- and out-coupling of particles. To unveil the complete bifurcation
structure and the properties of the exceptional points we perform an analytical
continuation of the Gross-Pitaevskii equation, which is used to describe the
system. We use an elegant and numerically efficient method for the analytical
continuation of the Gross-Pitaevskii equation with dipolar interactions
containing bicomplex numbers. The Bose-Einstein condensate with dipole
interaction shows a much richer bifurcation scenario than a condensate without
long-range interactions. The inclusion of analytically continued states can
also explain property changes of bifurcation points which were hidden before,
and allows for the examination of the properties of the exceptional points
associated with the branch points. With the new analytically continued states
we are able to prove the existence of an exceptional point of fifth order.


  Herein we demonstrate that the performance of mesoporous silica SBA-15 and
SBA-15-Ag impregnated during plasma ammonia synthesis depend on the plasma
conditions. At high power the mesoporous silica SBA-15 without Ag produces the
largest amount of ammonia observed in our experiments, but the addition of Ag
provides a minor benefit at lower powers. Plasma conditions were analyzed using
optical emission spectroscopy using N2, N2+ and NH molecular bands and
H{\alpha} lines. The analysis of optical emission, specifically Ha lines and N2
molecular bands data shows that SBA-15 has higher electron density, and higher
N2 vibrational temperature. Stronger N2+ emission relative to the neutral N2
provides additional evidence for higher electron density in the SBA-15 packed
bed reactors. The addition of Ag resulted in a lower concentration of atomic
hydrogen. Where Ag acts as a hydrogen sink facilitating surface reactions with
nitrogen. The presence NH in the spectrum may indicate a higher concentration
of H in the SBA-15 reactor. From the materials point of view SBA-15 is a more
robust catalyst with a commendable surface area retention after plasma exposure
due to the lack of local heating generated when a metal is in the structure.


  We present a learning-based method for synthesizing novel views of complex
scenes using only unstructured collections of in-the-wild photographs. We build
on Neural Radiance Fields (NeRF), which uses the weights of a multilayer
perceptron to model the density and color of a scene as a function of 3D
coordinates. While NeRF works well on images of static subjects captured under
controlled settings, it is incapable of modeling many ubiquitous, real-world
phenomena in uncontrolled images, such as variable illumination or transient
occluders. We introduce a series of extensions to NeRF to address these issues,
thereby enabling accurate reconstructions from unstructured image collections
taken from the internet. We apply our system, dubbed NeRF-W, to internet photo
collections of famous landmarks, and demonstrate temporally consistent novel
view renderings that are significantly closer to photorealism than the prior
state of the art.


  For a commutative ring $R$ with a unit, an $R$-homology rose is a topological
space whose homology groups with $R$-coefficients agree with those of a bouquet
of cirlces. In this paper, we study some special properties of covering spaces
and fundamental groups of $R$-homology roses, from which we obtain some result
supporting the Carlsson conjecture on free $(Z_p)^r$ actions. In addition, for
a group $G$ and a field $F$, we define an integer called the $F$-gap of $G$,
which is an obstruction for $G$ to be realized as the fundamental group of a
2-dimensional $F$-homology rose. Furthermore, we discuss how to search
candidates of the counterexamples of Wall's D(2)-problem among $F$-homology
roses and $F$-acyclic spaces.


  Multi-domain text classification can automatically classify texts in various
scenarios. Due to the diversity of human languages, texts with the same label
in different domains may differ greatly, which brings challenges to the
multi-domain text classification. Current advanced methods use the
private-shared paradigm, capturing domain-shared features by a shared encoder,
and training a private encoder for each domain to extract domain-specific
features. However, in realistic scenarios, these methods suffer from
inefficiency as new domains are constantly emerging. In this paper, we propose
a robust contrastive alignment method to align text classification features of
various domains in the same feature space by supervised contrastive learning.
By this means, we only need two universal feature extractors to achieve
multi-domain text classification. Extensive experimental results show that our
method performs on par with or sometimes better than the state-of-the-art
method, which uses the complex multi-classifier in a private-shared framework.


  Consider an undirected graph $G=(V,E)$. A subgraph of $G$ is a subset of its
edges, whilst an orientation of $G$ is an assignment of a direction to each
edge. Provided with an integer circulation-demand $d:V\to \mathbb{Z}$, we show
an explicit and efficiently computable bijection between subgraphs of $G$ on
which a $d$-flow exists and orientations on which a $d$-flow exists. Moreover,
given a cost function $w:E\to (0,\infty)$ we can find such a bijection which
preserves the $w$-min-cost-flow.
  In 2013, Kozma and Moran showed, using dimensional methods, that the number
of subgraphs $k$-connecting a vertex $s$ to a vertex $t$ is the same as the
number of orientations $k$-connecting $s$ to $t$. An application of our result
is an efficient, bijective proof of this fact.


  The rank modulation scheme has been proposed for efficient writing and
storing data in non-volatile memory storage. Error-correction in the rank
modulation scheme is done by considering permutation codes. In this paper we
consider codes in the set of all permutations on $n$ elements, $S_n$, using the
Kendall's $\tau$-metric. We prove that there are no perfect
single-error-correcting codes in $S_n$, where $n>4$ is a prime or $4\leq n\leq
10$. We also prove that if such a code exists for $n$ which is not a prime then
the code should have some uniform structure. We define some variations of the
Kendall's $\tau$-metric and consider the related codes and specifically we
prove the existence of a perfect single-error-correcting code in $S_5$.
Finally, we examine the existence problem of diameter perfect codes in $S_n$
and obtain a new upper bound on the size of a code in $S_n$ with even minimum
Kendall's $\tau$-distance.


  We study well-posedness of viscous nonlinear wave equations (vNLW) on the
two-dimensional torus with a stochastic forcing. In particular, we prove
pathwise global well-posedness of the stochastic defocusing vNLW with an
additive stochastic forcing $D^\alpha \xi$, where $\alpha < \frac 12$ and $\xi$
denotes the space-time white noise.


  In dusty plasma environments, the spontaneous growth of nanoparticles from
reactive gases has been extensively studied for over three decades, primarily
focusing on hydrocarbons and silicate particles. Here, we introduce the growth
of titanium dioxide, a wide band gap semiconductor, as dusty plasma
nanoparticles. The resultant particles exhibited a spherical morphology and
reached a maximum homogeneous radius of 230 $\pm$ 17 nm after an elapsed time
of 70 seconds. The particle grew linearly and the growth displayed a cyclic
behavior; that is, upon reaching their maximum radius, the largest particles
fell out of the plasma, and a new growth cycle immediately followed. The
particles were collected after being grown for different amounts of time and
imaged using scanning electron microscopy. Further characterization was carried
out using energy dispersive X-ray spectroscopy, X-ray diffraction and Raman
spectroscopy to elucidate the chemical composition and crystalline properties
of the maximally sized particles. Initially, the as-grown particles after 70
seconds exhibited an amorphous structure. However, annealing treatments at
temperatures of 400 $^\circ$C and 800 $^\circ$C induced crystallization,
yielding anatase and rutile phases, respectively. Notably, annealing at 600
$^\circ$C resulted in a mixed phase of anatase and rutile. These findings open
new avenues for a rapid and controlled growth technique of titanium dioxide as
dusty plasma.


  In this paper we investigate the massive Sine-Gordon model in the ultraviolet
finite regime in thermal states over the two-dimensional Minkowski spacetime.
We combine recently developed methods of perturbative algebraic quantum field
theory with techniques developed in the realm of constructive quantum field
theory over Euclidean spacetimes to construct the correlation functions of the
equilibrium state of the Sine-Gordon theory in the adiabatic limit. First of
all, the observables of the Sine-Gordon theory are seen as functionals over the
free configurations and are obtained as a suitable combination of the
S-matrices of the interaction Lagrangian restricted to compact spacetime
regions over the free massive theory. These S-matrices are given as power
series in the coupling constant with values in the algebra of fields over the
free massive theory. Adapting techniques like conditioning and inverse
conditioning to spacetimes with Lorentzian signature, we prove that these power
series converge when evaluated on a generic field configuration. The latter
observation implies convergence in the strong operator topology in the GNS
representations of the considered states. In the second part of the paper,
adapting the cluster expansion technique to the Lorentzian case, we prove that
the correlation functions of the interacting equilibrium state at finite
temperature (KMS state) can be constructed also in the adiabatic limit, where
the interaction Lagrangian is supported everywhere in space.


  Alweiss, Lovett, Wu, and Zhang introduced $q$-spread hypergraphs in their
breakthrough work regarding the sunflower conjecture, and since then $q$-spread
hypergraphs have been used to give short proofs of several outstanding problems
in probabilistic combinatorics. A variant of $q$-spread hypergraphs was
implicitly used by Kahn, Narayanan, and Park to determine the threshold for
when a square of a Hamiltonian cycle appears in the random graph $G_{n,p}$. In
this paper we give a common generalization of the original notion of $q$-spread
hypergraphs and the variant used by Kahn et al.


  We study the bulk viscosity in two (anti-spaghetti and Swiss cheese) phases
of non- spherical nuclei in the mantle of an oscillating neutron star near the
boundary with the stellar core. The bulk viscosity is produced by
non-equilibrium Urca neutrino emis- sion processes. In the mantle, the direct
Urca process may be open (Gusakov et al., 2004) if neutrons and protons move in
a periodic potential created by a lattice of non- spherical nuclei (which
allows the nucleons to have large quasi-momenta and satisfy direct Urca
momentum-conservation). This bulk viscosity can dominate over that due to the
modified Urca process in the outer stellar core and over the shear viscosity.
The bulk viscosity depends strongly on temperature, oscillation frequency and
nucleon superfluidity. The enhanced bulk viscosity in the mantle can control
propagation and damping of neutron star oscillations.


  This paper presents a model for head and body pose estimation (HBPE) when
labelled samples are highly sparse. The current state-of-the-art multimodal
approach to HBPE utilizes the matrix completion method in a transductive
setting to predict pose labels for unobserved samples. Based on this approach,
the proposed method tackles HBPE when manually annotated ground truth labels
are temporally sparse. We posit that the current state of the art approach
oversimplifies the temporal sparsity assumption by using Laplacian smoothing.
Our final solution uses: i) Gaussian process regression in place of Laplacian
smoothing, ii) head and body coupling, and iii) nuclear norm minimization in
the matrix completion setting. The model is applied to the challenging SALSA
dataset for benchmark against the state-of-the-art method. Our presented
formulation outperforms the state-of-the-art significantly in this particular
setting, e.g. at 5% ground truth labels as training data, head pose accuracy
and body pose accuracy is approximately 62% and 70%, respectively. As well as
fitting a more flexible model to missing labels in time, we posit that our
approach also loosens the head and body coupling constraint, allowing for a
more expressive model of the head and body pose typically seen during
conversational interaction in groups. This provides a new baseline to improve
upon for future integration of multimodal sensor data for the purpose of HBPE.


  We analyze dynamics of chiral gauge theories based on the $SU(N)$ gauge group
with one anti-symmetric tensor $A$ and $(N-4)$ anti-fundamentals $F_{i}$ when
$N$ is odd. Based on the continuity to the supersymmetric gauge theories with
anomaly-mediated supersymmetry breaking, we claim that the global $SU(N-4)$
symmetry is spontaneously broken to $Sp(N-5)$. There are $N-5$ massless
fermions as a fundamental representation of $Sp(N-5)$, and another massless
fermion, together saturating the anomaly matching conditions. When $N$ is even,
the unbroken flavor symmetry is $Sp(N-4)$ while there are no massless fermions.
Our result is different from the dynamics suggested by tumbling where the full
$SU(N-4)$ symmetry is unbroken, but the tumbling picture can be modified via
the addition of a second condensate to produce the symmetry breaking pattern
predicted from our method.


  In the Standard Model the process $q\bar{q}\rightarrow q\bar{q}V$ (where
$V=W^\pm,Z^0$ or $\gamma$) can occur via gluon exchange and also via $W^\pm$ or
$Z^0$ exchange. The corresponding chromodynamic and electroweak amplitudes can
interfere with one another. These interference cross sections are largest when
the exchanged $W^\pm$ or $Z^0$ is on-shell when they are also odd under parity.
Interference cross sections computed using helicity-amplitude techniques are
presented for all interesting subprocesses as well as for the processes
$q\bar{q}\rightarrow q\bar{q} l\bar{l}$ in which the lepton pair $l\bar{l}$
comes from the decay of $V$ on-shell. Parity-violating asymmetries are defined
and presented at the parton level and for the hadronic processes $pp$ or
$p\bar{p}\rightarrow V$ + 2 jets or $l\bar{l}$ + 2 jets. These asymmetries are
independent of the polarizations of all particles involved, and do not require
that the flavors of the jet partons be measured. They are generally of order
0.01 pb at energies $\sqrt{s} \gtrsim 1$ TeV.


  The cubic nonlinear Schrodinger equation with repulsive nonlinearity and
elliptic function potential in two-dimensions models a repulsive dilute gas
Bose--Einstein condensate in a lattice potential. A family of exact stationary
solutions is presented and its stability is examined using analytical and
numerical methods. All stable trivial-phase solutions are off-set from the zero
level. Our results imply that a large number of condensed atoms is sufficient
to form a stable, periodic condensate.


  We report on tests of the AdS/CFT correspondence that are made possible by
complete knowledge of the Kaluza-Klein mass spectrum of type IIB supergravity
on $AdS_5 \times T^{11}$ with T^{11}=SU(2)^2/U(1). After briefly discussing
general multiplet shortening conditions in SU(2,2|1) and PSU(2,2|4), we compare
various types of short SU(2,2|1) supermultiplets on AdS_5 and different
families of boundary operators with protected dimensions. The supergravity
analysis predicts the occurrence in the SCFT at leading order in N and g_s N,
of extra towers of long multiplets whose dimensions are rational but not
protected by supersymmetry.


  Using numerical simulations, here we demonstrate that a single sheet of
graphene with properly designed inhomogeneous, nonuniform conductivity
distributions can act as a convex lens for focusing and collimating the
transverse-magnetic (TM) surface Plasmon polariton (SPP) surface wave
propagating along the graphene. Consequently, we show that the graphene can act
as a platform capable of obtaining spatial Fourier transform of infra-red (IR)
SPP signals. This may lead to rebirth of the field of Fourier Optics on a
one-atom-thick structure.


  The FLASH L-band (1.3 GHz) superconducting accelerator facility at DESY has a
Low Level RF (LLRF) system that is similar to that envisioned for ILC. This
system has extensive monitoring capability and was used to gather performance
data relevant to ILC. In particular, waveform data were recorded with beam off
for three, 8-cavity cryomodules to evaluate the input rf stability,
perturbations to the SC cavity frequencies and the rf overhead required to
achieve constant gradient during the 800 microseconds pulses. In this paper, we
discuss the measurements and data analysis procedures and present key findings
on the pulse-to-pulse input rf and cavity field stability.


  Inspired by the effectiveness of adversarial training in the area of
Generative Adversarial Networks we present a new approach for learning feature
representations in person re-identification. We investigate different types of
bias that typically occur in re-ID scenarios, i.e., pose, body part and camera
view, and propose a general approach to address them. We introduce an
adversarial strategy for controlling bias, named Bias-controlled Adversarial
framework (BCA), with two complementary branches to reduce or to enhance
bias-related features. The results and comparison to the state of the art on
different benchmarks show that our framework is an effective strategy for
person re-identification. The performance improvements are in both full and
partial views of persons.


  Regular languages (RL) are the simplest family in Chomsky's hierarchy. Thanks
to their simplicity they enjoy various nice algebraic and logic properties that
have been successfully exploited in many application fields. Practically all of
their related problems are decidable, so that they support automatic
verification algorithms. Also, they can be recognized in real-time.
  Context-free languages (CFL) are another major family well-suited to
formalize programming, natural, and many other classes of languages; their
increased generative power w.r.t. RL, however, causes the loss of several
closure properties and of the decidability of important problems; furthermore
they need complex parsing algorithms. Thus, various subclasses thereof have
been defined with different goals, spanning from efficient, deterministic
parsing to closure properties, logic characterization and automatic
verification techniques.
  Among CFL subclasses, so-called structured ones, i.e., those where the
typical tree-structure is visible in the sentences, exhibit many of the
algebraic and logic properties of RL, whereas deterministic CFL have been
thoroughly exploited in compiler construction and other application fields.
  After surveying and comparing the main properties of those various language
families, we go back to operator precedence languages (OPL), an old family
through which R. Floyd pioneered deterministic parsing, and we show that they
offer unexpected properties in two fields so far investigated in totally
independent ways: they enable parsing parallelization in a more effective way
than traditional sequential parsers, and exhibit the same algebraic and logic
properties so far obtained only for less expressive language families.


  We classify the stable solutions (positive or sign-changing, radial or not)
to the following nonlocal Lane-Emden equation: $(-\Delta)^s u=|u|^{p-1}u$ in
$\mathbb{R}^n$ for $2<s<3$.


  We have observed two spin density wave (SDW) phases in hole doped metallic
V_{2-y}O_3, one evolves from the other as a function of doping, pressure or
temperature. They differ in their response to an external magnetic field, which
can also induce a transition between them. The phase boundary between these two
states in the temperature-, doping-, and pressure-dependent phase diagram has
been determined by magnetization and magnetotransport measurements. One phase
exists at high doping level and has already been described in the literature.
The second phase is found in a small parameter range close to the boundary to
the antiferromagnetic insulating phase (AFI). The quantum phase transitions
between these states as a function of pressure and doping and the respective
metamagnetic behavior observed in these phases are discussed in the light of
structurally induced changes of the band structure.


  Many quantum algorithms make use of oracles which evaluate classical
functions on a superposition of inputs. In order to facilitate implementation,
testing, and resource estimation of such algorithms, we present quantum
circuits for evaluating functions that are often encountered in the quantum
algorithm literature. This includes Gaussians, hyperbolic tangent, sine/cosine,
inverse square root, arcsine, and exponentials. We use insights from classical
high-performance computing in order to optimize our circuits and implement a
quantum software stack module which allows to automatically generate circuits
for evaluating piecewise smooth functions in the computational basis. Our
circuits enable more detailed cost analyses of various quantum algorithms,
allowing to identify concrete applications of future quantum computing devices.
Furthermore, our resource estimates may guide future research aiming to reduce
the costs or even the need for arithmetic in the computational basis
altogether.


  A self-consistent theoretical treatment of a triple-well atomtronic
transistor circuit reveals the mechanism of gain, conditions of oscillation,
and properties of the subsequent coherent matterwaves emitted by the circuit. A
Bose-condensed reservoir of atoms in a large source well provides a chemical
potential that drives circuit dynamics. The theory is based on the ansatz that
a condensate arises in the transistor gate well as a displaced ground state,
that is, one that undergoes dipole oscillation in the well. That gate atoms
remain condensed and oscillating is shown to be a consequence of the cooling
induced by the emission of a matterwave into the vacuum. Key circuit parameters
such as the transistor transconductance and output current are derived by
transitioning to a classical equivalent circuit model. Voltage-like and
current-like matterwave circuit wave fields are introduced in analogy with
microwave circuits, as well as an impedance relationship between the two. This
leads to a new notion of a classical coherent matterwave that is the dual of a
coherent electromagnetic wave and which is distinct from a deBroglie matterwave
associated with cold atoms. Subjecting the emitted atom flux to an atomic
potential that will reduce the deBroglie wavelength, for example, will increase
the classical matterwave wavelength. Quantization of the classical matterwave
fields leads to the dual of the photon that is identified not as an atom but as
something else, which is here dubbed a "matteron".


  In addition to generating data and annotations, devising sensible data
splitting strategies and evaluation metrics is essential for the creation of a
benchmark dataset. This practice ensures consensus on the usage of the data,
homogeneous assessment, and uniform comparison of research methods on the
dataset. This study focuses on CholecT50, which is a 50 video surgical dataset
that formalizes surgical activities as triplets of <instrument, verb, target>.
In this paper, we introduce the standard splits for the CholecT50 and CholecT45
datasets and show how they compare with existing use of the dataset. CholecT45
is the first public release of 45 videos of CholecT50 dataset. We also develop
a metrics library, ivtmetrics, for model evaluation on surgical triplets.
Furthermore, we conduct a benchmark study by reproducing baseline methods in
the most predominantly used deep learning frameworks (PyTorch and TensorFlow)
to evaluate them using the proposed data splits and metrics and release them
publicly to support future research. The proposed data splits and evaluation
metrics will enable global tracking of research progress on the dataset and
facilitate optimal model selection for further deployment.


  We obtain closed-form solutions of several inhomogeneous Lienard equations by
the factorization method. The two factorization conditions involved in the
method are turned into a system of first-order differential equations
containing the forcing term. In this way, one can find the forcing terms that
lead to integrable cases. Because of the reduction of order feature of
factorization, the solutions are simultaneously solutions of first-order
differential equations with polynomial nonlinearities. The illustrative
examples of Lienard solutions obtained in this way generically have rational
parts, and consequently display singularities.


  Most existing policy learning solutions require the learning agents to
receive high-quality supervision signals such as well-designed rewards in
reinforcement learning (RL) or high-quality expert demonstrations in behavioral
cloning (BC). These quality supervisions are usually infeasible or
prohibitively expensive to obtain in practice. We aim for a unified framework
that leverages the available cheap weak supervisions to perform policy learning
efficiently. To handle this problem, we treat the "weak supervision" as
imperfect information coming from a peer agent, and evaluate the learning
agent's policy based on a "correlated agreement" with the peer agent's policy
(instead of simple agreements). Our approach explicitly punishes a policy for
overfitting to the weak supervision. In addition to theoretical guarantees,
extensive evaluations on tasks including RL with noisy rewards, BC with weak
demonstrations, and standard policy co-training show that our method leads to
substantial performance improvements, especially when the complexity or the
noise of the learning environments is high.


  Novel coronavirus (COVID-19) outbreak, has raised a calamitous situation all
over the world and has become one of the most acute and severe ailments in the
past hundred years. The prevalence rate of COVID-19 is rapidly rising every day
throughout the globe. Although no vaccines for this pandemic have been
discovered yet, deep learning techniques proved themselves to be a powerful
tool in the arsenal used by clinicians for the automatic diagnosis of COVID-19.
This paper aims to overview the recently developed systems based on deep
learning techniques using different medical imaging modalities like Computer
Tomography (CT) and X-ray. This review specifically discusses the systems
developed for COVID-19 diagnosis using deep learning techniques and provides
insights on well-known data sets used to train these networks. It also
highlights the data partitioning techniques and various performance measures
developed by researchers in this field. A taxonomy is drawn to categorize the
recent works for proper insight. Finally, we conclude by addressing the
challenges associated with the use of deep learning methods for COVID-19
detection and probable future trends in this research area. This paper is
intended to provide experts (medical or otherwise) and technicians with new
insights into the ways deep learning techniques are used in this regard and how
they potentially further works in combatting the outbreak of COVID-19.


  We answer the natural question: when are a regular Poisson structure along
with a complex structure transverse to its symplectic leaves induced by
generalized complex structure? The leafwise symplectic form and transverse
complex structure determine an obstruction class in a certain cohomology, which
vanishes if and only if our question has an affirmative answer. We first study
a component of this obstruction, which gives the condition that the leafwise
cohomology class of the symplectic form must be transversely pluriharmonic. As
a consequence, under certain topological hypotheses, we infer that we actually
have a symplectic fibre bundle over a complex base. We then show how to compute
the full obstruction via a spectral sequence. We give various concrete
necessary and sufficient conditions for the vanishing of the obstruction.
Throughout, we give examples to test the sharpness of these conditions,
including a symplectic fibre bundle over a complex base which does not come
from a generalized complex structure, and a regular generalized complex
structure which is very unlike a symplectic fibre bundle, i.e., for which
nearby leaves are not symplectomorphic.


  We define an algebraic/combinatorial object on the front projection $\Sigma$
of a Legendrian knot called a Morse complex sequence, abbreviated MCS. This
object is motivated by the theory of generating families and provides new
connections between generating families, normal rulings, and augmentations of
the Chekanov-Eliashberg DGA. In particular, we place an equivalence relation on
the set of MCSs on $\Sigma$ and construct a surjective map from the equivalence
classes to the set of chain homotopy classes of augmentations of $L_\Sigma$,
where $L_\Sigma$ is the Ng resolution of $\Sigma$. In the case of Legendrian
knot classes admitting representatives with two-bridge front projections, this
map is bijective. We also exhibit two standard forms for MCSs and give explicit
algorithms for finding these forms.


  In physical human-robot interaction, the coexistence of robots and humans in
the same workspace requires the guarantee of a stable interaction, trying to
minimize the effort for the operator. To this aim, the admittance control is
widely used and the appropriate selection of the its parameters is crucial,
since they affect both the stability and the ability of the robot to interact
with the user. In this paper, we present a strategy for detecting deviations
from the nominal behavior of an admittance-controlled robot and for adapting
the parameters of the controller while guaranteeing the passivity. The proposed
methodology is validated on a KUKA LWR 4+.


  We present the full-resolution correspondence learning for cross-domain
images, which aids image translation. We adopt a hierarchical strategy that
uses the correspondence from coarse level to guide the fine levels. At each
hierarchy, the correspondence can be efficiently computed via PatchMatch that
iteratively leverages the matchings from the neighborhood. Within each
PatchMatch iteration, the ConvGRU module is employed to refine the current
correspondence considering not only the matchings of larger context but also
the historic estimates. The proposed CoCosNet v2, a GRU-assisted PatchMatch
approach, is fully differentiable and highly efficient. When jointly trained
with image translation, full-resolution semantic correspondence can be
established in an unsupervised manner, which in turn facilitates the
exemplar-based image translation. Experiments on diverse translation tasks show
that CoCosNet v2 performs considerably better than state-of-the-art literature
on producing high-resolution images.


  We define and study a fractional Gaussian field $X$ with Hurst parameter $H$
on the Sierpi\'nski gasket $K$ equipped with its Hausdorff measure $\mu$. It
appears as a solution, in a weak sense, of the equation $(-\Delta)^s X =W$
where $W$ is a Gaussian white noise on $L_0^2(K,\mu)$, $\Delta$ the Laplacian
on $K$ and $s= \frac{d_h+2H}{2d_w}$, where $d_h$ is the Hausdorff dimension of
$K$ and $d_w$ its walk dimension. The construction of those fields is then
extended to other fractals including the Sierpi\'nski carpet.


  The blessing of ubiquitous data also comes with a curse: the communication,
storage, and labeling of massive, mostly redundant datasets. We seek to solve
this problem at its core, collecting only valuable data and throwing out the
rest via submodular maximization. Specifically, we develop algorithms for the
online and distributed version of the problem, where data selection occurs in
an uncoordinated fashion across multiple data streams. We design a general and
flexible core selection routine for our algorithms which, given any stream of
data, any assessment of its value, and any formulation of its selection cost,
extracts the most valuable subset of the stream up to a constant factor while
using minimal memory. Notably, our methods have the same theoretical guarantees
as their offline counterparts, and, as far as we know, provide the first
guarantees for online distributed submodular optimization in the literature.
Finally, in learning tasks on ImageNet and MNIST, we show that our selection
methods outperform random selection by $5-20\%$.


  We numerically study the dynamics of a discrete spring-block model introduced
by Olami, Feder and Christensen (OFC) to mimic earthquakes and investigate to
which extent this simple model is able to reproduce the observed spatiotemporal
clustering of seismicty. Following a recently proposed method to characterize
such clustering by networks of recurrent events [Geophys. Res. Lett. {\bf 33},
L1304, 2006], we find that for synthetic catalogs generated by the OFC model
these networks have many non-trivial statistical properties. This includes
characteristic degree distributions -- very similar to what has been observed
for real seismicity. There are, however, also significant differences between
the OFC model and earthquake catalogs indicating that this simple model is
insufficient to account for certain aspects of the spatiotemporal clustering of
seismicity.


  We examine the formation and evolution of primordial black holes (PBH's)
after hybrid inflation. Our goal is to assess the effects of various
theoretical uncertainties on the extrapolation from a given inflation model to
a spectrum of primordial black hole masses. The context of our work is an
examination of the possibility [Chen and Adler (2003), MacGibbon (1987)] that
the dark matter is comprised of Planck-mass black hole remnants (BHR's). As an
example we focus on a particular scenario [Chen (2003)] in which the black
holes form from quantum perturbations that were generated during hybrid
inflation. We find the correspondence between hybrid inflation parameters and
the range of initial PBH masses that would allow BHR's to comprise the dark
matter, taking account of the possible early presence of radiation and its
accretion onto the PBH's.


  We consider two social consensus models, the AB-model and the Naming Game
restricted to two conventions, which describe a population of interacting
agents that can be in either of two equivalent states (A or B) or in a third
mixed (AB) state. Proposed in the context of language competition and
emergence, the AB state was associated with bilingualism and synonymy
respectively. We show that the two models are equivalent in the mean field
approximation, though the differences at the microscopic level have non-trivial
consequences. To point them out, we investigate an extension of these dynamics
in which confidence/trust is considered, focusing on the case of an underlying
fully connected graph, and we show that the consensus-polarization phase
transition taking place in the Naming Game is not observed in the AB model. We
then consider the interface motion in regular lattices. Qualitatively, both
models show the same behavior: a diffusive interface motion in a
one-dimensional lattice, and a curvature driven dynamics with diffusing
stripe-like metastable states in a two-dimensional one. However, in comparison
to the Naming Game, the AB-model dynamics is shown to slow down the diffusion
of such configurations.


  Results from an analysis of the intrinsic UV absorption in the Seyfert 1
galaxy NGC 3783 are presented. We focus on two new results that demonstrate
techniques for deriving important constraints on the physical conditions and
geometry of the absorbers. First, using variability in the spectrum, the
emission-line profile is separated into distinct kinematic components and the
effect on the interpretation of covering factors and column densities is
demonstrated. Second, measurements of the 2s2p 3^P metastable levels of C^+2
derived from the C III* 1175-76 absorption multiplet are presented. New
calculations of the metastable level populations are given and shown to provide
a powerful diagnostic of the density (and thus location) and temperature in an
absorber.


  Heavy fermion pair production in $e^+e^-$ annihilation is a fundamental
process in hadron physics and is of considerable interest for various
phenomena. In this paper, we will apply the Principle of Maximum Conformality
(PMC) to provide a comprehensive analysis of these processes. The PMC provides
a systematic, unambiguous method for determining the renormalization scales of
the QCD coupling constant for single-scale and multiple-scale applications. The
resulting predictions eliminate any renormalization scheme-and-scale
ambiguities, eliminate the factorial renormalon divergences, and are consistent
with the requirements of the renormalization group. It is remarkable that two
distinctly different scales are determined by using the PMC for heavy fermion
pair production near the threshold region. One scale is the order of the
fermion mass $m_f$, which enters the hard virtual corrections, and the other
scale is of order $ v\,m_f$, where $v$ is the quark velocity, which enters the
Coulomb rescattering amplitude. The PMC scales yield the correct physical
behavior and reflect the virtuality of the propagating gluons (photons) for the
QCD (QED) processes. Moreover, we demonstrate the consistency of PMC scale
setting from QCD to QED. Perfect agreement between the Abelian unambiguous
Gell-Mann-Low and the PMC scale-setting methods in the limit of zero number of
colors is demonstrated.


  We develop a framework based on microeconomic theory from which the ideal gas
like market models can be addressed. A kinetic exchange model based on that
framework is proposed and its distributional features have been studied by
considering its moments. Next, we derive the moments of the CC model (Eur.
Phys. J. B 17 (2000) 167) as well. Some precise solutions are obtained which
conform with the solutions obtained earlier. Finally, an output market is
introduced with global price determination in the model with some necessary
modifications.


  We present a model for implementing fast entangling gates (${\sim}1~\mu$s)
with ultra-fast pulses in arbitrarily long ion chains, that requires low
numbers of pulses and can be implemented with laser repetition rates well
within experimental capability. We demonstrate that we are able to optimise
pulse sequences that have theoretical fidelities above $99.99\%$ in arbitrarily
long ion-chains, for laser repetition rates on the order of $100-300$~MHz.
Notably, we find higher repetition rates are not required for gates in longer
ion chains, which is in contrast to scaling analyses with other gate schemes.
When pulse imperfections are considered in our calculations, we find that
achievable gate fidelity is independent of the number of ions in the chain. We
also show that pulse control requirements do not scale up with the number of
ions. We find that population transfer efficiencies of above $99.9\%$ from
individual ultra-fast pulses is the threshold for realising high-fidelity
gates, which may be achievable in near-future experiments.


  In this work, the concept of Braced Fourier Continuation and Regression
(BFCR) is introduced. BFCR is a novel and computationally efficient means of
finding nonlinear regressions or trend lines in arbitrary one-dimensional data
sets. The Braced Fourier Continuation (BFC) and BFCR algorithms are first
outlined, followed by a discussion of the properties of BFCR as well as
demonstrations of how BFCR trend lines may be used effectively for anomaly
detection both within and at the edges of arbitrary one-dimensional data sets.
Finally, potential issues which may arise while using BFCR for anomaly
detection as well as possible mitigation techniques are outlined and discussed.
All source code and example data sets are either referenced or available via
GitHub, and all associated code is written entirely in Python.


  Earlier, we introduced Partial Quantifier Elimination (PQE). It is a
$\mathit{generalization}$ of regular quantifier elimination where one can take
a $\mathit{part}$ of the formula out of the scope of quantifiers. We apply PQE
to CNF formulas of propositional logic with existential quantifiers. The appeal
of PQE is that many problems like equivalence checking and model checking can
be solved in terms of PQE and the latter can be very efficient. The main flaw
of current PQE solvers is that they do not $\mathit{reuse}$ learned
information. The problem here is that these PQE solvers are based on the notion
of clause redundancy and the latter is a $\mathit{structural}$ rather than
$\mathit{semantic}$ property. In this paper, we provide two important
theoretical results that enable reusing the information learned by a PQE
solver. Such reusing can dramatically boost the efficiency of PQE like conflict
clause learning boosts SAT solving.


  We consider the ionisation of atomic hydrogen by a strong infrared field. We
extend and study in more depth an existing semi-analytical model. Starting from
the time-dependent Schroedinger equation in momentum space and in the velocity
gauge we substitute the kernel of the non-local Coulomb potential by a sum of N
separable potentials, each of them supporting one hydrogen bound state. This
leads to a set of N coupled one-dimensional linear Volterra integral equations
to solve. We analyze the gauge problem for the model, the different ways of
generating the separable potentials and establish a clear link with the strong
field approximation which turns out to be a limiting case of the present model.
We calculate electron energy spectra as well as the time evolution of electron
wave packets in momentum space. We compare and discuss the results obtained
with the model and with the strong field approximation and examine in this
context, the role of excited states.


  We explore the dynamics of artificial one- and two-dimensional Ising-like
quantum antiferromagnets with different lattice geometries by using a Rydberg
quantum simulator of up to 36 spins in which we dynamically tune the parameters
of the Hamiltonian. We observe a region in parameter space with
antiferromagnetic (AF) ordering, albeit with only finite-range correlations. We
study systematically the influence of the ramp speeds on the correlations and
their growth in time. We observe a delay in their build-up associated to the
finite speed of propagation of correlations in a system with short-range
interactions. We obtain a good agreement between experimental data and
numerical simulations taking into account experimental imperfections measured
at the single particle level. Finally, we develop an analytical model, based on
a short-time expansion of the evolution operator, which captures the observed
spatial structure of the correlations, and their build-up in time.


  This paper is concerned with the distributed control and stabilization
problems for linear discrete-time large scale systems with imposed constraints.
The main contributions of this paper are: Firstly, by using the maximum
principle (necessary condition) for the finite horizon optimal control
developed in this paper, the optimal distributed controller is thus derived,
which can be easily calculated; Secondly, by defining the Lyapunov function
with the optimal cost function, we show that the systems with imposed
constraints can be stabilized by the optimal distributed controller for the
infinite horizon case. The main techniques adopted in this paper are the
maximum principle and the solution to the forward and backward difference
equations (FBDE), which are the basis in solving distributed control and
stabilization problems for linear systems with constraints.


  In order to deal with the systematic verification with uncertain infromation
in possibility theory, Li and Li \cite{li12} introduced model checking of
linear-time properties in which the uncertainty is modeled by possibility
measures. Xue, Lei and Li \cite{Xue09} defined computation tree logic (CTL)
based on possibility measures, which is called possibilistic CTL (PoCTL). This
paper is a continuation of the above work. First, we study the expressiveness
of PoCTL. Unlike probabilistic CTL, it is shown that PoCTL (in particular,
qualitative PoCTL) is more powerful than CTL with respect to their
expressiveness. The equivalent expressions of basic CTL formulae using
qualitative PoCTL formulae are presented in detail. Some PoCTL formulae that
can not be expressed by any CTL formulae are presented. In particular, some
qualitative properties of repeated reachability and persistence are expressed
using PoCTL formulae. Next, adapting CTL model-checking algorithm, a method to
solve the PoCTL model-checking problem and its time complexity are discussed in
detail. Finally, an example is given to illustrate the PoCTL model-checking
method.


  In this paper an improved bound on the chromatic number of the Pancake graph
$P_n, n\geqslant 2$, is presented. The bound is obtained using a subadditivity
property of the chromatic number of the Pancake graph. We also investigate an
equitable coloring of $P_n$. An equitable $(n-1)$-coloring based on efficient
dominating sets is given and optimal equitable $4$-colorings are considered for
small $n$. It is conjectured that the chromatic number of $P_n$ coincides with
its equitable chromatic number for any $n\geqslant 2$.


  Backdoor attacks involve the injection of a limited quantity of poisoned
examples containing triggers into the training dataset. During the inference
stage, backdoor attacks can uphold a high level of accuracy for normal
examples, yet when presented with trigger-containing instances, the model may
erroneously predict them as the targeted class designated by the attacker. This
paper explores strategies for mitigating the risks associated with backdoor
attacks by examining the filtration of poisoned samples.We primarily leverage
two key characteristics of backdoor attacks: the ability for multiple backdoors
to exist simultaneously within a single model, and the discovery through
Composite Backdoor Attack (CBA) that altering two triggers in a sample to new
target labels does not compromise the original functionality of the triggers,
yet enables the prediction of the data as a new target class when both triggers
are present simultaneously.Therefore, a novel three-stage poisoning data
filtering approach, known as Composite Backdoor Poison Filtering (CBPF), is
proposed as an effective solution. Firstly, utilizing the identified
distinctions in output between poisoned and clean samples, a subset of data is
partitioned to include both poisoned and clean instances. Subsequently, benign
triggers are incorporated and labels are adjusted to create new target and
benign target classes, thereby prompting the poisoned and clean data to be
classified as distinct entities during the inference stage. The experimental
results indicate that CBPF is successful in filtering out malicious data
produced by six advanced attacks on CIFAR10 and ImageNet-12. On average, CBPF
attains a notable filtering success rate of 99.91% for the six attacks on
CIFAR10. Additionally, the model trained on the uncontaminated samples exhibits
sustained high accuracy levels.


  Word alignment is essential for the downstream cross-lingual language
understanding and generation tasks. Recently, the performance of the neural
word alignment models has exceeded that of statistical models. However, they
heavily rely on sophisticated translation models. In this study, we propose a
super lightweight unsupervised word alignment model named MirrorAlign, in which
bidirectional symmetric attention trained with a contrastive learning objective
is introduced, and an agreement loss is employed to bind the attention maps,
such that the alignments follow mirror-like symmetry hypothesis. Experimental
results on several public benchmarks demonstrate that our model achieves
competitive, if not better, performance compared to the state of the art in
word alignment while significantly reducing the training and decoding time on
average. Further ablation analysis and case studies show the superiority of our
proposed MirrorAlign. Notably, we recognize our model as a pioneer attempt to
unify bilingual word embedding and word alignments. Encouragingly, our approach
achieves {16.4X speedup} against GIZA++, and {50X parameter compression}
compared with the Transformer-based alignment methods. We release our code to
facilitate the community: https://github.com/moore3930/MirrorAlign.


  In this note we consider the iteration complexity of solving strongly convex
multi objective optimization. We discuss the precise meaning of this problem,
and indicate it is loosely defined, but the most natural notion is to find a
set of Pareto optimal points across a grid of scalarized problems. We derive
that in most cases, performing sensitivity based path-following after obtaining
one solution is the optimal strategy for this task in terms of iteration
complexity.


  Many real-world problems require trading off multiple competing objectives.
However, these objectives are often in different units and/or scales, which can
make it challenging for practitioners to express numerical preferences over
objectives in their native units. In this paper we propose a novel algorithm
for multi-objective reinforcement learning that enables setting desired
preferences for objectives in a scale-invariant way. We propose to learn an
action distribution for each objective, and we use supervised learning to fit a
parametric policy to a combination of these distributions. We demonstrate the
effectiveness of our approach on challenging high-dimensional real and
simulated robotics tasks, and show that setting different preferences in our
framework allows us to trace out the space of nondominated solutions.


  In some cases, solutions to nonlinear PDEs happen to be asymptotically (for
large $x$ and/or $t$) invariant under a group $G$ which is not a symmetry of
the equation. After recalling the geometrical meaning of symmetries of
differential equations -- and solution-preserving maps -- we provide a precise
definition of asymptotic symmetries of PDEs; we deal in particular, for ease of
discussion and physical relevance, with scaling and translation symmetries of
scalar equations. We apply the general discussion to a class of
``Richardson-like'' anomalous diffusion and reaction-diffusion equations, whose
solution are known by numerical experiments to be asymptotically scale
invariant; we obtain an analytical explanation of the numerically observed
asymptotic scaling properties. We also apply our method to a different class of
anomalous diffusion equations, relevant in optical lattices. The methods
developed here can be applied to more general equations, as clear by their
geometrical construction.


  Computing in the network (COIN) is a promising technology that allows
processing to be carried out within network devices such as switches and
network interface cards. Time sensitive application can achieve their quality
of service (QoS) target by flexibly distributing the caching and computing
tasks in the cloud-edge-mist continuum. This paper highlights the advantages of
in-network computing, comparing to edge computing, in terms of latency and
traffic filtering. We consider a critical use case related to Covid-19 alert
application in an airport setting. Arriving travelers are monitored through
cough analysis so that potentially infected cases can be detected and isolated
for medical tests. A performance comparison has been done between an
architecture using in-network computing and another one using edge computing.
We show using simulations that in-network computing outperforms edge computing
in terms of Round Trip Time (RTT) and traffic filtering.


  Star-forming galaxies display a close relation among stellar mass,
metallicity and star-formation rate (or molecular-gas mass). This is known as
the fundamental metallicity relation (FMR) (or molecular-gas FMR), and it has a
profound implication on models of galaxy evolution. However, there still
remains a significant residual scatter around the FMR. We show here that a
fourth parameter, the surface density of stellar mass, reduces the dispersion
around the molecular-gas FMR. In a principal component analysis of 29 physical
parameters of 41,338 star-forming galaxies, the surface density of stellar mass
is found to be the fourth most important parameter. The new four-dimensional
fundamental relation forms a tighter hypersurface that reduces the metallicity
dispersion to 50\% of that of the molecular-gas FMR. We suggest that future
analyses and models of galaxy evolution should consider the FMR in a
four-dimensional space that includes surface density. The dilution time scale
of gas inflow and the star-formation efficiency could explain the observational
dependence on surface density of stellar mass.


  We consider the problem of identifying possibly discontinuous doping profiles
in semiconductor devices from data obtained by\,stationary voltage-current
maps. In particular, we focus on the so-called unipolar case, a system of PDE's
derived directly from the drift diffusion equations. The related inverse
problem corresponds to an inverse conductivity problem with partial data.
  The identification issue for this inverse problem is considered. In
particular, for a discretized version of the problem, we derive a result
connected to diffusion tomography theory. A numerical approach for the
identification problem using level set methods is presented. Our method is
compared with previous results in the literature, where Landweber-Kaczmarz type
methods were used to solve a similar problem.


  In this paper we present a new family of high order accurate
Arbitrary-Lagrangian-Eulerian (ALE) one-step ADER-WENO finite volume schemes
for the solution of nonlinear systems of conservative and non-conservative
hyperbolic partial differential equations with stiff source terms on moving
tetrahedral meshes in three space dimensions. A WENO reconstruction technique
is used to achieve high order of accuracy in space, while an element-local
space-time Discontinuous Galerkin finite element predictor on moving meshes is
used to obtain a high order accurate one-step time discretization. Within the
space-time predictor the physical element is mapped onto a reference element
using an isoparametric approach, where the space-time basis and test functions
are given by the Lagrange interpolation polynomials passing through a
predefined set of space-time nodes. Since our algorithm is cell-centered, the
final mesh motion is computed by using a suitable node solver algorithm. A
rezoning step as well as a flattener strategy are used in some of the test
problems to avoid mesh tangling or excessive element deformations that may
occur when the computation involves strong shocks or shear waves. We apply our
new high order unstructured ALE schemes to the 3D Euler equations of
compressible gas dynamics, for which a set of classical numerical test problems
has been solved and for which convergence rates up to sixth order of accuracy
in space and time have been obtained. We furthermore consider the equations of
classical ideal magnetohydrodynamics (MHD) as well as the non-conservative
seven-equation Baer-Nunziato model of compressible multi-phase flows with stiff
relaxation source terms.


  Coded computing has emerged as a promising framework for tackling significant
challenges in large-scale distributed computing, including the presence of
slow, faulty, or compromised servers. In this approach, each worker node
processes a combination of the data, rather than the raw data itself. The final
result then is decoded from the collective outputs of the worker nodes.
However, there is a significant gap between current coded computing approaches
and the broader landscape of general distributed computing, particularly when
it comes to machine learning workloads. To bridge this gap, we propose a novel
foundation for coded computing, integrating the principles of learning theory,
and developing a new framework that seamlessly adapts with machine learning
applications. In this framework, the objective is to find the encoder and
decoder functions that minimize the loss function, defined as the mean squared
error between the estimated and true values. Facilitating the search for the
optimum decoding and functions, we show that the loss function can be
upper-bounded by the summation of two terms: the generalization error of the
decoding function and the training error of the encoding function. Focusing on
the second-order Sobolev space, we then derive the optimal encoder and decoder.
We show that in the proposed solution, the mean squared error of the estimation
decays with the rate of $O(S^4 N^{-3})$ and
$O(S^{\frac{8}{5}}N^{\frac{-3}{5}})$ in noiseless and noisy computation
settings, respectively, where $N$ is the number of worker nodes with at most
$S$ slow servers (stragglers). Finally, we evaluate the proposed scheme on
inference tasks for various machine learning models and demonstrate that the
proposed framework outperforms the state-of-the-art in terms of accuracy and
rate of convergence.


  Social media have substantially altered the way brands and businesses
advertise: Online Social Networks provide brands with more versatile and
dynamic channels for advertisement than traditional media (e.g., TV and radio).
Levels of engagement in such media are usually measured in terms of content
adoption (e.g., likes and retweets) and sentiment, around a given topic.
However, sentiment analysis and topic identification are both non-trivial
tasks.
  In this paper, using data collected from Twitter as a case study, we analyze
how engagement and sentiment in promoted content spread over a 10-day period.
We find that promoted tweets lead to higher positive sentiment than promoted
trends; although promoted trends pay off in response volume. We observe that
levels of engagement for the brand and promoted content are highest on the
first day of the campaign, and fall considerably thereafter. However, we show
that these insights depend on the use of robust machine learning and natural
language processing techniques to gather focused, relevant datasets, and to
accurately gauge sentiment, rather than relying on the simple keyword- or
frequency-based metrics sometimes used in social media research.


  We develop an Easy Java Simulation (EJS) model for students to experience the
physics of idealized one-dimensional collision carts. The physics model is
described and simulated by both continuous dynamics and discrete transition
during collision. In the field of designing computer simulations, we discuss
briefly three pedagogical considerations such as 1) consistent simulation world
view with pen paper representation, 2) data table, scientific graphs and
symbolic mathematical representations for ease of data collection and multiple
representational visualizations and 3) game for simple concept testing that can
further support learning. We also suggest using physical world setup to be
augmented complimentary with simulation while highlighting three advantages of
real collision carts equipment like tacit 3D experience, random errors in
measurement and conceptual significance of conservation of momentum applied to
just before and after collision. General feedback from the students has been
relatively positive, and we hope teachers will find the simulation useful in
their own classes. 2015 Resources added:
http://iwant2study.org/ospsg/index.php/interactive-resources/physics/02-newtonian-mechanics/02-dynamics/46-one-dimension-collision-js-model
http://iwant2study.org/ospsg/index.php/interactive-resources/physics/02-newtonian-mechanics/02-dynamics/195-elastic-collision


  We present a simple proof of the factorization of (complex) symmetric
matrices into a product of a square matrix and its transpose, and discuss its
application in establishing a uniqueness property of certain antilinear
operators.


  Stacking bilayer structures is an efficient way to tune the topology of
polaritons in in-plane anisotropic films, e.g., by leveraging the twist angle
(TA). However, the effect of another geometric parameter, film thickness ratio
(TR), on manipulating the plasmon topology in bilayers is elusive. Here, we
fabricate bilayer structures of WTe2 films, which naturally host in-plane
hyperbolic plasmons in the terahertz range. Plasmon topology is successfully
modified by changing the TR and TA synergistically, manifested by the
extinction spectra of unpatterned films and the polarization dependence of the
plasmon intensity measured in skew ribbon arrays. Such TR- and TA-tunable
topological transitions can be well explained based on the effective sheet
optical conductivity by adding up those of the two films. Our study
demonstrates TR as another degree of freedom for the manipulation of plasmonic
topology in nanophotonics, exhibiting promising applications in bio-sensing,
heat transfer and the enhancement of spontaneous emission.


  Brown dwarfs occupy a middle ground in mass space between gaseous giant
planets and ultra-cool dwarf stars, and the characterisation of their orbital
orientations may shed light on how these neighbouring objects form. We present
an analysis of the Rossiter-McLaughlin (RM) effect across the transit of
TOI-2533 $b$, a brown dwarf on a moderately eccentric ($e_b = 0.2476\pm0.0090$)
and wide-separation ($a_b/R_\star = 13.34\pm0.30$) orbit around an F8-type
star, using data from the NEID/WIYN spectrograph in combination with archival
photometry and radial velocity observations. Spin-orbit analyses of brown
dwarfs are relatively rare, and TOI-2533 stands out as the fifth brown dwarf
system with a measured spin-orbit constraint. We derive a sky-projected stellar
obliquity of $\lambda = -7\pm14^{\circ}$ for TOI-2533 $b$, finding that the
brown dwarf is consistent with spin-orbit alignment. Our joint model also
indicates that TOI-2533 $b$ falls near the lower bound of the hydrogen-burning
minimum mass range (M$_b$ = $74.9\pm5.3$ M$_{\rm \tiny Jup}$). Ultimately, we
find that TOI-2533 $b$ is consistent with formation from disc fragmentation in
a primordially spin-orbit aligned orientation, although we cannot rule out the
possibility that the system has been tidally realigned during its lifetime.


  Recent approaches for semantic correspondence have focused on obtaining
high-quality correspondences using a complicated network, refining the
ambiguous or noisy matching points. Despite their performance improvements,
they remain constrained by the limited training pairs due to costly point-level
annotations. This paper proposes a simple yet effective method that performs
training with unlabeled pairs to complement both limited image pairs and sparse
point pairs, requiring neither extra labeled keypoints nor trainable modules.
We fundamentally extend the data quantity and variety by augmenting new
unannotated pairs not primitively provided as training pairs in benchmarks.
Using a simple teacher-student framework, we offer reliable pseudo
correspondences to the student network via machine supervision. Finally, the
performance of our network is steadily improved by the proposed iterative
training, putting back the student as a teacher to generate refined labels and
train a new student repeatedly. Our models outperform the milestone baselines,
including state-of-the-art methods on semantic correspondence benchmarks.


  We will show that the famous, intractible 1959 Kadison-Singer problem in
$C^{*}$-algebras is equivalent to fundamental unsolved problems in a dozen
areas of research in pure mathematics, applied mathematics and Engineering.
This gives all these areas common ground on which to interact as well as
explaining why each of these areas has volumes of literature on their
respective problems without a satisfactory resolution. In each of these areas
we will reduce the problem to the minimum which needs to be proved to solve
their version of Kadison-Singer. In some areas we will prove what we believe
will be the strongest results ever available in the case that Kadison-Singer
fails. Finally, we will give some directions for constructing a counter-example
to Kadison-Singer.


  The traditional secondary frequency control of power systems restores nominal
frequency by steering Area Control Errors (ACEs) to zero. Existing methods are
a form of integral control with the characteristic that large control gain
coefficients introduce an overshoot and small ones result in a slow convergence
to a steady state. In order to deal with the large frequency deviation problem,
which is the main concern of the power system integrated with a large number of
renewable energy, a faster convergence is critical. In this paper, we propose a
secondary frequency control method named Power-Imbalance Allocation Control
(PIAC) to restore the nominal frequency with a minimized control cost,in which
a coordinator estimates the power imbalance and dispatches the control inputs
to the controllers after solving an economic power dispatch problem. The power
imbalance estimation converges exponentially in PIAC, both overshoots and large
frequency deviations are avoided. In addition, when PIAC is implemented in a
multi-area controlled network, the controllers of an area are independent of
the disturbance of the neighbor areas, which allows an asynchronous control in
the multi-area network. A Lyapunov stability analysis shows that PIAC is
locally asymptotically stable and simulation results illustrates that it
effectively eliminates the drawback of the traditional integral control based
methods.


  Transformer has achieved impressive successes for various computer vision
tasks. However, most of existing studies require to pretrain the Transformer
backbone on a large-scale labeled dataset (e.g., ImageNet) for achieving
satisfactory performance, which is usually unavailable for medical images.
Additionally, due to the gap between medical and natural images, the
improvement generated by the ImageNet pretrained weights significantly degrades
while transferring the weights to medical image processing tasks. In this
paper, we propose Bootstrap Own Latent of Transformer (BOLT), a self-supervised
learning approach specifically for medical image classification with the
Transformer backbone. Our BOLT consists of two networks, namely online and
target branches, for self-supervised representation learning. Concretely, the
online network is trained to predict the target network representation of the
same patch embedding tokens with a different perturbation. To maximally
excavate the impact of Transformer from limited medical data, we propose an
auxiliary difficulty ranking task. The Transformer is enforced to identify
which branch (i.e., online/target) is processing the more difficult perturbed
tokens. Overall, the Transformer endeavours itself to distill the
transformation-invariant features from the perturbed tokens to simultaneously
achieve difficulty measurement and maintain the consistency of self-supervised
representations. The proposed BOLT is evaluated on three medical image
processing tasks, i.e., skin lesion classification, knee fatigue fracture
grading and diabetic retinopathy grading. The experimental results validate the
superiority of our BOLT for medical image classification, compared to ImageNet
pretrained weights and state-of-the-art self-supervised learning approaches.


  This paper describes a data-driven framework to parse musical sequences into
dependency trees, which are hierarchical structures used in music cognition
research and music analysis. The parsing involves two steps. First, the input
sequence is passed through a transformer encoder to enrich it with contextual
information. Then, a classifier filters the graph of all possible dependency
arcs to produce the dependency tree. One major benefit of this system is that
it can be easily integrated into modern deep-learning pipelines. Moreover,
since it does not rely on any particular symbolic grammar, it can consider
multiple musical features simultaneously, make use of sequential context
information, and produce partial results for noisy inputs. We test our approach
on two datasets of musical trees -- time-span trees of monophonic note
sequences and harmonic trees of jazz chord sequences -- and show that our
approach outperforms previous methods.


  A model of nonlinear optical system surrounded by two loops of feedback is
investigated. The cell with the vapor of rubidium - type atoms is taken in the
capacity of nonlinear element. Two modes of near-resonant electromagnetic field
interacting with the cell are involved in the feedback. Two-dimensional optical
bistability domain in location of input field intensities is obtained and
dependence of its form and magnitude from the system parameters (photon
detunings, feedback factor etc.) is investigated. "Input - output" relations
corresponding to different trajectories in the bistability domain are obtained.
Cross-hysteresis is studied.


  Personas are commonly used in software projects to gain a better
understanding of end-users' needs. However, there is a limited understanding of
their usage and effectiveness in practice. This paper presents the results of a
two-step investigation, comprising interviews with 26 software developers,
UI/UX designers, business analysts and product managers and a survey of 203
practitioners, aimed at shedding light on the current practices, methods and
challenges of using personas in software development. Our findings reveal
variations in the frequency and effectiveness of personas across different
software projects and IT companies, the challenges practitioners face when
using personas and the reasons for not using them at all. Furthermore, we
investigate the coverage of human aspects in personas, often assumed to be a
key feature of persona descriptions. Contrary to the general perception, our
study shows that human aspects are often ignored for various reasons in
personas or requirements engineering in general. Our study provides actionable
insights for practitioners to overcome challenges in using personas during
requirements engineering stages, and we identify areas for future research.


  We investigate the role of local force balance in the transition from a
microcanonical ensemble of static granular packings, characterized by an
invariant stress, to a canonical ensemble. Packings in two dimensions admit a
reciprocal tiling, and a collective effect of force balance is that the area of
this tiling is also invariant in a microcanonical ensemble. We present
analytical relations between stress, tiling area and tiling area fluctuations,
and show that a canonical ensemble can be characterized by an intensive
thermodynamic parameter conjugate to one or the other. We test the equivalence
of different ensembles through the first canonical simulations of the force
network ensemble, a model system.


  In this paper we study deformation classes of moduli spaces of sheaves on a
projective K3 surface. More precisely, let $(S1,H1)$ and $(S2,H2)$ be two
polarized K3 surfaces, $m\in\mathbb{N}$, and for $i=1,2$ let $mv_{i}$ be a
Mukai vector on $S_{i}$ such that $H_{i}$ is $mv_{i}-$generic. Moreover,
suppose that the moduli spaces $M_{mv_{1}}(S_{1},H_{1})$ of $H_{1}-$semistable
sheaves on $S_{1}$ of Mukai vector $mv_{1}$ and $M_{mv_{2}}(S_{2},H_{2})$ of
$H_{2}-$semistable sheaves on $S_{2}$ with Mukai vector $mv_{2}$, have the same
dimension. The aim of this paper is to prove that $M_{mv_{1}}(S_{1},H_{1})$ is
deformation equivalent to $M_{mv_{2}}(S_{2},H_{2})$, showing a conjecture of Z.
Zhang contained in [18].


  Offline reinforcement learning (RL) algorithms can learn better
decision-making compared to behavior policies by stitching the suboptimal
trajectories to derive more optimal ones. Meanwhile, Decision Transformer (DT)
abstracts the RL as sequence modeling, showcasing competitive performance on
offline RL benchmarks. However, recent studies demonstrate that DT lacks of
stitching capacity, thus exploiting stitching capability for DT is vital to
further improve its performance. In order to endow stitching capability to DT,
we abstract trajectory stitching as expert matching and introduce our approach,
ContextFormer, which integrates contextual information-based imitation learning
(IL) and sequence modeling to stitch sub-optimal trajectory fragments by
emulating the representations of a limited number of expert trajectories. To
validate our approach, we conduct experiments from two perspectives: 1) We
conduct extensive experiments on D4RL benchmarks under the settings of IL, and
experimental results demonstrate ContextFormer can achieve competitive
performance in multiple IL settings. 2) More importantly, we conduct a
comparison of ContextFormer with various competitive DT variants using
identical training datasets. The experimental results unveiled ContextFormer's
superiority, as it outperformed all other variants, showcasing its remarkable
performance.


  A search is conducted for new physics in multijet final states using 3.6
inverse femtobarns of data from proton-proton collisions at $\sqrt{s}$ = 13 TeV
taken at the CERN Large Hadron Collider with the ATLAS detector. Events are
selected containing at least three jets with scalar sum of jet transverse
momenta ($H_T$) greater than 1 TeV. No excess is seen at large $H_T$ and limits
are presented on new physics: models which produce final states containing at
least three jets and having cross sections larger than 1.6 fb with $H_T$ > 5.8
TeV are excluded. Limits are also given in terms of new physics models of
strong gravity that hypothesize additional space-time dimensions.


  Advances in artificial intelligence and human-computer interaction will
likely lead to extended reality (XR) becoming pervasive. While XR can provide
users with interactive, engaging, and immersive experiences, non-player
characters are often utilized in pre-scripted and conventional ways. This paper
argues for using large language models (LLMs) in XR by embedding them in
avatars or as narratives to facilitate inclusion through prompt engineering and
fine-tuning the LLMs. We argue that this inclusion will promote diversity for
XR use. Furthermore, the versatile conversational capabilities of LLMs will
likely increase engagement in XR, helping XR become ubiquitous. Lastly, we
speculate that combining the information provided to LLM-powered spaces by
users and the biometric data obtained might lead to novel privacy invasions.
While exploring potential privacy breaches, examining user privacy concerns and
preferences is also essential. Therefore, despite challenges, LLM-powered XR is
a promising area with several opportunities.


  We propose a novel score-based approach to learning a directed acyclic graph
(DAG) from observational data. We adapt a recently proposed continuous
constrained optimization formulation to allow for nonlinear relationships
between variables using neural networks. This extension allows to model complex
interactions while avoiding the combinatorial nature of the problem. In
addition to comparing our method to existing continuous optimization methods,
we provide missing empirical comparisons to nonlinear greedy search methods. On
both synthetic and real-world data sets, this new method outperforms current
continuous methods on most tasks, while being competitive with existing greedy
search methods on important metrics for causal inference.


  We study the Wick symbol of a solution of the time dependent Hartree Fock
equation, under weaker hypotheses than those needed for the Weyl symbol in the
first paper with thesame title. With similar, we prove some kind of Ehrenfest
theorem for observables that are not pseudo-differential operators.


  I derive a temporally propagated uni-directional optical pulse equation valid
in the few cycle limit. Temporal propagation is advantageous because it
naturally preserves causality, unlike the competing spatially propagated
models. The exact coupled bi-directional equations that this approach generates
can be efficiently approximated down to a uni-directional form in cases where
an optical pulse changes little over one optical cycle. They also permit a
direct term-to-term comparison of the exact bi-directional theory with its
corresponding approximate uni-directional theory. Notably, temporal propagation
handles dispersion in a different way, and this difference serves to highlight
existing approximations inherent in spatially propagated treatments of
dispersion. Accordingly, I emphasise the need for future work in clarifying the
limitations of the dispersion conversion required by these types of approaches;
since the only alternative in the few cycle limit may be to resort to the much
more computationally intensive full Maxwell equation solvers.


  Intelligent reflecting surface (IRS) is a promising technology for beyond 5th
Generation of the wireless communications. In fully passive IRS-assisted
systems, channel estimation is challenging and should be carried out only at
the base station or at the terminals since the elements of the IRS are
incapable of processing signals. In this letter, we formulate a tensor-based
semi-blind receiver that solves the joint channel and symbol estimation problem
in an IRS-assisted multi-user multiple-input multiple-output system. The
proposed approach relies on a generalized PARATUCK tensor model of the signals
reflected by the IRS, based on a two-stage closed-form semi-blind receiver
using Khatri-Rao and Kronecker factorizations. Simulation results demonstrate
the superior performance of the proposed semi-blind receiver, in terms of the
normalized mean squared error and symbol error rate, as well as a lower
computational complexity, compared to recently proposed parallel factor
analysis-based receivers.


  The magnetic and transport properties of (GaMn)As are known to be influenced
by postgrowth annealing, and it is generally accepted that these modifications
are due to outdiffusion of Mn interstitials. We show that the annealing-induced
modifications are strongly accelerated if the treatment is carried out under As
capping. This means that the modification rate is not limited by the diffusion
process, but rather by the surface trapping of the diffusing species.


  Using data from a simulated cup drawing process, we demonstrate how the
inherent geometrical structure of cup meshes can be used to effectively prune
an artificial neural network in a straightforward way.


  A global interpretation of several significant indications of scalar
resonances observed at LHC is achieved using the Georgi Machacek model. Among
many other consequences, one predicts large cross sections for the processes
ggFH(320)->h(125)h(125) and A(151)A(151) and ggF->A(420)->H(320)Z, where H(320)
has been observed in A(420)->H(320)Z->bbbbl+l- and where A(151) is observed
into two photons accompanied by a b jet, a lepton or missing transverse energy.
We predict that ggF->H(320) has a cross section of about 2000 fb with a BR into
h(125)(125) of 17% and into A(151)A(151) of 45%. Henceforth we predict that
ggF->H(320)->hh will dominate over the SM process h*->hh, where h is the SM
h(125) scalar. One expects that H(320)->A(151)A(151) can be observed into bbbb,
becoming one of the most significant BSM phenomena identified so far. Arguments
in favor of a SUSY version of GM are also provided.


  (Abridged) We report on the structure of the nuclear star cluster in the
innermost 0.16 pc of the Galaxy as measured by the number density profile of
late-type giants. Using laser guide star adaptive optics in conjunction with
the integral field spectrograph, OSIRIS, at the Keck II telescope, we are able
to differentiate between the older, late-type (~ 1 Gyr) stars, which are
presumed to be dynamically relaxed, and the unrelaxed young (~ 6 Myr)
population. This distinction is crucial for testing models of stellar cusp
formation in the vicinity of a black hole, as the models assume that the cusp
stars are in dynamical equilibrium in the black hole potential. We find that
contamination from young stars is significant, with more than twice as many
young stars as old stars in our sensitivity range (K < 15.5) within the central
arcsecond. Based on the late-type stars alone, the surface stellar number
density profile, is flat, with a projected power law slope of -0.26+-0.24.
These results are consistent with the nuclear star cluster having no cusp, with
a core profile that is significantly flatter than predicted by most cusp
formation theories. Here, we also review the methods for further constraining
the true three-dimensional radial profile using kinematic measurements. Precise
acceleration measurements in the plane of the sky as well as along the line of
sight has the potential to directly measure the density profile to establish
whether there is a "hole" in the distribution of late-type stars in the inner
0.1 pc.


  The lepton pair production in collisions of antiproton beam (E_beam = 14 GeV)
with proton target is studied on the basis of event samples simulated with
PYTHIA6 generator. Two types of quark level subprocesses are considered. The
first one goes through the production of virtual photon which converts into
lepton pair (q qbar --> gamma* --> l+l-) having a continuous energy spectrum of
the final lepton pair invariant mass. The other subprocess proceeds through the
J/Psi resonance production (p pbar --> J/Psi + X --> l+l- + X) with the
following decay of J/Psi into a pair of leptons. The distributions of different
kinematical variables which may be useful for the design of the muon system and
the electromagnetic calorimeter of the detector of PANDA experiment at FAIR are
presented. The analysis of these distribution shows the possibility to measure
the proton structure function in a new kinematical region defined by the
time-like values of the square of the momentum transferred 1 < Q^2 < 6.25 GeV^2
and withing a rather wide interval 0.05 < x < 0.7 of Bjorken x-variable. The
problems due to the presence of fake leptons that appear from meson decays, as
well as due to the background caused by minimum bias events and other QCD
processes, are also discussed. The set of cuts which allows one to separate the
signal events with lepton pairs from this kind of background events is
proposed.


  Fokker-Planck equations describe time evolution of probability densities of
stochastic dynamical systems and play an important role in quantifying
propagation and evolution of uncertainty. Although Fokker-Planck equations can
be written explicitly for nonlinear dynamical systems excited by Gaussian white
noise, they are not available in general for nonlinear dynamical systems
excited by multiplicative non-Gaussian white noise. Marcus stochastic
differential equations are often appropriate models in engineering and physics
for stochastic dynamical systems excited by non-Gaussian white noise. In this
paper, we derive explicit forms of Fokker-Planck equations for one dimensional
systems modeled by Marcus stochastic differential equations under
multiplicative non-Gaussian white noise. As examples to illustrate the
theoretical results, the derived formula is used to obtain Fokker-Plank
equations for nonlinear dynamical systems under excitation of (i)
$\alpha$-stable white noise; (ii) combined Gaussian and Poisson white noise,
respectively.


  This paper presents the design concept, modeling and motion planning solution
for the aerial robotic chain. This design represents a configurable robotic
system of systems, consisting of multi-linked micro aerial vehicles that
simultaneously presents the ability to cross narrow sections, morph its shape,
ferry significant payloads, offer the potential of distributed sensing and
processing, and allow system extendability. We contribute an approach to
address the motion planning problem of such a connected robotic system of
systems, making full use of its reconfigurable nature, to find collision free
paths in a fast manner despite the increased number of degrees of freedom. The
presented approach exploits a library of aerial robotic chain configurations,
optimized either for cross-section size or sensor coverage, alongside a
probabilistic strategy to sample random shape configurations that may be needed
to facilitate continued collision-free navigation. Evaluation studies in
simulation involve traversal of constrained and obstacle-laden environments,
having narrow corridors and cross sections.


  The success of models operating on tokenized data has led to an increased
demand for effective tokenization methods, particularly when applied to vision
or auditory tasks, which inherently involve non-discrete data. One of the most
popular tokenization methods is Vector Quantization (VQ), a key component of
several recent state-of-the-art methods across various domains. Typically, a VQ
Variational Autoencoder (VQVAE) is trained to transform data to and from its
tokenized representation. However, since the VQVAE is trained with a
reconstruction objective, there is no constraint for the embeddings to be well
disentangled, a crucial aspect for using them in discriminative tasks.
Recently, several works have demonstrated the benefits of utilizing hyperbolic
spaces for representation learning. Hyperbolic spaces induce compact latent
representations due to their exponential volume growth and inherent ability to
model hierarchical and structured data. In this work, we explore the use of
hyperbolic spaces for vector quantization (HyperVQ), formulating the VQ
operation as a hyperbolic Multinomial Logistic Regression (MLR) problem, in
contrast to the Euclidean K-Means clustering used in VQVAE. Through extensive
experiments, we demonstrate that hyperVQ performs comparably in reconstruction
and generative tasks while outperforming VQ in discriminative tasks and
learning a highly disentangled latent space.


  Isotope effects (IEs) are powerful tool to probe directly the dependence of
many physical properties on the lattice dynamics. In this paper we invenstigate
the onset of anomalous IEs in the spinless Holstein model by employing the
dynamical mean field theory. We show that the isotope coefficients of the
electron effective mass and of the dressed phonon frequency are sizeable also
far away from the strong coupling polaronic crossover and mark the importance
of nonadiabatic lattice fluctuations in the weak to moderate coupling region.
We characterize the polaronic regime by the appearence of huge IEs. We draw a
nonadiabatic phase diagram in which we identify a novel crossover, not related
to polaronic features, where the IEs attain their largest anomalies.


  We review some recent results obtained for black holes using effective field
theory methods applied to quantum gravity, in particular the unique effective
action. Black holes are complex thermodynamical objects that not only have a
temperature but also have a pressure. Furthermore, they have quantum hair which
provides a solution to the black hole information paradox.


  Although the cosmological constant has primarily cosmological consequences,
its smallness poses one of the basic problems in particle physics. Various
attempts have been made to explain this mystery, but no satisfactory solution
has been found yet. The appearance of extra dimensions in the framework of
brane world systems seems to provide some new ideas to address this problem
form a different point of view. We shall discuss some of these new approaches
and see whether or not they lead to an improvement of the situation. We shall
conclude that we are still far from a solution of the problem.


  Let R be a commutative ring and S be an R-algebra. It is well-known that if N
is an injective R-module, then Hom(S,N) is an injective S-module. The converse
is not true, not even if R is a commutative noetherian local ring and S is its
completion, but it is close: It is a special case of our main theorem that in
this setting, an R-module N with Ext^i(S,N)=0 for all i>0 is injective if
Hom(S,N) is an injective S-module.


  Nonparametric generalized likelihood ratio test is popularly used for model
checking for regressions. However, there are two issues that may be the
barriers for its powerfulness. First, the bias term in its liming null
distribution causes the test not to well control type I error and thus Monte
Carlo approximation for critical value determination is required. Second, it
severely suffers from the curse of dimensionality due to the use of
multivariate nonparametric function estimation. The purpose of this paper is
thus two-fold: a bias-correction is suggested to this test and a dimension
reduction-based model-adaptive enhancement is recommended to promote the power
performance. The proposed test still possesses the Wilks phenomenon, and the
test statistic can converge to its limit at a much faster rate and is much more
sensitive to alternative models than the original nonparametric generalized
likelihood ratio test as if the dimension of covariates were one. Simulation
studies are conducted to evaluate the finite sample performance and to compare
with other popularly used tests. A real data analysis is conducted for
illustration.


  Pre-departure flight plan scheduling for Urban Air Mobility (UAM) and cargo
delivery drones will require on-demand scheduling of large numbers of aircraft.
We examine the scalability of an algorithm known as FastMDP which was shown to
perform well in deconflicting many dozens of aircraft in a dense airspace
environment with terrain. We show that the algorithm can adapted to perform
first-come-first-served pre-departure flight plan scheduling where conflict
free flight plans are generated on demand. We demonstrate a parallelized
implementation of the algorithm on a Graphics Processor Unit (GPU) which we
term FastMDP-GPU and show the level of performance and scaling that can be
achieved. Our results show that on commodity GPU hardware we can perform flight
plan scheduling against 2000-3000 known flight plans and with server-class
hardware the performance can be higher. We believe the results show promise for
implementing a large scale UAM scheduler capable of performing on-demand flight
scheduling that would be suitable for both a centralized or distributed flight
planning system


  In this article, we establish the Fefferman-Stein inequalities for the Dunkl
maximal operator associated with a finite reflection group generated by the
sign changes. Similar results are also given for a large class of operators
related to Dunkl's analysis.


  If G is a finite group and H is a subgroup of G, we characterize when
restriction of Brauer characters from G to H is injective.


  We analyse some open debates in cosmology in light of the most updated quasar
(QSO) sample, covering a wide redshift range up to $z\sim7.5$, combined with
type Ia supernovae (SNe) and baryon acoustic oscillations (BAO). Indeed,
extending the cosmological analyses with high-redshift data is key to
distinguishing between different cosmological models that are degenerate at low
redshifts, and allowing better constraints on a possible dark energy (DE)
evolution. Also, we discuss combinations of BAO, SNe, and QSO data to
understand their compatibility and implications for extensions of the standard
cosmological model. Specifically, we consider a flat and non-flat $\Lambda
\mathrm{CDM}$ cosmology, a flat and non-flat DE model with a constant DE
equation of state parameter ($w$), and four flat DE models with variable $w$,
namely the Chevallier-Polarski-Linder and Jassal-Bagla-Padmanabhan models, and
an "exponential" and Barboza-Alcaniz parameterisations. We find that a joint
analysis of QSO+SNe with BAO is only possible in the context of a flat
Universe. Indeed BAO confirms the flatness condition assuming a curved
geometry, whilst SNe+QSO show evidence of a closed space. We also find
$\Omega_{M,0}=0.3$ in all data sets assuming a flat $\Lambda \mathrm{CDM}$
model. Yet, all the other models show a statistically significant deviation at
2-3$\sigma$ with the combined SNe+QSO+BAO data set. In the models where DE
density evolves with time, SNe+QSO+BAO data always prefer $\Omega_{M,0}>0.3$,
$w_{0}<-1$ and $w_{a}>0$. This DE phantom behaviour is mainly driven by
SNe+QSO, while BAO are closer to the flat $\Lambda \mathrm{CDM}$ model.


  We investigate the possibility of a (sizable) nonperturbative contribution to
the charm parton distribution function (PDF) in a nucleon, theoretical issues
arising in its interpretation, and its potential impact on LHC scattering
processes. The "fitted charm" PDF obtained in various QCD analyses contains a
process-dependent component that is partly traced to power-suppressed radiative
contributions in DIS and is generally different at the LHC. We discuss
separation of the universal component of the nonperturbative charm from the
rest of the radiative contributions and estimate its magnitude in the CT14
global QCD analysis at the next-to-next-to leading order in the QCD coupling
strength, including the latest experimental data from HERA and the Large Hadron
Collider. Models for the nonperturbative charm PDF are examined as a function
of the charm quark mass and other parameters. The prospects for testing these
models in the associated production of a Z boson and a charm jet at the LHC are
studied under realistic assumptions, including effects of the final-state
parton showering.


  The advent of the Hohenberg-Kohn theorem in 1964, its extension to finite-T,
Kohn-Sham theory, and relativistic extensions provide the well-established
formalism of density-functional theory (DFT). This theory enables the
calculation of all static properties of quantum systems {\it without} the need
for an n-body wavefunction \psi. DFT uses the one-body density distribution
instead of \psi. The more recent time-dependent formulations of DFT attempt to
describe the time evolution of quantum systems without using the time-dependent
wavefunction. Although DFT has become the standard tool of condensed-matter
computational quantum mechanics, its foundational implications have remained
largely unexplored. While all systems require quantum mechanics (QM) at T=0,
the pair-distribution functions (PDFs) of such quantum systems have been
accurately mapped into classical models at effective finite-T, and using
suitable non-local quantum potentials (e.g., to mimic Pauli exclusion effects).
These approaches shed light on the quantum \to hybrid \to classical models, and
provide a new way of looking at the existence of non- local correlations
without appealing to Bell's theorem. They also provide insights regarding
Bohmian mechanics. Furthermore, macroscopic systems even at 1 Kelvin have de
Broglie wavelengths in the micro-femtometer range, thereby eliminating
macroscopic cat states, and avoiding the need for {\it ad hoc} decoherence
models.


  We report the discovery with the Proportional Counter Array on board the
Rossi X-ray Timing Explorer of highly coherent 582 Hz pulsations during the
February 22, 2001 (UT) ``superburst'' from 4U 1636-53. The pulsations are
detected during an 800 s interval spanning the flux maximum of the burst.
Within this interval the barycentric oscillation frequency increases in a
monotonic fashion from 581.89 to 581.93 Hz. The predicted orbital motion of the
neutron star during this interval is consistent with such an increase as long
as optical maximum corresponds roughly with superior conjunction of V801 Arae,
the optical companion to the neutron star in 4U 1636-53. We show that a range
of circular orbits with velocity 90 < v sin i < 175 km/s and fractional phase
0.336 > p > 0.277 for the neutron star can provide an excellent description of
the frequency and phase evolution. The average pulse profile is sinusoidal and
the time averaged pulsation amplitude, as inferred from the half amplitude of
the sinusoid is 1%, smaller than typical for burst oscillations observed in
normal thermonuclear bursts. We do not detect any higher harmonics nor the
putative subharmonic near 290 Hz. The 90% upper limits on signal amplitude at
the subharmonic and first harmonic are 0.1% and 0.06%, respectively. The
coherent pulsation provides compelling evidence for a rapidly rotating neutron
star in 4U 1636-53, and further supports the connection of burst oscillation
frequencies with the spin frequencies of neutron stars. Our results provide
further evidence that some millisecond pulsars are spun up via accretion in
LMXBs. We also discuss the implications of our orbital velocity constraint for
the masses of the components of 4U 1636-53.


  The discrete-time quantum walk dynamics can be generated by a time-dependent
Hamiltonian, repeatedly switching between the coin and the shift generators. We
change the model and consider the case where the Hamiltonian is
time-independent, including both the coin and the shift terms in all times. The
eigenvalues and the related Bloch vectors for the time-independent Hamiltonian
are then compared with the corresponding quantities for the effective
Hamiltonian generating the quantum walk dynamics. Restricted to the
non-localized initial quantum walk states, we optimize the parameters in the
time-independent Hamiltonian such that it generates a dynamics similar to the
Hadamard quantum walk. We find that the dynamics of the walker probability
distribution and the corresponding standard deviation, the coin-walker
entanglement, and the quantum-to-classical transition of the discrete-time
quantum walk model can be approximately generated by the optimized
time-independent Hamiltonian. We, further, show both dynamics are equivalent in
the classical regime, as expected.


  The definitions of para-Grassmann variables and q-oscillator algebras are
recalled. Some new properties are given. We then introduce appropriate coherent
states as well as their dual states. This allows us to obtain a formula for the
trace of a operator expressed as a function of the creation and annihilation
operators.