  Modeling mass flows is classically based on hydrostatic balance equations.
However, if momentum transfers scale similarly in slope parallel and flow depth
directions, then the gravity and acceleration can have the same order of
magnitude effects, urging for a non-hydrostatic model formulation. Here, we
extend existing single-phase Boussinesq-type models by developing a new
non-hydrostatic model for multi-phase mass flows consisting of solid and
fine-solid particles and viscous fluid (Pudasaini & Mergili, 2019). The new
model includes enhanced gravity and dispersion considering various interfacial
momentum transfers. We outline new contributions in the non-hydrostatic
Boussinesq-type multi-phase gravity waves emerging from phase-interactions. We
present a general, well-structured framework of multi-phase mass flows with
enhanced gravity and dispersion, setting a foundation for comprehensive
simulations. We discuss situations where non-hydrostatic, dispersive effects
are more pronounced for such flows. Reduced models demonstrate the importance
of non-hydrostatic contributions. Simplified analytical solutions reveal how
the new dispersive model can be reduced to non-dispersive ones, yet largely
generalizing existing models. We postulate a novel, spatially varying
dissipative force, called the prime-force, which physically controls the
dynamics and run-out of the mass flow in a precise way. Practitioners and
engineers may find this force very useful in technical applications. This
illuminates the need of formally including the prime-force in momentum
equations. A simple dispersion equation is derived highlighting the essence of
dispersion on mass flow dynamics. Dispersion produces a wavy velocity field
about a reference state without dispersion, the first of this kind for
avalanching debris mass. Dispersion intensity increases energetically as the
solid volume fraction or friction decreases.


  While designed primarily for X-ray imaging applications, the Medipix3 ASIC
can also be used for charged-particle tracking. In this work, results from a
beam test at the CERN SPS with irradiated and non-irradiated sensors are
presented and shown to be in agreement with simulation, demonstrating the
suitability of the Medipix3 ASIC as a tool for characterising pixel sensors.


  The unique sensitivity of the Arecibo telescope at 430 MHz motivated a drift
scan search of the visible sky divided up into eight declination strips. Based
on an analysis of the data collected in the region assigned to us, eight new
long-period pulsars have so far been discovered.


  The entanglement entropy of the incompressible states of a realistic quantum
Hall system are studied by direct diagonalization. The subdominant term to the
area law, the topological entanglement entropy, which is believed to carry
information about topologic order in the ground state, was extracted for
filling factors 1/3, 1/5 and 5/2. The results for 1/3 and 1/5 are consistent
with the topological entanglement entropy for the Laughlin wave function. The
5/2 state exhibits a topological entanglement entropy consistent with the
Moore-Read wave function.


  We discuss a strong relationship between Majorana fermions and odd-frequency
Cooper pairs which appear at a disordered normal (N) nano wire attached to a
topologically nontrivial superconducting (S) one. The transport properties in
superconducting nano wire junctions show universal behaviors irrespective of
the degree of disorder: the quantized zero-bias differential conductance at
2e^{2}/h in NS junction and the fractional current-phase (J-\varphi)
relationship of the Josephson effect in SNS junction J\propto \sin(\varphi/2).
Such behaviors are exactly the same as those in the anomalous proximity effect
of odd-parity spin-triplet superconductors. We show that odd-frequency Cooper
pairs support the universal transport properties. The odd-frequency pairs exist
wherever the Majorana fermions stay.


  In this study we analyze {\sl Atmospheric Imaging Assembly (AIA)} and Hi-C
images in order to investigate absolute limits for the finest loop strands. We
develop a model of the occurrence-size distribution function of coronal loop
widths, characterized by a lower limit of widths $w_{min}$, a peak width $w_p$,
a peak occurrence number $n_p$, and a power law slope $a$. Our data analysis
includes automated tracing of curvi-linear features with the OCCULT-2 code,
automated sampling of the cross-sectional widths of coronal loops, and fitting
of the theoretical size distribution to the observed distribution. With
Monte-Carlo simulations and variable pixel sizes $\Delta x$ we derive a first
diagnostic criterion to discriminate whether the loop widths are unresolved
$(w_p/\Delta x \approx 2.5\pm0.2)$, or fully resolved (if $w_p/\Delta x >
2.7$). For images with resolved loop widths we can apply a second diagnostic
criterion that predicts the lower limit of loop widths, $w_{min} \approx 3
(\Delta x_{crit}-0.37")$ as a function of the critical resolution $\Delta
x_{crit}$. We find that the loop widths are marginally resolved in AIA images,
but are fully resolved in Hi-C images, where our model predicts a lower limit
of loop widths at $w_{min} \approx 100$ km and a most frequent (peak) value at
$w_p \approx 300$ km, in agreement with recent results of Brooks et al. This
result agrees with the statistics of photospheric granulation sizes and thus
supports coronal heating mechanisms operating on the macroscopic scale of
photospheric magneto-convection, rather than nanoflare heating models with
unresolved microscopic scales.


  It is sometimes speculated that the sign problem that afflicts many quantum
field theories might be reduced or even eliminated by choosing an alternative
domain of integration within a complexified extension of the path integral (in
the spirit of the stationary phase integration method). In this paper we start
to explore this possibility somewhat systematically. A first inspection reveals
the presence of many difficulties but - quite surprisingly - most of them have
an interesting solution. In particular, it is possible to regularize the
lattice theory on a Lefschetz thimble, where the imaginary part of the action
is constant and disappears from all observables. This regularization can be
justified in terms of symmetries and perturbation theory. Moreover, it is
possible to design a Monte Carlo algorithm that samples the configurations in
the thimble. This is done by simulating, effectively, a five dimensional
system. We describe the algorithm in detail and analyze its expected cost and
stability. Unfortunately, the measure term also produces a phase which is not
constant and it is currently very expensive to compute. This residual sign
problem is expected to be much milder, as the dominant part of the integral is
not affected, but we have still no convincing evidence of this. However, the
main goal of this paper is to introduce a new approach to the sign problem,
that seems to offer much room for improvements. An appealing feature of this
approach is its generality. It is illustrated first in the simple case of a
scalar field theory with chemical potential, and then extended to the more
challenging case of QCD at finite baryonic density.


  First-principles calculation of nonlinear magneto-optical effects has become
an indispensable tool to reveal the geometric and topological nature of
electronic states and to understand light-matter interactions. While
intriguingly rich physics could emerge in magnetic materials, further
methodological developments are required to deal with time-reversal symmetry
breaking, due to the degeneracy and gauge problems caused by symmetry and the
low-frequency divergence problem in the existing calculation formalism. Here we
present a gauge-covariant and low-frequency convergent formalism for the
first-principles computation. Remarkably, this formalism generally works for
both non-magnetic and magnetic materials with or without band degeneracy.
Reliability and capability of our method are demonstrated by studying example
materials (i.e., bilayers of MnBi$_2$Te$_4$ and CrI$_3$) and comparing with
published results. Moreover, an importance correction term that ensures gauge
covariance of degenerate states is derived, whose influence on physical
responses is systematically checked. Our method enables computation of
nonlinear magneto-optical effects in magnetic materials and paves the way for
exploring rich physics created by the interplay of light and magnetism.


  HESS J1809-193 is an extended TeV gamma-ray source in the Galactic Plane.
Multiwavelength observations of the HESS J1809-193 field reveal a complex
picture. We present results from three CXO and two Suzaku observations of a
region in the northeastern outskirts of HESS J1809-193, where enhanced TeV
emission has been reported. Our analysis also includes GeV gamma-ray and radio
data. One of the X-ray sources in the field is the X-ray binary XTE J1810-189,
for which we present the outburst history from multiple observatories and
confirm that XTE J1810-189 is a strongly variable type I X-ray burster, which
can hardly produce TeV emission. We investigate whether there is any connection
between the possible TeV extension of HESS J1809-193 and the sources seen at
lower energies. We find that another X-ray binary candidate, Suzaku J1811-1900,
and a radio supernova remnant, SNR G11.4-0.1, can hardly be responsible for the
putative TeV emission. Our multiwavelength classification of fainter X-ray
point sources also does not produce a plausible candidate. We conclude that the
northeast extension of HESS J1809-193, if confirmed by deeper observations, can
be considered as a dark accelerator - a TeV source without visible counterpart
at lower energies.


  Augmentative and Alternative Communication (AAC) are essential techniques
that help people with communication disabilities. AAC demonstrates its
transformative power by replacing spoken language with symbol sequences.
However, to unlock its full potential, AAC materials must adhere to specific
characteristics, placing the onus on educators to create custom-tailored
materials and symbols. This paper introduces AMBRA (Pervasive and Personalized
Augmentative and Alternative Communication based on Federated Learning and
Generative AI), an open platform that aims to leverage the capabilities of
foundation models to tackle many AAC issues, opening new opportunities (but
also challenges) for AI-enhanced AAC. We thus present a compelling vision--a
roadmap towards a more inclusive society. By leveraging the capabilities of
modern technologies, we aspire to not only transform AAC but also guide the way
toward a world where communication knows no bounds.


  We prove that the asymptotic of the bulk local statistics in models of random
lozenge tilings is universal in the vicinity of straight boundaries of the
tiled domains. The result applies to uniformly random lozenge tilings of large
polygonal domains on triangular lattice and to the probability measures
describing the decomposition in Gelfand-Tsetlin bases of tensor products of
representations of unitary groups. In a weaker form our theorem also applies to
random domino tilings.


  In this paper we investigate effects of a lattice dimension on strongly
correlated electronic systems at $T=0 K$. The model for numerical calculations
is formalized in terms of the integral equations which were obtained previously
for the half -- filled paramagnetic Hubbard model. The assumption that all
relevant functions of the system have a magnitude dependence of momentus
vectors makes ultimately possible treating a lattice dimension $D$ as an
external parameter. We here submit for consideration the number of doubly
occupied lattice sites, the electronic density of states, and the momentum
distribution in electronic occupation numbers at $D$=1, $D$=2, $D$=3


  Many problems in linear algebra -- such as those arising from non-Hermitian
physics and differential equations -- can be solved on a quantum computer by
processing eigenvalues of the non-normal input matrices. However, the existing
Quantum Singular Value Transformation (QSVT) framework is ill-suited to this
task, as eigenvalues and singular values are different in general. We present a
Quantum EigenValue Transformation (QEVT) framework for applying arbitrary
polynomial transformations on eigenvalues of block-encoded non-normal
operators, and a related Quantum EigenValue Estimation (QEVE) algorithm for
operators with real spectra. QEVT has query complexity to the block encoding
nearly recovering that of the QSVT for a Hermitian input, and QEVE achieves the
Heisenberg-limited scaling for diagonalizable input matrices. As applications,
we develop a linear differential equation solver with strictly linear time
query complexity for average-case diagonalizable operators, as well as a ground
state preparation algorithm that upgrades previous nearly optimal results for
Hermitian Hamiltonians to diagonalizable matrices with real spectra.
Underpinning our algorithms is an efficient method to prepare a quantum
superposition of Faber polynomials, which generalize the nearly-best uniform
approximation properties of Chebyshev polynomials to the complex plane. Of
independent interest, we also develop techniques to generate $n$ Fourier
coefficients with $\mathbf{O}(\mathrm{polylog}(n))$ gates compared to prior
approaches with linear cost.


  We study quark-gluon plasma (QGP) and hadronic gas (HG) models of the central
fireball presumed to be the source of abundantly produced strange
(anti-)baryons in S -> W collisions at 200 GeV A. We consider how multi-strange
(anti-)baryon multiplicities depend on strangeness conservation and compare the
HG and QGP fireball scenarios. We argue that the total particle multiplicity
emerging from the central rapidity region as well as the variation of
production rates with changes in the beam energy allows to distinguish between
the two reaction scenarios.


  We present an intuitive physical picture for fractures in nacre-type
stratified materials via scaling arguments: strain distributions around a
fracture are rather different depending on directions and size of fractures. We
thus observe that viscoelastic effects are important for parallel fractures.
These effect are taken into account via the simplest viscoelastic model for
weakly cross-linked polymer. Within a certain limit, we find a trumpet crack
shape similar to that in certain polymers and make predictions for the fracture
energy of certain stratified materials in this mode.


  Using the weak-noise theory, we evaluate the probability distribution
$\mathcal{P}(H,t)$ of large deviations of height $H$ of the evolving surface
height $h(x,t)$ in the Kardar-Parisi-Zhang (KPZ) equation in one dimension when
starting from a flat interface. We also determine the optimal history of the
interface, conditioned on reaching the height $H$ at time $t$. We argue that
the tails of $\mathcal{P}$ behave, at arbitrary time $t>0$, and in a proper
moving frame, as $-\ln \mathcal{P}\sim |H|^{5/2}$ and $\sim |H|^{3/2}$. The
$3/2$ tail coincides with the asymptotic of the Gaussian orthogonal ensemble
Tracy-Widom distribution, previously observed at long times.


  A search for a dark photon, a new light neutral particle, which decays
promptly into collimated pairs of electrons or muons is presented. The search
targets dark photons resulting from the exotic decay of the Standard Model
Higgs boson, assuming its production via the dominant gluon-gluon fusion mode.
The analysis is based on 140 fb$^{-1}$ of data collected with the ATLAS
detector at the Large Hadron Collider from proton-proton collisions at a
center-of-mass energy of 13 TeV. Events with collimated pairs of electrons or
muons are analysed and background contributions are estimated using data-driven
techniques. No significant excess in the data above the Standard Model
background is observed. Upper limits are set at 95% confidence level on the
branching ratio of the Higgs boson decay into dark photons between 0.001% and
5%, depending on the assumed dark photon mass and signal model.


  We examine the properties of white dwarfs (WDs) accreting hydrogen-rich
matter in and near the stable burning regime of accretion rates as modeled by
time-dependent calculations done with Modules for Experiments in Stellar
Astrophysics (MESA). We report the stability boundary for WDs of masses between
0.51 solar masses and 1.34 solar masses as found via time-dependent
calculations. We also examine recurrent novae that are accreting at rates close
to, but below, the stable burning limit and report their recurrence times and
ignition masses. Our dense grid in accretion rates finds the expected minimum
possible recurrence times as a function of the WD mass. This enables inferences
to be made about the minimum WD mass possible to reach a specific recurrence
time. We compare our computational models of post-outburst novae to the stably
burning WDs and explicitly calculate the duration and effective temperature
(Teff) of the post-novae WD in the supersoft phase. We agree with the measured
turnoff time - Teff relation in M31 by Henze and collaborators, infer WD masses
in the 1.0-1.3 solar masses range, and predict ejection masses consistent with
those observed. We close by commenting on the importance of the hot helium
layer generated by stable or unstable hydrogen burning for the short- and
long-term evolution of accreting white dwarfs.


  This is an exhaustive study of the seventeen elements of Pythagorean
triangles, from the point of view of when such an element is an irrational
number, a rational number, or an integer. For each of these 17 elements,precice
conditions for their integrality,rationality, or irrationality; are given.
These 17 elements are:The radius of the incircle, the radius of the
circumscribed circle,the three radii of the three exterior circles tangential
to the three lines containing the triangles'sides, the lengths of the three
heights, the lenghts of the three internal angle bisectors, the lengthe of the
three external angle bisectors, and the lengths of the three medians.


  A notion of one-dimensional formal ring is presented. It consists of a triple
$(A,\Phi,\Psi)$ where $A$ is a unital ring and $\Phi$ and $\Psi$ are two formal
power series in $2$ variables ${\Phi(x,y),\Psi(x,y)\in A\llbracket
x,y\rrbracket}$, the first one defining a one-dimensional formal group law over
$A$ and the second one providing a second composition law satisfying axiomatic
properties of compatibility with the first one. For a characteristic-zero ring
$A$, a large class of one-dimensional formal rings can be obtained by
constructing a new composition law, defined in terms of the group logarithm
associated with a given formal group law $\Phi(x,y)$, and associative and
distributive with respect to it. A natural $n$-dimensional generalization of
the previous construction is also proposed; curves on $n$-dimensional formal
rings are introduced. The higher-dimensional theory allows us to define a
generalization of the ring $W(A)$ of Witt vectors over a ring $A$, which is
recovered by means of a specific choice of the associated group logarithm. The
composition laws of our generalized Witt ring are defined in terms of an
underlying formal ring structure. Examples of formal rings related to
Hirzebruch's theory of genera are explicitly computed. Finally, we also propose
the examples of Euler's and Abel's formal rings.


  As machine learning becomes more and more available to the general public,
theoretical questions are turning into pressing practical issues. Possibly, one
of the most relevant concerns is the assessment of our confidence in trusting
machine learning predictions. In many real-world cases, it is of utmost
importance to estimate the capabilities of a machine learning algorithm to
generalize, i.e., to provide accurate predictions on unseen data, depending on
the characteristics of the target problem. In this work, we perform a
meta-analysis of 109 publicly-available classification data sets, modeling
machine learning generalization as a function of a variety of data set
characteristics, ranging from number of samples to intrinsic dimensionality,
from class-wise feature skewness to $F1$ evaluated on test samples falling
outside the convex hull of the training set. Experimental results demonstrate
the relevance of using the concept of the convex hull of the training data in
assessing machine learning generalization, by emphasizing the difference
between interpolated and extrapolated predictions. Besides several predictable
correlations, we observe unexpectedly weak associations between the
generalization ability of machine learning models and all metrics related to
dimensionality, thus challenging the common assumption that the \textit{curse
of dimensionality} might impair generalization in machine learning.


  A simple mechanical device is introduced, the parallelometer, that can be
used to measure curvatures of surfaces. The device can be used as a practical
illustration of parallel transport of a vector and to study Berry phase shift
when it is carried along a loop on the surface. Its connection to the Foucault
pendulum is discussed. The experimental results can be successfully compared
with the theoretical expectations. The experiment is inexpensive and
conceptually easy to perform and understand for a beginner.


  Study of time series data often involves measuring the strength of temporal
dependence, on which statistical properties like consistency and central limit
theorem are built. Historically, various dependence measures have been
proposed. In this note, we first survey some of the most well-used dependence
measures as well as various probability and moment inequalities built upon them
under a high-dimensional triangular array time series setting. We then argue
that this triangular array setting will pose substantially new challenges to
the verification of some dependence conditions. In particular, ``textbook
results" could now be misleading, and hence are recommended to be used with
caution.


  (Abridged) We here investigate, by means of fully 3-D Smoothed Particle
Magnetohydrodynamic numerical simulations, the effects of magnetic fields on
overdense, radiatively cooling, pulsed jets, using different initial magnetic
field topologies and strengths ($B \simeq 260 \mu$G-0). The relative
differences that have been previously detected in 2-D simulations involving
distinct magnetic field configurations are diminished in the 3-D flows. While
the presence of toroidal magnetic components can modify the morphology close to
the jet head inhibiting its fragmentation in the early jet evolution, as
previously reported in the literature, the impact of the pulsed-induced
internal knots causes the appearance of a complex morphology at the jet head
(as required by the observations of H-H jets) even in the MHD jet models with
toroidal components. The detailed structure and emission properties of the
internal working surfaces can be also significantly altered by the presence of
magnetic fields. The increase of the magnetic field strength improves the jet
collimation, and amplifies the density (by factors up to 1.4, and 4) and the
H\alpha$ intensity (by factors up to 4, and 5) behind the knots of jets with
helical field and $\beta \simeq 1-0.1$ (respectively), relative to a non
magnetic jet. As a consequence, the corresponding $I_{[SII]}}/I_{H}\alpha}$
ratio (which is frequently used to determine the excitation level of HH
objects) can be decreased in the MHD models with toroidal components relative
to non-magnetic calculations. We also find that the helical mode of the K-H
instability can be triggered in MHD models with helical magnetic fields,
causing some jet wiggling. No evidence for the formation of the nose cones is
found in the 3-D flows, nor even in the $\beta \simeq 0.1$ case.


  Applying the HSD transport approach to charmonium dynamics within the
'hadronic comover model' and the 'QGP melting scenario', we show that the
suppression pattern seen at RHIC cannot be explained by the interaction with
baryons, comoving mesons and/or by color screening mechanism. The interaction
with hadrons in the late stages of the collision (when the energy density falls
below the critical) gives a sizable contribution to the suppression. On the
other hand, it does not account for the observed additional charmonium
dissociation and its dependence on rapidity. Together with the failure of the
hadron-string models to reproduce high v2 of open charm mesons, this suggests
strong pre-hadronic interaction of c-cbar with the medium at high energy
densities.


  Intra Cluster Media (ICMs) located at galaxy clusters is in the state of hot,
tenuous, magnetized, and highly ionized X-ray emitting plasmas. This overall
collisionless, viscous, and conductive magnetohydrodynamic (MHD) turbulence in
ICM is simulated using hyper and physical magnetic diffusivity. The results
show that fluctuating random plasma motion amplifies the magnetic field, which
cascades toward the diffusivity scale passing through the viscous scale. The
kinetic eddies in the subviscous scale are driven and constrained by the
magnetic tension which finally gets balanced with the highly damping effect of
the kinetic eddies. However, the saturated kinetic energy spectrum is deeper
than that of the incompressible or compressible hydrodynamics fluid. To explain
this unusual field profile we set up two simultaneous differential equations
for the kinetic and magnetic energy spectrum using an Eddy Damped Quasi Normal
Markovianized (EDQNM) approximation. The analytic solution tells us that the
magnetic energy in addition to the viscous damping effect constrains the plasma
motion leading to the power spectra: kinetic energy spectrum $E_V^k\sim k^{-3}$
and corresponding representative magnetic energy spectrum $E_M^k\sim k^{-1/2}$.
Also the comparison of simulation results with different resolutions and
magnetic diffusivities implies the role of small scale magnetic energy in
dynamo.


  To improve the overall efficiency and reliability of Byzantine protocols in
large sparse networks, we propose a new system assumption for developing
multi-scale fault-tolerant systems, with which several kinds of multi-scale
Byzantine protocols are developed in large sparse networks with high system
assumption coverage. By extending the traditional Byzantine adversary to the
multi-scale adversaries, it is shown that efficient deterministic Byzantine
broadcast and Byzantine agreement can be built in logarithmic-degree networks.
Meanwhile, it is shown that the multi-scale adversary can make a finer
trade-off between the system assumption coverage and the overall efficiency of
the Byzantine protocols, especially when a small portion of the low-layer
small-scale protocols are allowed to fail arbitrarily. With this, efficient
Byzantine protocols can be built in large sparse networks with high system
reliability.


  We explore some mathematical features of the loss landscape of
overparameterized neural networks. A priori one might imagine that the loss
function looks like a typical function from $\mathbb{R}^n$ to $\mathbb{R}$ - in
particular, nonconvex, with discrete global minima. In this paper, we prove
that in at least one important way, the loss function of an overparameterized
neural network does not look like a typical function. If a neural net has $n$
parameters and is trained on $d$ data points, with $n>d$, we show that the
locus $M$ of global minima of $L$ is usually not discrete, but rather an $n-d$
dimensional submanifold of $\mathbb{R}^n$. In practice, neural nets commonly
have orders of magnitude more parameters than data points, so this observation
implies that $M$ is typically a very high-dimensional subset of $\mathbb{R}^n$.


  We study the manganese oxides from the viewpoint of the strongly correlated
doped Mott insulator. The magnetic ordering and the charge transport are
governed by the orbital degrees of freedom, and their dimensionality is
controlled by the anisotropic transfer integrals between the $e_g$ orbitals. As
x increases the magnetic structure is predicted to change as $A \to F \to A \to
C \to G$ (F: ferromagnet, A: layered antiferromagnet, C: rod-type
antiferromagnet, G: usual antiferromagnet), in agreement with experiments.
Especially the orbital is aligned as $d_{x^2-y^2}$ in the metallic A state,
which explains the quasi 2D transport and no canting of the spin observed
experimentally. Next we discuss the ferromagnetic state without the orbital
ordering due to the quantum fluctuation. Here the interplay between the
electron repulsion U and the Jahn-Teller electron-phonon interation $E_{LR}$ is
studied with a large d model. In addition to this strong correlation, we
propose that the dynamical phase separation could explain the specific heat as
well as the various anomalous physical properties, e.g., resistivity,
photo-emission, etc.


  We present a new technique for the numerical simulation of axisymmetric
systems. This technique avoids the coordinate singularities which often arise
when cylindrical or polar-spherical coordinate finite difference grids are
used, particularly in simulating tensor partial differential equations like
those of 3+1 numerical relativity. For a system axisymmetric about the z axis,
the basic idea is to use a 3-dimensional Cartesian (x,y,z) coordinate grid
which covers (say) the y=0 plane, but is only one
finite-difference-molecule--width thick in the y direction. The field variables
in the central y=0 grid plane can be updated using normal (x,y,z)--coordinate
finite differencing, while those in the y \neq 0 grid planes can be computed
from those in the central plane by using the axisymmetry assumption and
interpolation. We demonstrate the effectiveness of the approach on a set of
fully nonlinear test computations in 3+1 numerical general relativity,
involving both black holes and collapsing gravitational waves.


  In this paper we study the extremality of translation-invariant Gibbs measure
for the HC-model on a Cayley tree. It is known that for this model the
translation-invariant measure is unique. We give a new proof of this statement
and found regions of the extremality of this measure on the Cayley tree of
order k.


  Our infrastructure touches the day-to-day life of each of our fellow
citizens, and its capabilities, integrity and sustainability are crucial to the
overall competitiveness and prosperity of our country. Unfortunately, the
current state of U.S. infrastructure is not good: the American Society of Civil
Engineers' latest report on America's infrastructure ranked it at a D+ -- in
need of $3.9 trillion in new investments. This dire situation constrains the
growth of our economy, threatens our quality of life, and puts our global
leadership at risk. The ASCE report called out three actions that need to be
taken to address our infrastructure problem: 1) investment and planning in the
system; 2) bold leadership by elected officials at the local and federal state;
and 3) planning sustainability and resiliency in our infrastructure.
  While our immediate infrastructure needs are critical, it would be
shortsighted to simply replicate more of what we have today. By doing so, we
miss the opportunity to create Intelligent Infrastructure that will provide the
foundation for increased safety and resilience, improved efficiencies and civic
services, and broader economic opportunities and job growth. Indeed, our
challenge is to proactively engage the declining, incumbent national
infrastructure system and not merely repair it, but to enhance it; to create an
internationally competitive cyber-physical system that provides an immediate
opportunity for better services for citizens and that acts as a platform for a
21st century, high-tech economy and beyond.


  We calculate the amplitude of the Lyman discontinuity for an accretion disk
around a maximally rotating black hole. The locally emitted disk spectrum is
computed with a previously developed numerical model (Sincell and Krolik 1998)
and then a new general relativistic ray-tracing code is used to determine the
observed spectra as a function of inclination angle, theta_o. We find that
Lyman discontinuities are undetectable for disks viewed at inclinations
cos(theta_o) < 0.8, but a significant feature remains for disks seen face-on.


  A new strategy was developed to investigate the effect of volatile fatty
acids (VFAs) on the efficiency of biogas production with a focus on improving
bio-H$_2$. The inoculum used, anaerobic granular sludge obtained from a UASB
reactor treating poultry slaughterhouse wastewater, was pretreated with five
different pretreatments. The relationship between VFAs and biogas compounds was
studied as time-dependent components. In time-dependent processes with small
sample size data, regression models may not be good enough at estimating
responses. Therefore, a deep learning neural network (DNN) model was developed
to estimate the biogas compounds based on the VFAs. The accuracy of this model
to predict the biogas compounds was higher than that of multivariate regression
models. Further, it could predict the effect of time changes on biogas
compounds. Analysis showed that all the pretreatments were able to increase the
ratio of butyric acid / acetic acid successfully, decrease propionic acid
drastically, and increase the efficiency of bio-H$_2$ production. As
discovered, butyric acid had the greatest effect on bio-H$_2$, and propionic
acid had the greatest effect on CH$_4$ production. The best amounts of the VFAs
were determined using an optimization method, integrated DNN and desirability
analysis, dynamically retrained based on digestion time. Accordingly, optimal
ranges of acetic, propionic, and butyric acids were 823.2 - 1534.3, 36.3 -
47.4, and 1522 - 1822 mg/L, respectively, determined for digestion time of
25.23 - 123.63 h. These values resulted in the production of bio-H$_2$, N$_2$,
CO$_2$, and CH$_4$ in ranges of 6.4 - 26.2, 12.2 - 43.2, 5 - 25.3, and 0 - 1.4
mmol/L, respectively. The optimum ranges of VFAs are relatively wide ranges and
practically can be used in biogas plants.


  A dynamical model for Landau's original approach to superfluid Helium is
presented, with two velocities but only one mass density. Second sound is an
adiabatic perturbation that involves the temperature and the roton, aka the
notoph. The action incorporates all the conservation laws, including the
equation of continuity. With only 4 canonical variables it has a higher power
of prediction than Landau's later, more complicated model, with its 8 degrees
of freedom. The roton is identified with the massless notoph. This theory gives
a very satisfactory account of second and fourth sounds.
  Second sound is an adiabatic oscillation of the temperature and both vector
fields, with no net material motion. Fourth sound involves the roton, the
temperature and the density. With the experimental confirmation of
gravitational waves the relations between Hydrodynamics and Relativity and
particle physics have become more clear, and urgent. The appearance of the
Newtonian potential in irrotational hydrodynamics comes directly from
Einstein's equations for the metric. The density factor $\rho$ is essential; it
is time to acknowledge the role that it plays in particle theory.
  To complete the 2-vector theory we include the massless roton mode. Although
this mode too is affected by the mass density, it turns out that the wave
function of the unique notoph propagating mode $\mathcal{N}$ satisfies the
normal massless wave equation $\Box\mathcal{N}$ = 0; the roton propagates as a
free particle in the bulk of the superfluid without meeting resistance. In this
circumstance we may have discovered the mechanism that lies behind the flow of
He-II through very thin pores.


  (Abridged) We present results from 13776 radial-velocity (RV) measurements of
1278 candidate members of the old (4 Gyr) open cluster M67 (NGC 2682). The
measurements are the results of a long-term survey that includes data from
seven telescopes with observations for some stars spanning over 40 years. For
narrow-lined stars, RVs are measured with precisions ranging from about 0.1 to
0.8 km/s. The combined stellar sample reaches from the brightest giants in the
cluster down to about 4 magnitudes below the main-sequence turnoff (V = 16.5),
covering a mass range of about 1.34 MSun to 0.76 MSun. Spatially, the sample
extends to a radius of 30 arcmin (7.4 pc in projection at a distant of 850 pc
or 6-7 core radii). We find M67 to have a mean RV of +33.64 km/s (with an
internal precision of +/- 0.03 km/s). For stars with >=3 measurements, we
derive RV membership probabilities and identify RV variables, finding 562
cluster members, 142 of which show significant RV variability. We use these
cluster members to construct a color-magnitude diagram and identify a rich
sample of stars that lie far from the standard single star isochrone, including
the well-known blue stragglers, sub-subgiants and yellow giants. These exotic
stars have a binary frequency of (at least) 80%, more than three times that
detected for stars in the remainder of the sample. We confirm that the cluster
is mass segregated, finding the binaries to be more centrally concentrated than
the single stars in our sample at the 99.8% confidence level. The blue
stragglers are centrally concentrated as compared to the solar-type
main-sequence single stars in the cluster at the 99.7% confidence level.
Accounting for both measurement precision and undetected binaries, we derive a
RV dispersion in M67 of 0.59 +0.07 -0.06 km/s, which yields a virial mass for
the cluster of 2100 +610 -550 MSun.
  WIYN Open Cluster Study. LXVII.


  An extension of the Tamari lattice to the multiplihedra is discussed, along
with projections to the composihedra and the Boolean lattice. The multiplihedra
and composihedra are sequences of polytopes that arose in algebraic topology
and category theory. Here we describe them in terms of the composition of
combinatorial species. We define lattice structures on their vertices, indexed
by painted trees, which are extensions of the Tamari lattice and projections of
the weak order on the permutations. The projections from the weak order to the
Tamari lattice and the Boolean lattice are shown to be different from the
classical ones. We generalize the Tamari lattice to graph tubings--as is also
described by Ronco. We review how lattice structures often interact with the
Hopf algebra structures, following Aguiar and Sottile who discovered the
applications of Mobius inversion on the Tamari lattice to the Loday-Ronco Hopf
algebra.


  We review the background of the cluster algorithms in Monte Carlo simulation
of statistical physics problems. One of the first such successful algorithm was
developed by Swendsen and Wang eight years ago. In contrast to the local
algorithms, cluster algorithms update dynamical variables in a global fashion.
Therefore, large changes are made in a single step. The method is very
efficient, especially near the critical point of a second-order phase
transition. Studies of various statistical mechanics models and some
generalizations of the algorithm will be briefly reviewed. We mention
applications in other fields, especially in imaging processing.


  Given an algebraic torus action on a normal projective variety with finitely
generated total coordinate ring, we study the GIT-equivalence for not
necessarily ample linearized divisors, and we provide a combinatorial
description of the partially ordered set of GIT-equivalence classes. As an
application, we extend in the $\QQ$-factorial case a basic feature of the
collection of ample GIT-classes to the partially ordered collection of maximal
subsets with a quasiprojective quotient: for any two members there is at most
one minimal member comprising both of them. Moreover, we demonstrate in an
example, how our theory can be applied for a systematic treatment of ``exotic
projective orbit spaces'', i.e., projective geometric quotients that do not
arise from any linearized ample divisor.


  The possible application of boosted neural network to particle classification
in high energy physics is discussed. A two-dimensional toy model, where the
boundary between signal and background is irregular but not overlapping, is
constructed to show how boosting technique works with neural network. It is
found that boosted neural network not only decreases the error rate of
classification significantly but also increases the efficiency and
signal-background ratio. Besides, boosted neural network can avoid the
disadvantage aspects of single neural network design. The boosted neural
network is also applied to the classification of quark- and gluon- jet samples
from Monte Carlo \EE collisions, where the two samples show significant
overlapping. The performance of boosting technique for the two different
boundary cases -- with and without overlapping is discussed.


  Differential equations are used in a wide variety of disciplines, describing
the complex behavior of the physical world. Analytic solutions to these
equations are often difficult to solve for, limiting our current ability to
solve complex differential equations and necessitating sophisticated numerical
methods to approximate solutions. Trained neural networks act as universal
function approximators, able to numerically solve differential equations in a
novel way. In this work, methods and applications of neural network algorithms
for numerically solving differential equations are explored, with an emphasis
on varying loss functions and biological applications. Variations on
traditional loss function and training parameters show promise in making neural
network-aided solutions more efficient, allowing for the investigation of more
complex equations governing biological principles.


  Traditional adversarial attacks concentrate on manipulating clean examples in
the pixel space by adding adversarial perturbations. By contrast, semantic
adversarial attacks focus on changing semantic attributes of clean examples,
such as color, context, and features, which are more feasible in the real
world. In this paper, we propose a framework to quickly generate a semantic
adversarial attack by leveraging recent diffusion models since semantic
information is included in the latent space of well-trained diffusion models.
Then there are two variants of this framework: 1) the Semantic Transformation
(ST) approach fine-tunes the latent space of the generated image and/or the
diffusion model itself; 2) the Latent Masking (LM) approach masks the latent
space with another target image and local backpropagation-based interpretation
methods. Additionally, the ST approach can be applied in either white-box or
black-box settings. Extensive experiments are conducted on CelebA-HQ and AFHQ
datasets, and our framework demonstrates great fidelity, generalizability, and
transferability compared to other baselines. Our approaches achieve
approximately 100% attack success rate in multiple settings with the best FID
as 36.61. Code is available at
https://github.com/steven202/semantic_adv_via_dm.


  Graph neural networks can be effectively applied to find solutions for many
real-world problems across widely diverse fields. The success of graph neural
networks is linked to the message-passing mechanism on the graph, however, the
message-aggregating behavior is still not entirely clear in most algorithms. To
improve functionality, we propose a new transparent network called Graph
Decipher to investigate the message-passing mechanism by prioritizing in two
main components: the graph structure and node attributes, at the graph,
feature, and global levels on a graph under the node classification task.
However, the computation burden now becomes the most significant issue because
the relevance of both graph structure and node attributes are computed on a
graph. In order to solve this issue, only relevant representative node
attributes are extracted by graph feature filters, allowing calculations to be
performed in a category-oriented manner. Experiments on seven datasets show
that Graph Decipher achieves state-of-the-art performance while imposing a
substantially lower computation burden under the node classification task.
Additionally, since our algorithm has the ability to explore the representative
node attributes by category, it is utilized to alleviate the imbalanced node
classification problem on multi-class graph datasets.


  The corrections to g-mode frequencies caused by the presence of a central
magnetic field and rotation of the Sun are calculated. The calculations are
carried out in the simple one dimensional magnetohydrodynamical model using the
approximations which allow one to find the purely analytical spectra of
magneto-gravity waves beyond the scope of the JWKB approximation and avoid in a
small background magnetic field the appearance of the cusp resonance which
locks a wave within the radiative zone. These analytic results are compared
with the satellite observations of the g-mode frequency shifts which are of the
order one per cent as given in the GOLF experiment at the SoHO board. The main
contribution turns out to be the magnetic frequency shift in the strong
magnetic field which obeys the used approximations. In particular, the fixed
magnetic field strength 700 KG results in the mentioned value of the frequency
shift for the g-mode of the radial order n=-10. The rotational shift due to the
Coriolis force appears to be small and does not exceed a fracton of per cent,
\alpha_\Omega < 0.003.


  The Euclidean method is usually discussed in the context of the metric
avoiding the typical delta of the conical singularity. We introduce a new way
to calculate the Hawking temperature using the vierbein and spin connection.
The conical singularity is seen globally through an effect on parallel
transport, the so called holonomy of the spin connection. The period of the
Euclidean time is calculated requiring that the holonomy of the spin connection
is trivial at the event horizon.


  Deep learning methods exhibit promising performance for predictive modeling
in healthcare, but two important challenges remain: -Data insufficiency:Often
in healthcare predictive modeling, the sample size is insufficient for deep
learning methods to achieve satisfactory results. -Interpretation:The
representations learned by deep learning methods should align with medical
knowledge. To address these challenges, we propose a GRaph-based Attention
Model, GRAM that supplements electronic health records (EHR) with hierarchical
information inherent to medical ontologies. Based on the data volume and the
ontology structure, GRAM represents a medical concept as a combination of its
ancestors in the ontology via an attention mechanism. We compared predictive
performance (i.e. accuracy, data needs, interpretability) of GRAM to various
methods including the recurrent neural network (RNN) in two sequential
diagnoses prediction tasks and one heart failure prediction task. Compared to
the basic RNN, GRAM achieved 10% higher accuracy for predicting diseases rarely
observed in the training data and 3% improved area under the ROC curve for
predicting heart failure using an order of magnitude less training data.
Additionally, unlike other methods, the medical concept representations learned
by GRAM are well aligned with the medical ontology. Finally, GRAM exhibits
intuitive attention behaviors by adaptively generalizing to higher level
concepts when facing data insufficiency at the lower level concepts.


  Over the years of challenges on detecting the crash consistency of
non-volatile persistent memory (PM) bugs and developing new tools to identify
those bugs are quite stretching due to its inconsistent behavior on the file or
storage systems. In this paper, we evaluated an open-sourced automatic bug
detector tool (i.e. AGAMOTTO) to test NVM level hashing PM application to
identify performance and correctness PM bugs in the persistent (main) memory.
Furthermore, our faithful validation tool able to discovered 65 new NVM level
hashing bugs on PMDK library and it outperformed the number of bugs (i.e. 40
bugs) that WITCHER framework was able to identified. Finally, we will propose a
Deep-Q Learning search heuristic algorithm over the PM-Aware search algorithm
in the state selection process to improve the searching strategy efficiently.


  The commercialization of Cloud-RAN, and Open-RAN in particular, is a key
factor to enable 5G cell densification, by providing lower cost and more agile
deployment of small cells. In addition, the adoption of MEC is important to
support ultra-low latency and high reliability required by mission-critical
applications, which constitute a milestone of the 5G and beyond vision of a
fully connected society. However, connecting antenna site, C-RAN processing and
MEC at low cost is challenging, as it requires high-capacity, low latency
connectivity delivered through a highly inter-connected topology. While PON is
being considered as a solution for providing low-cost connectivity to C-RAN,
they only allow data transmission from the endpoints (for example hosting RU at
the antenna site) towards a central node (e.g., the central office, hosting
computing equipment), thus cannot support traffic from RU towards MEC end nodes
that could host DU and possibly CU and network core. This led to research into
the evolution of PON architectures with the ability to provide direct
communications between endpoints, thus supporting mesh traffic patterns
required by MEC installations. In this context, virtualization plays a key role
in enabling efficient resource allocation (i.e. optical transmission capacity)
to endpoints, according to their communication patterns. In this article, we
address the challenge of dynamic allocation of virtual PON slices over mesh-PON
architectures to support C-RAN and MEC nodes. We make use of a mixed
analytical-iterative model to compute optimal virtual PON slice allocation,
with the objective of minimizing the use of MEC node resources, while meeting a
target latency threshold (100 $\mu s$ in our scenario). Our method is
particularly effective in reducing computation time, enabling virtual PON slice
allocation in timescales compatible with real-time or near real-time
operations.


  The power domination problem seeks to determine the minimum number of phasor
measurement units (PMUs) needed to monitor an electric power network. We
introduce random sensor failure before the power domination process occurs and
call this the fragile power domination process. For a given graph, PMU
placement, and probability of PMU failure $q$, we study the expected number of
observed vertices at the termination of the fragile power domination process.
This expected value is a polynomial in $q$, which we relate to fault-tolerant
and PMU-defect-robust power domination. We also study the probability of that
the entire graph becomes observed and give results for some graph families.


  In free electron laser facilities, almost every kind of device will generate
wakefield when an electron beam passes through it. Most of the wakefields are
undesired and have a negative effect on the electron beam, which means a
decrease of FEL performance. As for the SXFEL test facility, the sophisticated
layout and the cumulative effect of such a long undulator section lead to an
obvious wakefield, which is strong enough that can not be ignored. Based on two
deflecting cavities at the entrance and the exit of the undulator section with
corresponding profile monitors, we measured the wakefield of the undulator
section. In this paper, we give the theoretical and simulation results of
resistive wall wakefields which agree well with each other. In addition, the
experimental and the simulation results of the overall undulator wakefield are
given showing small difference. In order to explore the impact of this
wakefield on FEL lasing, we give the simulation results of FEL with and without
wakefield for comparison. There is almost no impact on 44 nm FEL in stage-1 of
cascaded EEHG-HGHG mode, while the impact on 8.8 nm FEL in stage-2 becomes
critical decreasing the pulse energy and peak power by 42% and 27% and
broadening the bandwidth.


  Extensive air showers are detectable by radio signals with a radio surface
detector. A promising theory of the dominant emission process is the coherent
synchrotron radiation emitted by e+ e- shower particles in the Earth's magnetic
field (geosynchrotron effect). A radio air shower detector can extend IceTop,
the air shower detector on top of IceCube. This could increase the sensitivity
of IceTop to higher shower energies and for inclined showers significantly.
Muons from air showers are a major part of the background of the neutrino
telescope IceCube. Thus a surface radio air shower detector could act as a veto
detector for this muonic background. Initial radio background measurements with
a single antenna in 2007 revealed a continuous electromagnetic background
promising a low energy threshold of radio air shower detection. However, short
pulsed radio interferences can mimic real signals and have to be identified in
the frequency range of interest. These properties of the electromagnetic
background was being measured at the South Pole during the Antarctic winter
2009 with two different types of surface antennas. In total four antennas are
placed at distances ranging up to 400m from each other. In 2010 a small eight
channel surface detector will test an amplitude threshold self trigger strategy
with large dipole antennas on the South Pole snow ground. The installation will
be described.


  This research reports investigates an edge on-device stream processing
platform, which extends the serverless com- puting model to the edge to help
facilitate real-time data analytics across the cloud and edge in a uniform
manner. We investigate associated use cases and architectural design. We
deployed and tested our system on edge devices (Raspberry Pi and Android
Phone), which proves that stream processing analytics can be performed at the
edge of the network with single board computers in a real-time fashion.


  The increasing use of Machine Learning (ML) components embedded in autonomous
systems -- so-called Learning-Enabled Systems (LESs) -- has resulted in the
pressing need to assure their functional safety. As for traditional functional
safety, the emerging consensus within both, industry and academia, is to use
assurance cases for this purpose. Typically assurance cases support claims of
reliability in support of safety, and can be viewed as a structured way of
organising arguments and evidence generated from safety analysis and
reliability modelling activities. While such assurance activities are
traditionally guided by consensus-based standards developed from vast
engineering experience, LESs pose new challenges in safety-critical application
due to the characteristics and design of ML models. In this article, we first
present an overall assurance framework for LESs with an emphasis on
quantitative aspects, e.g., breaking down system-level safety targets to
component-level requirements and supporting claims stated in reliability
metrics. We then introduce a novel model-agnostic Reliability Assessment Model
(RAM) for ML classifiers that utilises the operational profile and robustness
verification evidence. We discuss the model assumptions and the inherent
challenges of assessing ML reliability uncovered by our RAM and propose
solutions to practical use. Probabilistic safety argument templates at the
lower ML component-level are also developed based on the RAM. Finally, to
evaluate and demonstrate our methods, we not only conduct experiments on
synthetic/benchmark datasets but also scope our methods with case studies on
simulated Autonomous Underwater Vehicles and physical Unmanned Ground Vehicles.


  We present a quantitative study of the far-ultraviolet (FUV) and optical
morphology in 32 nearby galaxies and estimate the ``morphological
k-correction'' expected if these objects were observed unevolved at high
redshift. Using the common indices of central concentration (C) and rotational
asymmetry (A) to quantify morphology, we consider independently two phenomena
that give rise to this k-correction. Bandshifting, the decrease in rest-frame
wavelength of light observed through optical filters, is explored by measuring
these indices in several passbands for each galaxy, and it is found to be the
primary driver of changes in C and A. In general, the optical trend found for
decreasing C and increasing A when going to shorter wavelengths extends to the
FUV. However, the patchy nature of recent star-formation in late-type galaxies,
which is accentuated in the FUV, results in poor quantitative correspondence
between morphologies determined in the optical and FUV. We then artificially
redshift our FUV images into the Hubble Deep Field (HDF) filters to simulate
various cosmological distance effects such as surface brightness dimming and
loss of spatial resolution. Hubble types of many galaxies in our sample are not
readily identifiable at redshifts beyond z ~ 1, and the galaxies themselves are
difficult to detect beyond z ~ 3. Only features of the highest surface
brightness remain visible at cosmological distances. Our simulations suggest
that k-corrections alone are indeed capable of producing the peculiar
morphologies observed at high redshift.


  In the framework of the planar CR3BP for mass parameter mu=0.0121505,
corresponding to the Earth-Moon system, we identify and describe 80 families of
periodic orbits encircling both the Earth and the Moon ("transfer" orbits). All
the orbits in these families have very low energies, most of them corresponding
to values of the Jacobi constant C for which the Hill surface is closed at the
Lagrangian point L2. All of these orbits have also short period T, generally
under six months. Most of the families are composed of orbits that are
asymmetric with respect to the Earth-Moon axis.
  The main results presented for each family are: (i) the characteristic curves
T(h), y(h), v_y(h), and v_x(h) on the Poincare section
Sigma_1={x=0.836915310,y,v_x>0,v_y} normal to the Earth-Moon axis at the
Lagrangian point L1, parameterized by their energy h=-C/2 in the synodic
coordinate system; (ii) the stability parameter along each family; (iii) the
intersections x_i(h) of the orbits with the Earth-Moon axis, on the Poincare
section Sigma_2={x,y=0,v_x},v_y>0}; (iv) plots of some selected orbits and
details of their circumlunar region; and (v) numerical data for the
intersection of an orbit with Sigma_1 at a reference value of h. Some possible
extensions and applications of this work are also discussed.


  In this paper, we present an algorithm for reparametrizing algebraic plane
curves from a numerical point of view. That is, we deal with mathematical
objects that are assumed to be given approximately. More precisely, given a
tolerance $\epsilon>0$ and a rational parametrization $\cal P$ with perturbed
float coefficients of a plane curve $\cal C$, we present an algorithm that
computes a parametrization $\cal Q$ of a new plane curve $\cal D$ such that
${\cal Q}$ is an {\it $\epsilon$--proper reparametrization} of $\cal D$. In
addition, the error bound is carefully discussed and we present a formula that
measures the "closeness" between the input curve $\cal C$ and the output curve
$\cal D$.


  The scalar form factor of the nucleon and related physical quantities are
investigated in the framework of the semibosonized SU(3) Nambu-Jona-Lasinio
soliton model. We take into account the rotational $1/N_c$ corrections and
linear $m_s$ corrections. The strangeness content of the nucleon in the scalar
form factor is discussed in detail. In particular, it is found that the $m_s$
corrections play an essential role of reducing the $\langle N | \bar{s} s | N
\rangle$ arising from the leading order and rotational $1/N_c$ contributions.
We obtain the $\sigma_{\pi N} (0)=40.80\;\mbox{MeV}$, $\Delta \sigma =
\sigma_{\pi N} (2m^{2}_{\pi})-\sigma_{\pi N} (0) = 18.18\;\mbox{MeV}$ and
$\langle r^2\rangle^{S}_{N} = 1.50\;\mbox{fm}^2$.
  The results are in a remarkable agreement with empirical data analyzed by
Gasser, Leutwyler, and Sainio~\cite{gls}.


  This article is an extended version of the minicourse given by the second
author at the summer school of the conference "Interactions of quantum affine
algebras with cluster algebras, current algebras and categorification", held in
June 2018 in Washington. The aim of the minicourse, consisting of three
lectures, was to present a number of results and conjectures on certain
monoidal categories of finite-dimensional representations of quantum affine
algebras, obtained by exploiting the fact that their Grothendieck rings have
the natural structure of a cluster algebra.


  Most classical dynamical systems are chaotic. The trajectories of two
identical systems prepared in infinitesimally different initial conditions
diverge exponentially with time. Quantum systems, instead, exhibit
quasi-periodicity due to their discrete spectrum. Nonetheless, the dynamics of
quantum systems whose classical counterparts are chaotic are expected to show
some features that resemble chaotic motion. Among the many controversial
aspects of the quantum-classical boundary, the emergence of chaos remains among
the least experimentally verified. Time-resolved observations of quantum
chaotic dynamics are particularly rare, and as yet unachieved in a single
particle, where the subtle interplay between chaos and quantum measurement
could be explored at its deepest levels. We present here a realistic proposal
to construct a chaotic driven top from the nuclear spin of a single donor atom
in silicon, in the presence of a nuclear quadrupole interaction. This system is
exquisitely measurable and controllable, and possesses extremely long intrinsic
quantum coherence times, allowing for the observation of subtle dynamical
behavior over extended periods. We show that signatures of chaos are expected
to arise for experimentally realizable parameters of the system, allowing the
study of the relation between quantum decoherence and classical chaos, and the
observation of dynamical tunneling.


  The scope of this review is to give a pedagogical introduction to some new
calculations and methods developed by the author in the context of quantum
groups and their applications. The review is self- contained and serves as a
"first aid kit" before one ventures into the beautiful but bewildering
landscape of Woronowicz's theory. First, we present an up-to-date account of
the methods and definitions used in quantum gauge theories. Then, we highlight
our new results. The present paper is by no means an exhaustive overview of
this swiftly developing subject.


  We develop a methodology that enables us to extract accurately the
electromagnetic Delta form factors and their momentum dependence. We test our
approach in the quenched approximation as a preparation for a study using
dynamical fermions. Our calculation of the four form factors covers pion masses
between about 410 MeV and 560 MeV on lattices with a size of 2.9 fm and a
lattice spacing 0.09 fm. From the form factors we are able to obtain estimates
of the magnetic moment and the charge radius of the Delta, which we compare to
existing experimental and theoretical results.Our non-zero result for the
electric quadrupole form factor signals a deformation of the Delta, pointing to
an oblate charge distribution.


  A combination of small-cluster exact-diagonalization calculations and a
well-controlled approximative method is used to examine the ground-state phase
diagrams of the spin-one-half Falicov-Kimball model extended by the
spin-dependent on-site interaction ($J$) between localized spins and itinerant
($d$) electrons, as well as by external magnetic field ($h$). Both the magnetic
ordering and metamagnetic transitions are analysed as functions of $h$ and the
number of itinerant electrons ($N_d$) at selected $J$. Various magnetic
superstructures including axial and diagonal spin stripes are observed for
nonzero values of $J$ and $h$. Moreover, it is shown that increasing $h$
strongly stabilizes the fully and partially polarized states, while the non
polarized state is reduced.


  It is shown that the surface gravity and temperature of a stationary black
hole are invariant under conformal transformations of the metric that are the
identity at infinity. More precisely, we find a conformal invariant definition
of the surface gravity of a conformal Killing horizon that agrees with the
usual definition(s) for a true Killing horizon and is proportional to the
temperature as defined by Hawking radiation. This result is reconciled with the
intimate relation between the trace anomaly and the Hawking effect, despite the
{\it non}invariance of the trace anomaly under conformal transformations.


  We formulate evaporation phenomena in a generic model of generalized
teleparallel gravity in Weitzenbock spacetime with diagonal and non-diagonal
tetrads basis. We also perform the perturbation analysis around the constant
torsion scalar solution named Nariai spacetime which is an exact solution of
field equations as the limiting case of the Schwarzschild-de Sitter and in the
limit where two back hole and their cosmological horizons coincide. By a
carefully analysis of the horizon perturbation equation, we show that
(anti)evaporation can not happen if we use a diagonal tetrad basis. This result
implies that a typical black hole in any generic form of generalized
teleparallel gravity is frozen in its initial state if we use the diagonal
tetrads. But in the case of non-diagonal tetrads the analysis is completely
different. By a suitable non trivial non-diagonal tetrad basis we investigate
the linear stability of the model under perturbations of the metric and torsion
simultaneously. We observe that in spite of the diagonal case, both evaporation
and anti evaporation can happen. The phenomena depend on the initial phase of
the horizon perturbation. In the first mode when we restrict ourselves to the
first lower modes the (anti)evaporation happens. So, in non-diagonal case the
physical phenomena is reasonable. This is an important advantage of using
non-diagonal tetrads instead of the diagonal ones. We also see that this is an
universal feature, completely independent from the form of the model.


  Deep multi-view stereo (MVS) methods have been developed and extensively
compared on simple datasets, where they now outperform classical approaches. In
this paper, we ask whether the conclusions reached in controlled scenarios are
still valid when working with Internet photo collections. We propose a
methodology for evaluation and explore the influence of three aspects of deep
MVS methods: network architecture, training data, and supervision. We make
several key observations, which we extensively validate quantitatively and
qualitatively, both for depth prediction and complete 3D reconstructions.
First, complex unsupervised approaches cannot train on data in the wild. Our
new approach makes it possible with three key elements: upsampling the output,
softmin based aggregation and a single reconstruction loss. Second, supervised
deep depthmap-based MVS methods are state-of-the art for reconstruction of few
internet images. Finally, our evaluation provides very different results than
usual ones. This shows that evaluation in uncontrolled scenarios is important
for new architectures.


  We present the theory of Cauchy-Fantappi\'e integral operators, with emphasis
on the situation when the domain of integration, $D$, has minimal boundary
regularity. Among these operators we focus on those that are more closely
related to the classical Cauchy integral for a planar domain, whose kernel is a
holomorphic function of the parameter $z\in D$. The goal is to prove $L^p$
estimates for these operators and, as a consequence, to obtain $L^p$ estimates
for the canonical Cauchy-Szeg\"o and Bergman projection operators (which are
not of Cauchy-Fantappi\'e type).


  If our universe has appeared in a result of Big Bang or something like this,
whether we have reasons to deny an existence of other universes appearing by
the same or similar way? An objection that there is no anything like it, is
doubtful, because nobody knows: what could we observe in this case? A model of
a multi-space universe with mutual coupling of spaces is being proposed and
investigated.


  Interaction of two skyrmions stabilized by the ferromagnetic exchange,
Dzyaloshinskii-Moriya interaction (DMI), and external magnetic field has been
studied numerically on a 2D lattice of size large compared to the separation,
$d$, between the skyrmions. We show that two skyrmions of the same chirality
(determined by the symmetry of the crystal) repel. In accordance with earlier
analytical results, their long-range pair interaction falls out with the
separation as $\exp(-d/\delta_H)$, where $\delta_H$ is the magnetic screening
length, independent of the DMI. The prefactor in this expression depends on the
DMI that drives the repulsion. The latter results in the spiral motion of the
two skyrmions around each other, with the separation between them growing
logarithmically with time. When two skyrmions of the total topological charge
$Q = 2$ are pushed close to each other, the discreteness of the atomic lattice
makes them collapse into one skyrmion of charge $Q = 1$ below a critical
separation. Experiment is proposed that would allow one to measure the
interaction between two skyrmions by holding them in positions with two
magnetic tips. Our findings should be of value for designing topologically
protected magnetic memory based upon skyrmions.


  We introduce and demonstrate the variational autoencoder (VAE) for
probabilistic non-negative matrix factorisation (PAE-NMF). We design a network
which can perform non-negative matrix factorisation (NMF) and add in aspects of
a VAE to make the coefficients of the latent space probabilistic. By
restricting the weights in the final layer of the network to be non-negative
and using the non-negative Weibull distribution we produce a probabilistic form
of NMF which allows us to generate new data and find a probability distribution
that effectively links the latent and input variables. We demonstrate the
effectiveness of PAE-NMF on three heterogeneous datasets: images, financial
time series and genomic.


  Understanding factors that contribute to the increased likelihood of disease
transmission between two individuals is important for infection control.
However, analyzing measures of genetic relatedness is complicated due to
correlation arising from the presence of the same individual across multiple
dyadic outcomes, potential spatial correlation caused by unmeasured
transmission dynamics, and the distinctive distributional characteristics of
some of the outcomes. We develop two novel hierarchical Bayesian spatial
methods for analyzing dyadic genetic relatedness data, in the form of patristic
distances and transmission probabilities, that simultaneously address each of
these complications. Using individual-level spatially correlated random effect
parameters, we account for multiple sources of correlation between the outcomes
as well as other important features of their distribution. Through simulation,
we show the limitations of existing approaches in terms of estimating key
associations of interest, and the ability of the new methodology to correct for
these issues across datasets with different levels of correlation. All methods
are applied to Mycobacterium tuberculosis data from the Republic of Moldova
where we identify previously unknown factors associated with disease
transmission and, through analysis of the random effect parameters, key
individuals and areas with increased transmission activity. Model comparisons
show the importance of the new methodology in this setting. The methods are
implemented in the R package GenePair.


  An adapted bubble approach which is a modifiation of the residual-free
bubbles (RFB) method, is proposed for the Helmhotz problem in 2D. A new
two-level finite element method is introduced for the approximations of the
bubble functions. Unlike the other equations such as the advection-diffusion
equation, RFB method when applied to the Helmholtz equation, does not depend on
another stabilized method to obtain approximations to the solutions of the
sub-problems. Adapted bubbles (AB) are obtained by a simple modification of the
sub-problems. This modification increases the accuracy of the numerical
solution impressively. The AB method is able to solve the Helmholtz equation
efficiently in 2D up to ch = 3.5 where c is the wave number and h is the mesh
size. We provide analysis to show how the AB method mitigates the pollution
error.


  Gas electron multiplier (GEM) is widely used in modern gas detectors of
ionizing radiation in experiments on high-energy physics at accelerators and in
other fields of science. Typically the GEM devices are based on a dielectric
foil with holes and electrodes on both sides. GEMs made by radiation-hard
dielectrics or wide band-gap semiconductors are desirable for some
applications. The results of the first tests of the gas electron multiplier
made of radiation-hard materials, such as polycrystalline CVD diamond with a
thickness of 100 microns is described. Here we report on fabrication of GEM
based on free-standing polycrystalline CVD diamond film and its first test.


  Here we report the monolayer characteristics of a biodegradable polymer
chitosan using Langmuir-Blodgett film technique. Usually chitosan has not form
stable monolayer film. However they form stable monolayer film when mixed with
fatty acid. Our result may be useful for the biological application of chitosan
mainly in drug delivery. Since, the stability during the time of drug delivery
is one of the important criteria for the choice of biomaterials suitable for
forming the carrier film matrix and the barrier films.


  This version of the paper has been withdrawn due to an error. Please contact
one of the authors for an updated copy.


  Indian free ranging dogs live in a carbohydrate rich environment as
scavengers in and around human settlements. They rarely hunt and consequently
do not encounter rich sources of protein. Instead they have adapted to a diet
of primarily carbohydrates. As descendants of the exclusively carnivorous
wolves, they are subjected to the evolutionary load of a physiological demand
for proteins. To meet their protein needs they resort to a thumb rule, if it
smells like meat, eat it. Pups face high competition from group and non group
members and are in a phase of rapid growth with high protein demands. Following
the thumb rule, then they can acquire more protein at the cost of increased
competition and reduced supplementary non protein nutrition. However, if the
mother supplements their diet with protein rich regurgitates and milk, then the
pups can benefit by being generalists. Using a choice test in the field we show
that while adults have a clear preference for meat, pups have no such
preference, and they even eat degraded protein eagerly. Thus the thumb rule
used by adult dogs for efficient scavenging is not innate, and needs to be
learned. The thumb rule might be acquired by cultural transmission, through
exposure to meat in the regurgitate of the mother, or while accompanying her on
foraging trips.


  We have observed the X-ray transient XTE J0421+56 in quiescence with
XMM-Newton. The observed spectrum is highly unusual being dominated by an
emission feature at ~6.5 keV. The spectrum can be fit using a partially covered
power-law and Gaussian line model, in which the emission is almost completely
covered (covering fraction of 0.98_{-0.06}^{+0.02}) by neutral material and is
strongly absorbed with an N_H of (5_{-2}^{+3}) x 10^{23} atom cm^{-2}. This
absorption is local and not interstellar. The Gaussian has a centroid energy of
6.4 +/- 0.1 keV, a width < 0.28 keV and an equivalent width of 940
^{+650}_{-460} eV. It can be interpreted as fluorescent emission line from
iron. Using this model and assuming XTE J0421+56 is at a distance of 5 kpc, its
0.5-10 keV luminosity is 3.5 x 10^{33} erg s^{-1}. The Optical Monitor onboard
XMM-Newton indicates a V magnitude of 11.86 +/- 0.03. The spectra of X-ray
transients in quiescence are normally modeled using advection dominated
accretion flows, power-laws, or by the thermal emission from a neutron star
surface. The strongly locally absorbed X-ray emission from XTE J0421+56 is
therefore highly unusual and could result from the compact object being
embedded within a dense circumstellar wind emitted from the supergiant B[e]
companion star. The uncovered and unabsorbed component observed below 5 keV
could be due either to X-ray emission from the supergiant B[e] star itself, or
to the scattering of high-energy X-ray photons in a wind or ionized corona,
such as observed in some low-mass X-ray binary systems.


  Part-based approaches for fine-grained recognition do not show the expected
performance gain over global methods, although explicitly focusing on small
details that are relevant for distinguishing highly similar classes. We assume
that part-based methods suffer from a missing representation of local features,
which is invariant to the order of parts and can handle a varying number of
visible parts appropriately. The order of parts is artificial and often only
given by ground-truth annotations, whereas viewpoint variations and occlusions
result in not observable parts. Therefore, we propose integrating a Fisher
vector encoding of part features into convolutional neural networks. The
parameters for this encoding are estimated by an online EM algorithm jointly
with those of the neural network and are more precise than the estimates of
previous works. Our approach improves state-of-the-art accuracies for three
bird species classification datasets.


  We report on the use of an interferometric weak value technique to amplify
very small transverse deflections of an optical beam. By entangling the beam's
transverse degrees of freedom with the which-path states of a Sagnac
interferometer, it is possible to realize an optical amplifier for polarization
independent deflections. The theory for the interferometric weak value
amplification method is presented along with the experimental results, which
are in good agreement. Of particular interest, we measured the angular
deflection of a mirror down to 560 femtoradians and the linear travel of a
piezo actuator down to 20 femtometers.


  This paper provides an unique dual representation of set-valued lower
semi-continuous quasiconvex and convex functions. The results are based on a
duality result for increasing set valued functions.


  A knot in the 3-sphere is called an L--space knot if it admits a nontrivial
Dehn surgery yielding an L--space. Like torus knots and Berge knots, many
L--space knots admit also a Seifert fibered surgery. We give a concrete example
of a hyperbolic, L-space knot which has no exceptional surgeries, in
particular, no Seifert fibered surgeries.


  Blockchains have witnessed widespread adoption in the past decade in various
fields. The growing demand makes their scalability and sustainability
challenges more evident than ever. As a result, more and more blockchains have
begun to adopt proof-of-stake (PoS) consensus protocols to address those
challenges. One of the fundamental characteristics of any blockchain technology
is its crypto-economics and incentives. Lately, each PoS blockchain has
designed a unique reward mechanism, yet, many of them are prone to free-rider
and nothing-at-stake problems. To better understand the ad-hoc design of reward
mechanisms, in this paper, we develop a reward mechanism framework that could
apply to many PoS blockchains. We formulate the block validation game wherein
the rewards are distributed for validating the blocks correctly. Using
evolutionary game theory, we analyze how the participants' behaviour could
potentially evolve with the reward mechanism. Also, penalties are found to play
a central role in maintaining the integrity of blockchains.


  The main purpose of this paper is to introduce and investigate the various
properties of a new generalization of Apostol Hermite-Genocchi polynomials. We
derive many useful results involving new generalized Apostol Hermite-Genocchi
polynomials. We also consider some statistical application of the new family in
probability distribution theory and reliability.


  This volume contains the proceedings of the Twelfth International Workshop on
the ACL2 Theorem Prover and Its Applications, ACL2'14, a two-day workshop held
in Vienna, Austria, on July 12-13, 2014. ACL2 workshops occur at approximately
18-month intervals and provide a major technical forum for researchers to
present and discuss improvements and extensions to the theorem prover,
comparisons of ACL2 with other systems, and applications of ACL2 in formal
verification. These proceedings include 13 peer reviewed technical papers.
  ACL2 is a state-of-the-art automated reasoning system that has been
successfully applied in academia, government, and industry for specification
and verification of computing systems and in teaching computer science courses.
In 2005, Boyer, Kaufmann, and Moore were awarded the 2005 ACM Software System
Award for their work in ACL2 and the other theorem provers in the Boyer-Moore
family.


  Given $\Omega$ bounded open set of $\mathbb R^{n}$ and $\alpha\in \mathbb R$,
let us consider \[ \mu(\Omega,\alpha)=\min_{\substack{v\in
W_{0}^{1,2}(\Omega)\\v\not\equiv 0}} \frac{\displaystyle\int_{\Omega} |\nabla
v|^{2}dx+\alpha \left|\displaystyle\int_{\Omega}|v|v\,dx
\right|}{\displaystyle\int_{\Omega} |v|^{2}dx}. \] We study some properties of
$\mu(\Omega,\alpha)$ and of its minimizers, and, depending on $\alpha$, we
determine the set $\Omega_{\alpha}$ among those of fixed measure such that
$\mu(\Omega_{\alpha},\alpha)$ is the smallest possible.


  In this paper we use near-infrared (NIR) spectral observations of Type Ia
supernovae (SNe Ia) to study the uncertainties inherent to NIR K corrections.
To do so, 75 previously published NIR spectra of 33 SNe Ia are employed to
determine K-correction uncertainties in the YJHK_s passbands as a function of
temporal phase and redshift. The resultant K corrections are then fed into an
interpolation algorithm that provides mean K corrections as a function of
temporal phase and robust estimates of the associated errors. These
uncertainties are both statistical and intrinsic --- i.e., due to the diversity
of spectral features from object to object --- and must be included in the
overall error budget of cosmological parameters constrained through the use of
NIR observations of SNe Ia. Intrinsic variations are likely the dominant source
of error for all four passbands at maximum light. Given the present data, the
total Y-band K-correction uncertainties at maximum are smallest, amounting to
+/- 0.04 mag at a redshift of z = 0.08. The J-band K-term errors are also
reasonably small (+/- 0.06 mag), but intrinsic variations of spectral features
and noise introduced by telluric corrections in the H band currently limit the
total K-correction errors at maximum to +/- 0.10 mag at z = 0.08. Finally,
uncertainties in the K_s-band K terms at maximum amount to +/- 0.07 mag at this
same redshift. These results are largely constrained by the small number of
published NIR spectra of SNe Ia, which do not yet allow spectral templates to
be constructed as a function of the light curve decline rate.


  In the wake of the growing popularity of machine learning in particle
physics, this work finds a new application of geometric deep learning on
Feynman diagrams to make accurate and fast matrix element predictions with the
potential to be used in analysis of quantum field theory. This research uses
the graph attention layer which makes matrix element predictions to 1
significant figure accuracy above 90% of the time. Peak performance was
achieved in making predictions to 3 significant figure accuracy over 10% of the
time with less than 200 epochs of training, serving as a proof of concept on
which future works can build upon for better performance. Finally, a procedure
is suggested, to use the network to make advancements in quantum field theory
by constructing Feynman diagrams with effective particles that represent
non-perturbative calculations.


  Tree transductions are binary relations of finite trees. For tree
transductions defined by non-deterministic top-down tree transducers,
inclusion, equivalence and synthesis problems are known to be undecidable.
Adding origin semantics to tree transductions, i.e., tagging each output node
with the input node it originates from, is a known way to recover decidability
for inclusion and equivalence. The origin semantics is rather rigid, in this
work, we introduce a similarity measure for transducers with origin semantics
and show that we can decide inclusion, equivalence and synthesis problems for
origin-close non-deterministic top-down tree transducers.


  The interior angle vector ($\widehat{\alpha}$-vector) of a polytope is a
metric analogue of the $f$-vector in which faces are weighted by their solid
angle. For simplicial polytopes, Dehn-Sommerville-type relations on the
$\widehat{\alpha}$-vector were introduced by Sommerville (1927) and H\"ohn
(1953). Camenga (2006) defined the $\widehat{\gamma}$-vector, a linear
transformation analogous to the $h$-vector and conjectured it to be
non-negative. Using tools from geometric and algebraic combinatorics, we prove
this conjecture and show that the $\widehat{\gamma}$-vector increases in the
first half and is flawless. In contrast to the $h$-vector, we construct a
six-dimensional polytope with non-unimodal $\widehat{\gamma}$-vector. More
generally, all result remain valid when solid angles are replaced by simple and
non-negative cone valuations.


  We present the results of our long term radio monitoring campaign at 1.3GHz
(MeerKAT) and 15.5GHz (Arcminute Microkelvin Imager - Large Array, AMI-LA) for
the outburst of the recently discovered neutron star X-ray binary Swift
J1858.6-0814. Throughout the outburst, we observe radio emission consistent
with a quasi-persistent, self-absorbed jet. In addition, we see two flares at
MJD 58427 and 58530. The second flare allows us to place constraints on the
magnetic field and minimum energy of the jet at 0.2G and 5x10^37erg,
respectively. We use the multi-frequency radio data in conjunction with data
from Swift-BAT to place Swift J1858.6-0814 on the radio/X-ray correlation. We
find that the quasi-simultaneous radio and BAT data makes Swift J1858.6-0814
appears to bridge the gap in the radio/X-ray plane between atoll and Z sources.
Furthermore, AMI-LA observations made whilst Swift J1858.6-0814 was in the soft
state have allowed us to show that the radio emission during the soft state is
quenched by at least a factor of four.


  The current status of measurements of the strong coupling constant from
different reactions is reviewed. Including new results presented at the 1996
ICHEP conference, a global average alpha_s(Mz) = 0.118 +- 0.003 is obtained.


  For many years the FORS2 instrument has suffered from artefacts in the flat
fields caused by surface inhomogeneities on the longitudinal atmospheric
dispersion corrector (LADC) that affected high-precision photometric and
spectroscopic measurements. Recently, the FORS LADC prisms were exchanged, and
our analysis of a large number of flat fields shows that this exchange has
resulted in a significant decrease in the level of small-scale artefacts.


  Mobility edge (ME), a critical energy separating localized and extended
states in spectrum, is a central concept in understanding the localization
physics. However, there are few models with exact MEs. In the paper, we
generalize the Aubry-Andr\'{e}-Harper model proposed in [Phys. Rev. Lett. 114,
146601 (2015)] and recently realized in [Phys. Rev. Lett. 126, 040603 (2021)],
by introducing a relative phase in the quasiperiodic potential. Applying
Avila's global theory we analytically compute localization lengths of all
single-particle states and determine the exact expression of ME, which both
significantly depend on the relative phase. They are verified by numerical
simulations, and a physical perception of the exact expression is also
provided. We further demonstrate that the exact expression of ME works for an
even broad class of generalized Aubry-Andr\'{e}-Harper models. Moreover, we
show that the exact ME is related to the one in the dual model which has
long-range hoppings.


  The interaction of coherent light with a nonlinear medium is modeled here by
a general quantum anharmonic oscillator. The model is not exactly solvable in a
closed analytical form. But we need operator solutions of the equations of
motion corresponding to these models in order to study the quantum fluctuations
of coherent light in nonlinear media. In the present work we derive approximate
operator solutions. From these solutions we observe that there exists an
apparent discrepancy between the solutions obtained by different techniques. We
compare different solutions and conclude that all correct solutions are
equivalent and the apparent discrepancy is due to the use of different ordering
of the operators. We use these solutions to investigate the possibilities of
observing different optical phenomena in a nonlinear dielectric medium. To be
precise, we have studied quantum phase fluctuations of coherent light in a
third order inversion symmetric nonlinear medium. Fluctuations in phase space
quadrature for the same system are studied and the possibility of generating
squeezed state is reported. Fluctuations in photon number are studied and the
nonclassical phenomenon of antibunching is predicted. We have generalized the
results obtained for third order nonlinear medium and have studied the
interaction of an intense laser beam with a general (m-1)-th order nonlinear
medium. Aharanov Anandan nonadiabatic geometric phase is also discussed in the
context of (m-1)-th order nonlinear medium.


  We introduce a novel strategy, based on the use of modular variables, to
encode and deterministically process quantum information using states described
by continuous variables. Our formalism leads to a general recipe to adapt
existing quantum information protocols, originally formulated for finite
dimensional quantum systems, to infinite dimensional systems described by
continuous variables. This is achieved by using non unitary and non-gaussian
operators, obtained from the superposition of gaussian gates, together with
adaptative manipulations in qubit systems defined in infinite dimensional
Hilbert spaces. We describe in details the realization of single and two qubit
gates and briefly discuss their implementation in a quantum optical set-up.


  ATLAS measurements of the production of muons from heavy-flavor decays in
$\sqrt{s_{\mathrm{NN}}} = 2.76$ TeV Pb+Pb collisions and $\sqrt{s} = 2.76$ TeV
$pp$ collisions at the LHC are presented. Integrated luminosities of 0.14
$\mathrm{nb}^{-1}$ and 570 $\mathrm{nb}^{-1}$ are used for the Pb+Pb and $pp$
measurements, respectively, which are performed over the muon transverse
momentum range $4 < p_{\mathrm{T}} < 14$ GeV and for five Pb+Pb centrality
intervals. Backgrounds arising from in-flight pion and kaon decays, hadronic
showers, and mis-reconstructed muons are statistically removed using a
template-fitting procedure. The heavy-flavor muon differential cross-sections
and per-event yields are measured in $pp$ and Pb+Pb collisions, respectively.
The nuclear modification factor $R_{\mathrm{AA}}$ obtained from these is
observed to be independent of $p_{\mathrm{T}}$, within uncertainties, and to be
less than unity, which indicates suppressed production of heavy-flavor muons in
Pb+Pb collisions. For the 10% most central Pb+Pb events, the measured
$R_{\mathrm{AA}}$ is approximately 0.35. The azimuthal modulation of the
heavy-flavor muon yields is also measured and the associated Fourier
coefficients $v_n$ for $n =$ 2, 3 and 4 are given as a function of
$p_{\mathrm{T}}$ and centrality. They vary slowly with $p_{\mathrm{T}}$ and
show a systematic variation with centrality which is characteristic of other
anisotropy measurements, such as that observed for inclusive hadrons. The
measured $R_{\mathrm{AA}}$ and $v_n$ values are also compared with theoretical
calculations.


  We carefully review the basic examples of anomaly cancellation in M-theory:
the 5-brane anomalies and the anomalies on S^1/Z_2. This involves cancellation
between quantum anomalies and classical inflow from topological terms. To
correctly fix all coefficients and signs, proper attention is paid to issues of
orientation, chirality and the Euclidean continuation. Independent of the
conventions chosen, the Chern-Simons and Green-Schwarz terms must always have
the same sign. The reanalysis of the reduction to the heterotic string on
S^1/Z_2 yields a surprise: a previously neglected factor forces us to slightly
modify the Chern-Simons term, similar to what is needed for cancelling the
normal bundle anomaly of the 5-brane. This modification leads to a local
cancellation of the anomaly, while maintaining the periodicity on S^1.


  Deep learning models are challenged by the distribution shift between the
training data and test data. Recently, the large models pre-trained on diverse
data have demonstrated unprecedented robustness to various distribution shifts.
However, fine-tuning these models can lead to a trade-off between
in-distribution (ID) performance and out-of-distribution (OOD) robustness.
Existing methods for tackling this trade-off do not explicitly address the OOD
robustness problem. In this paper, based on causal analysis of the
aforementioned problems, we propose a novel fine-tuning method, which uses
masked images as counterfactual samples that help improve the robustness of the
fine-tuning model. Specifically, we mask either the semantics-related or
semantics-unrelated patches of the images based on class activation map to
break the spurious correlation, and refill the masked patches with patches from
other images. The resulting counterfactual samples are used in feature-based
distillation with the pre-trained model. Extensive experiments verify that
regularizing the fine-tuning with the proposed masked images can achieve a
better trade-off between ID and OOD performance, surpassing previous methods on
the OOD performance. Our code is available at
https://github.com/Coxy7/robust-finetuning.


  The basic mechanism driving aneurysm growth is unknown. Currently, clinical
diagnosis of an aneurysm is mainly informed by retrospective tracking of its
size and growth rate. However, aneurysms can rupture before reactive criteria
are met or remain stable when they are exceeded. Here, we identify a
fluid-structure instability that is associated with abnormal aortic dilatation.
Our analysis yields a measurable dimensionless number and its analytically
derived critical threshold. This threshold pinpoints the transition from stable
flow to unstable aortic fluttering as a function of the physiological
properties composing the dimensionless number, like blood pressure and aortic
compliance. A retrospective study was then conducted with 4D-flow MRI data from
117 patients indicated for cardiac imaging and 100 healthy volunteers recruited
prospectively. The difference between the dimensionless number and its critical
threshold was calculated for every subject from their earliest MRI data and
used as an aneurysm physiomarker to forecast future growth. As a binary
predictor for abnormal growth and subsequent surgical intervention reported
from follow-up imaging, the aneurysm physiomarker yielded an AUC of 0.997 in a
receiving operator characteristic analysis. Though validated here for thoracic
ascending aortic aneurysms, this instability mechanism may be used to
understand, predict and inform patient-specific treatment of aneurysms in any
location without fundamental differences.


  Armed conflict exhibits regularities beyond known power law distributions of
fatalities and duration over varying culture and geography. We systematically
cluster conflict reports from a database of $10^5$ events from Africa spanning
20 years into conflict avalanches. Conflict profiles collapse over a range of
scales. Duration, diameter, extent, fatalities, and report totals satisfy
mutually consistent scaling relations captured with a model combining
geographic spread and local conflict-site growth. The emergence of such social
scaling laws hints at principles guiding conflict evolution.


  Vision-Language Pretraining (VLP) models have recently successfully
facilitated many cross-modal downstream tasks. Most existing works evaluated
their systems by comparing the fine-tuned downstream task performance. However,
only average downstream task accuracy provides little information about the
pros and cons of each VLP method, let alone provides insights on how the
community can improve the systems in the future. Inspired by the CheckList for
testing natural language processing, we exploit VL-CheckList, a novel framework
to understand the capabilities of VLP models. The proposed method divides the
image-texting ability of a VLP model into three categories: objects,
attributes, and relations, and uses a novel taxonomy to further break down
these three aspects. We conduct comprehensive studies to analyze seven recently
popular VLP models via the proposed framework. Results confirm the
effectiveness of the proposed method by revealing fine-grained differences
among the compared models that were not visible from downstream task-only
evaluation. Further results show promising research direction in building
better VLP models. Our data and code are available at:
https://github.com/om-ai-lab/VL-CheckList.


  The recent claim by the NANOGrav collaboration of a possible detection of an
isotropic gravitational wave background stimulated a series of investigations
searching for the origin of such a signal. The QCD phase transition appears as
a natural candidate and in this paper the gravitational spectrum generated
during the conversion of quarks into hadrons is calculated. Here, contrary to
recent studies, equations of state for the quark-gluon plasma issued from the
lattice approach were adopted. The duration of the transition, an important
parameter affecting the amplitude of the gravitational wave spectrum, was
estimated self-consistently with the dynamics of the universe controlled by the
Einstein equations. The gravitational signal generated during the transition
peaks around 0,28 \mu Hz, being unable to explain the claimed NANOGrav signal.
However, the expected QCD gravitational wave background could be detected by
the planned spatial interferometer Big Bang Observer in its advanced version
for frequencies above 1.0 mHz. This possible detection assumes that algorithms
recently proposed will be able to disentangle the cosmological signal from that
expected for the astrophysical background generated by black hole binaries.