title:
Current and future neutrino limits on the abundance of primordial black
  holes

abstract:
  Primordial black holes (PBHs) formed in the early Universe are sources of
neutrinos emitted via Hawking radiation. Such astrophysical neutrinos could be
detected at Earth and constraints on the abundance of comet-mass PBHs could be
derived from the null observation of this neutrino flux. Here, we consider
non-rotating PBHs and improve constraints using Super-Kamiokande neutrino data,
as well as we perform forecasts for next-generation neutrino (Hyper-Kamiokande,
JUNO, DUNE) and dark matter (DARWIN, ARGO) detectors, which we compare. For
PBHs less massive than $\sim \textrm{few} \times 10^{14}$ g, PBHs would have
already evaporated by now, whereas more massive PBHs would still be present and
would constitute a fraction of the dark matter of the Universe. We consider
monochromatic and extended (log-normal) mass distributions, and a PBH mass
range spanning from $10^{12}$ g to $\sim 10^{16}$ g. Finally, we also compare
our results with previous ones in the literature.


title: Pure Quark and Gluon Observables in Collinear Drop

abstract:
  We construct a class of pure quark and gluon observables by using the
collinear drop grooming technique. The construction is based on linear
combinations of multiple cumulative distributions of the jet mass in collinear
drop, whose specific weights are fully predicted perturbatively. This yields
observables which obtain their values purely from quarks (or purely from
gluons) in a wide region of phase space. We demonstrate this by showing that
these observables are effective in two phase space regions, one dominated by
perturbative resummation and one dominated by nonperturbative effects. The
nonperturbative effects are included using shape functions which only appear as
a common factor in the linear combinations constructed. We test this
construction using a numerical analysis with next-to-leading logarithmic
resummation and various shape function models, as well as analyzing these
observables with Pythia and Vincia. Choices for the collinear drop parameters
are optimized for experimental use.


title: Large $N$ Topologically Twisted Indices, Holography, and Black Holes

abstract:
  We present a simple closed form expression for the topologically twisted
index of the ABJM theory as a function of the magnetic fluxes and complex
chemical potentials valid at fixed $k$ and to all orders in the $1/N$
expansion. This in turn leads to analytic expressions for the topologically
twisted index at fixed genus in the 't Hooft limit to all orders in the
$1/\sqrt{\lambda}$ expansion. These results have important implications for
holography and the microscopic entropy counting of supersymmetric static
AdS$_4$ black holes. Generalizations to other SCFTs arising from M2-branes are
also briefly discussed.


title: Rare radiative decays of charm baryons

abstract:
  We study weak radiative $|\Delta c|=|\Delta u|=1$ decays of the charmed
anti-triplett ($\Lambda_c$, $\Xi_c^{+}$, $\Xi_c^{0}$) and sextet
($\Sigma_c^{++}$, $\Sigma_c^+$, $\Sigma_c^0$, $\Xi_c^{\prime +}$,
$\Xi_c^{\prime 0}$, $\Omega_c$) baryons in the standard model (SM) and beyond.
We work out $SU(2)$ and $SU(3)_F$-symmetry relations. We propose to study
self-analyzing decay chains such as $\Xi_c^+ \to \Sigma^+ (\to p \pi^0) \gamma$
and $\Xi_c^0 \to \Lambda (\to p \pi^-) \gamma$, which enable new physics
sensitive polarization studies. SM contributions can be controlled by
corresponding analysis of the Cabibbo-favored decays $\Lambda_c^+ \to \Sigma^+
(\to p \pi^0) \gamma$ and $\Xi_c^0 \to \Xi^0 (\to \Lambda \pi^0) \gamma$.
Further tests of the SM are available with initially polarized baryons
including $\Lambda_c \to p \gamma$ together with $\Lambda_c \to \Sigma^+
\gamma$ decays, or $\Omega_c \to \Xi^0 \gamma$ together with $\Omega_c \to
(\Lambda,\Sigma^0) \gamma$. In addition, CP-violating new physics contributions
to dipole operators can enhance CP-asymmetries up to few percent.


title: Dark Matter and $(g-2)_{\mu,e}$ in radiative Dirac neutrino mass models

abstract:
  The origin of neutrino mass is a mystery, so is its nature, namely, whether
neutrinos are Dirac or Majorana particles. On top of that, hints of large
deviations of the muon and the electron anomalous magnetic moments (AMMs) are
strong evidence for physics beyond the Standard Model. In this work, piecing
these puzzles together, we propose a class of radiative Dirac neutrino mass
models to reconcile $(g-2)_{\mu,e}$ anomalies with neutrino oscillation data.
In this framework, a common set of new physics (NP) states run through the
loops that generate non-zero neutrino mass and, due to chiral enhancement,
provide substantial NP contributions to lepton AMMs. In addition, one of the
three models studied in this work offers a Dark Matter candidate automatically
stabilized by the residual symmetry, whose phenomenology is non-trivially
connected to the other two puzzles mentioned above. Finally, our detailed
numerical analysis reveals a successful resolution to these mysteries while
being consistent with all colliders and cosmological constraints.


title: New Higgs decays to axion-like particles

abstract:
  We investigate the interactions of a light scalar with the Higgs boson and
second-generation fermions, which trigger new rare decays of the Higgs boson
into $4\mu$, $2\mu 2\gamma$, $6\mu$ and $4\mu 2j$. We recast current LHC
searches to constrain these decays and develop new collider analyses for those
channels which are only poorly tested by existing studies. With the currently
collected data we can probe branching ratios as small as $1.5 \times10^{-5}$,
$8.7 \times10^{-5}$, $5.7 \times10^{-8}$ and $1.6 \times10^{-7}$, respectively.
For the High-Luminosity LHC run, considered here to involve 3 ab$^{-1}$ of
integrated luminosity, these numbers go down to $1.3 \times10^{-5}$, $2.0
\times10^{-6}$, $3.0 \times10^{-9}$ and $5.4 \times10^{-9}$, respectively. We
also comment on other channels that remain still unexplored.


title:
Simulations of black hole fueling in isolated and merging galaxies with
  an explicit, multiphase ISM

abstract:
  We study gas inflows onto supermassive black holes using hydrodynamics
simulations of isolated galaxies and idealized galaxy mergers with an explicit,
multiphase interstellar medium (ISM). Our simulations use the recently
developed ISM and stellar evolution model called Stars and MUltiphase Gas in
GaLaxiEs (SMUGGLE). We implement a novel super-Lagrangian refinement scheme
that increases the gas mass resolution in the immediate neighborhood of the
black holes (BHs) to accurately resolve gas accretion. We do not include black
hole feedback in our simulations. We find that the complex and turbulent nature
of the SMUGGLE ISM leads to highly variable BH accretion. BH growth in SMUGGLE
converges at gas mass resolutions $\lesssim3\times10^3{\rm M_\odot}$. We show
that the low resolution simulations combined with the super-Lagrangian
refinement scheme are able to produce central gas dynamics and BH accretion
rates very similar to that of the uniform high resolution simulations. We
further explore BH fueling by simulating galaxy mergers. The interaction
between the galaxies causes an inflow of gas towards the galactic centres and
results in elevated and bursty star formation. The peak gas densities near the
BHs increase by orders of magnitude resulting in enhanced accretion. Our
results support the idea that galaxy mergers can trigger AGN activity, although
the instantaneous accretion rate depends strongly on the local ISM. We also
show that the level of merger-induced enhancement of BH fueling predicted by
the SMUGGLE model is much smaller compared to the predictions by simulations
using an effective equation of state model of the ISM.


title: A Torsion-Driven Generalisation of Unimodular Gravity

abstract:
  We present an Einstein Cartan Kalb Ramond model that yields a generalisation
of unimodular gravity in which the dynamics are driven by torsion rather than
curvature. Using a simple ansatz, we discuss how different possible
cosmological scenarios can arise on considering geometries that include torsion
tensors and provide a framework for modifying GR using Emergent Reimann
Geometry.


title:
Multilingual Knowledge Graph Completion with Self-Supervised Adaptive
  Graph Alignment

abstract:
  Predicting missing facts in a knowledge graph (KG) is crucial as modern KGs
are far from complete. Due to labor-intensive human labeling, this phenomenon
deteriorates when handling knowledge represented in various languages. In this
paper, we explore multilingual KG completion, which leverages limited seed
alignment as a bridge, to embrace the collective knowledge from multiple
languages. However, language alignment used in prior works is still not fully
exploited: (1) alignment pairs are treated equally to maximally push parallel
entities to be close, which ignores KG capacity inconsistency; (2) seed
alignment is scarce and new alignment identification is usually in a noisily
unsupervised manner. To tackle these issues, we propose a novel self-supervised
adaptive graph alignment (SS-AGA) method. Specifically, SS-AGA fuses all KGs as
a whole graph by regarding alignment as a new edge type. As such, information
propagation and noise influence across KGs can be adaptively controlled via
relation-aware attention weights. Meanwhile, SS-AGA features a new pair
generator that dynamically captures potential alignment pairs in a
self-supervised paradigm. Extensive experiments on both the public multilingual
DBPedia KG and newly-created industrial multilingual E-commerce KG empirically
demonstrate the effectiveness of SS-AG


title: (Half) Wormholes under Irrelevant Deformation

abstract:
  Recently it has been shown by Almheiri and Lin [1] that the reconstruction of
black-hole interior is sensitive to knowing the exact coupling of the boundary
theory even if the coupling is irrelevant. This motivates us to enlarge the set
of the one-time and two-time toy models inspired from the SYK by deforming the
same with \textit{irrelevant} coupling. We find that both half-wormholes as
well as the wormholes persist in presence of the deformation, leading to a
similar mechanism for curing the factorization problem. While for the one time
case, the deformed partition function and its moments change by an overall
factor, which can completely be absorbed into a renormalization of coupling,
for the two time (or coupled one time) SYK we find non-trivial dynamics of the
saddles as the couplings are varied. Curiously, the \textit{irrelevant}
deformations that we consider can also be thought of as an ensemble average
over an overall scaling of the original undeformed Hamiltionian with an
appropriate probability distribution, this allows for the possibility that
half-wormholes may also be present in suitably defined ensemble of theories.


title: Widespread subsonic turbulence in Ophiuchus North 1

abstract:
  Supersonic motions are common in molecular clouds. (Sub)sonic turbulence is
usually detected toward dense cores and filaments. However, it remains unknown
whether (sub)sonic motions at larger scales ($\gtrsim$1~pc) can be present in
different environments or not. Located at a distance of about 110 pc, Ophiuchus
North 1 (Oph N1) is one of the nearest molecular clouds that allows in-depth
investigation of its turbulence properties by large-scale mapping observations
of single-dish telescopes. We carried out the $^{12}$CO ($J=1-0$) and C$^{18}$O
($J=1-0$) imaging observations toward Oph N1 with the Purple Mountain
Observatory 13.7 m telescope. The observations have an angular resolution of
$\sim$55\arcsec (i.e., 0.03~pc). Most of the whole C$^{18}$O emitting regions
have Mach numbers of $\lesssim$1, demonstrating the large-scale (sub)sonic
turbulence across Oph N1. Based on the polarization measurements, we estimate
the magnetic field strength of the plane-of-sky component to be
$\gtrsim$9~$\mu$G. We infer that Oph N1 is globally sub-Alfv{\'e}nic, and is
supported against gravity mainly by the magnetic field. The steep velocity
structure function can be caused by the expansion of the Sh~2-27 H{\scriptsize
II} region or the dissipative range of incompressible turbulence. Our
observations reveal a surprising case of clouds characterised by widespread
subsonic turbulence and steep size-linewidth relationship. This cloud is
magnetized where ion-neutral friction should play an important role.


title:
On the fate of the secondary white dwarf in double-degenerate
  double-detonation Type Ia supernovae

abstract:
  The progenitor systems and explosion mechanism of Type Ia supernovae are
still unknown. Currently favoured progenitors include double-degenerate systems
consisting of two carbon-oxygen white dwarfs with thin helium shells. In the
double-detonation scenario, violent accretion leads to a helium detonation on
the more massive primary white dwarf that turns into a carbon detonation in its
core and explodes it. We investigate the fate of the secondary white dwarf,
focusing on changes of the ejecta and observables of the explosion if the
secondary explodes as well rather than survives. We simulate a binary system of
a $1.05\,M_\odot$ and a $0.7\,M_\odot$ carbon-oxygen white dwarf with
$0.03\,M_\odot$ helium shells each. We follow the system self-consistently from
inspiral to ignition, through the explosion, to synthetic observables. We
confirm that the primary white dwarf explodes self-consistently. The helium
detonation around the secondary white dwarf, however, fails to ignite a carbon
detonation. We restart the simulation igniting the carbon detonation in the
secondary white dwarf by hand and compare the ejecta and observables of both
explosions. We find that the outer ejecta at $v>15000\,\mathrm{km\,s^{-1}}$ are
indistinguishable. Light curves and spectra are very similar until $\sim 40$d
after explosion and the ejecta are much more spherical than for violent merger
models. The inner ejecta differ significantly which slows down the decline rate
of the bolometric light curve after maximum of the model with a secondary
explosion by about 20 per cent. We expect future synthetic 3D nebular spectra
to confirm or rule out either model.


title:
The Gaia-ESO Survey: Chemical tagging in the thin disk. Open clusters
  blindly recovered in the elemental abundance space

abstract:
  The chemical makeup of a star provides the fossil information of the
environment where it formed. Under this premise, it should be possible to use
chemical abundances to tag stars that formed within the same stellar
association. This idea - known as chemical tagging - has not produced the
expected results, especially within the thin disk where open stellar clusters
have chemical patterns that are difficult to disentangle. The ultimate goal of
this study is to probe the feasibility of chemical tagging within the thin disk
population using high-quality data from a controlled sample of stars. We also
aim at improving the existing techniques of chemical tagging and giving
guidance on different strategies of clustering analysis in the elemental
abundance space. Here we develop the first blind search of open clusters'
members through clustering analysis in the elemental abundance space using the
OPTICS algorithm applied to data from the Gaia-ESO survey. First, we evaluate
different strategies of analysis, determining which ones are more performing.
Second, we apply these methods to a data set including both field stars and
open clusters attempting a blind recover of as many open clusters as possible.
We show how specific strategies of data analysis can improve the final results.
Specifically, we demonstrate that open clusters can be more efficaciously
recovered with the Manhattan metric and on a space whose dimensions are
carefully selected. Using these (and other) prescriptions we are able to
recover open clusters hidden in our data set and find new members of these
stellar associations. Our results indicate that there are chances of recovering
open clusters' members via clustering analysis in the elemental abundance
space. Presumably, the performances of chemical tagging will further increase
with higher quality data and more sophisticated clustering algorithms.


title:
Operator fusion from wavefunction overlaps: Universal finite-size
  corrections and application to Haagerup model

abstract:
  Given a critical quantum spin chain described by a conformal field theory
(CFT) at long distances, it is crucial to understand the universal conformal
data. One most important ingredient is the operator product expansion (OPE)
coefficients, which describe how operators fuse into each other. It has been
proposed in [Zou, Vidal, Phys. Rev. B 105, 125125] that the OPE coefficients
can be computed from overlaps of low-energy wavefunctions of the spin chain. In
this work, we establish that all conformal data including central charge,
conformal dimensions, and OPE coefficients are encoded in the wavefunction
overlaps, with universal finite-size corrections that depend on the operator
content of the cyclic orbifold CFT. Thus this method allows us to numerically
compute all the conformal data based solely on the low-energy eigenstates. The
predictions are verified in the Ising and XXZ model. As an application, we
study the recently proposed Haagerup model built from the Haagerup fusion
category. We find that the CFT has central charge $c \approx 2.1$ and the
lowest spin-$1$ operator in the twisted sector has scaling dimension $1 <
\Delta_J \leq 1.4$.


title:
Characterization of planar symmetric central configurations of four
  bodies, models, and an application

abstract:
  We overview angle-based models to study planar symmetric central
configurations of four bodies. We present models to determine the masses given
the shape of the configuration and the shape-type of the configuration given
the masses. We also describe a diagram-based method for the counting of the
shape-types in the case of the concave configurations. As an application, we
determine planar symmetric central configurations containing bodies of Earth's
and Moon's masses.


title:
Long-term evolution of supercritical black hole accretion with outflows:
  a subgrid feedback model for cosmological simulations

abstract:
  We study the long-term evolution of the global structure of axisymmetric
accretion flows onto a black hole (BH) at rates substantially higher than the
Eddington value ($\dot{M}_{\rm Edd}$), performing two-dimensional
hydrodynamical simulations with and without radiative diffusion. In the
high-accretion optically-thick limit, where the radiation energy is efficiently
trapped within the inflow, the accretion flow becomes adiabatic and comprises
of turbulent gas in the equatorial region and strong bipolar outflows. As a
result, the mass inflow rate decreases toward the center as $\dot{M}_{\rm
in}\propto r^{p}$ with $p\sim 0.5-0.7$ and a small fraction of the inflowing
gas feeds the nuclear BH. Thus, super-Eddington accretion is sustained only
when a larger amount of gas is supplied from larger radii at $>
100-1000~\dot{M}_{\rm Edd}$. The global structure of the flow settles down to a
quasi-steady state in millions of the orbital timescale at the BH event
horizon, which is $> 10-100$ times longer than that addressed in previous
(magneto-)RHD simulation studies. Energy transport via radiative diffusion
accelerates the outflow near the poles in the inner region but does not change
the overall properties of the accretion flow compared to the cases without
diffusion. Based on our simulation results, we provide a mechanical feedback
model for super-Eddington accreting BHs. This can be applied as a sub-grid
model in large-scale cosmological simulations that do not sufficiently resolve
galactic nuclei, and to the formation of the heaviest gravitational-wave
sources via accretion in dense environments.


title:
Measurement-based interleaved randomised benchmarking using IBM
  processors

abstract:
  Quantum computers have the potential to outperform classical computers at
certain computational tasks, such as prime factorisation and unstructured
searching. However, experimental realisations of quantum computers are subject
to noise. Quantifying the noise is of fundamental importance, since noise is
often the dominant factor preventing the successful realisation of advanced
quantum computations. Here we propose an interleaved randomised benchmarking
protocol for measurement-based quantum computers, in which any single-qubit
measurement-based 2-design can be used to estimate the fidelity of any
single-qubit measurement-based gate. We test our protocol by using a weak
approximate measurement-based 2-design to estimate the fidelity of the Hadamard
gate and the T gate (a universal single-qubit set) on IBM superconducting
quantum computers. To this end, single-qubit measurements were performed on
entangled linear cluster states of up to 31 qubits. Our estimated gate
fidelities show good agreement with gate fidelities calculated from process
tomography results. Furthermore, by artificially increasing noise in the
measurement-based gates, we were able to show that our protocol is able to
detect large noise variations in different measurement-based implementations of
a gate.


title:
Comparing in context: Improving cosine similarity measures with a metric
  tensor

abstract:
  Cosine similarity is a widely used measure of the relatedness of pre-trained
word embeddings, trained on a language modeling goal. Datasets such as
WordSim-353 and SimLex-999 rate how similar words are according to human
annotators, and as such are often used to evaluate the performance of language
models. Thus, any improvement on the word similarity task requires an improved
word representation. In this paper, we propose instead the use of an extended
cosine similarity measure to improve performance on that task, with gains in
interpretability. We explore the hypothesis that this approach is particularly
useful if the word-similarity pairs share the same context, for which distinct
contextualized similarity measures can be learned. We first use the dataset of
Richie et al. (2020) to learn contextualized metrics and compare the results
with the baseline values obtained using the standard cosine similarity measure,
which consistently shows improvement. We also train a contextualized similarity
measure for both SimLex-999 and WordSim-353, comparing the results with the
corresponding baselines, and using these datasets as independent test sets for
the all-context similarity measure learned on the contextualized dataset,
obtaining positive results for a number of tests.


title: Intermediate determinism in general probabilistic theories

abstract:
  Quantum theory is indeterministic, but not completely so. When a system is in
a pure state there are properties it possesses with certainty, known as actual
properties. The actual properties of a quantum system (in a pure state) fully
determine the probability of finding the system to have any other property. We
call this feature intermediate determinism. In dimensions of at least three,
the intermediate determinism of quantum theory is guaranteed by the structure
of its lattice of properties. This observation follows from Gleason's theorem,
which is why it fails to hold in dimension two. In this work we extend the idea
of intermediate determinism from properties to measurements. Under this
extension intermediate determinism follows from the structure of quantum
effects for separable Hilbert spaces of any dimension, including dimension two.
Then, we find necessary and sufficient conditions for a general probabilistic
theory to obey intermediate determinism. We show that, although related, both
the no-restriction hypothesis and a Gleason-type theorem are neither necessary
nor sufficient for intermediate determinism.


title:
Thermoelectric effect and temperature-gradient-driven electrokinetic
  flow of electrolyte solutions in charged nanocapillaries

abstract:
  A systematic theoretical study of thermoelectric effect and
temperature-gradient-driven electrokinetic flow of electrolyte solutions in
charged nanocapillaries is presented. The study is based on a semianalytical
model developed by simultaneously solving the non-isothermal
Poisson-Nernst-Planck-Navier-Stokes equations with the lubrication theory.
Particularly, this paper clarifies the interplay and relative importance of the
thermoelectric mechanisms due to (a) the convective transport of ions caused by
the fluid flow, (b) the dependence of ion electrophoretic mobility on
temperature, (c) the difference in the intrinsic Soret coefficients of cation
and anion. Additionally, synergy conditions for the three thermoelectric
mechanisms to fully cooperate are proposed for thermo-phobic/philic
electrolytes. The temperature-gradient-driven electrokinetic flow is shown to
be a nearly unidirectional flow whose axial velocity profiles vary with the
axial location. Also, the flow can be regarded as a consequence of the
counteraction or cooperation between a thermoelectric-field-driven
electroosmotic flow and a thermo-osmotic flow driven by the osmotic pressure
gradient and dielectric body force. Moreover, the Seebeck coefficient and the
fluid average velocity are demonstrated to be affected by electrolyte-related
parameters. The results are beneficial for understanding the
temperature-gradient-driven electrokinetic transport in nanocapillaries and
also serve as theoretical foundation for the design of low-grade waste heat
recovery devices and thermoosmotic pumps.


title: Partial Skew Motzkin Paths

abstract:
  Motzkin paths consist of up-steps, down-steps, level-steps, and never go
below the $x$-axis. They return to the $x$-axis at the end. The concept of skew
Dyck path \cite{Deutsch-italy} is transferred to skew Motzkin paths, namely, a
left step $(-1,-1)$ is additionally allowed, but the path is not allowed to
intersect itself. The enumeration of these combinatorial objects was known
\cite{Qing}; here, using the kernel method, we extend the results by allowing
them to end at a prescribed level $j$. The approach is completely based on
generating functions.
  Asymptotics of the total number of objects as well as the average height are
also given.


title:
The Lick AGN Monitoring Project 2016: Dynamical Modeling of
  Velocity-Resolved H\b{eta} Lags in Luminous Seyfert Galaxies

abstract:
  We have modeled the velocity-resolved reverberation response of the H\b{eta}
broad emission line in nine Seyfert 1 galaxies from the Lick Active Galactic
Nucleus (AGN) Monitioring Project 2016 sample, drawing inferences on the
geometry and structure of the low-ionization broad-line region (BLR) and the
mass of the central supermassive black hole. Overall, we find that the H\b{eta}
BLR is generally a thick disk viewed at low to moderate inclination angles. We
combine our sample with prior studies and investigate line-profile shape
dependence, such as log10(FWHM/{\sigma}), on BLR structure and kinematics and
search for any BLR luminosity-dependent trends. We find marginal evidence for
an anticorrelation between the profile shape of the broad H\b{eta} emission
line and the Eddington ratio, when using the root-mean-square spectrum.
However, we do not find any luminosity-dependent trends, and conclude that AGNs
have diverse BLR structure and kinematics, consistent with the hypothesis of
transient AGN/BLR conditions rather than systematic trends.


title:
$L^p(\mathbb{R}^2)$ bounds for geometric maximal operators associated to
  homothecy invariant convex bases

abstract:
  Let $\mathcal{B}$ be a nonempty homothecy invariant collection of convex sets
of positive finite measure in $\mathbb{R}^2$. Let $M_\mathcal{B}$ be the
geometric maximal operator defined by $$M_\mathcal{B}f(x) = \sup_{x \in R \in
\mathcal{B}}\frac{1}{|R|}\int_R |f|\;.$$ We show that either $M_\mathcal{B}$ is
bounded on $L^p(\mathbb{R}^2)$ for every $1 < p \leq \infty$ or that
$M_\mathcal{B}$ is unbounded on $L^p(\mathbb{R}^2)$ for every $1 \leq p <
\infty$. As a corollary, we have that any density basis that is a homothecy
invariant collection of convex sets in $\mathbb{R}^2$ must differentiate
$L^p(\mathbb{R}^2)$ for every $1 < p \leq \infty$.


title:
Galaxy Rotation Parameters from OB2 Stars with Proper Motions and
  Parallaxes from the Gaia EDR3 Catalog

abstract:
  We have analyzed the kinematics of OB2 stars with proper motions and
parallaxes selected by Xu et al. from the Gaia EDR3 catalog. The relative
parallax errors for all the stars in this sample do not exceed 10\%. Based on a
sample of 9750 stars, the group velocity components
$(U,V,W)_\odot=(7.21,7.46,8.52)\pm(0.13,0.20,0.10)$~km/s were obtained and the
parameters of the angular velocity of rotation of the Galaxy:
  $\Omega_0 =29.712\pm0.062$~km/s/kpc,
$\Omega^{'}_0=-4.014\pm0.018$~km/s/kpc$^{2}$ and
  $\Omega^{''}_0=0.674\pm0.009$~km/s/kpc$^{3}$. The circular velocity of
rotation of the solar neighborhood around the center of the Galaxy is
$V_0=240.7\pm3.0$~km/s for the assumed distance of the Sun to the galactic
center $R_0=8.1\pm0.1$~kpc. It is shown that the influence of the systematic
correction to the trigonometric parallaxes of the Gaia EDR3 catalog with the
value $\Delta\pi=-0.040$~mas does not exceed the $\sim1\sigma$ level of the
errors of the sought-for kinematic parameters of the model. Based on the proper
motions of OB stars, the following variances of the residual velocities were
found:
$(\sigma_1,\sigma_2,\sigma_3)=(11.79,9.66,7.21)\pm(0.06,0.05,0.04)$~km/s. It is
shown that the first axis of this ellipsoid slightly deviates from the
direction to the center of the Galaxy $L_1=12.4\pm0.1^\circ$, and the third
axis is oriented almost exactly to the north pole of the Galaxy,
$B_3=87.7\pm0.1^\circ.$


title: Covering complexity, scalar curvature, and quantitative $K$-theory

abstract:
  We establish a relationship between a certain notion of covering complexity
of a Riemannian spin manifold and positive lower bounds on its scalar
curvature. This makes use of a pairing between quantitative operator $K$-theory
and Lipschitz topological $K$-theory, combined with an earlier vanishing
theorem for the quantitative higher index.


title:
Offline-Online Learning of Deformation Model for Cable Manipulation with
  Graph Neural Networks

abstract:
  Manipulating deformable linear objects by robots has a wide range of
applications, e.g., manufacturing and medical surgery. To complete such tasks,
an accurate dynamics model for predicting the deformation is critical for
robust control. In this work, we deal with this challenge by proposing a hybrid
offline-online method to learn the dynamics of cables in a robust and
data-efficient manner. In the offline phase, we adopt Graph Neural Network
(GNN) to learn the deformation dynamics purely from the simulation data. Then a
linear residual model is learned in real-time to bridge the sim-to-real gap.
The learned model is then utilized as the dynamics constraint of a trust region
based Model Predictive Controller (MPC) to calculate the optimal robot
movements. The online learning and MPC run in a closed-loop manner to robustly
accomplish the task. Finally, comparative results with existing methods are
provided to quantitatively show the effectiveness and robustness.


title:
Controlling thermodynamics of a quantum heat engine with modulated
  amplitude drivings

abstract:
  External driving of bath temperatures with a phase difference of a
nonequilibrium quantum engine leads to the emergence of geometric effects on
the thermodynamics. In this work, we modulate the amplitude of the external
driving protocols by introducing envelope functions and study the role of
geometric effects on the flux, noise and efficiency of a four-level driven
quantum heat engine coupled with two thermal baths and a unimodal cavity. We
observe that having a finite width of the modulation envelope introduces an
additional control knob for studying the thermodynamics in the adiabatic limit.
The optimization of the flux as well as the noise with respect to thermally
induced quantum coherences becomes possible in presence of geometric effects,
which is hitherto not possible with sinusoidal driving without an envelope. We
also report the deviation of the slope and generation of an intercept in the
standard expression for efficiency at maximum power as a function of Carnot
efficiency in presence of geometric effects under the amplitude modulation.
Further, a recently developed universal bound on the efficiency obtained from
thermodynamic uncertainty relation is shown not to hold when a small width of
the modulation envelope along with a large value of cavity temperature is
maintained.


title:
TL-GAN: Improving Traffic Light Recognition via Data Synthesis for
  Autonomous Driving

abstract:
  Traffic light recognition, as a critical component of the perception module
of self-driving vehicles, plays a vital role in the intelligent transportation
systems. The prevalent deep learning based traffic light recognition methods
heavily hinge on the large quantity and rich diversity of training data.
However, it is quite challenging to collect data in various rare scenarios such
as flashing, blackout or extreme weather, thus resulting in the imbalanced
distribution of training data and consequently the degraded performance in
recognizing rare classes. In this paper, we seek to improve traffic light
recognition by leveraging data synthesis. Inspired by the generative
adversarial networks (GANs), we propose a novel traffic light generation
approach TL-GAN to synthesize the data of rare classes to improve traffic light
recognition for autonomous driving. TL-GAN disentangles traffic light sequence
generation into image synthesis and sequence assembling. In the image synthesis
stage, our approach enables conditional generation to allow full control of the
color of the generated traffic light images. In the sequence assembling stage,
we design the style mixing and adaptive template to synthesize realistic and
diverse traffic light sequences. Extensive experiments show that the proposed
TL-GAN renders remarkable improvement over the baseline without using the
generated data, leading to the state-of-the-art performance in comparison with
the competing algorithms that are used for general image synthesis and data
imbalance tackling.


title:
Registering Explicit to Implicit: Towards High-Fidelity Garment mesh
  Reconstruction from Single Images

abstract:
  Fueled by the power of deep learning techniques and implicit shape learning,
recent advances in single-image human digitalization have reached unprecedented
accuracy and could recover fine-grained surface details such as garment
wrinkles. However, a common problem for the implicit-based methods is that they
cannot produce separated and topology-consistent mesh for each garment piece,
which is crucial for the current 3D content creation pipeline. To address this
issue, we proposed a novel geometry inference framework ReEF that reconstructs
topology-consistent layered garment mesh by registering the explicit garment
template to the whole-body implicit fields predicted from single images.
Experiments demonstrate that our method notably outperforms its counterparts on
single-image layered garment reconstruction and could bring high-quality
digital assets for further content creation.


title:
Relativistic Angular Redshift Fluctuations embedded in Large Scale
  Varying Gravitational Potentials

abstract:
  We compute the linear order, general relativistic corrections to angular
redshift fluctuations (ARF), a new cosmological observable built upon
density-weighted two-dimensional (2D) maps of galaxy redshifts. We start with
an existing approach for galaxy/source counts developed in the Newtonian gauge,
and generalize it to ARF, modifying for this purpose a standard Boltzmann code.
Our calculations allow us identifying the velocity terms as the leading
corrections on large scales, emphasizing the sensitivity of ARF to peculiar,
cosmological velocity fields. Just like for standard 2D clustering, the impact
of gravitational lensing on ARF is dominant on small angular scales and for
wide redshift shells, while the signatures associated to gravitational
potentials are extremely small and hardly detectable. The ARF also present
interesting correlation properties to anisotropies of the Cosmic Microwave
Background (CMB): they are highly correlated to CMB lensing potential
fluctuations, while also exhibiting a significant (S/N$\sim 4$-$5$) {\em
anti-}correlation with the Integrated Sachs-Wolfe effect (ISW). This negative
ARF$\times$ISW signal is quite complementary to the standard 2D
clustering$\times$ISW correlation, since the former appears mostly at higher
redshift ($z\sim 2$) than the latter ($z\lesssim 1)$, and the combination of
the two observables significantly increases the $\chi^2$ statistics testing the
null (no ISW) hypothesis. We conclude that ARF constitute a novel, alternative,
and potentially powerful tool to constrain the nature of Dark Energy component
that gives rise to the ISW.


title:
DAMNETS: A Deep Autoregressive Model for Generating Markovian Network
  Time Series

abstract:
  In this work, we introduce DAMNETS, a deep generative model for Markovian
network time series. Time series of networks are found in many fields such as
trade or payment networks in economics, contact networks in epidemiology or
social media posts over time. Generative models of such data are useful for
Monte-Carlo estimation and data set expansion, which is of interest for both
data privacy and model fitting. Using recent ideas from the Graph Neural
Network (GNN) literature, we introduce a novel GNN encoder-decoder structure in
which an encoder GNN learns a latent representation of the input graph, and a
decoder GNN uses this representation to simulate the network dynamics. We show
using synthetic data sets that DAMNETS can replicate features of network
topology across time observed in the real world, such as changing community
structure and preferential attachment. DAMNETS outperforms competing methods on
all of our measures of sample quality over several real and synthetic data
sets.


title: Quantum monadic algebras

abstract:
  We introduce quantum monadic and quantum cylindric algebras. These are
adaptations to the quantum setting of the monadic algebras of Halmos, and
cylindric algebras of Henkin, Monk and Tarski, that are used in algebraic
treatments of classical and intuitionistic predicate logic. Primary examples in
the quantum setting come from von Neumann algebras and subfactors. Here we
develop the basic properties of these quantum monadic and cylindric algebras
and relate them to quantum predicate logic.


title: Quantum Yu-Shiba-Rusinov dimers

abstract:
  Magnetic adatoms on a superconducting substrate undergo a quantum phase
transition as their exchange coupling to the conduction electrons increases.
For quantum spins, this transition is accompanied by screening of the adatom
spin. Here, we explore the consequences of this screening for the phase
diagrams and subgap excitation spectra of dimers of magnetic adatoms coupled by
hybridization of their Yu-Shiba-Rusinov states and spin-spin interactions. We
specifically account for higher spins, single-ion anisotropy,
Ruderman-Kittel-Kasuya-Yosida coupling, and Dzyaloshinsky-Moriya interactions
relevant in transition-metal and rare-earth systems. Our flexible approach
based on a zero-bandwidth approximation provides detailed physical insight and
is in excellent qualitative agreement with available numerical-renormalization
group calculations on monomers and dimers. Remarkably, we find that even in the
limit of large impurity spins or strong single-ion anisotropy, the phase
diagrams for dimers of quantum spins remain qualitatively distinct from phase
diagrams based on classical spins, highlighting the need for a theory of
quantum Yu-Shiba-Rusinov dimers.


title:
Electron-spin spectral diffusion in an erbium doped crystal at
  millikelvin temperatures

abstract:
  Erbium-doped crystals offer a versatile platform for hybrid quantum devices
because they combine magnetically-sensitive electron-spin transitions with
telecom-wavelength optical transitions. At the high doping concentrations
necessary for many quantum applications, however, strong magnetic interactions
of the electron-spin bath lead to excess spectral diffusion and rapid
decoherence. Here we lithographically fabricate a 4.4 GHz superconducting
planar micro-resonator on a $\text{CaWO}_{4}$ crystal doped with Er ions at a
concentration of twenty parts per million relative to Ca. Using the microwave
resonator, we characterize the spectral diffusion processes that limit the
electron-spin coherence of Er ions at millikelvin temperatures by applying 2-
and 3-pulse echo sequences. The coherence time shows a strong temperature
dependence, reaching 1.3 ms at 23 mK for an electron-spin transition of
$^{167}\text{Er}$.


title:
Net energy up-conversion processes in CdSe/CdS (core/shell) quantum
  dots, a possible pathway to towards optical cooling

abstract:
  The investigation of the possibility of optical refrigeration (OR) on
zinc-blende cadmium selenide/cadmium sulfide (CdSe/CdS) core/shell structure
quantum dots (QDs) has been carried out. Quality samples were synthesized in
our lab, and significant energy up-conversion photoluminescence (UCPL) was
observed in these samples, showing the potential of generating net cooling
effects. To better understand and predict the UCPL characteristics of the QDs,
a semi-empirical model has been developed, showing good agreement with our
experimental results. The model takes into account the corresponding quantum
yield and cooling efficiency, predicting the possibility of realizing optical
refrigeration on a CdSe QDs system.


title: Spectral localization for semimetals and Callias operators

abstract:
  A semiclassical argument is used to show that the low-lying spectrum of a
selfadjoint operator, the so-called spectral localizer, determines the number
of Dirac or Weyl points of an ideal semimetal. Apart from the IMS localization
procedure, an explicit computation for the local toy models given by a Dirac or
Weyl point is the key element of proof. The argument has numerous similarities
to Witten's reasoning leading to the strong Morse inequalities. The same
techniques allow to prove a spectral localization for the Callias operator in
terms of a multi-parameter spectral flow of selfadjoint Fredholm operators.


title:
Deep Interactive Learning-based ovarian cancer segmentation of
  H&E-stained whole slide images to study morphological patterns of BRCA
  mutation

abstract:
  Deep learning has been widely used to analyze digitized hematoxylin and eosin
(H&E)-stained histopathology whole slide images. Automated cancer segmentation
using deep learning can be used to diagnose malignancy and to find novel
morphological patterns to predict molecular subtypes. To train pixel-wise
cancer segmentation models, manual annotation from pathologists is generally a
bottleneck due to its time-consuming nature. In this paper, we propose Deep
Interactive Learning with a pretrained segmentation model from a different
cancer type to reduce manual annotation time. Instead of annotating all pixels
from cancer and non-cancer regions on giga-pixel whole slide images, an
iterative process of annotating mislabeled regions from a segmentation model
and training/finetuning the model with the additional annotation can reduce the
time. Especially, employing a pretrained segmentation model can further reduce
the time than starting annotation from scratch. We trained an accurate ovarian
cancer segmentation model with a pretrained breast segmentation model by 3.5
hours of manual annotation which achieved intersection-over-union of 0.74,
recall of 0.86, and precision of 0.84. With automatically extracted high-grade
serous ovarian cancer patches, we attempted to train another deep learning
model to predict BRCA mutation. The segmentation model and code have been
released at https://github.com/MSKCC-Computational-Pathology/DMMN-ovary.


title:
Theory-guided investigation on magnetic evolution of MnPt$_{5-x}$Pd$_x$P
  and discovery of anti-CeCoIn$_5$-type ferromagnetic MnPd$_5$P

abstract:
  We report the magnetic changes from canted antiferromagnetic to ferromagnetic
orderings in anti-115-type MnPt$_{5-x}$Pd$_x$P ($x$ = 1, 2, 2.5, 3, 4, and 5)
and the discovery of a new rare-earth-free ferromagnet, MnPd$_5$P by both
theoretical prediction and experimental investigation. The family compounds
were synthesized using high temperature solid state method and characterized to
crystalize in the anti-CeCoIn$_5$ type with the space group P4/mmm exhibiting a
two-dimensional layered structural feature. The magnetic property measurements
indicate that the compounds ordered from canted A-type antiferromagnet in
MnPt$_5$P to ferromagnet above the room temperature with varying degrees of
coercivity and magnetic moments in MnPd$_5$P by reducing the spin orbital
coupling. The results of the MnPt$_{5-x}$Pd$_x$P have been analyzed in
comparison to the other candidates of the 151 family of Mn(Pt/Pd)$_5$(P/As) to
understand the complex structure-magnetism relationships.


title: Differential Modules with Complete Intersection Homology

abstract:
  Differential modules are natural generalizations of complexes. In this paper,
we study differential modules with complete intersection homology, comparing
and contrasting the theory of these differential modules with that of the
Koszul complex. We construct a Koszul differential module that directly
generalizes the classical Koszul complex and investigate which properties of
the Koszul complex can be generalized to this setting.


title: Mp-residuated lattices

abstract:
  This paper is devoted to the study of a fascinating class of residuated
lattices, the so-called mp-residuated lattice, in which any prime filter
contains a unique minimal prime filter. A combination of algebraic and
topological methods is applied to obtain new and structural results on
mp-residuated lattices. It is demonstrated that mp-residuated lattices are
strongly tied up with the dual hull-kernel topology. Especially, it is shown
that a residuated lattice is mp if and only if its minimal prime spectrum,
equipped with the dual hull-kernel topology, is Hausdorff if and only if its
prime spectrum, equipped with the dual hull-kernel topology, is normal. The
class of mp-residuated lattices is characterized by means of pure filters. It
is shown that a residuated lattice is mp if and only if its pure filters are
precisely its minimal prime filters, if and only if its pure spectrum is
homeomorphic to its minimal prime spectrum, equipped with the dual hull-kernel
topology.


title:
Sacrificing CSI for a Greater Good: RIS-enabled Opportunistic Rate
  Splitting

abstract:
  In reconfigurable intelligent surface (RIS)-assisted systems, the
optimization of the phase shifts requires separate acquisition of the channel
state information (CSI) for the direct and RIS-assisted channels, posing
significant design challenges. In this paper, a novel scheme is proposed, which
considers practical limitations like pilot overhead and channel estimation (CE)
errors to increase the net performance. More specifically, at the cost of
unpredictable interference, a portion of the CSI for the RIS-assisted channels
is sacrificed in order to reduce the CE time. By alternating the CSI between
coherence blocks and employing rate splitting, it becomes possible to mitigate
the interference, thereby compensating the adverse effect of the sacrificed
CSI. Numerical simulations validate that the proposed scheme exhibits better
performance in terms of achievable net rate, resulting in gains of up to 160%
compared non-orthogonal multiple access (NOMA), when CE time and CE errors are
considered.


title:
Quintessence Dark Energy from strongly-coupled Higgs mass gap: Local and
  Non-local higher-derivative non-perturbative scenarios

abstract:
  We entertain the possibility that dark energy arises from the Higgs field of
quintessence type in the non-perturbative regimes. For this purpose we utilize
a set of exact solutions of Higgs field theory recently devised, in terms of
Jacobi elliptical function for a massless quartic scalar field, that satisfy a
massive dispersion relation. In certain regions of the parameter space,
determined by the quartic coupling value, we show that such solutions have the
property to give the correct behavior for the equation of state of the dark
energy depending on the initial $\theta$ value of periodicity of the Jacobi
elliptical function solution. It is seen that on a time scale determined by the
Hubble constant and the strength of the self-interaction of the scalar field,
when conformal invariance is restored, the equation of state for the dark
energy becomes manifest. Further we investigate scenarios within standard field
theory framework and also extending to higher-derivative theories, namely
infinite-derivative theory, motivated from p-adic string field theory, and
Lee-Wick theories and in all cases, we find suitable choices of the parameter
space leads to dark energy behavior of the Higgs field, which we compare.


title: Few-Shot Object Detection with Fully Cross-Transformer

abstract:
  Few-shot object detection (FSOD), with the aim to detect novel objects using
very few training examples, has recently attracted great research interest in
the community. Metric-learning based methods have been demonstrated to be
effective for this task using a two-branch based siamese network, and calculate
the similarity between image regions and few-shot examples for detection.
However, in previous works, the interaction between the two branches is only
restricted in the detection head, while leaving the remaining hundreds of
layers for separate feature extraction. Inspired by the recent work on vision
transformers and vision-language transformers, we propose a novel Fully
Cross-Transformer based model (FCT) for FSOD by incorporating cross-transformer
into both the feature backbone and detection head. The asymmetric-batched
cross-attention is proposed to aggregate the key information from the two
branches with different batch sizes. Our model can improve the few-shot
similarity learning between the two branches by introducing the multi-level
interactions. Comprehensive experiments on both PASCAL VOC and MSCOCO FSOD
benchmarks demonstrate the effectiveness of our model.


title:
Room temperature deep UV photoluminescence from low dimensional
  hexagonal boron nitride prepared using a facile synthesis

abstract:
  Evaluation of the defect levels in low-dimensional materials is an important
aspect of quantum science. In this article, we report a facile synthesis method
of hexagonal boron nitride (h-BN) and evaluate the defects and their light
emission characteristics. The thermal annealing procedure is optimized to
obtain clean h-BN. The UV-Vis spectroscopy shows the optical energy gap of 5.28
eV which is comparable to the reported energy gap for exfoliated, clean h-BN
samples. The optimized synthesis route of h-BN has generated two kinds of
defects which are characterised using room temperature photoluminescence
measurements. The defects emit light at 4.18 eV (in deep ultraviolet region)
and 3.44 eV (ultraviolet), respectively. The defect emitting deep ultraviolet
(DUV) has oscillatory dependency on the excitation energy, while that emitting
3.44 eV light (ZPL3.44 eV) has a phonon bands with mean energy level separation
of 125 meV measured at room temperature. This agrees very well with the
Franck-Condon-like structure having regularly spaced energy levels, which are
typical indications of single defect levels in the low dimensional h-BN.


title: A two-dimensional programmable tweezer array of fermions

abstract:
  We prepare high-filling two-component arrays of up to fifty fermionic atoms
in optical tweezers, with the atoms in the ground motional state of each
tweezer. Using a stroboscopic technique, we configure the arrays in various
two-dimensional geometries with negligible Floquet heating. Full spin- and
density-resolved readout of individual sites allows us to post-select near-zero
entropy initial states for fermionic quantum simulation. We prepare a
correlated state in a two-by-two tunnel-coupled Hubbard plaquette,
demonstrating all the building blocks for realizing a programmable fermionic
quantum simulator.


title: Epidemic outbreaks with adaptive prevention on complex networks

abstract:
  The adoption of prophylaxis attitudes, such as social isolation and use of
face masks, to mitigate epidemic outbreaks strongly depends on the support of
the population. In this work, we investigate a susceptible-infected-recovered
(SIR) epidemic model, in which the epidemiological perception of the
environment can adapt the behavior of susceptible individuals towards
preventive behavior. {Two compartments of susceptible individuals are
considered, to distinguish those that adopt or not prophylaxis attitudes.} Two
rules, depending on local and global epidemic prevalence, for the spread of the
epidemic in heterogeneous networks are investigated. We present the results of
both heterogeneous mean-field theory and stochastic simulations. The former
performs well for the global rule, but misses relevant outcomes of simulations
in the local case. In simulations, only local awareness can significantly raise
the epidemic threshold, delay the peak of prevalence, and reduce the outbreak
size. Interestingly, we observed that increasing the local perception rate
leads to less individuals recruited to the protected state, but still enhances
the effectiveness in mitigating the outbreak. We also report that network
heterogeneity substantially reduces the efficacy of local awareness mechanisms
since hubs, the super-spreaders of the SIR dynamics, are little responsive to
epidemic environments in the low epidemic prevalence regime. Our results
indicate that strategies that improve the perception of who is socially very
active can improve the mitigation of epidemic outbreaks.


title: Strict derivation of angular-averaged Ewald potential

abstract:
  In this work we strictly derive an angular-averaged Ewald potential suitable
for numerical simulations of disordered Coulomb systems. The potential was
first introduced by E. Yakub and C. Ronchi without strict mathematical
justification. Two methods are used to find the coefficients of the series
expansion of the potential: based on the Euler-Maclaurin and Poisson summation
formulas. The expressions for each coefficient is represented as a finite
series containing derivatives of Jacobi theta functions. We also demonstrate
the formal equivalence of the Poisson and Euler-Maclaurin summation formulas in
the three-dimensional case. The effectiveness of the angular-averaged Ewald
potential is shown by the example of calculating the Madelung constant for a
number of crystal lattices.


title:
A systematic review and meta-analysis of Digital Elevation Model (DEM)
  fusion: pre-processing, methods and applications

abstract:
  The remote sensing community has identified data fusion as one of the key
challenging topics of the 21st century. The subject of image fusion in
two-dimensional (2D) space has been covered in several published reviews.
However, the special case of 2.5D/3D Digital Elevation Model (DEM) fusion has
not been addressed till date. DEM fusion is a key application of data fusion in
remote sensing. It takes advantage of the complementary characteristics of
multi-source DEMs to deliver a more complete, accurate and reliable elevation
dataset. Although several methods for fusing DEMs have been developed, the
absence of a well-rounded review has limited their proliferation among
researchers and end-users. It is often required to combine knowledge from
multiple studies to inform a holistic perspective and guide further research.
In response, this paper provides a systematic review of DEM fusion: the
pre-processing workflow, methods and applications, enhanced with a
meta-analysis. Through the discussion and comparative analysis, unresolved
challenges and open issues were identified, and future directions for research
were proposed. This review is a timely solution and an invaluable source of
information for researchers within the fields of remote sensing and spatial
information science, and the data fusion community at large.


title:
Nonequilibrium thermodynamic characterization of chimeras in a continuum
  chemical oscillator system

abstract:
  The emergence of the chimera state as counterintuitive spatial coexistence of
synchronous and asynchronous regimes is addressed here in a continuum chemical
oscillator system by implementing a relevant complex Ginzburg-Landau equation
with global coupling. This study systematically acquires and characterizes the
evolution of nonequilibrium thermodynamic entities corresponding to the chimera
state. The temporal evolution of the entropy production rate exhibits a beat
pattern with a series of equidistant spectral lines in the frequency domain.
Symmetric profiles associated with the incoherence regime appear in
descriptions of the dynamics and thermodynamics of the chimera. It is shown
that identifying the semigrand Gibbs free energy of the state as the Gabor
elementary function can unveil the guiding role of the information uncertainty
principle in shaping the chimera energetics.

categories: nlin.AO nlin.CD nlin.PS physics.app-ph physics.chem-ph


title: Shape Deformations of Charged R\'enyi Entropies from Holography

abstract:
  Charged and symmetry-resolved R\'enyi entropies are entanglement measures
quantifying the degree of entanglement within different charge sectors of a
theory with a conserved global charge. We use holography to determine the
dependence of charged R\'enyi entropies on small shape deformations away from a
spherical or planar entangling surface in general dimensions. This dependence
is completely characterized by a single coefficient appearing in the two point
function of the displacement operator associated with the R\'enyi defect. We
extract this coefficient using its relation to the one point function of the
stress tensor in the presence of a deformed entangling surface. This is mapped
to a holographic calculation in the background of a deformed charged black hole
with hyperbolic horizon. We obtain numerical solutions for different values of
the chemical potential and replica number $n$ in various spacetime dimensions,
as well as analytic expressions for small chemical potential near $n=1$. When
the R\'enyi defect becomes supersymmetric, we demonstrate a conjectured
relation between the two point function of the displacement operator and the
conformal weight of the twist operator.

categories: hep-th cond-mat.stat-mech cond-mat.str-el quant-ph


title: Almost every path structure is not variational

abstract:
  Given a smooth family of unparameterized curves such that through every point
in every direction there passes exactly one curve, does there exist a
Lagrangian with extremals being precisely this family? It is known that in
dimension 2 the answer is positive. In dimension 3, it follows from the work of
Douglas that the answer is, in general, negative. We generalise this result to
all higher dimensions and show that the answer is actually negative for almost
every such a family of curves, also known as path structure or path geometry.
On the other hand, we consider path geometries possessing infinitesimal
symmetries and show that path and projective structures with submaximal
symmetry dimensions are variational. Note that the projective structure with
the submaximal symmetry algebra, the so-called Egorov structure, is not
pseudo-Riemannian metrizable; we show that it is metrizable in the class of
Kropina pseudo-metrics and explicitly construct the corresponding Kropina
Lagrangian.


title:
Solving Disjunctive Temporal Networks with Uncertainty under Restricted
  Time-Based Controllability using Tree Search and Graph Neural Networks

abstract:
  Planning under uncertainty is an area of interest in artificial intelligence.
We present a novel approach based on tree search and graph machine learning for
the scheduling problem known as Disjunctive Temporal Networks with Uncertainty
(DTNU). Dynamic Controllability (DC) of DTNUs seeks a reactive scheduling
strategy to satisfy temporal constraints in response to uncontrollable action
durations. We introduce new semantics for reactive scheduling: Time-based
Dynamic Controllability (TDC) and a restricted subset of TDC, R-TDC. We design
a tree search algorithm to determine whether or not a DTNU is R-TDC. Moreover,
we leverage a graph neural network as a heuristic for tree search guidance.
Finally, we conduct experiments on a known benchmark on which we show R-TDC to
retain significant completeness with regard to DC, while being faster to prove.
This results in the tree search processing fifty percent more DTNU problems in
R-TDC than the state-of-the-art DC solver does in DC with the same time budget.
We also observe that graph neural network search guidance leads to substantial
performance gains on benchmarks of more complex DTNUs, with up to eleven times
more problems solved than the baseline tree search.


title:
An efficient GPU-Parallel Coordinate Descent Algorithm for Sparse
  Precision Matrix Estimation via Scaled Lasso

abstract:
  The sparse precision matrix plays an essential role in the Gaussian graphical
model since a zero off-diagonal element indicates conditional independence of
the corresponding two variables given others. In the Gaussian graphical model,
many methods have been proposed, and their theoretical properties are given as
well. Among these, the sparse precision matrix estimation via scaled lasso
(SPMESL) has an attractive feature in which the penalty level is automatically
set to achieve the optimal convergence rate under the sparsity and
invertibility conditions. Conversely, other methods need to be used in
searching for the optimal tuning parameter. Despite such an advantage, the
SPMESL has not been widely used due to its expensive computational cost. In
this paper, we develop a GPU-parallel coordinate descent (CD) algorithm for the
SPMESL and numerically show that the proposed algorithm is much faster than the
least angle regression (LARS) tailored to the SPMESL. Several comprehensive
numerical studies are conducted to investigate the scalability of the proposed
algorithm and the estimation performance of the SPMESL. The results show that
the SPMESL has the lowest false discovery rate for all cases and the best
performance in the case where the level of the sparsity of the columns is high.


title:
Reverse Link Analysis for Full-Duplex Cellular Networks with Low
  Resolution ADC/DAC

abstract:
  In this work, we consider a full-duplex (FD) massive multiple-input
multiple-output (MIMO) cellular network with low-resolution analog-to-digital
converters (ADCs) and digital-to-analog converter (DACs). Our first
contribution is to provide a unified framework for reverse link analysis where
matched filters are applied at the FD base stations (BSs) under channel
hardening. Second, we derive the expressions of the
signal-to-quantization-plus-interference-plus-noise ratio (SQINR) for general
and special cases. Finally, we quantify effects of quantization error, pilot
contamination, and full duplexing for a hexagonal cell lattice on spectral
efficiency and cumulative distribution function (CDF) to show that FD
outperforms half duplex (HD) in a wide variety of scenarios.


title:
Nonextensive percolation and Lee-Yang edge singularity from nonextensive
  $\lambda\phi^{3}$ scalar field theory

abstract:
  We compute the critical exponents for nonextensive $\lambda\phi^{3}$ scalar
field theory for all loop orders and $|q - 1| < 1$. We apply the results for
both nonextensive percolation and Lee-Yang edge singularity problems. The
corresponding systems are nonextensive generalizations of their extensive
counterparts. For that we employ tools from the recently introduced
nonextensive statistical field theory. The results for the nonextensive
critical exponents computed depend on the nonextensive parameter $q$, which
encodes global correlations among the degrees of freedom of the system. The
extensive results are recovered in the limit $q\rightarrow 1$. There is an
interplay between global correlations and fluctuations, once the nonextensive
critical exponents depend on $q$. This dependence is in agreement with the
universality hypothesis.


title:
Learning Parameterized Task Structure for Generalization to Unseen
  Entities

abstract:
  Real world tasks are hierarchical and compositional. Tasks can be composed of
multiple subtasks (or sub-goals) that are dependent on each other. These
subtasks are defined in terms of entities (e.g., "apple", "pear") that can be
recombined to form new subtasks (e.g., "pickup apple", and "pickup pear"). To
solve these tasks efficiently, an agent must infer subtask dependencies (e.g.
an agent must execute "pickup apple" before "place apple in pot"), and
generalize the inferred dependencies to new subtasks (e.g. "place apple in pot"
is similar to "place apple in pan"). Moreover, an agent may also need to solve
unseen tasks, which can involve unseen entities. To this end, we formulate
parameterized subtask graph inference (PSGI), a method for modeling subtask
dependencies using first-order logic with subtask entities. To facilitate this,
we learn entity attributes in a zero-shot manner, which are used as quantifiers
(e.g. "is_pickable(X)") for the parameterized subtask graph. We show this
approach accurately learns the latent structure on hierarchical and
compositional tasks more efficiently than prior work, and show PSGI can
generalize by modelling structure on subtasks unseen during adaptation.


title: Slow-roll inflation in $f(R,T)$ gravity with a $RT$ mixing term

abstract:
  We consider slow-roll inflationary models in a class of modified theories of
gravity which contains non-minimal curvature-inflaton couplings, i.e., the
$f(R,T)$ gravity, where $R$ is the Ricci scalar and $T$ is the trace of the
inflaton energy-momentum tensor. On top of the minimally coupled $T$ that has
been widely investigated in the literature, we further include a $RT$ mixing
term in the theory. This mixing term introduces non-minimal derivative
couplings and plays an important role in inflationary dynamics. Taking chaotic
and natural inflation as examples, we find that the predictions for spectral
tilt and the tensor-to-scalar ratio are sensitive to the existence of the $RT$
mixing term. In particular, by turning on this mixing term, it is possible to
bring chaotic and natural inflation into better agreement with observational
data.


title: Ergodicity of unlabeled dynamics of Dyson's model in infinite dimensions

abstract:
  Dyson's model in infinite dimensions is a system of Brownian particles that
interact via a logarithmic potential with an inverse temperature of $ \beta =
2$. The stochastic process can be represented by the solution to an
infinite-dimensional stochastic differential equation. The associated unlabeled
dynamics (diffusion process) are given by the Dirichlet form with the sine$ _2$
point process as a reference measure. In a previous study, we proved that
Dyson's model in infinite dimensions is irreducible, but left the ergodicity of
the unlabeled dynamics as an open problem. In this paper, we prove that the
unlabeled dynamics of Dyson's model in infinite dimensions are ergodic.


title: Online Algorithms for Matching Platforms with Multi-Channel Traffic

abstract:
  Two-sided platforms rely on their recommendation algorithms to help visitors
successfully find a match. However, on platforms such as VolunteerMatch (VM) --
which has facilitated millions of connections between volunteers and nonprofits
-- a sizable fraction of website traffic arrives directly to a nonprofit's
volunteering page via an external link, thus bypassing the platform's
recommendation algorithm. We study how such platforms should account for this
external traffic in the design of their recommendation algorithms, given the
goal of maximizing successful matches. We model the platform's problem as a
special case of online matching, where (using VM terminology) volunteers arrive
sequentially and probabilistically match with one opportunity, each of which
has finite need for volunteers. In our framework, external traffic is
interested only in their targeted opportunity; by contrast, internal traffic
may be interested in many opportunities, and the platform's online algorithm
selects which opportunity to recommend. In evaluating different algorithms, we
parameterize the competitive ratio based on the amount of external traffic.
After demonstrating the shortcomings of a commonly-used algorithm that is
optimal in the absence of external traffic, we propose a new algorithm --
Adaptive Capacity (AC) -- which accounts for matches differently based on
whether they originate from internal or external traffic. We provide a lower
bound on AC's competitive ratio that is increasing in the amount of external
traffic and that is close to the parameterized upper bound we establish on the
competitive ratio of any online algorithm. Our analysis utilizes a path-based,
pseudo-rewards approach, which we further generalize to settings where the
platform can recommend a ranked set of opportunities. Beyond our theoretical
results, we show the strong performance of AC in a case study motivated by VM
data.


title:
Accelerating innovation with software abstractions for scalable
  computational geophysics

abstract:
  We present the SLIM (https://github.com/slimgroup) open-source software
framework for computational geophysics, and more generally, inverse problems
based on the wave-equation (e.g., medical ultrasound). We developed a software
environment aimed at scalable research and development by designing multiple
layers of abstractions. This environment allows the researchers to easily
formulate their problem in an abstract fashion, while still being able to
exploit the latest developments in high-performance computing. We illustrate
and demonstrate the benefits of our software design on many geophysical
applications, including seismic inversion and physics-informed machine learning
for geophysics (e.g., loop unrolled imaging, uncertainty quantification), all
while facilitating the integration of external software.


title: Quantum Genetic Algorithm with Individuals in Multiple Registers

abstract:
  Genetic algorithms are heuristic optimization techniques inspired by
Darwinian evolution, which are characterized by successfully finding robust
solutions for optimization problems. Here, we propose a subroutine-based
quantum genetic algorithm with individuals codified in independent registers.
This distinctive codification allows our proposal to depict all the fundamental
elements characterizing genetic algorithms, i.e. population-based search with
selection of many individuals, crossover, and mutation. Our subroutine-based
construction permits us to consider several variants of the algorithm. For
instance, we firstly analyze the performance of two different quantum cloning
machines, a key component of the crossover subroutine. Indeed, we study two
paradigmatic examples, namely, the biomimetic cloning of quantum observables
and the Bu\v zek-Hillery universal quantum cloning machine, observing a faster
average convergence of the former, but better final populations of the latter.
Additionally, we analyzed the effect of introducing a mutation subroutine,
concluding a minor impact on the average performance. Furthermore, we introduce
a quantum channel analysis to prove the exponential convergence of our
algorithm and even predict its convergence-ratio. This tool could be extended
to formally prove results on the convergence of general non-unitary
iteration-based algorithms.


title:
Confirming new changing--look AGNs discovered through optical
  variability using a random-forest based light curve classifier

abstract:
  Determining the frequency and duration of changing--look (CL) active galactic
nuclei (AGNs) phenomena, where the optical broad emission lines appear or
disappear, is crucial to understand the evolution of the accretion flow around
supermassive black holes. We present a strategy to select new CL candidates
starting from a spectroscopic type 2 AGNs sample and searching for current type
1 photometric variability. We use the publicly available Zwicky Transient
Facility (ZTF) alert stream and the Automatic Learning for the Rapid
Classification of Events (ALeRCE) light curve classifier to produce a list of
CL candidates with a highly automated algorithm, resulting in 60 candidates.
Visual inspection reduced the sample to 30. We performed new spectroscopic
observations of six candidates of our clean sample, without further refinement,
finding the appearance of clear broad Balmer lines in four of them and
tentative evidence of type changes in the remaining two, which suggests a
promising success rate of $\geq66$ per cent for this CL selection method.


title:
Socially Compliant Navigation Dataset (SCAND): A Large-Scale Dataset of
  Demonstrations for Social Navigation

abstract:
  Social navigation is the capability of an autonomous agent, such as a robot,
to navigate in a 'socially compliant' manner in the presence of other
intelligent agents such as humans. With the emergence of autonomously
navigating mobile robots in human populated environments (e.g., domestic
service robots in homes and restaurants and food delivery robots on public
sidewalks), incorporating socially compliant navigation behaviors on these
robots becomes critical to ensuring safe and comfortable human robot
coexistence. To address this challenge, imitation learning is a promising
framework, since it is easier for humans to demonstrate the task of social
navigation rather than to formulate reward functions that accurately capture
the complex multi objective setting of social navigation. The use of imitation
learning and inverse reinforcement learning to social navigation for mobile
robots, however, is currently hindered by a lack of large scale datasets that
capture socially compliant robot navigation demonstrations in the wild. To fill
this gap, we introduce Socially CompliAnt Navigation Dataset (SCAND) a large
scale, first person view dataset of socially compliant navigation
demonstrations. Our dataset contains 8.7 hours, 138 trajectories, 25 miles of
socially compliant, human teleoperated driving demonstrations that comprises
multi modal data streams including 3D lidar, joystick commands, odometry,
visual and inertial information, collected on two morphologically different
mobile robots a Boston Dynamics Spot and a Clearpath Jackal by four different
human demonstrators in both indoor and outdoor environments. We additionally
perform preliminary analysis and validation through real world robot
experiments and show that navigation policies learned by imitation learning on
SCAND generate socially compliant behaviors


title:
Tone generation in an open-end organ pipe: How a resonating sphere of
  air stops the pipe

abstract:
  According to the classical Helmholtz picture, an organ pipe while generating
its eigentone has two anti-nodes at the two open ends of a cylinder, the
anti-nodes being taken as boundary condition for the corresponding sound. Since
1860 it is also known that according to the classical picture the pipe actually
sounds lower, which is to say that the pipe so-to-speak sounds longer than it
is, a long-standing enigma. As for the pipe's end, we have resolved this
acoustic enigma by detailing the physics of the airflow at the pipe's open end
and showing that the boundary condition is actually the pipe's acoustically
resonating vortical sphere (PARVS). The PARVS geometry entails a
sound-radiating hemisphere based on the pipe's open end and enclosing a vortex
ring. In this way we obtain not only a physical explanation of sound radiation
from the organ-pipe's open end, in particular, of its puzzling dependence upon
the pipe's radius, but also an appreciation of it as realization of the sound
of the flute, mankind's oldest musical instrument.


title: Efficient Algorithm for Deterministic Search of Hot Elements

abstract:
  When facing a very large stream of data, it is often desirable to extract
most important statistics online in a short time and using small memory. For
example, one may want to quickly find the most influential users generating
posts online or check if the stream contains many identical elements. In this
paper, we study streams containing insertions and deletions of elements from a
possibly large set $N$ of size $|N| = n$, that are being processed by online
deterministic algorithms. At any point in the stream the algorithm may be
queried to output elements of certain frequency in the already processed
stream. More precisely, the most frequent elements in the stream so far. The
output is considered correct if the returned elements it contains all elements
with frequency greater than a given parameter $\varphi$ and no element with
frequency smaller than $\varphi-\epsilon$. We present an efficient online
deterministic algorithm for solving this problem using $O(\min(n,
\frac{polylog(n)}{\epsilon}))$ memory and $O(polylog(n))$ time per processing
and outputting an element. It is the first such algorithm as the previous
algorithms were either randomized, or processed elements in substantially
larger time $\Omega(\min(n, \frac{\log n}{\epsilon}))$, or handled only
insertions and required two passes over the stream (i.e., were not truly
online). Our solution is almost-optimally scalable (with only a polylogarithmic
overhead) and does not require randomness or scanning twice through the stream.
We complement the algorithm analysis with a lower bound $\Omega(\min(n,
\frac{1}{\epsilon}))$ on required memory.


title:
Are Deepfakes Concerning? Analyzing Conversations of Deepfakes on Reddit
  and Exploring Societal Implications

abstract:
  Deepfakes are synthetic content generated using advanced deep learning and AI
technologies. The advancement of technology has created opportunities for
anyone to create and share deepfakes much easier. This may lead to societal
concerns based on how communities engage with it. However, there is limited
research available to understand how communities perceive deepfakes. We
examined deepfake conversations on Reddit from 2018 to 2021 -- including major
topics and their temporal changes as well as implications of these
conversations. Using a mixed-method approach -- topic modeling and qualitative
coding, we found 6,638 posts and 86,425 comments discussing concerns of the
believable nature of deepfakes and how platforms moderate them. We also found
Reddit conversations to be pro-deepfake and building a community that supports
creating and sharing deepfake artifacts and building a marketplace regardless
of the consequences. Possible implications derived from qualitative codes
indicate that deepfake conversations raise societal concerns. We propose that
there are implications for Human Computer Interaction (HCI) to mitigate the
harm created from deepfakes.


title:
The Initial Calibration Date of the Antikythera Mechanism after the
  Saros spiral mechanical Apokatastasis

abstract:
  This work analyzes the phase correlation of the three lunar cycles and the
Saros/Exeligmos cycle, after the study of the chapter About Exeligmos in
Introduction to the Phenomena by Geminus. Geminus, refers that each Exeligmos
cycle began on very specific and rare dates, when the Moon positioned at the
points of the three lunar cycles beginning: New moon at Apogee and at the Node.
The extremely large duration of the Annular Solar eclipse occurred on December
22 178BC (Saros series 58), marks the start of the Prominent Saros Cycle
Apokatastasis. The next day, 23 December 178BC, the Winter Solstice started.
During these two neighboring dates, the celebration of the religious festival
of Isia started in Egypt and the Hellenistic Greece. After the analysis of the
Mechanism's Parapegma events specific position, 22/23 December 178BC is an
ideal, functional and representative initial date, in order to calibrate the
initial position of the Mechanism's pointers.

categories: physics.hist-ph astro-ph.EP astro-ph.IM astro-ph.SR


title: AUC Maximization in the Era of Big Data and AI: A Survey

abstract:
  Area under the ROC curve, a.k.a. AUC, is a measure of choice for assessing
the performance of a classifier for imbalanced data. AUC maximization refers to
a learning paradigm that learns a predictive model by directly maximizing its
AUC score. It has been studied for more than two decades dating back to late
90s and a huge amount of work has been devoted to AUC maximization since then.
Recently, stochastic AUC maximization for big data and deep AUC maximization
for deep learning have received increasing attention and yielded dramatic
impact for solving real-world problems. However, to the best our knowledge
there is no comprehensive survey of related works for AUC maximization. This
paper aims to address the gap by reviewing the literature in the past two
decades. We not only give a holistic view of the literature but also present
detailed explanations and comparisons of different papers from formulations to
algorithms and theoretical guarantees. We also identify and discuss remaining
and emerging issues for deep AUC maximization, and provide suggestions on
topics for future work.


title: Amalgamating the real Gamma and Zeta functions

abstract:
  We develop multisummability, in the positive real direction, for generalized
power series with natural support, and we prove o-minimality of the expansion
of the real field by all multisums of these series. This resulting structure
expands both $\mathbb{R}_{\mathcal{G}}$ and the reduct of
$\mathbb{R}_{\mathrm{an}^*}$ generated by all convergent generalized power
series with natural support; in particular, it defines both the Gamma function
on $(0,\infty)$ and the Zeta function on $(1,\infty)$.


title: A Methodology for CHF Prediction in VVER Rod Bundles

abstract:
  The critical heat flux (CHF) is an important thermal-hydraulic parameter that
must be determined during the design of water-cooled reactors to ensure safety
of operation. In this paper, a methodology is proposed to predict the CHF in
VVER rod bundles. A whole-reactor model of VVER-1000 is implemented in
RELAP-SCDAPSIM, and, based on the bundle-average approach, the parameters of
the hot channel are used to assess four CHF predictors (i.e., the 2006
Groeneveld look-up table (LUT), and the Bowring, W-3, Biasi, and OKB-Gidropress
correlations) upon comparison with the 2011 Bobkov LUT. A brief critique is
given of the correction factors for both the Groeneveld and Bobkov LUTs. The
effects of channel diameter and non-uniform axial heat flux on the predicted
values of CHF in subcooled conditions are also studied. The performance and
time complexity of the standard and Bourke trilinear interpolation algorithms
are assessed for use with LUTs in safety codes. It has been concluded that the
non-uniform axial heat flux does not affect the CHF in subcooled conditions and
that the Groeneveld diameter factor exponent gives better predictability of the
CHF in VVER rod bundles over the Tanase and Wong exponent correlations.
Additionally, the Groeneveld LUT has been found to nearly reproduces the Bobkov
LUT CHF values when used in conjunction with the Bobkov heated length factor.
Values of MDNBR equal to 1.83 and 1.96 can be used as thermal limits at 12\%
overpower when the Bowring and W-3 correlations, respectively, are used in
subcooled conditions. The standard and Bourke algorithms nearly exhibit the
same average CPU time. The Bourke algorithm, however, is less affected by
noise. Further research is needed to ensure the smoothness of the Bobkov LUT
and to derive reactor type-dependent correction factor formulas for use with
the Groeneveld LUT.


title: Compressible fluid motion with uncertain data

abstract:
  We propose a suitable analytical framework to perform numerical analysis of
problems arising in compressible fluid models with uncertain data. We discuss
both weak and strong stochastic approach, where the former is based on the
knowledge of the mere distribution (law) of the random data typical for the
Monte-Carlo and related methods, while the latter assumes the data to be known
as a random variabble on a given probability space aiming at obtaining the
associated solution in the same form. As an example of the strong approach, we
discuss the stochastic collocation method based on a piecewise constant
approximation of the random data.


title: Critical Casimir Effect: Exact Results

abstract:
  If a material body is immersed into a fluctuating medium, its shape and the
properties of its constituents modify the fluctuations in the surrounding
medium. If in the same medium there is a second body, modifications of the
fluctuation due to the first one influence the modifications due to the second
one. This mutual influence results in a force between these bodies. If the
fluctuating medium consists of the confined electromagnetic field in vacuum,
one speaks of the quantum mechanical Casimir effect. In the case that the order
parameter of material fields fluctuates - such as differences of number
densities or concentrations - and that the corresponding fluctuations of the
order parameter are long-ranged, one speaks of the critical Casimir effect.
This holds, e.g., in the case of systems which undergo a second-order phase
transition and which are thermodynamically located near the corresponding
critical point, or for systems with a continuous symmetry exhibiting Goldstone
mode excitations. Here we review the currently available exact results
concerning the critical Casimir effect in systems encompassing the
one-dimensional Ising, XY, and Heisenberg models, the two-dimensional Ising
model, the Gaussian and the spherical models, as well as the mean field results
for the Ising and the XY model. Special attention is paid to the influence of
the boundary conditions on the behavior of the Casimir force.

categories: cond-mat.stat-mech cond-mat.soft hep-th math-ph math.MP quant-ph


title: Ultra-long photonic quantum walks via spin-orbit metasurfaces

abstract:
  The possibility of fine-tuning the couplings between optical modes is a key
requirement in programmable photonic simulators and quantum computers.
Engineering particle evolutions across large lattices is a challenging task,
which requires sophisticated setups that are often intrinsically lossy. Here we
report ultra-long photonic quantum walks across several hundred optical modes,
obtained by propagating a light beam through very few closely-stacked
liquid-crystal metasurfaces, without any optical amplification. By exploiting
spin-orbit effects, these metasurfaces realize a space-dependent polarization
transformation which mixes circularly polarized optical modes carrying
quantized transverse momentum. With this setup we engineer quantum walks up to
320 discrete steps, far beyond state-of-the-art experiments. To showcase the
potential of this method, we experimentally demonstrate that in the long-time
limit a quantum walk affected by dynamical disorder generates maximal
entanglement between two system partitions. Our platform grants experimental
access to ultra-long unitary evolutions while keeping optical losses constant,
thereby paving the way to massive multi-photon multi-mode quantum simulations.


title: Learning Minimum-Time Flight in Cluttered Environments

abstract:
  We tackle the problem of minimum-time flight for a quadrotor through a
sequence of waypoints in the presence of obstacles while exploiting the full
quadrotor dynamics. Early works relied on simplified dynamics or polynomial
trajectory representations that did not exploit the full actuator potential of
the quadrotor, and, thus, resulted in suboptimal solutions. Recent works can
plan minimum-time trajectories; yet, the trajectories are executed with control
methods that do not account for obstacles. Thus, a successful execution of such
trajectories is prone to errors due to model mismatch and in-flight
disturbances. To this end, we leverage deep reinforcement learning and
classical topological path planning to train robust neural-network controllers
for minimum-time quadrotor flight in cluttered environments. The resulting
neural network controller demonstrates significantly better performance of up
to 19% over state-of-the-art methods. More importantly, the learned policy
solves the planning and control problem simultaneously online to account for
disturbances, thus achieving much higher robustness. As such, the presented
method achieves 100% success rate of flying minimum-time policies without
collision, while traditional planning and control approaches achieve only 40%.
The proposed method is validated in both simulation and the real world.


title:
Application of Stabilized Explicit Runge-Kutta Methods to the
  Incompressible Navier-Stokes Equations by means of a Projection Method and a
  Differential Algebraic Approach

abstract:
  In this master thesis we have compared different second order stabilized
explicit Runge-Kutta methods when applied to the incompressible Navier-Stokes
equations by means of a projection method and a differential algebraic
approach. We explored the stability and accuracy properties of the RKC, ROCK2
and PIROCK schemes when coupled with the projection and the differential
algebraic approach. PIROCK has shown unexpected instabilities, ROCK2 resulted
to be the most efficient and versatile Runge-Kutta method taken into account.
The differential algebraic approach sounds computationally costly but it
exhibits better accuracy and a larger stability region. These properties make
it more efficient than the projection method. The theory presented in the first
chapters is supported by numerical experiments.


title: Local extrema for hypercube sections

abstract:
  Consider the hyperplanes at a fixed distance $t$ from the center of the
hypercube $[0,1]^d$. Significant attention has been given to determining the
hyperplanes $H$ among these such that the $(d-1)$-dimensional volume of
$H\cap[0,1]^d$ is maximal or minimal. In the spirit of a question by Vitali
Milman, the corresponding local problem is considered here when $H$ is
orthogonal to a diagonal or a sub-diagonal of the hypercube. It is proven in
particular that this volume is strictly locally maximal at the diagonals in all
dimensions greater than $3$ within a range for $t$ that grows like
$\sqrt{d}/\log{d}$. At lower order sub-diagonals, this volume is shown to be
strictly locally maximal when $t$ is close to $0$ and not locally extremal when
$t$ is large. This relies on a characterisation of local extremality at the
diagonals and sub-diagonals that allows to solve the problem over the whole
possible range for $t$ in any fixed, reasonably low dimension.


title:
High-ionisation oxygen absorption from the Warm-Hot Intergalactic Medium
  in Simba

abstract:
  We examine the physical conditions, environments, and statistical properties
of intergalactic Ovi, Ovii and Oviii absorbers in the Simba cosmological
hydrodynamic simulation suite. The goal is to understand the nature of these
high ionisation absorbers, and test Simba's surprising prediction that $\sim
70\%$ of cosmic baryons at $z=0$ are in the Warm-Hot Intergalactic Medium
(WHIM) driven by jet feedback from active galactic nuclei (AGN). By comparing a
full-physics Simba run versus one with jets turned off, we find that jet
feedback causes widespread heating that impacts the absorption morphology
particularly of the higher ions. However, the distribution of the physical
properties of detectable absorbers are not dramatically affected. Higher
ionisation absorbers probe hotter gas as expected, but in Simba all ions arise
at similar overdensities (typically $\delta\sim20-30$), similar environments
(predominantly filaments), and similar nearest-halo distances (typically
$\sim2-3r_{200c}$). Simba matches the observed Ovi column density distribution
function (CDDF) fairly well, but under-predicts the CDDF preliminarily derived
from two detected intergalactic Ovii absorbers. Predicted CDDFs are very
similar at $z=1$ with or without jets, but show differences by $z=0$
particularly at the high-column end. Despite some discrepancies, Simba
reproduces available observations as well as or better than other comparable
simulations, suggesting that Simba's widespread jet heating cannot be ruled out
by these data. These results offer hope that future X-ray and ultraviolet
facilities could provide significant constraints on galactic feedback models
from high-ionisation IGM metal absorbers.


title:
Monte Carlo Calculations of the Extraction of Scintillation Light from
  Cryogenic N-type GaAs

abstract:
  The high scintillation luminosity of n-type GaAs at 10K is surprising because
(1) with a refractive index of about 3.5, escape is inhibited by total internal
reflection and (2) narrow-beam experiments at 90K report infrared absorption
coefficients of several per cm. This paper presents Monte Carlo calculations
showing that the high luminosity at 10K can be explained if (1) narrow-beam
absorption is almost all optical scattering and (2) the absolute absorption
coefficient is below 0.1 per cm. Sixteen surface reflector configurations are
simulated for a range of internal scattering and absolute absorption
coefficients, and these can guide the design of cryogenic scintillating GaAs
targets for the direct detection of dark matter. The discussion section
presents a possible infrared scattering mechanism based on the metallic nature
of n-type GaAs. Appendix A describes the Monte Carlo program steps in detail.
Appendix B shows how narrow-beam and integrating sphere experiments can measure
the cryogenic optical scattering and absolute absorption coefficients.


title:
Emergence of unidirectional coherent quasiparticles from
  high-temperature rotational symmetry broken phase of AV3Sb5 kagome
  superconductors

abstract:
  Kagome metals AV3Sb5 display a rich phase diagram of correlated electron
states, including superconductivity and novel density waves. Within this
landscape, recent experiments reveal signs of a new transition below T ~ 35 K
attributed to the highly sought-after electronic nematic phase that
spontaneously breaks rotational symmetry of the lattice. We use
spectroscopic-imaging scanning tunneling microscopy to study atomic-scale
signatures of electronic symmetry breaking as a function of temperature across
several materials in this family: CsV3Sb5, KV3Sb5 and Sn-doped CsV3Sb5. We find
that rotational symmetry breaking onsets universally at a high temperature in
these materials, toward the 2 x 2 charge density wave (CDW) transition
temperature T*. At a significantly lower temperature of about 30 K, we discover
a striking emergence of the quantum interference of coherent quasiparticles, a
key signature for the formation of a coherent electronic state. These
quasiparticles display a pronounced unidirectional reciprocal-space
fingerprint, which strengthens on approaching the superconducting state. Our
experiments reveal that the high-temperature charge ordering states are
separated from the superconducting ground state by an intermediate-temperature
regime with coherent unidirectional quasiparticles. Their emergence that occurs
significantly below the onset of rotational symmetry breaking is
phenomenologically different compared to high-temperature superconductors,
shedding light on the complex nature of electronic nematicity in AV3Sb5 kagome
superconductors.


title:
A distribution-dependent Mumford-Shah model for unsupervised
  hyperspectral image segmentation

abstract:
  Hyperspectral images provide a rich representation of the underlying spectrum
for each pixel, allowing for a pixel-wise classification/segmentation into
different classes. As the acquisition of labeled training data is very
time-consuming, unsupervised methods become crucial in hyperspectral image
analysis. The spectral variability and noise in hyperspectral data make this
task very challenging and define special requirements for such methods.
  Here, we present a novel unsupervised hyperspectral segmentation framework.
It starts with a denoising and dimensionality reduction step by the
well-established Minimum Noise Fraction (MNF) transform. Then, the Mumford-Shah
(MS) segmentation functional is applied to segment the data. We equipped the MS
functional with a novel robust distribution-dependent indicator function
designed to handle the characteristic challenges of hyperspectral data. To
optimize our objective function with respect to the parameters for which no
closed form solution is available, we propose an efficient fixed point
iteration scheme. Numerical experiments on four public benchmark datasets show
that our method produces competitive results, which outperform two
state-of-the-art methods substantially on three of these datasets.


title:
A New Analysis of 8 Spitzer Phase Curves and Hot Jupiter Population
  Trends: Qatar-1b, Qatar-2b, WASP-52b, WASP-34b, and WASP-140b

abstract:
  With over 30 phase curves observed during the warm Spitzer mission, the
complete data set provides a wealth of information relating to trends and
three-dimensional properties of hot Jupiter atmospheres. In this work we
present a comparative study of seven new Spitzer phase curves for four planets
with equilibrium temperatures of T$_{eq}\sim$ 1300K: Qatar-2b, WASP-52b,
WASP-34b, and WASP-140b, as well as the reanalysis of the 4.5 $\micron$
Qatar-1b phase curve due to the similar equilibrium temperature. In total, five
4.5 $\micron$ phase curves and three 3.6 $\micron$ phase curves are analyzed
here with a uniform approach. Using these new results, in combination with
literature values for the entire population of published Spitzer phase curves
of hot Jupiters, we present evidence for a linear trend of increasing hot spot
offset with increasing orbital period, as well as observational evidence for
two classes of planets in apparent redistribution vs. equilibrium temperature
parameter space, and tentative evidence for a dependence of hot spot offset on
planetary surface gravity in our $\sim$ 1300 K sample. We do not find trends in
apparent heat redistribution with orbital period or gravity. Non-uniformity in
literature Spitzer data analysis techniques precludes a definitive
determination of the sources or lack of trends.