  Fractons are a type of emergent quasiparticle which cannot move freely in
isolation, but can easily move in bound pairs. Similar phenomenology is found
in boson-affected hopping models, encountered in the study of polaron systems
and hole-doped Ising antiferromagnets, in which motion of a particle requires
the creation or absorption of bosonic excitations. We show that boson-affected
hopping models can provide a natural realization of fractons, either
approximately or exactly, depending on the details of the system. We first
consider a generic one-dimensional boson-affected hopping model, in which we
show that single particles move only at sixth order in perturbation theory,
while motion of bound states occurs at second order, allowing for a broad
parameter regime exhibiting approximate fracton phenomenology. We explicitly
map the model onto a fracton Hamiltonian featuring conservation of dipole
moment via integrating out the mediating bosons. We then consider a special
type of boson-affected hopping models with mutual hard-core repulsion between
particles and bosons, accessible in hole-doped mixed-dimensional Ising
antiferromagnets, in which the hole motion is one dimensional in an otherwise
two-dimensional antiferromagnetic background. We show that this system, which
is within the current reach of ultracold-atom experiments, exhibits perfect
fracton behavior to all orders in perturbation theory, thereby enabling the
experimental study of dipole-conserving field theories. We further discuss
diagnostic signatures of fractonic behavior in these systems. In studying these
models, we also identify simple effective one-dimensional microscopic
Hamiltonians featuring perfect fractonic behavior, paving the way to future
studies on fracton physics in lower dimensions.


  The smooth muscle bundles (SMBs) in the bladder act as contractile elements
which enable the bladder to void effectively. In contrast to skeletal muscles,
these bundles are not highly aligned, rather they are oriented more
heterogeneously throughout the bladder wall. In this work, for the first time,
this regional orientation of the SMBs is quantified across the whole bladder,
without the need for optical clearing or cryosectioning. Immunohistochemistry
staining was utilized to visualize smooth muscle cell actin in multiphoton
microscopy (MPM) images of bladder smooth muscle bundles (SMBs). Feature
vectors for each pixel were generated using a range of filters, including
Gaussian blur, Gaussian gradient magnitude, Laplacian of Gaussian, Hessian
eigenvalues, structure tensor eigenvalues, Gabor, and Sobel gradients. A Random
Forest classifier was subsequently trained to automate the segmentation of SMBs
in the MPM images. Finally, the orientation of SMBs in each bladder region was
quantified using the CT-FIRE package. This information is essential for
biomechanical models of the bladder that include contractile elements.


  Millimeter wavelength observations are presented of NGC 1333 IRAS 4, a group
of highly-embedded young stellar objects in Perseus, that reveal motions of
infall, outflow, rotation, and turbulence in the dense gas around its two
brightest continuum objects, 4A and 4B. These data have finest angular
resolution of approximately 2" (0.0034 pc) and finest velocity resolution of
0.13 km/s. Infall motions are seen from inverse P-Cygni profiles observed in
H2CO 3_12-2_11 toward both objects, but also in CS 3-2 and N2H+ 1-0 toward 4A,
providing the least ambiguous evidence for such motions toward low-mass
protostellar objects. Outflow motions are probed by bright line wings of H2CO
3_12-2_11 and CS 3-2 observed at positions offset from 4A and 4B, likely
tracing dense cavity walls. Rotational motions of dense gas are traced by a
systematic variation of the N2H+ line velocities, and such variations are found
around 4A but not around 4B. Turbulent motions appear reduced with scale, given
N2H+ line widths around both 4A and 4B that are narrower by factors of 2 or 3
than those seen from single-dish observations. Minimum observed line widths of
approximately 0.2 km/s provide a new low, upper bound to the velocity
dispersion of the parent core to IRAS 4, and demonstrate that turbulence within
regions of clustered star formation can be reduced significantly. A third
continuum object in the region, 4B', shows no detectable line emission in any
of the observed molecular species.


  Gender is playing an important role in the 2016 U.S. presidential election,
especially with Hillary Clinton becoming the first female presidential nominee
and Donald Trump being frequently accused of sexism. In this paper, we
introduce computer vision to the study of gender politics and present an
image-driven method that can measure the effects of gender in an accurate and
timely manner. We first collect all the profile images of the candidates'
Twitter followers. Then we train a convolutional neural network using images
that contain gender labels. Lastly, we classify all the follower and unfollower
images. Through two case studies, one on the `woman card' controversy and one
on Sanders followers, we demonstrate how gender is informing the 2016
presidential election. Our framework of analysis can be readily generalized to
other case studies and elections.


  While many see the prospect of autonomous machines as threatening, autonomy
may be exactly what we want in a superintelligent machine. There is a sense of
autonomy, deeply rooted in the ethical literature, in which an autonomous
machine is necessarily an ethical one. Development of the theory underlying
this idea not only reveals the advantages of autonomy, but it sheds light on a
number of issues in the ethics of artificial intelligence. It helps us to
understand what sort of obligations we owe to machines, and what obligations
they owe to us. It clears up the issue of assigning responsibility to machines
or their creators. More generally, a concept of autonomy that is adequate to
both human and artificial intelligence can lead to a more adequate ethical
theory for both.


  This study introduces a novel framework, G3Reg, for fast and robust global
registration of LiDAR point clouds. In contrast to conventional complex
keypoints and descriptors, we extract fundamental geometric primitives,
including planes, clusters, and lines (PCL) from the raw point cloud to obtain
low-level semantic segments. Each segment is represented as a unified Gaussian
Ellipsoid Model (GEM), using a probability ellipsoid to ensure the ground truth
centers are encompassed with a certain degree of probability. Utilizing these
GEMs, we present a distrust-and-verify scheme based on a Pyramid Compatibility
Graph for Global Registration (PAGOR). Specifically, we establish an upper
bound, which can be traversed based on the confidence level for compatibility
testing to construct the pyramid graph. Then, we solve multiple maximum cliques
(MAC) for each level of the pyramid graph, thus generating the corresponding
transformation candidates. In the verification phase, we adopt a precise and
efficient metric for point cloud alignment quality, founded on geometric
primitives, to identify the optimal candidate. The algorithm's performance is
validated on three publicly available datasets and a self-collected
multi-session dataset. Parameter settings remained unchanged during the
experiment evaluations. The results exhibit superior robustness and real-time
performance of the G3Reg framework compared to state-of-the-art methods.
Furthermore, we demonstrate the potential for integrating individual GEM and
PAGOR components into other registration frameworks to enhance their efficacy.
Code: https://github.com/HKUST-Aerial-Robotics/G3Reg


  We present multi-frequency radio continuum as well as HI observations of the
composite galaxy NGC6764, which has a young, circumnuclear starburst and also
harbours an active galactic nucleus (AGN). These observations have been made at
a number of frequencies ranging from ~600 MHz to 15 GHz using both the GMRT and
the VLA. They reveal the structure of the bipolar bubbles of non-thermal, radio
emission which are along the minor axis of the galaxy and extend up to ~1.1 and
1.5 kpc on the northern and southern sides respectively. Features in the radio
bubbles appear to overlap with filaments of H_alpha emission. The
high-resolution observations reveal a compact source, likely to be associated
with the nucleus of the galaxy, and a possible radio jet towards the
south-west. We have compiled a representative sample of galaxies with bubbles
of non-thermal radio emission and find that these are found in galaxies with an
AGN.
  The HI observations with the GMRT show two peaks of emission on both ends of
the stellar-bar and depletion of HI in the central region of the galaxy. We
also detect HI in absorption against the central radio peak at the systemic
velocity of the galaxy. The HI-absorption spectrum also suggests a possible
weak absorption feature blue-shifted by ~120 km/s, which requires confirmation.
A similar feature has also been reported from observations of CO in emission,
suggesting that the circumnuclear starburst and nuclear activity affect the
kinematics of the atomic and molecular gas components, in addition to the
ionised gas seen in H_alpha and [N II].


  Learning deep neural networks that are generalizable across different domains
remains a challenge due to the problem of domain shift. Unsupervised domain
adaptation is a promising avenue which transfers knowledge from a source domain
to a target domain without using any labels in the target domain. Contemporary
techniques focus on extracting domain-invariant features using domain
adversarial training. However, these techniques neglect to learn discriminative
class boundaries in the latent representation space on a target domain and
yield limited adaptation performance. To address this problem, we propose
distance metric guided feature alignment (MetFA) to extract discriminative as
well as domain-invariant features on both source and target domains. The
proposed MetFA method explicitly and directly learns the latent representation
without using domain adversarial training. Our model integrates class
distribution alignment to transfer semantic knowledge from a source domain to a
target domain. We evaluate the proposed method on fetal ultrasound datasets for
cross-device image classification. Experimental results demonstrate that the
proposed method outperforms the state-of-the-art and enables model
generalization.


  Some examples and basic properties of ultrametric spaces are briefly
discussed.


  Quantum Monte Carlo estimates of the spectrum of rotationally invariant
states of noble gas clusters suggest inter-dimensional degeneracy in $N-1$ and
$N+1$ spacial dimensions. We derive this property by mapping the Schr\"odinger
eigenvalue problem onto an eigenvalue equation in which $D$ appears as a
continuous variable. We discuss implications for quantum Monte Carlo and
dimensional scaling methods.


  Background: The nuclear kinetic density is one of many fundamental quantities
in density functional theory (DFT) dependent on the nonlocal nuclear density.
Often, approximations may be made when computing the density that may result in
spurious contributions in other DFT quantities. With the ability to compute the
nonlocal nuclear density from ab initio wave functions, it is now possible to
estimate effects of such spurious contributions. Purpose: We derive the kinetic
density using ab initio nonlocal scalar one-body nuclear densities computed
within the no-core shell model (NCSM) approach, utilizing two- and
three-nucleon chiral interactions as the sole input. With the ability to
compute translationally invariant nonlocal densities, it is possible to
directly gauge the impact of the spurious center-of-mass (COM) contributions in
DFT quantities such as the kinetic density. Methods: The nonlocal nuclear
densities are derived from the NCSM one-body densities calculated in second
quantization. We present a review of COM contaminated and translationally
invariant nuclear densities. We then derive an analytic expression for the
kinetic density using these nonlocal densities, producing an ab initio kinetic
density. Results: The ground state nonlocal densities of
\textsuperscript{4,6,8}He, \textsuperscript{12}C, and \textsuperscript{16}O are
used to compute the kinetic densities of the aforementioned nuclei. The impact
of the COM removal technique in the densities is discussed. The results of this
work can be extended to other fundamental quantities in DFT. Conclusions: The
use of a general nonlocal density allows for the calculation of fundamental
quantities taken as input in theories such as DFT. This allows benchmarking of
procedures for COM removal in different many-body techniques.


  The enduring legacy of Euclidean geometry underpins classical machine
learning, which, for decades, has been primarily developed for data lying in
Euclidean space. Yet, modern machine learning increasingly encounters richly
structured data that is inherently nonEuclidean. This data can exhibit
intricate geometric, topological and algebraic structure: from the geometry of
the curvature of space-time, to topologically complex interactions between
neurons in the brain, to the algebraic transformations describing symmetries of
physical systems. Extracting knowledge from such non-Euclidean data
necessitates a broader mathematical perspective. Echoing the 19th-century
revolutions that gave rise to non-Euclidean geometry, an emerging line of
research is redefining modern machine learning with non-Euclidean structures.
Its goal: generalizing classical methods to unconventional data types with
geometry, topology, and algebra. In this review, we provide an accessible
gateway to this fast-growing field and propose a graphical taxonomy that
integrates recent advances into an intuitive unified framework. We subsequently
extract insights into current challenges and highlight exciting opportunities
for future development in this field.


  We propose a new framework for the sampling, compression, and analysis of
distributions of point sets and other geometric objects embedded in Euclidean
spaces. Our approach involves constructing a tensor called the RaySense sketch,
which captures nearest neighbors from the underlying geometry of points along a
set of rays. We explore various operations that can be performed on the
RaySense sketch, leading to different properties and potential applications.
Statistical information about the data set can be extracted from the sketch,
independent of the ray set. Line integrals on point sets can be efficiently
computed using the sketch. We also present several examples illustrating
applications of the proposed strategy in practical scenarios.


Part I. Some Facts From p-Adic Analysis. Part II. Tables of Integrals.


  Low temperature M\"ossbauer spectroscopic and magnetization measurements were
performed on a crystalline sample of Fe-gluconate. Fe atoms were revealed to
exist in two phases i.e. a major (90-94 pct.) and a minor (6-10 pct.). Based on
values of spectral parameters the former can be regarded as ferrous and the
latter as ferric. A sub spectrum associated with the ferric phase shows a
significant broadening below ca. 30 K corresponding to 7.5 kGs. A magnetic
origin of the effect was confirmed by the magnetization measurements. Evidence
on the effect of the magnetism on the lattice vibrations of Fe atoms in both
components was found. The Debye temperature, T_D, associated with the
vibrations of Fe2+ ions is by a factor of about 2 smaller in the temperature
range below ca. 30 K than the one determined from the data measured above ca.
30 K. Interestingly, the T_D-value found for the Fe3+ ions from the data
recorded below ca.30 K is about two times smaller than the corresponding value
determined for the Fe2+ ions.


  Most face super-resolution methods assume that low-resolution and
high-resolution manifolds have similar local geometrical structure, hence learn
local models on the lowresolution manifolds (e.g. sparse or locally linear
embedding models), which are then applied on the high-resolution manifold.
However, the low-resolution manifold is distorted by the oneto-many
relationship between low- and high- resolution patches. This paper presents a
method which learns linear models based on the local geometrical structure on
the high-resolution manifold rather than on the low-resolution manifold. For
this, in a first step, the low-resolution patch is used to derive a globally
optimal estimate of the high-resolution patch. The approximated solution is
shown to be close in Euclidean space to the ground-truth but is generally
smooth and lacks the texture details needed by state-ofthe-art face
recognizers. This first estimate allows us to find the support of the
high-resolution manifold using sparse coding (SC), which are then used as
support for learning a local projection (or upscaling) model between the
low-resolution and the highresolution manifolds using Multivariate Ridge
Regression (MRR). Experimental results show that the proposed method
outperforms six face super-resolution methods in terms of both recognition and
quality. These results also reveal that the recognition and quality are
significantly affected by the method used for stitching all super-resolved
patches together, where quilting was found to better preserve the texture
details which helps to achieve higher recognition rates.


  This research explores the potential to analyze bank card payments and ATM
cash withdrawals in order to map and quantify how people are impacted by and
recover from natural disasters. Our approach defines a disaster-affected
community's economic recovery time as the time needed to return to baseline
activity levels in terms of number of bank card payments and ATM cash
withdrawals. For Hurricane Odile, which hit the state of Baja California Sur
(BCS) in Mexico between 15 and 17 September 2014, we measured and mapped
communities' economic recovery time, which ranged from 2 to 40 days in
different locations. We found that -- among individuals with a bank account --
the lower the income level, the shorter the time needed for economic activity
to return to normal levels. Gender differences in recovery times were also
detected and quantified. In addition, our approach evaluated how communities
prepared for the disaster by quantifying expenditure growth in food or gasoline
before the hurricane struck. We believe this approach opens a new frontier in
measuring the economic impact of disasters with high temporal and spatial
resolution, and in understanding how populations bounce back and adapt.


  In proving the Fermionic formulae, combinatorial bijection called the
Kerov--Kirillov--Reshetikhin (KKR) bijection plays the central role. It is a
bijection between the set of highest paths and the set of rigged
configurations. In this paper, we give a proof of crystal theoretic
reformulation of the KKR bijection. It is the main claim of Part I
(math.QA/0601630) written by A. Kuniba, M. Okado, T. Takagi, Y. Yamada, and the
author. The proof is given by introducing a structure of affine combinatorial
$R$ matrices on rigged configurations.


  Effect of biaxial tensile strains on optical functions and band edge
transitions of ultra thin epitaxial films was studied using as an example a 13
nm thick SrTiO3 films deposited on KTaO3(100) single-crystal substrates.
Optical functions were determined by spectroscopic ellipsometry technique. It
was found that tensile strains result in a shift of the low energy band gap
optical transitions to higher energies and decrease the refractive index in the
visible region. Comparison of the optical spectra for strained SrTiO3 films and
for homoepitaxial strain-free SrTiO3:Cr (0.01 %at.) films deposited on
SrTiO3(100) single crystalline substrates showed that this shift could not be
related to technological imperfections or to reduced thickness. The observed
effect is connected with changes in the lowest conduction and in the top
valence bands that are due to increase of the in-plane lattice constant and/or
onset of polar phase in the tensile strain-induced ultra-thin epitaxial SrTiO3
films.


  The daily variation of the solar photocenter over some 11 years is derived
from the Mount Wilson data reprocessed by Ulrich et al. 2010 to closely match
the surface distribution of solar irradiance. The standard deviations of
astrometric jitter are 0.52 $\mu$AU and 0.39 $\mu$AU in the equatorial and the
axial dimensions, respectively. The overall dispersion is strongly correlated
with the solar cycle, reaching $0.91 \mu$AU at the maximum activity in 2000.
The largest short-term deviations from the running average (up to 2.6 $\mu$AU)
occur when a group of large spots happen to lie on one side with respect to the
center of the disk. The amplitude spectrum of the photocenter variations never
exceeds 0.033 $\mu$AU for the range of periods 0.6--1.4 yr, corresponding to
the orbital periods of planets in the habitable zone. Astrometric detection of
Earth-like planets around stars as quiet as the Sun is not affected by star
spot noise, but the prospects for more active stars may be limited to giant
planets.


  Recently, a secrecy measure based on list-reconstruction has been proposed
[2], in which a wiretapper is allowed to produce a list of $2^{mR_{L}}$
reconstruction sequences and the secrecy is measured by the minimum distortion
over the entire list. In this paper, we show that this list secrecy problem is
equivalent to the one with secrecy measured by a new quantity
\emph{lossy-equivocation}, which is proven to be the minimum optimistic
1-achievable source coding rate (the minimum coding rate needed to reconstruct
the source within target distortion with positive probability for
\emph{infinitely many blocklengths}) of the source with the wiretapped signal
as two-sided information, and also can be seen as a lossy extension of
conventional equivocation. Upon this (or list) secrecy measure, we study
source-channel secrecy problem in the discrete memoryless Shannon cipher system
with \emph{noisy} wiretap channel. Two inner bounds and an outer bound on the
achievable region of secret key rate, list rate, wiretapper distortion, and
distortion of legitimate user are given. The inner bounds are derived by using
uncoded scheme and (operationally) separate scheme, respectively. Thanks to the
equivalence between lossy-equivocation secrecy and list secrecy, information
spectrum method is leveraged to prove the outer bound. As special cases, the
admissible region for the case of degraded wiretap channel or lossless
communication for legitimate user has been characterized completely. For both
these two cases, separate scheme is proven to be optimal. Interestingly,
however, separation indeed suffers performance loss for other certain cases.
Besides, we also extend our results to characterize the achievable region for
Gaussian communication case. As a side product optimistic lossy source coding
has also been addressed.


  We study a Ginzburg-Landau model of structural phase transition in two
dimensions, in which a single order parameter is coupled to the tetragonal and
dilational strains. Such elastic coupling terms in the free energy much affect
the phase transition behavior particularly near the tricriticality. A
characteristic feature is appearance of intermediate states, where the ordered
and disordered regions coexist on mesoscopic scales in nearly steady states in
a temperature window. The window width increases with increasing the strength
of the dilational coupling. It arises from freezing of phase ordering in
inhomogeneous strains. No impurity mechanism is involved. We present a simple
theory of the intermediate states to produce phase diagrams consistent with
simulation results.


  We report on a systematic study of the thermodynamic, electronic and charge
transport properties of high-quality single crystals of BaNiS$_2$, the metallic
end-member of the quasi-twodimensional BaCo$_{1-x}$Ni$_x$S$_2$ system
characterized by a metal-insulator transition at $x_{cr}=0.22$. Our analysis of
magnetoresistivity and specific heat data consistently suggests a picture of
compensated semimetal with two hole- and one electron-bands, where
electron-phonon scattering dominates charge transport and the minority holes
exhibit, below $\sim$100 K, a very large mobility, $\mu_h\sim$ 15000
cm$^2$V$^{-1}$s$^{-1}$, which is explained by a Dirac-like band. Evidence of
unconventional metallic properties is given by an intriguing crossover of the
resistivity from a Bloch-Gr\"uneisen regime to a linear$-T$ regime occurring at
2 K and by a strong linear term in the paramagnetic susceptibility above 100 K.
We discuss the possibility that these anomalies reflect a departure from
conventional Fermi-liquid properties in presence of short-range AF fluctuations
and of a large Hund coupling.


  All extensions of the standard model that generate Majorana neutrino masses
at the electro-weak scale introduce some "heavy" mediators, either fermions
and/or scalars, weakly coupled to leptons. Here, by "heavy" we understand
implicitly the mass range between a few 100 GeV up to, say, roughly 2 TeV, such
that these particles can be searched for at the LHC. We study decay widths of
these mediators for several different tree-level neutrino mass models. The
models we consider range from the simplest $d=5$ seesaw up to $d=11$ neutrino
mass models. For each of the models we identify the most interesting parts of
the parameter space, where the heavy mediator fields are particularly
long-lived and can decay with experimentally measurable decay lengths. One has
to distinguish two different scenarios, depending on whether fermions or
scalars are the lighter of the heavy particles. For fermions we find that the
decay lengths correlate with the inverse of the overall neutrino mass scale.
Thus, since no lower limit on the lightest neutrino mass exists, nearly
arbitrarily long decay lengths can be obtained for the case where fermions are
the lighter of the heavy particles. For charged scalars, on the other hand,
there exists a maximum value for the decay length. This maximum value depends
on the model and on the electric charge of the scalar under consideration, but
can at most be of the order of a few millimeters. Interestingly, independent of
the model, this maximum occurs always in a region of parameter space, where
leptonic and gauge boson final states have similar branching ratios, i.e. where
the observation of lepton number violating final states from scalar decays is
possible.


  We prove uniform convergence of metrics $g_k$ on a closed surface with
bounded integral curvature (measure) in the sense of A.D. Alexandrov, under the
assumption that the curvature measures $\mathbb{K}_{g_k}=\mu^1_k-\mu^2_k$,
where $\mu^1_k,\mu^2_k$ are nonnegative Radon measures that converge weakly to
measures $\mu^1,\mu^2$ respectively, and $\mu^1$ is less than $2\pi$ at each
point. This generalizes Yu. G. Reshetnyak's well-known result on uniform
convergence of metrics on a domain in $\mathbb{C}$, and answers affirmatively
the open question on the metric convergence on a closed surface.We also show
that a metric $g=e^{2u}g_0$ with bounded integral curvature in the sense of
Alexandrov on a closed Riemannian surface $(\Sigma,g_0)$ can be approximated by
smooth metrics in the fixed conformal class $[g_0]$ in terms of distance
functions, curvature measures and conformal factors. Results on a closed
surface with varying conformal classes and on complete noncompact surfaces are
obtained as well.


  To adequately model mathematical arguments the analyst must be able to
represent the mathematical objects under discussion and the relationships
between them, as well as inferences drawn about these objects and relationships
as the discourse unfolds. We introduce a framework with these properties, which
has been used to analyse mathematical dialogues and expository texts. The
framework can recover salient elements of discourse at, and within, the
sentence level, as well as the way mathematical content connects to form larger
argumentative structures. We show how the framework might be used to support
computational reasoning, and argue that it provides a more natural way to
examine the process of proving theorems than do Lamport's structured proofs.


  The latest Planck results reconfirm the existence of a slight but chronic
tension between the best-fit Cosmic Microwave Background (CMB) and low-redshift
observables: power seems to be consistently lacking in the late universe across
a range of observables (e.g.~weak lensing, cluster counts). We propose a
two-parameter model for dark energy where the dark energy is sufficiently like
dark matter at large scales to keep the CMB unchanged but where it does not
cluster at small scales, preventing concordance collapse and erasing power. We
thus exploit the generic scale-dependence of dark energy instead of the more
usual time-dependence to address the tension in the data. The combination of
CMB, distance and weak lensing data somewhat prefer our model to $\Lambda$CDM,
at $\Delta\chi^2=2.4$. Moreover, this improved solution has $\sigma_8=0.79 \pm
0.02$, consistent with the value implied by cluster counts.


  Depth perception is considered an invaluable source of information in the
context of 3D mapping and various robotics applications. However, point cloud
maps acquired using consumer-level light detection and ranging sensors (lidars)
still suffer from bias related to local surface properties such as measuring
beam-to-surface incidence angle, distance, texture, reflectance, or
illumination conditions. This fact has recently motivated researchers to
exploit traditional filters, as well as the deep learning paradigm, in order to
suppress the aforementioned depth sensors error while preserving geometric and
map consistency details. Despite the effort, depth correction of lidar
measurements is still an open challenge mainly due to the lack of clean 3D data
that could be used as ground truth. In this paper, we introduce two novel point
cloud map consistency losses, which facilitate self-supervised learning on real
data of lidar depth correction models. Specifically, the models exploit
multiple point cloud measurements of the same scene from different view-points
in order to learn to reduce the bias based on the constructed map consistency
signal. Complementary to the removal of the bias from the measurements, we
demonstrate that the depth correction models help to reduce localization drift.
Additionally, we release a data set that contains point cloud data captured in
an indoor corridor environment with precise localization and ground truth
mapping information.


  Deformation theory of complex manifolds is a classical subject with recent
new advances in the noncompact case using both algebraic and analytic methods.
  In this note, we recall some concepts of the existing theory and introduce
new notions of deformations for manifolds with boundary, for compactifiable
manifolds, and for $q$-concave spaces. We highlight some of the possible
applications and give a list of open questions which we intend as a guide for
further research in this rich and beautiful subject.


  A vast majority of the school choice literature focuses on designing
mechanisms to simultaneously assign students to many schools, and employs a
"make it up as you go along" approach when it comes to each school's admissions
policy. An alternative approach is to focus on the admissions policy for one
school. This is especially relevant for effectively communicating policy
objectives such as achieving a diverse student body or implementing affirmative
action. I argue that the latter approach is relatively under-examined and
deserves more attention in the future.


  We consider the two dimensional generalization of the Korteweg-de Vries
equation, the generalized Zakharov-Kuznetsov (ZK) equation, $u_t +
\partial_{x_1}(\Delta u + u^p) = 0, (x_1,x_2) \in \mathbb{R}^2$. It is known
that solitons are stable for nonlinearities $p < 3$ and unstable for $p > 3$,
which was established by Anne de Bouard in [5] generalizing the arguments of
Bona-Souganidis-Strauss in [1] for the gKdV equation. The $L^2$-critical case
with $p=3$ has been open and in this paper we prove that solitons are unstable
in the cubic ZK equation. This matches the situation with the critical gKdV
equation, proved in 2001 by Martel and Merle in [22]. While the general
strategy follows [22], the two dimensional case creates several difficulties
and to deal with them, we design a new virial-type quantity, revisit
monotonicity properties and, most importantly, develop new pointwise decay
estimates, which can be useful in other contexts.


  The exact optical response of quantum few-level systems depends crucially on
the exact choice of the incoming pulse areas. We use four-wave mixing (FWM)
spectroscopy to infer the coherent response and dynamics of single InAs quantum
dots (QDs) and study their pulse area dependence. By combining atomic force
microscopy with FWM hyperspectral imaging, we show that the retrieved FWM
signals originate from individual QDs enclosed in natural photonic defects. The
optimized light-matter coupling in these defects allows us to perform our
studies in a wide range of driving field amplitudes. When varying the pulse
areas of the exciting laser pulses the so-called Rabi rotations can be resolved
by the two-pulse FWM technique. We investigate these Rabi rotations within two-
and three-level systems, both theoretically and experimentally, and explain
their damping by the coupling to acoustic phonons. To highlight the importance
of the pulse area influence, we show that the phonon-induced dephasing of QD
excitons depends on the pulse intensity.


  Estimating the dimension of an Hilbert space is an important component of
quantum system identification. In quantum technologies, the dimension of a
quantum system (or its corresponding accessible Hilbert space) is an important
resource, as larger dimensions determine e.g. the performance of quantum
computation protocols or the sensitivity of quantum sensors. Despite being a
critical task in quantum system identification, estimating the Hilbert space
dimension is experimentally challenging. While there have been proposals for
various dimension witnesses capable of putting a lower bound on the dimension
from measuring collective observables that encode correlations, in many
practical scenarios, especially for multiqubit systems, the experimental
control might not be able to engineer the required initialization, dynamics and
observables.
  Here we propose a more practical strategy, that relies not on directly
measuring an unknown multiqubit target system, but on the indirect interaction
with a local quantum probe under the experimenter's control. Assuming only that
the interaction model is given and the evolution correlates all the qubits with
the probe, we combine a graph-theoretical approach and realization theory to
demonstrate that the dimension of the Hilbert space can be exactly estimated
from the model order of the system. We further analyze the robustness in the
presence of background noise of the proposed estimation method based on
realization theory, finding that despite stringent constrains on the allowed
noise level, exact dimension estimation can still be achieved.


  We discuss the problem of the separation and description of the underlying
event (UE) within two existing approaches to UE measurement: the "traditional"
method, widely used at Tevatron, and a recently proposed jet-area/median
method. A simple toy model of UE is developed in order to understand how these
approaches perform. We find that both methods are comparably good for measuring
average properties of the UE but the jet-area/median approach is favorable for
determining fluctuations. We also use the latter method to study the UE from
several existing Monte Carlo generator tunes. We investigate which
characteristics of the underlying event might be useful to measure in order to
improve understanding of its properties and to simulate it well. These include
transverse momentum density per area, intra and inter-event fluctuations and
correlations.


  We analyze the spectrum of massive scalar bound states in the background of
extremal Kerr black holes, focusing on modes in the superradiant regime, which
grow exponentially in time and quickly deplete the black hole's mass and spin.
Previous analytical estimates for the growth rate of this instability were
limited to the $\mu M\ll1$ and $\mu M\gg1$ regimes, where $\mu$ and $M$ denote
the scalar field and black hole masses, respectively. In this work, we discuss
an analytical method to compute the superradiant spectrum for generic values of
these parameters, namely in the phenomenologically interesting regime $\mu
M\sim 1$. To do this, we solve the radial mode equation in two overlapping
regions and match the solutions in their common domain of validity. We show
that matching the functional forms of these functions involves approximations
that are not valid for the whole range of scalar masses, exhibiting unphysical
poles that produce a large enhancement of the growth rate. Alternatively, we
match the functions at a single point and show that, despite the uncertainty in
the choice of the match point, this method eliminates the spurious poles and
agrees with previous numerical computations of the spectrum using a
continued-fraction method.


  We discuss tipping phenomena (critical transitions) in nonautonomous systems
using an example of a bistable ecosystem model with environmental changes
represented by time-varying parameters [Scheffer et al., Ecosystems, 11 (2008),
pp. 275--279]. We give simple testable criteria for the occurrence of
nonautonomous tipping from the herbivore-dominating equilibrium to the
plant-only equilibrium using global properties of the autonomous frozen system
with fixed-in-time parameters. To begin with, we use classical autonomous
bifurcation analysis to identify a codimension-three degenerate Bogdanov-Takens
bifurcation: the source of a dangerous subcritical Hopf bifurcation and the
organizing center for bifurcation-induced tipping (B-tipping). Then, we
introduce the concept of basin instability for equilibria to identify parameter
paths along which genuine nonautonomous rate-induced tipping (R-tipping) occurs
without crossing any classical autonomous bifurcations. We explain
nonautonomous R-tipping in terms of maximal canard trajectories and produce
nonautonomous tipping diagrams in the plane of the magnitude and rate of a
parameter shift to uncover intriguing R-tipping tongues and wiggling
tipping-tracking bifurcation curves. Discussion of nontrivial dynamics arising
from the interaction between B-tipping and R-tipping identifies "points of no
return" where tipping cannot be prevented by the parameter trend reversal and
"points of return tipping" where tipping is inadvertently induced by the
parameter trend reversal. Our results give new insight into the sensitivity of
ecosystems to the magnitudes and rates of environmental change. Finally, a
comparison between "tilted" saddle-node and subcritical Hopf normal forms
reveals some universal tipping properties due to basin instability, a generic
dangerous bifurcation, or the combination of both.


  Generating new samples from data sets can mitigate extra expensive
operations, increased invasive procedures, and mitigate privacy issues. These
novel samples that are statistically robust can be used as a temporary and
intermediate replacement when privacy is a concern. This method can enable
better data sharing practices without problems relating to identification
issues or biases that are flaws for an adversarial attack.


  We analyze the emergence of diffractive focusing in the transition from
discrete to continuous space-time variables. Three types of dynamical equations
are studied in a top-to-bottom approach, starting with the most general system.
First we solve a linear cellular automaton of two species, then a nearest
neighbour tight-binding array and finally the time-dependent Schr\"odinger
equation. All models are shown to produce diffractive solutions for square
packet distributions. The main result of this paper is that in discrete
variables, the nature of the solutions depends strongly on the size of
wavepackets, whereas in continuous variables, diffraction due to
discontinuities exists at every scale. A transition in the number of
participants or cells is identified by means of a measure and the corresponding
phenomenon is further analyzed by a generalization of Wigner functions to
discrete variables and crystals.


  Traditional design cycles for new materials and assemblies have two
fundamental drawbacks. The underlying physical relationships are often too
complex to be precisely calculated and described. Aside from that, many unknown
uncertainties, such as exact manufacturing parameters or materials composition,
dominate the real assembly behavior. Machine learning (ML) methods overcome
these fundamental limitations through data-driven learning. In addition, modern
approaches can specifically increase system knowledge. Representation Learning
allows the physical, and if necessary, even symbolic interpretation of the
learned solution. In this way, the most complex physical relationships can be
considered and quickly described. Furthermore, generative ML approaches can
synthesize possible morphologies of the materials based on defined conditions
to visualize the effects of uncertainties. This modern approach accelerates the
design process for new materials and enables the prediction and interpretation
of realistic materials behavior.


  We present a benchmark in the parameter space of the Next-to-Minimal
Supersymmetric Standard Model (NMSSM) that provides for a dramatic multi-lepton
signal and no jets containing 5 or more leptons resulting from the cascade
decays of the third lightest neutralino, $\chi^{0}_{3}$, and the lightest
chargino, $\chi^{\pm}_{1}$, via light charged sleptons. This is a very clean
signal with almost no Standard Model (SM) background. In some cases, a total
signal of $\ge 3 $leptons + 0 jets can be detected at the $5 \sigma$ level at
the LHC running at $\sqrt{s}=7$ TeV with approximately 3 fb$^{-1}$ of data and
with less than 1 fb$^{-1}$ when running at $\sqrt{s}=14$ TeV. In addition,
kinematic edges in the invariant mass distributions of 2, 3, and 4 leptons are
easily detectable with large integrated luminosities ($\sim 600$ fb$^{-1}$)
which can lead to simple measurements of the mass differences of heavy
particles in the decay chains, including all combinations of the three lightest
neutralinos.


  Here are studied pairs of transversal foliations with singularities, defined
on the Elliptic region (where the Gaussian curvature $\mathcal K$ is positive)
of an oriented surface immersed in $\mathbb R^3$. The leaves of the foliations
are the lines of geometric mean curvature, along which the normal curvature is
given by $\sqrt {\mathcal K}$, which is the geometric mean curvature of the
principal curvatures $ k_1, k_2$ of the immersion. The singularities of the
foliations are the umbilic points and parabolic curves}, where $ k_1 = k_2$ and
${\mathcal K} = 0$, respectively. Here are determined the structurally stable
patterns of geometric mean curvature lines near the umbilic points, parabolic
curves and geometric mean curvature cycles, the periodic leaves of the
foliations. The genericity of these patterns is established. This provides the
three essential local ingredients to establish sufficient conditions, likely to
be also necessary, for Geometric Mean Curvature Structural Stability. This
study, outlined at the end of the paper, is a natural analog and complement for
the Arithmetic Mean Curvature and Asymptotic Structural Stability of immersed
surfaces studied previously by the authors.


  Magnetic Particle Imaging is an emerging imaging modality through which it is
possible to detect tracers containing superparamagnetic nanoparticles. The
exposure of the particles to dynamic magnetic fields generates a non-linear
response that is used to locate the particles and produce an image of their
distribution. The bounding box that can be covered by a single scan curve
depends on the strength of the gradients of the magnetic fields applied, which
is limited due to the risk of causing peripheral nerve stimulation (PNS) in the
patients. To address this issue, multiple scans are performed. The scan data
must be merged together to produce reconstructions of larger regions of
interest. In this paper we propose a mathematical framework which can deal with
rather general multi-patching scenarios including rigid transformations of the
field of view (FoV), the specimen and of the scanner. We show the flexibility
of this framework in a variety of different scanning scenarios. Moreover, we
describe an iterative reconstruction algorithm that yields a reconstruction of
the target distribution by minimizing a convex functional which includes
positivity constraints and sparsity enforcing priors. We show its convergence
to a minimizer and perform numerical experiments on simulated data.


  Quantum phase-slip (QPS) is the exact dual to the well-known Josephson
effect. Although there are numerous proposals for applications of QPS devices,
experimental work to develop these remains in the relatively early stages.
Significant barriers to exploiting QPS nanowires for useful technologies still
exist, such as establishing robust nanowire fabrication methods that allow
coupling to low-loss circuits, and demonstrating control over the QPS process
with an experimenter-controlled external bias. Here we report experiments which
show that both of these barriers have been overcome. We present measurements at
300 mK of NbN coplanar waveguide (CPW) resonators embedded with nanowires
fabricated using a neon focused ion-beam. The internal quality factor exceeds
$2\times10^{4}$ -- significantly higher than previously reported in comparable
experiments. The resonator frequency tunes periodically with an applied
magnetic field, revealing tunnelling of the order parameter that always occurs
at half-integer values of the applied flux. In contrast to previous studies of
single QPS, the order-parameter tunnelling is shown to be adiabatic,
demonstrating improved control over energy dissipation in nanowire QPS
circuits. Our results highlight a promising pathway towards realising low-loss
nanowire-based QPS devices.


  Phase space structures such as dividing surfaces, normally hyperbolic
invariant manifolds, their stable and unstable manifolds have been an integral
part of computing quantitative results such as transition fraction, stability
erosion in multi-stable mechanical systems, and reaction rates in chemical
reaction dynamics. Thus, methods that can reveal their geometry in high
dimensional phase space (4 or more dimensions) need to be benchmarked by
comparing with known results. In this study, we assess the capability of one
such method called Lagrangian descriptor for revealing the types of high
dimensional phase space structures associated with index-1 saddle in
Hamiltonian systems. The Lagrangian descriptor based approach is applied to two
and three degree-of-freedom quadratic Hamiltonian systems where the high
dimensional phase space structures are known, that is as closed-form analytical
expressions. This leads to a direct comparison of features in the Lagrangian
descriptor plots and the phase space structures' intersection with an
isoenergetic two-dimensional surface and hence provides a validation of the
approach.


  This paper studies various aspects of the world volume dynamics of the
M-theory five-brane, including: non-BPS solutions and solution generating
symmetries; the scattering properties of world volume solutions; and the
equivalence with probe brane dynamics.


  In this work we propose the use of quantile regression and dilated recurrent
neural networks with temporal scaling (MQ-DRNN-s) and apply it to the inventory
management task. This model showed a better performance of up to 3.2\% over a
statistical benchmark (the quantile autoregressive model with exogenous
variables, QAR-X), being better than the MQ-DRNN without temporal scaling by
6\%. The above on a set of 10,000 time series of sales of El Globo over a
53-week horizon using rolling windows of 7-day ahead each week.


  This paper describes two explicit couplings of standard Brownian motions $B$
and $V$, which naturally extend the mirror coupling and the synchronous
coupling and respectively maximise and minimise (uniformly over all time
horizons) the coupling time and the tracking error of two regime-switching
martingales. The generalised mirror coupling minimizes the coupling time of the
two martingales while simultaneously maximising the tracking error for all time
horizons. The generalised synchronous coupling maximises the coupling time and
minimises the tracking error over all co-adapted couplings. The proofs are
based on the Bellman principle. We give counterexamples to the conjectured
optimality of the two couplings amongst a wider classes of stochastic
integrals.


  A large number of safety-critical control systems are based on N-modular
redundant architectures, using majority voters on the outputs of independent
computation units. In order to assess the compliance of these architectures
with international safety standards, the frequency of hazardous failures must
be analyzed by developing and solving proper formal models. Furthermore, the
impact of maintenance faults has to be considered, since imperfect maintenance
may degrade the safety integrity level of the system. In this paper we present
both a failure model for voting architectures based on Bayesian Networks and a
maintenance model based on Continuous Time Markov Chains, and we propose to
combine them according to a compositional multiformalism modeling approach in
order to analyze the impact of imperfect maintenance on the system safety. We
also show how the proposed approach promotes the reuse and the interchange of
models as well the interchange of solving tools.


  We present space efficient Monte Carlo algorithms that solve Subset Sum and
Knapsack instances with $n$ items using $O^*(2^{0.86n})$ time and polynomial
space, where the $O^*(\cdot)$ notation suppresses factors polynomial in the
input size. Both algorithms assume random read-only access to random bits.
Modulo this mild assumption, this resolves a long-standing open problem in
exact algorithms for NP-hard problems. These results can be extended to solve
Binary Linear Programming on $n$ variables with few constraints in a similar
running time. We also show that for any constant $k\geq 2$, random instances of
$k$-Sum can be solved using $O(n^{k-0.5}polylog(n))$ time and $O(\log n)$
space, without the assumption of random access to random bits.
  Underlying these results is an algorithm that determines whether two given
lists of length $n$ with integers bounded by a polynomial in $n$ share a common
value. Assuming random read-only access to random bits, we show that this
problem can be solved using $O(\log n)$ space significantly faster than the
trivial $O(n^2)$ time algorithm if no value occurs too often in the same list.


  We report high-resolution high-energy photoemission spectra together with
parameter-free LDA+DMFT (local density approximation + dynamical mean-field
theory) results for Sr1-xCaxVO3, a prototype 3d1 system. In contrast to earlier
investigations the bulk spectra are found to be insensitive to x. The good
agreement between experiment and theory confirms the bulk-sensitivity of the
high-energy photoemission spectra.


  Crisis response requires information intensive efforts utilized for reducing
uncertainty, calculating and comparing costs and benefits, and managing
resources in a fashion beyond those regularly available to handle routine
problems. This paper presents an Artificial Immune Systems (AIS) metaphor for
agent based modeling of crisis response operations. The presented model
proposes integration of hybrid set of aspects (multi-agent systems, built-in
defensive model of AIS, situation management, and intensity-based learning) for
crisis response operations. In addition, the proposed response model is applied
on the spread of pandemic influenza in Egypt as a case study.


  We present an extension of the random--phase approximation (RPA) where the
RPA phonons are used as building blocks to construct the excited states. In our
model, that we call double RPA (DRPA), we include up to two RPA phonons. This
is an approximate and simplified way, with respect to the full second
random--phase approximation (SRPA), to extend the RPA by including two
particle--two hole configurations. Some limitations of the standard SRPA model,
related to the violation of the stability condition, are not encountered in the
DRPA. We also verify in this work that the energy--weighted sum rules are
satisfied. The DRPA is applied to low--energy modes and giant resonances in the
nucleus $^{16}$O. We show that the model (i) produces a global downwards shift
of the energies with respect to the RPA spectra; (ii) provides a shift that is
however strongly reduced compared to that generated by the standard SRPA. This
model represents an alternative way of correcting for the SRPA anomalous energy
shift, compared to a recently developed extension of the SRPA, where a
subtraction procedure is applied. The DRPA provides results in good agreeement
with the experimental energies, with the exception of those low--lying states
that have a dominant two particle--two hole nature. For describing such states,
higher--order calculations are needed.


  We study theoretically the Josephson effect in d-wave superconductor /
diffusive normal metal /insulator/ diffusive normal metal/ d-wave
superconductor (D/DN/I/DN/D) junctions. This model is aimed to describe
practical junctions in high-$T_C$ cuprate superconductors, in which the product
of the critical Josephson current ($I_C$) and the normal state resistance ($R$)
(the so-called $I_{\rm C}R$ product) is very small compared to the prediction
of the standard theory. We show that the $I_{\rm C}R$ product in D/DN/I/DN/D
junctions can be much smaller than that in d-wave superconductor / insulator /
d-wave superconductor junctions and formulate the conditions necessary to
achieve large $I_{\rm C}R$ product in D/DN/I/DN/D junctions. The proposed
theory describes the behavior of $I_{\rm C}R$ products quantitatively in
high-$T_{\rm C}$ cuprate junctions.


  We have carried out a generalized Schrieffer-Wolff transformation of an
Anderson two-impurity Hamiltonian to study the low-energy spin interactions of
the system. The second-order expansion yields the standard Kondo Hamiltonian
for two impurities with additional scattering terms. At fouth-order, we get the
well-known RKKY interaction. In addition, we also find an antiferromagnetic
superexchange coupling and a correlated Kondo coupling between the two
impurities.


  The present paper proposes a waveform boundary detection system for audio
spoofing attacks containing partially manipulated segments. Partially
spoofed/fake audio, where part of the utterance is replaced, either with
synthetic or natural audio clips, has recently been reported as one scenario of
audio deepfakes. As deepfakes can be a threat to social security, the detection
of such spoofing audio is essential. Accordingly, we propose to address the
problem with a deep learning-based frame-level detection system that can detect
partially spoofed audio and locate the manipulated pieces. Our proposed method
is trained and evaluated on data provided by the ADD2022 Challenge. We evaluate
our detection model concerning various acoustic features and network
configurations. As a result, our detection system achieves an equal error rate
(EER) of 6.58% on the ADD2022 challenge test set, which is the best performance
in partially spoofed audio detection systems that can locate manipulated clips.


  We study inclusive nu_e and nu_mu cross sections in C-12 in a theory that
takes into account significant nuclear renormalization of strengths. Our
calculation is in excellent agreement with the measured inclusive muon capture
rate and the flux-averaged nu_e cross section, but overestimates the
flux-averaged nu_mu inclusive cross section. These reactions are of crucial
relevance to the issue of possible nu_mu to nu_e oscillations.We also calculate
the flux-averaged cross sections in C-13 and Al-27, which are found to be
consistent with the available experimental result.


  Pion electro- and photoproduction off the nucleon close to threshold is
studied in covariant baryon chiral perturbation theory at O($p^3$) in the
extended-on-mass-shell scheme, with the explicit inclusion of the
$\Delta(1232)$ resonance. The relevant low energy constants are fixed by
fitting the available experimental data with the theoretical model. The
inclusion of the $\Delta$ resonance as an explicit degree of freedom
substantially improves the agreement with data and the convergence of the
model.


  An overview of the Conquest linear scaling density functional theory (DFT)
code is given, focussing particularly on the scaling behaviour on modern high-
performance computing (HPC) platforms. We demonstrate that essentially perfect
linear scaling and weak parallel scaling (with fixed atoms per processor core)
can be achieved, and that DFT calculations on millions of atoms are now
possible.


  The problem of vertex coloring in random graphs is studied using methods of
statistical physics and probability. Our analytical results are compared to
those obtained by exact enumeration and Monte-Carlo simulations. We critically
discuss the merits and shortcomings of the various methods, and interpret the
results obtained. We present an exact analytical expression for the 2-coloring
problem as well as general replica symmetric approximated solutions for the
thermodynamics of the graph coloring problem with p colors and K-body edges.


  The lower bound W(K_{2n})>=3n-2 is proved for the greatest possible number of
colors in an interval edge coloring of the complete graph K_{2n}.


  Deep learning models have achieved tremendous success in most of the
industries in recent years. The evolution of these models has also led to an
increase in the model size and energy requirement, making it difficult to
deploy in production on low compute devices. An increase in the number of
connected devices around the world warrants compressed models that can be
easily deployed at the local devices with low compute capacity and power
accessibility. A wide range of solutions have been proposed by different
researchers to reduce the size and complexity of such models, prominent among
them are, Weight Quantization, Parameter Pruning, Network Pruning, low-rank
representation, weights sharing, neural architecture search, knowledge
distillation etc. In this research work, we investigate the performance impacts
on various trained deep learning models, compressed using quantization and
pruning techniques. We implemented both, quantization and pruning, compression
techniques on popular deep learning models used in the image classification,
object detection, language models and generative models-based problem
statements. We also explored performance of various large language models
(LLMs) after quantization and low rank adaptation. We used the standard
evaluation metrics (model's size, accuracy, and inference time) for all the
related problem statements and concluded this paper by discussing the
challenges and future work.


  A time-dependent vibrational electronic coupled-cluster (VECC) approach is
proposed to simulate photoelectron/ UV-VIS absorption spectra, as well as
time-dependent properties for non-adiabatic vibronic models, going beyond the
Born-Oppenheimer approximation. A detailed derivation of the equations of
motion and a motivation of the ansatz are presented. The VECC method employs
second-quantized bosonic construction operators and a mixed linear and
exponential ansatz to form a compact representation of the time-dependent
wave-function. Importantly, the method does not require a basis set, has only
few user-defined inputs, and has a classical (polynomial) scaling with respect
to the number of degrees of freedom (of the vibronic model), resulting in a
favourable computational cost. In benchmark applications to small models and
molecules the VECC method provides accurate results, compared to
Multi-Configurational Time-dependent Hartree (MCTDH) calculations when
predicting short-time dynamical properties (i.e. photo-elecron / UV-VIS
absorption spectra) for non-adiabatic vibronic models. To illustrate the
capabilities the VECC method is also applied successfully to a large vibronic
model for hexahelicene with 14 electronic states and 63 normal modes, developed
in the group by Santoro.


  We present a method to assess the plausibility of alternative mechanisms to
explain the damping of magnetohydrodynamic (MHD) transverse waves in solar
coronal loops. The considered mechanisms are resonant absorption of kink waves
in the Alfv\'en continuum, phase-mixing of Alfv\'en waves, and wave leakage.
Our methods make use of Bayesian inference and model comparison techniques. We
first infer the values for the physical parameters that control the wave
damping, under the assumption of a particular mechanism, for typically observed
damping time-scales. Then, the computation of marginal likelihoods and Bayes
factors enable us to quantify the relative plausibility between the alternative
mechanisms. We find that, in general, the evidence is not large enough to
support a single particular damping mechanism as the most plausible one.
Resonant absorption and wave leakage offer the most probable explanations in
strong damping regimes, while phase mixing is the best candidate for
weak/moderate damping. When applied to a selection of 89 observed transverse
loop oscillations, with their corresponding measurements of damping times
scales and taking into account data uncertainties, we find that only in a few
cases positive evidence for a given damping mechanism is available.


  Quantum random number generators (QRNGs) produce random numbers based on the
intrinsic probabilistic nature of quantum mechanics, making them true random
number generators (TRNGs). In this paper, we design and fabricate an embedded
QRNG that produces random numbers based on fluctuations of spontaneous emission
and absorption in a Light-Emitting Diode (LED). To achieve a robust and
reliable QRNG, we compare some usual post-processing methods and select the
finite impulse response (FIR) method for a real-time device. This device could
pass NIST tests, the generation rate is 1 Mbit/s and the randomness of the
output data is invariant in time.


  Shallow water environments create a challenging channel for communications.
In this paper, we focus on the challenges posed by the frequency-selective
signal distortion called the Doppler effect. We explore the design and
performance of machine learning (ML) based demodulation methods --- (1) Deep
Belief Network-feed forward Neural Network (DBN-NN) and (2) Deep Belief
Network-Convolutional Neural Network (DBN-CNN) in the physical layer of Shallow
Water Acoustic Communication (SWAC). The proposed method comprises of a ML
based feature extraction method and classification technique. First, the
feature extraction converts the received signals to feature images. Next, the
classification model correlates the images to a corresponding binary
representative. An analysis of the ML based proposed demodulation shows that
despite the presence of instantaneous frequencies, the performance of the
algorithm shows an invariance with a small 2dB error margin in terms of bit
error rate (BER).


  With a goal of understanding the conditions under which jets might be
produced in novae and related objects, I consider the conditions under which
jets are produced from other classes of accreting compact objects. I give an
overview of accretion disk spectral states, including a discussion of in which
states these jets are seen. I highlight the differences between neutron stars
and black holes, which may help give us insights about when and how the
presence of a solid surface may help or inhibit jet production.


  Final measurements and analysis are reported from the first-generation
Holometer, the first instrument capable of measuring correlated variations in
space-time position at strain noise power spectral densities smaller than a
Planck time. The apparatus consists of two co-located, but independent and
isolated, 40 m power-recycled Michelson interferometers, whose outputs are
cross-correlated to 25 MHz. The data are sensitive to correlations of
differential position across the apparatus over a broad band of frequencies up
to and exceeding the inverse light crossing time, 7.6 MHz. By measuring with
Planck precision the correlation of position variations at spacelike
separations, the Holometer searches for faint, irreducible correlated position
noise backgrounds predicted by some models of quantum space-time geometry. The
first-generation optical layout is sensitive to quantum geometrical noise
correlations with shear symmetry---those that can be interpreted as a
fundamental noncommutativity of space-time position in orthogonal directions.
General experimental constraints are placed on parameters of a set of models of
spatial shear noise correlations, with a sensitivity that exceeds the
Planck-scale holographic information bound on position states by a large
factor. This result significantly extends the upper limits placed on models of
directional noncommutativity by currently operating gravitational wave
observatories.


  The Euler characteristic and the volume are two best-known multiplicative
invariants of manifolds under finite covers. On the other hand, quantum
invariants of 3-manifolds are not multiplicative. We show that a perturbative
power series, introduced by Dimofte and the first author and shown to be a
topological invariant of cusped hyperbolic 3-manifolds by Storzer--Wheeler and
the first author, and conjectured to agree with the asymptotics of the Kashaev
invariant to all orders in perturbation theory, is asymptotically
multiplicative under cyclic covers. Moreover, its coefficients are determined
by polynomials constructed out of twisted Neumann--Zagier data. This gives a
new $t$-deformation of the perturbative quantum invariants, different than the
$x$-deformation obtained by deforming the geometric representation. We
illustrate our results with several hyperbolic knots.


  In this paper, we consider the inverse problem of determining the location
and the shape of a sound-soft obstacle from the modulus of the far-field data
for a single incident plane wave. By adding a reference ball artificially to
the inverse scattering system, we propose a system of nonlinear integral
equations based iterative scheme to reconstruct both the location and the shape
of the obstacle. The reference ball technique causes few extra computational
costs, but breaks the translation invariance and brings information about the
location of the obstacle. Several validating numerical examples are provided to
illustrate the effectiveness and robustness of the proposed inversion
algorithm.


  Recent experiments indicate that the Bernal stacked graphene multilayer (BGM)
have an interaction induced gapped (or pseudo gapped) ground state. Here, we
propose that, due to the electron correlation, the BGM can be induced into a
half metallic phase by applying a vertical electric field and doping. The half
metallic states in even-layer and odd-layer BGMs have totally different
behaviors, due to their different band structures. We systematically calculate
the graphene tetralayer (4L-BGM) and trilayer (3L-BGM) as the typical examples
of the even-layer and odd-layer BGMs, respectively. In 4L-BGM, we find an
interesting phenomenon of electric field induced inversion of the
spin-polarized bands. Namely, in the half metallic phase, the spin polarization
of the conducting channel and the net magnetic moment are inversed when the
applied electric field exceeds a critical value. In 3L-BGM, a remarkable
feature is that the inequivalence of the the two sublattices will intrinsically
break the degeneracy of the the spin-up and spin-down bands even in the zero
electric field case. Our results suggest that 4L-BGM should be an ideal
platform to detect the proposed half metallic phase in BGM systems.


  Temporal Sentence Grounding in Video (TSGV) is troubled by dataset bias
issue, which is caused by the uneven temporal distribution of the target
moments for samples with similar semantic components in input videos or query
texts. Existing methods resort to utilizing prior knowledge about bias to
artificially break this uneven distribution, which only removes a limited
amount of significant language biases. In this work, we propose the
bias-conflict sample synthesis and adversarial removal debias strategy
(BSSARD), which dynamically generates bias-conflict samples by explicitly
leveraging potentially spurious correlations between single-modality features
and the temporal position of the target moments. Through adversarial training,
its bias generators continuously introduce biases and generate bias-conflict
samples to deceive its grounding model. Meanwhile, the grounding model
continuously eliminates the introduced biases, which requires it to model
multi-modality alignment information. BSSARD will cover most kinds of coupling
relationships and disrupt language and visual biases simultaneously. Extensive
experiments on Charades-CD and ActivityNet-CD demonstrate the promising
debiasing capability of BSSARD. Source codes are available at
https://github.com/qzhb/BSSARD.


  Based on string theory's framework, the gauge/gravity duality, also known as
holography, has the ability to solve practical problems in low energy physical
systems like metals and fluids. Holographic applications open a path for
conversation and collaboration between the theory-driven, high energy culture
of string theory and fields like nuclear and condensed matter physics, which in
contrast place great emphasis on the empirical evidence that experiment
provides. This paper takes a look at holography's history, from its roots in
string theory to its present-day applications that are challenging the cultural
identity of the field. I will focus on two of these applications: holographic
QCD and holographic superconductivity, highlighting some of the (often
incompatible) historical influences, motives, and epistemic values at play, as
well as the subcultural shifts that help the collaborations work. The extent to
which holographic research -- arguably string theory's most successful and
prolific area -- must change its subcultural identity in order to function in
fields outside of string theory reflects its changing nature and the field's
uncertain future. Does string theory lose its identity in the low-energy
applications that holography provides? Does holography still belong under
string theory's umbrella, or is it destined to form new subcultures with each
of its fields of application? I find that the answers to these questions are
dynamic, interconnected, and highly dependent on string theory's relationship
with its field of application. In some cases, holography can maintain the goals
and values it inherited from string theory. In others, it instead adopts the
goals and values of the field in which it is applied. These examples highlight
a need for the STS community to expand its treatment of string theory beyond
its relationship with empiricism and role as a theory of quantum gravity.


  Recently, different approaches have been proposed for studying basic
properties of time series from a complex network perspective. In this work, the
corresponding potentials and limitations of networks based on recurrences in
phase space are investigated in some detail. We discuss the main requirements
that permit a feasible system-theoretic interpretation of network topology in
terms of dynamically invariant phase-space properties. Possible artifacts
induced by disregarding these requirements are pointed out and systematically
studied. Finally, a rigorous interpretation of the clustering coefficient and
the betweenness centrality in terms of invariant objects is proposed.


  A mathematical approach is adopted for optimizing the number of total device
elements required for obtaining high effective quantized resistances in
graphene-based quantum Hall array devices. This work explores an analytical
extension to the use of star-mesh transformations such that fractal-like, or
recursive, device designs can yield high enough resistances (like 1 E{\Omega},
arguably the highest resistance with meaningful applicability) while still
being feasible to build with modern fabrication techniques. Epitaxial graphene
elements are tested, whose quantized Hall resistance at the nu=2 plateau (R_H =
12906.4 {\Omega}) becomes the building block for larger effective, quantized
resistances. It is demonstrated that, mathematically, one would not need more
than 200 elements to achieve the highest pertinent resistances


  Quantum simulations of the hydroxide anion and hydroxyl radical are reported,
employing variational quantum algorithms for near-term quantum devices. The
energy of each species is calculated along the dissociation curve, to obtain
information about the stability of the molecular species being investigated. It
is shown that simulations restricted to valence spaces incorrectly predict the
hydroxyl radical to be more stable than the hydroxide anion. Inclusion of
dynamical electron correlation from non-valence orbitals is demonstrated,
through the integration of the variational quantum eigensolver and quantum
subspace expansion methods in the workflow of N-electron valence perturbation
theory, and shown to correctly predict the hydroxide anion to be more stable
than the hydroxyl radical, provided that basis sets with diffuse orbitals are
also employed. Finally, we calculate the electron affinity of the hydroxyl
radical using an aug-cc-pVQZ basis on IBM's quantum devices.


  The structure-thermodynamic stability relationship in vitreous silica is
investigated using machine learning and a library of 24,157 inherent structures
generated from melt-quenching and replica exchange molecular dynamics
simulations. We find the thermodynamic stability, i.e., enthalpy of the
inherent structure ($e_{\mathrm{IS}}$), can be accurately predicted by both
linear and nonlinear machine learning models from numeric structural
descriptors commonly used to characterize disordered structures. We find
short-range features become less indicative of thermodynamic stability below
the fragile-to-strong transition. On the other hand, medium-range features,
especially those between 2.8-~6 $\unicode{x212B}$;, show consistent
correlations with $e_{\mathrm{IS}}$ across the liquid and glass regions, and
are found to be the most critical to stability prediction among features from
different length scales. Based on the machine learning models, a set of five
structural features that are the most predictive of the silica glass stability
is identified.


  Increasing rates of global trade and travel, as well as changing climatic
patterns, have led to more frequent outbreaks of plant disease epidemics
worldwide. Mathematical modelling is a key tool in predicting where and how
these new threats will spread, as well as in assessing how damaging they might
be. Models can also be used to inform disease management, providing a rational
methodology for comparing the performance of possible control strategies
against one another. For emerging epidemics, in which new pathogens or pathogen
strains are actively spreading into new regions, the spatial component of
spread becomes particularly important, both to make predictions and to optimise
disease control. In this chapter we illustrate how the spatial spread of
emerging plant diseases can be modelled at the landscape scale via spatially
explicit compartmental models. Our particular focus is on the crucial role of
the dispersal kernel-which parameterises the probability of pathogen spread
from an infected host to susceptible hosts at any given distance-in determining
outcomes of epidemics. We add disease management to our model by testing
performance of a simple ''one off'' form of reactive disease control, in which
sites within a particular distance of locations detected to contain infection
are removed in a single round of disease management. We use this simplified
model to show how ostensibly arcane decisions made by the modeller-most notably
whether or not the underpinning disease model allows for stochasticity (i.e.
randomness)-can greatly impact on disease management recommendations. Our
chapter is accompanied by example code in the programming language R available
via an online repository, allowing the reader to run the models we present for
him/herself.


  Purpose: To develop a truly calibrationless reconstruction method that
derives ESPIRiT maps from uniformly-undersampled multi-channel MR data by deep
learning. Methods: ESPIRiT, one commonly used parallel imaging reconstruction
technique, forms the images from undersampled MR k-space data using ESPIRiT
maps that effectively represents coil sensitivity information. Accurate ESPIRiT
map estimation requires quality coil sensitivity calibration or autocalibration
data. We present a U-Net based deep learning model to estimate the
multi-channel ESPIRiT maps directly from uniformly-undersampled multi-channel
multi-slice MR data. The model is trained using fully-sampled multi-slice axial
brain datasets from the same MR receiving coil system. To utilize subject-coil
geometric parameters available for each dataset, the training imposes a hybrid
loss on ESPIRiT maps at the original locations as well as their corresponding
locations within the standard reference multi-slice axial stack. The
performance of the approach was evaluated using publicly available T1-weighed
brain and cardiac data. Results: The proposed model robustly predicted
multi-channel ESPIRiT maps from uniformly-undersampled k-space data. They were
highly comparable to the reference ESPIRiT maps directly computed from 24
consecutive central k-space lines. Further, they led to excellent ESPIRiT
reconstruction performance even at high acceleration, exhibiting a similar
level of errors and artifacts to that by using reference ESPIRiT maps.
Conclusion: A new deep learning approach is developed to estimate ESPIRiT maps
directly from uniformly-undersampled MR data. It presents a general strategy
for calibrationless parallel imaging reconstruction through learning from coil
and protocol specific data.


  In this study we examine the evolution of price, volume, and the bid-ask
spread after extreme 15 minute intraday price changes on the NYSE and the
NASDAQ. We find that due to strong behavioral trading there is an overreaction.
Furthermore we find that volatility which increases sharply at the event decays
according to a power law with an exponent of approximately 0.4, i.e., much
faster than the autocorrelation function of volatility.


  We study the problem of selecting the top-k candidates from a pool of
applicants, where each candidate is associated with a score indicating his/her
aptitude. Depending on the specific scenario, such as job search or college
admissions, these scores may be the results of standardized tests or other
predictors of future performance and utility. We consider a situation in which
some groups of candidates experience historical and present disadvantage that
makes their chances of being accepted much lower than other groups. In these
circumstances, we wish to apply an affirmative action policy to reduce
acceptance rate disparities, while avoiding any large decrease in the aptitude
of the candidates that are eventually selected. Our algorithmic design is
motivated by the frequently observed phenomenon that discrimination
disproportionately affects individuals who simultaneously belong to multiple
disadvantaged groups, defined along intersecting dimensions such as gender,
race, sexual orientation, socio-economic status, and disability. In short, our
algorithm's objective is to simultaneously: select candidates with high
utility, and level up the representation of disadvantaged intersectional
classes. This naturally involves trade-offs and is computationally challenging
due to the the combinatorial explosion of potential subgroups as more
attributes are considered. We propose two algorithms to solve this problem,
analyze them, and evaluate them experimentally using a dataset of university
application scores and admissions to bachelor degrees in an OECD country. Our
conclusion is that it is possible to significantly reduce disparities in
admission rates affecting intersectional classes with a small loss in terms of
selected candidate aptitude. To the best of our knowledge, we are the first to
study fairness constraints with regards to intersectional classes in the
context of top-k selection.


  In this paper, extending the results in \cite{F}, we compute Adams operations
on twisted $K$-theory of connected, simply-connected and simple compact Lie
groups $G$, in both equivariant and nonequivariant settings.


  Confusion and disagreement around the notion of time is due to the fact that
we often fail to recognize that we call 'time' a variety of distinct notions,
only partially related to one another. Many apparently obvious properties of
time are results of different kinds of approximations, idealizations, or
special contexts. I illustrate a number of distinct notions of time, their
differences and relations; they are all relevant for describing the real world.
To understand time, we have to break it apart.


  Confidence sets based on sparse estimators are shown to be large compared to
more standard confidence sets, demonstrating that sparsity of an estimator
comes at a substantial price in terms of the quality of the estimator. The
results are set in a general parametric or semiparametric framework.


  We investigate the relationship between endomorphisms of the Cuntz algebra
${\mathcal O}_2$ and endomorphisms of the Thompson groups $F$, $T$ and $V$
represented inside the unitary group of ${\mathcal O}_2$. For an endomorphism
$\lambda_u$ of ${\mathcal O}_2$, we show that $\lambda_u(V)\subseteq V$ if and
only if $u\in V$. If $\lambda_u$ is an automorphism of ${\mathcal O}_2$ then
$u\in V$ is equivalent to $\lambda_u(F)\subseteq V$. Our investigations are
facilitated by introduction of the concept of modestly scaling endomorphism of
${\mathcal O}_n$, whose properties and examples are investigated.


  Cubic chiral magnets, such as Cu$_{2}$OSeO$_{3}$, exhibit a variety of
non-collinear spin textures, including a trigonal lattice of spin whirls,
so-called skyrmions. Using magnetic resonant elastic x-ray scattering (REXS) on
a crystalline Bragg peak and its magnetic satellites while exciting the sample
with magnetic fields at GHz frequencies, we probe the ferromagnetic resonance
modes of these spin textures by means of the scattered intensity. Most notably,
the three eigenmodes of the skyrmion lattice are detected with large
sensitivity. As this novel technique, which we label REXS-FMR, is carried out
at distinct positions in reciprocal space, it allows to distinguish
contributions originating from different magnetic states, providing information
on the precise character, weight and mode mixing as a prerequisite of tailored
excitations for applications.


  The large tensor spectrum recently observed by the BICEP2 Collaboration
requires a super-Planckian field variation of the inflaton in the single-field
inflationary scenario. The required slow-roll parameter \epsilon \approx 0.01
would restrict the e-folding number to around 7 in (sub-)Planckian inflationary
models. To overcome such problems, we consider a two-field scenario based on
the natural assisted supersymmetric (SUSY) hybrid model ("natural SUSY hybrid
inflation" [1]), which combines the SUSY hybrid and the natural inflation
models. The axionic inflaton field from the natural inflation sector can admit
the right values for the tensor spectrum as well as a spectral index of 0.96
with a decay constant smaller than the Planck scale, f \lesssim M_P. On the
other hand, the vacuum energy of 2 x 10^{16} GeV with 50 e-folds is provided by
the inflaton coming from the SUSY hybrid sector, avoiding the eta problem.
These are achieved by introducing both the U(1)_R and a shift symmetry, and
employing the minimal Kahler potential.


  The thin-file borrowers are customers for whom a creditworthiness assessment
is uncertain due to their lack of credit history; many researchers have used
borrowers' relationships and interactions networks in the form of graphs as an
alternative data source to address this. Incorporating network data is
traditionally made by hand-crafted feature engineering, and lately, the graph
neural network has emerged as an alternative, but it still does not improve
over the traditional method's performance. Here we introduce a framework to
improve credit scoring models by blending several Graph Representation Learning
methods: feature engineering, graph embeddings, and graph neural networks. We
stacked their outputs to produce a single score in this approach. We validated
this framework using a unique multi-source dataset that characterizes the
relationships and credit history for the entire population of a Latin American
country, applying it to credit risk models, application, and behavior,
targeting both individuals and companies.
  Our results show that the graph representation learning methods should be
used as complements, and these should not be seen as self-sufficient methods as
is currently done. In terms of AUC and KS, we enhance the statistical
performance, outperforming traditional methods.
  In Corporate lending, where the gain is much higher, it confirms that
evaluating an unbanked company cannot solely consider its features. The
business ecosystem where these firms interact with their owners, suppliers,
customers, and other companies provides novel knowledge that enables financial
institutions to enhance their creditworthiness assessment.
  Our results let us know when and which group to use graph data and what
effects on performance to expect. They also show the enormous value of graph
data on the unbanked credit scoring problem, principally to help companies'
banking.


  This paper provides sharp Dirichlet heat kernel estimates in inner uniform
domains, including bounded inner uniform domains, in the context of certain
(possibly non-symmetric) bilinear forms resembling Dirichlet forms. For
instance, the results apply to the Dirichlet heat kernel associated with a
uniformly elliptic divergence form operator with symmetric second order part
and bounded measurable coefficients in inner uniform domains in $\mathbb R^n$.
The results are applicable to any convex domain, to the complement of any
convex domain, and to more exotic examples such as the interior and exterior of
the snowflake.


  The standard dissipation inequality for passivity is extended from storage
functions to general Lagrange subspaces. This is shown to have some interesting
consequences. A classical factorization result for passive systems is extended
to this generalized case, making use of the newly defined concept of the
Hamiltonian lift of a DAE system.


  Adiabatic potential energy curves of the 3sSigma+, 3tSigma+, 2sPi and 2tPi
states correlating for large internuclear distance with the K(4s) + Li(2p)
atomic asymptote were calculated. Very good agreement between the calculated
and the experimental curve of the 2sPi state allowed for a reliable description
of the dissociation process through a small (20 cm-1 for J = 0) potential
energy barrier. The barrier supports several rovibrational quasi-bound states
and explicit time evolution of these states via the time-dependent nuclear
Schroedinger equation, showed that the state populations decay exponentially in
time. We were able to precisely describe the time-dependent dissociation
process of several rovibrational levels and found that our calculated spectrum
match very well with the assigned experimental spectrum. Moreover, our approach
is able to predict the positions of previously unassigned lines despite their
low intensity


  Human factors, specifically visual impact and noise production, are the
current main limitations for broader urban wind energy exploitation. Trailing
edge noise, caused by the turbulent boundary layer interacting with the airfoil
surface, is the primary source of noise of modern horizontal and vertical axis
wind turbines. Low inflow turbulence levels do not affect the trailing edge
noise. However, an inflow of high turbulence intensity has flow structures that
can penetrate the boundary layer, increasing the velocity fluctuations inside
the boundary layer and, consequently, the wall pressure fluctuations and
trailing edge noise. This research investigates the effect of high free-stream
turbulence, observed in the atmospheric boundary layer of urban zones, in the
trailing edge noise generation. This was performed by measuring the increment
of the turbulence inside of the boundary layer and surface pressure
fluctuations near the trailing edge when an airfoil is submitted to high inflow
turbulence. Experimental measurements were performed in the Aeroacoustic Wind
Tunnel of the University of Twente on a NACA 0012 airfoil subjected to a
turbulent inflow. The chord-based Reynolds number of the experiments ranged
from 170 x 10^3 to 500 x 10^3. Results showed that high inflow turbulence
significantly increases the velocity fluctuations and the integral length scale
along the entire boundary layer, resulting in an increment of the surface
pressure spectrum more than 6 dB and 10 dB in the entire frequency range for
10% and 20% of free-stream turbulence, respectively. The 10% free-stream
turbulence increases the velocity fluctuations just in the low--frequency
range, whereas the 20% inflow turbulence influences the velocity spectrum in
the entire frequency range, increasing the size of the smallest structures of
the turbulence. Amiet's theory is applied to predict the trailing edge
far-field noise.


  The design of energy and spectrally efficient Wireless Sensor Networks (WSN)
is crucial to support the upcoming expansion of IoT/M2M mobile data traffic. In
this work, we consider an energy harvesting WSN where sensor data are
periodically reported to a Fusion Center (FC) by a sparse set of active
sensors. Unlike most existing works, the transmit power levels of each sensor
are assumed to be unknown at the FC in this distributed setting. We address the
inverse problem of joint signal / power restoration at the FC- a challenging
under-determined separation problem. To regularize the ill-posed problem, we
assume both a graph-signal smoothness prior (signal is smooth with respect to a
graph modeling spatial correlation among sensors) and a sparsity power prior
for the two unknown variables. We design an efficient algorithm by alternately
fixing one variable and solving for the other until convergence. Specifically,
when the signal is fixed, we solve for the power vector using Simplex pivoting
in linear programming (LP) to iteratively identify sparse feasible solutions,
locally minimizing an objective. Simulation results show that our proposal can
achieve very low reconstruction errors and outperform conventional schemes
under reasonable assumptions


  There has been much recent interest in near-term applications of quantum
computers, i.e., using quantum circuits that have short decoherence times due
to hardware limitations. Variational quantum algorithms (VQA), wherein an
optimization algorithm implemented on a classical computer evaluates a
parametrized quantum circuit as an objective function, are a leading framework
in this space. An enormous breadth of algorithms in this framework have been
proposed for solving a range of problems in machine learning, forecasting,
applied physics, and combinatorial optimization, among others.
  In this paper, we analyze the iteration complexity of VQA, that is, the
number of steps that VQA requires until its iterates satisfy a surrogate
measure of optimality. We argue that although VQA procedures incorporate
algorithms that can, in the idealized case, be modeled as classic procedures in
the optimization literature, the particular nature of noise in near-term
devices invalidates the claim of applicability of off-the-shelf analyses of
these algorithms. Specifically, noise makes the evaluations of the objective
function via quantum circuits biased. Commonly used optimization procedures,
such as SPSA and the parameter shift rule, can thus be seen as derivative-free
optimization algorithms with biased function evaluations, for which there are
currently no iteration complexity guarantees in the literature. We derive the
missing guarantees and find that the rate of convergence is unaffected.
However, the level of bias contributes unfavorably to both the constant
therein, and the asymptotic distance to stationarity, i.e., the more bias, the
farther one is guaranteed, at best, to reach a stationary point of the VQA
objective.


  In this paper, we aim to address the challenge of novel view rendering of
human performers who wear clothes with complex texture patterns using a sparse
set of camera views. Although some recent works have achieved remarkable
rendering quality on humans with relatively uniform textures using sparse
views, the rendering quality remains limited when dealing with complex texture
patterns as they are unable to recover the high-frequency geometry details that
are observed in the input views. To this end, we propose HDhuman, which uses a
human reconstruction network with a pixel-aligned spatial transformer and a
rendering network with geometry-guided pixel-wise feature integration to
achieve high-quality human reconstruction and rendering. The designed
pixel-aligned spatial transformer calculates the correlations between the input
views and generates human reconstruction results with high-frequency details.
Based on the surface reconstruction results, the geometry-guided pixel-wise
visibility reasoning provides guidance for multi-view feature integration,
enabling the rendering network to render high-quality images at 2k resolution
on novel views. Unlike previous neural rendering works that always need to
train or fine-tune an independent network for a different scene, our method is
a general framework that is able to generalize to novel subjects. Experiments
show that our approach outperforms all the prior generic or specific methods on
both synthetic data and real-world data.


  Recent studies have actively employed persistent homology (PH), a topological
data analysis technique, to analyze the topological information in time series
data. Many successful studies have utilized graph representations of time
series data for PH calculation. Given the diverse nature of time series data,
it is crucial to have mechanisms that can adjust the PH calculations by
incorporating domain-specific knowledge. In this context, we introduce a
methodology that allows the adjustment of PH calculations by reflecting
relevant domain knowledge in specific fields. We introduce the concept of
featured time series, which is the pair of a time series augmented with
specific features such as domain knowledge, and an influence vector that
assigns a value to each feature to fine-tune the results of the PH. We then
prove the stability theorem of the proposed method, which states that adjusting
the influence vectors grants stability to the PH calculations. The proposed
approach enables the tailored analysis of a time series based on the graph
representation methodology, which makes it applicable to real-world domains. We
consider two examples to verify the proposed method's advantages: anomaly
detection of stock data and topological analysis of music data.


  Recently, John Franks and Michael Handel proved that, for $g\geq 3$ and
$n\leq 2g-4$, every homomorphism from the mapping class group of an orientable
surface of genus $g$ to $\GL (n,\C)$ is trivial. We extend this result to
$n\leq 2g-1$, also covering the case $g=2$. As an application, we prove the
corresponding result for nonorientable surfaces. Another application is on the
triviality of homomorphisms from the mapping class group of a closed surface of
genus $g$ to $\Aut (F_n)$ or to $\Out (F_n)$ for $n\leq 2g-1$.


  Blazar spectra are one of the most important windows into the physical
processes occurring along jets. The spectrum, composed from the different
emitting regions along the jet, allows us to constrain the physical conditions
in the jet. I present my work modelling blazar spectra using an extended
inhomogeneous jet model with an accelerating, magnetically dominated, parabolic
base transitioning to a slowly decelerating, conical section motivated by
observations, simulations and theory. We set the inner geometry of our
multi-zone model using observations of the jet in M87 which transitions from
parabolic to conical at 10^5 Schwarzschild radii. This model is able to
reproduce quiescent blazar spectra very well across all wavelengths (including
radio observations) for a sample of 42 BL Lacs and FSRQs.
  Using this inhomogeneous model we are able to constrain the location at which
the synchrotron emission is brightest in these jets by fitting to the optically
thick to thin synchrotron break. We find that the radius of the jet at which
the synchrotron emission is brightest (where the jet first approaches
equipartition) scales approximately linearly with the jet power. We also find a
correlation between the length of the accelerating, parabolic section of the
jet and the maximum bulk Lorentz factor. In agreement with previous work we
find that BL Lacs are low power blazars whereas FSRQs are high power blazars.
Together with our simple jet power-radius relation this leads us to a deeper
understanding of the physics underlying the blazar sequence.