  Building vehicles capable of operating without human supervision requires the
determination of the agent's pose. Visual Odometry (VO) algorithms estimate the
egomotion using only visual changes from the input images. The most recent VO
methods implement deep-learning techniques using convolutional neural networks
(CNN) extensively, which add a substantial cost when dealing with
high-resolution images. Furthermore, in VO tasks, more input data does not mean
a better prediction; on the contrary, the architecture may filter out useless
information. Therefore, the implementation of computationally efficient and
lightweight architectures is essential. In this work, we propose the RAM-VO, an
extension of the Recurrent Attention Model (RAM) for visual odometry tasks.
RAM-VO improves the visual and temporal representation of information and
implements the Proximal Policy Optimization (PPO) algorithm to learn robust
policies. The results indicate that RAM-VO can perform regressions with six
degrees of freedom from monocular input images using approximately 3 million
parameters. In addition, experiments on the KITTI dataset demonstrate that
RAM-VO achieves competitive results using only 5.7% of the available visual
information.


  We demonstrate deterministic and robust generation of pulsed resonance
fluorescence single photons from a single InGaAs quantum dot using the method
of rapid adiabatic passage. Comparative study is performed with
transform-limited, negatively chirped and positively chirped pulses,
identifying the last one to be the most robust against fluctuation of driving
strength. The generated single photons are background free, have a vanishing
two-photon emission probability of 0.3% and a raw (corrected) two-photon
Hong-Ou-Mandel interference visibility of 97.9% (99.5%), reaching a precision
that places single photons at the threshold for fault-tolerant surface-code
quantum computing. The single-photon source can be readily scaled up to
multi-photon entanglement and used for quantum metrology, boson sampling and
linear optical quantum computing.


  In this paper, we present a novel two-level variable Horizon Model Predictive
Control (VH-MPC) framework for bipedal locomotion. In this framework, the
higher level computes the landing location and timing (horizon length) of the
swing foot to stabilize the unstable part of the center of mass (CoM) dynamics,
using feedback from the CoM states. The lower level takes into account the
swing foot dynamics and generates dynamically consistent trajectories for
landing at the desired time as close as possible to the desired location. To do
that, we use a simplified model of the robot dynamics projected in swing foot
space that takes into account joint torque constraints as well as the friction
cone constraints of the stance foot. We show the effectiveness of our proposed
control framework by implementing robust walking patterns on our
torque-controlled and open-source biped robot, Bolt. We report extensive
simulations and real robot experiments in the presence of various disturbances
and uncertainties.


  In the era of exponential technology growth, one unexpected guest has claimed
a seat in classrooms worldwide, Artificial Intelligence. Generative AI, such as
ChatGPT, promises a revolution in education, yet it arrives with a double-edged
sword. Its potential for personalized learning is offset by issues of cheating,
inaccuracies, and educators struggling to incorporate it effectively into their
lesson design. We are standing on the brink of this educational frontier, and
it is clear that we need to navigate this terrain with a lot of care. This is a
major challenge that could undermine the integrity and value of our educational
process. So, how can we turn these challenges into opportunities? When used
inappropriately, AI tools can become the perfect tool for the cut copy paste
mentality, and quickly begin to corrode critical thinking, creativity, and deep
understanding, the most important skills in our rapidly changing world.
Teachers feel that they are not equipped to leverage this technology, widening
the digital divide among educators and institutions. Addressing these concerns
calls for an in depth research approach. We will employ empirical research,
drawing on the Technology Acceptance Model, to assess the attitudes toward
generative AI among educators and students. Understanding their perceptions,
usage patterns, and hurdles is the first crucial step in creating an effective
solution. The present study will be used as a process manual for future
researchers to apply, running their own data, based on the steps explained here


  This paper has been withdrawn by the authors. Because of a misunderstanding,
the paper was submitted prematurely to the arXiv. A replacement will follow.


  Federated learning (FL) is a challenging setting for optimization due to the
heterogeneity of the data across different clients which gives rise to the
client drift phenomenon. In fact, obtaining an algorithm for FL which is
uniformly better than simple centralized training has been a major open problem
thus far. In this work, we propose a general algorithmic framework, Mime, which
i) mitigates client drift and ii) adapts arbitrary centralized optimization
algorithms such as momentum and Adam to the cross-device federated learning
setting. Mime uses a combination of control-variates and server-level
statistics (e.g. momentum) at every client-update step to ensure that each
local update mimics that of the centralized method run on iid data. We prove a
reduction result showing that Mime can translate the convergence of a generic
algorithm in the centralized setting into convergence in the federated setting.
Further, we show that when combined with momentum based variance reduction,
Mime is provably faster than any centralized method--the first such result. We
also perform a thorough experimental exploration of Mime's performance on real
world datasets.


  We present analysis of high spectral resolution NIR spectra of CO and H$_2$O
in comet C/2009 P1 (Garradd) taken during its 2011-2012 apparition with the
CSHELL instrument on NASA's Infrared Telescope Facility (IRTF). We also present
analysis of observations of atomic oxygen in comet Garradd obtained with the
ARCES echelle spectrometer mounted on the ARC 3.5-meter telescope at Apache
Point Observatory and the Tull Coude spectrograph on the Harlan J. Smith
2.7-meter telescope at McDonald Observatory. The observations of atomic oxygen
serve as a proxy for H$_2$O and CO$_2$. We confirm the high CO abundance in
comet Garradd and the asymmetry in the CO/H$_2$O ratio with respect to
perihelion reported by previous studies. From the oxygen observations, we infer
that the CO$_2$/H$_2$O ratio decreased as the comet moved towards the Sun,
which is expected based on current sublimation models. We also infer that the
CO$_2$/H$_2$O ratio was higher pre-perihelion than post-perihelion. We observe
evidence for the icy grain source of H$_2$O reported by several studies
pre-perihelion, and argue that this source is significantly less abundant
post-perihelion. Since H$_2$O, CO$_2$, and CO are the primary ices in comets,
they drive the activity. We use our measurements of these important volatiles
in an attempt to explain the evolution of Garradd's activity over the
apparition.


  We previously built a sample of 14,012 extremely variable quasars (EVQs)
based on SDSS and Pan-STARRS1 photometric observations. In this work we present
the spectral fitting to their SDSS spectra, and study the spectral variation in
1,259 EVQs with multi-epoch SDSS spectra (after prudently excluding spectra
with potentially unreliable spectroscopic photometry). We find clear
"bluer-when-brighter" trend in EVQs, consistent with previous findings of
normal quasars and AGNs. We detect significant intrinsic Baldwin effect (iBeff,
i.e., smaller line EW at higher continuum flux in individual AGNs) in the broad
MgII and CIV lines of EVQs. Meanwhile, no systematical iBeff is found for the
broad Hb line, which could be attributed to strong host contamination at longer
wavelengths. Remarkably, by comparing the iBeff slope of EVQs with archived
changing-look quasars (CLQs), we show that the CLQs identified in literature
are mostly likely a biased (due to its definition) sub-population of EVQs,
rather than a distinct population of quasars. We also found no significant
broad line breathing of either Hb, MgII or CIV, suggesting the broad line
breathing in quasars may disappear at longer timescales ($\sim$ 3000 days).


  Capillary forces significantly affect the stability of sandpiles. We analyze
the stability of sandpiles with such forces, and find that the critical angle
is unchanged in the limit of an infinitely large system; however, this angle is
increased for finite-sized systems. The failure occurs in the bulk of the
sandpile rather than at the surface. This is related to a standard result in
soil mechanics. The increase in the critical angle is determined by the surface
roughness of the particles, and exhibits three regimes as a function of the
added-fluid volume. Our theory is in qualitative agreement with the recent
experimental results of Hornbaker et al., although not with the interpretation
they make of these results.


  On the 17th of August 2017, the thriving discovery of gravitational waves
event GW170817 and its optical counterpart GRB170817A, owing to coalescing of
two neutron starts divulged a very small amount of difference around ${\cal
O}(10^{-16})$ between traveling speed of light and the velocity of
gravitational waves ($C_{T}$). This small deviation can be used as a strong
constraint on modified gravity models. We concentrate on the Higher-Order
expansion of Mimetic Gravity (HOMimG) model to specify the parametric space of
three parameters of our model ($a$, $b$, and $c$ ) utilizing the observational
constraint from GW170817-GRB170817A on $C_{T}$, besides two theoretical
constraints on $C_{T}^{2}$ and $C_{s}^{2}$ due to assurance of the stability of
the model and subluminal promulgation of the scalar and tensor perturbations.
Thereafter, we increase the accuracy of the parametric space with the aid of
imposing further limitation of $\gamma$ parameter (related to the age of the
Universe). In pursuance of determining the other parameter of the model
($\lambda$), the potential of the model is specified, and another observational
bound related to the Equation of State (EoS) parameter of dark energy is taken
into account. In consequence, we attain a viable HOMimG model confined to
numbers of observational and theoretical constraints. At the end, regarding the
concluded numerical ranges for the model parameters, and cogitating two
different potential (quadratic and quartic potentials) to specify $\lambda$
parameter, we illustrate that the values of the model parameters are
independent of the form of potential.


  Recently, Dvir, Golovnev, and Weinstein have shown that sufficiently strong
lower bounds for linear data structures would imply new bounds for rigid
matrices. However, their result utilizes an algorithm that requires an $NP$
oracle, and hence, the rigid matrices are not explicit. In this work, we derive
an equivalence between rigidity and the systematic linear model of data
structures. For the $n$-dimensional inner product problem with $m$ queries, we
prove that lower bounds on the query time imply rigidity lower bounds for the
query set itself. In particular, an explicit lower bound of
$\omega\left(\frac{n}{r}\log m\right)$ for $r$ redundant storage bits would
yield better rigidity parameters than the best bounds due to Alon, Panigrahy,
and Yekhanin. We also prove a converse result, showing that rigid matrices
directly correspond to hard query sets for the systematic linear model. As an
application, we prove that the set of vectors obtained from rank one binary
matrices is rigid with parameters matching the known results for explicit sets.
This implies that the vector-matrix-vector problem requires query time
$\Omega(n^{3/2}/r)$ for redundancy $r \geq \sqrt{n}$ in the systematic linear
model, improving a result of Chakraborty, Kamma, and Larsen. Finally, we prove
a cell probe lower bound for the vector-matrix-vector problem in the high error
regime, improving a result of Chattopadhyay, Kouck\'{y}, Loff, and
Mukhopadhyay.


  We explore a novel strategy of patterning nematic elastomers that does not
require inscribing the texture directly. It is based on varying the dopant
concentration that, beside shifting the phase transition point, affects the
nematic director field via coupling between the gradients of concentration and
nematic order parameter. Rotation of the director around a point dopant source
causes topological modification manifesting itself in a change of the number of
defects. A variety of shapes, dependent on the dopant distribution, are
obtained by anisotropic deformation following the nematic--isotropic
transition.


  We show that strong electron-electron interactions in cavity-coupled quantum
materials can enable collectively enhanced light-matter interactions with
ultrastrong effective coupling strengths. As a paradigmatic example we consider
a Fermi-Hubbard model coupled to a single-mode cavity and find that resonant
electron-cavity interactions result in the formation of a quasi-continuum of
polariton branches. The vacuum Rabi splitting of the two outermost branches is
collectively enhanced and scales with $g_{\text{eff}}\propto\sqrt{2L}$, where
$L$ is the number of electronic sites, and the maximal achievable value for
$g_{\text{eff}}$ is determined by the volume of the unit cell of the crystal.
We find that $g_{\text{eff}}$ for existing quantum materials can by far exceed
the width of the first excited Hubbard band. This effect can be experimentally
observed via measurements of the optical conductivity and does not require
ultra-strong coupling on the single-electron level. Quantum correlations in the
electronic ground state as well as the microscopic nature of the light-matter
interaction enhance the collective light-matter interaction compared to an
ensemble of independent two-level atoms interacting with a cavity mode.


  In this contribution we present the preliminary results regarding the
non-linear BAO signal in higher-order statistics of the cosmic density field.
We use ensembles of N-body simulations to show that the non-linear evolution
changes the amplitudes of the BAO signal, but has a negligible effect on the
scale of the BAO feature. The latter observation accompanied by the fact that
the BAO feature amplitude roughly doubles as one moves to higher orders,
suggests that the higher-order correlation amplitudes can be used as probe of
the BAO signal.


  In heterogeneous solids such as rocks and concrete, the speed of sound
diminishes with the strain amplitude of a dynamic loading (softening). This
decrease known as "slow dynamics" occurs at time scales larger than the period
of the forcing. Also, hysteresis is observed in the steady-state response. The
phenomenological model by Vakhnenko et al. is based on a variable that
describes the softening of the material [Phys. Rev. E 70-1, 2004]. However,
this model is 1D and it is not thermodynamically admissible. In the present
article, a 3D model is derived in the framework of the finite strain theory. An
internal variable that describes the softening of the material is introduced,
as well as an expression of the specific internal energy. A mechanical
constitu-tive law is deduced from the Clausius-Duhem inequality. Moreover, a
family of evolution equations for the internal variable is proposed. Here, an
evolution equation with one relaxation time is chosen. By construction, this
new model of continuum is thermodynamically admissible and dissipative
(inelas-tic). In the case of small uniaxial deformations, it is shown
analytically that the model reproduces qualitatively the main features of real
experiments.


  Complex organic molecules (COMs) have been detected ubiquitously in
protostellar systems. However, at shorter wavelengths (~0.8mm) it is more
difficult to detect larger molecules than at longer wavelengths (~3mm) because
of the increase of millimeter dust opacity, line confusion, and unfavorable
partition function. We aim to search for large molecules (>8 atoms) in the ALMA
Band 3 spectrum of IRAS 16293-2422 B. We search for more than 70 molecules and
identify as many lines as possible in the spectrum. The spectral settings were
set to specifically target three-carbon species such as propanol and glycerol.
We identify lines of 31 molecules including many oxygen-bearing COMs such as
CH3OH and c-C2H4O and a few nitrogen- and sulfur-bearing ones such as HOCH2CN
and CH3SH. The largest detected molecules are gGg-(CH2OH)2 and CH3COCH3. We do
not detect glycerol or propanol but provide upper limits for them which are in
line with previous laboratory and observational studies. The line density in
Band 3 is only ~2.5 times lower in frequency space than in Band 7. From the
detected lines in Band 3 at a $\gtrsim 6\sigma$ level, ~25-30% of them could
not be identified indicating the need for more laboratory data of rotational
spectra. We find similar column densities and column density ratios of COMs
(within a factor ~2) between Band 3 and Band 7. The effect of dust optical
depth for IRAS 16293-2422 B at an off-source location on column densities and
column density ratios is minimal. Moreover, for warm protostars, long
wavelength spectra are not only crowded, but also take longer integration times
to reach the same sensitivity limit. The 3mm search has not yet resulted in
detection of larger and more complex molecules in warm sources. A full deep
ALMA Band 2-3 (i.e., 3-4 mm) survey is needed to assess whether low frequency
data have the potential to reveal more complex molecules in warm sources.


  We review the methods and results obtained in an analysis of the experimental
heavy ion collision research program at nuclear beam energy of 160-200A GeV. We
study strange, and more generally, hadronic particle production experimental
data. We discuss present expectations concerning how these observables will
perform at other collision energies. We also present the dynamical theory of
strangeness production and apply it to show that it agrees with available
experimental results. We describe strange hadron production from the
baryon-poor quark-gluon phase formed at much higher reaction energies, where
the abundance of strange baryons and antibaryons exceeds that of nonstrange
baryons and antibaryons.


  Among those transversally elliptic operators initiated by Atiyah and Singer,
Kohn's $\Box_b$ operator on CR manifolds with $S^1$ action is a natural one of
geometric significance for complex analysts. Our first main result establishes
an asymptotic expansion for the heat kernel of such an operator with values in
its Fourier components, which involves an unprecedented contribution in terms
of a distance function from lower dimensional strata of the $S^1$-action. Our
second main result computes a local index density, in terms of
\emph{tangential} characteristic forms, on such manifolds including
\emph{Sasakian manifolds} of interest in String Theory, by showing that certain
non-trivial contributions from strata in the heat kernel expansion will
eventually cancel out by applying Getzler's rescaling technique to off-diagonal
estimates. This leads to a local result which can be thought of as a type of
local index theorem on these CR manifolds. As applications of our CR index
theorem we can prove a CR version of Grauert-Riemenschneider criterion, and
produce many CR functions on a weakly pseudoconvex CR manifold with transversal
$S^1$ action and many CR sections on some class of CR manifolds, answering (on
this class of manifolds) some long-standing questions in several complex
variables and CR geometry. We give examples of these CR manifolds, some of
which arise from Brieskorn manifolds. Moreover in some cases, without use of
equivariant cohomology method nor keeping contributions arising from lower
dimensional strata as done in previous works, we can reinterpret Kawasaki's
Hirzebruch-Riemann-Roch formula for a complex orbifold with an orbifold
holomorphic line bundle, as an index theorem obtained by a single integral over
a smooth CR manifold which is essentially the circle bundle of this line
bundle.


  The common idea behind complexity reduction in physical systems is separation
of information into "physically meaningful" and "safely ignorable". Here we
consider a generic notion of such separation -- implemented by coarse-graining
the state-space -- and address the question of what information is indeed
safely ignorable if we want to reduce the complexity of dynamics. The general
condition for reducibility of dynamics under coarse-graining will be presented
for stochastic and quantum systems. In the process we develop the quantum
notion of state-space coarse-graining that allows to marginalize selected
degrees of freedom. One of our main findings is that there is a broader class
of symmetries, beyond those that are considered by Noether's Theorem, that can
play a role in the reduction of dynamics. Some examples of quantum
coarse-grainings and the reduction of dynamics with symmetries will be
discussed.


  Generative adversarial networks (GANs) are known to benefit from
regularization or normalization of their critic (discriminator) network during
training. In this paper, we analyze the popular spectral normalization scheme,
find a significant drawback and introduce sparsity aware normalization (SAN), a
new alternative approach for stabilizing GAN training. As opposed to other
normalization methods, our approach explicitly accounts for the sparse nature
of the feature maps in convolutional networks with ReLU activations. We
illustrate the effectiveness of our method through extensive experiments with a
variety of network architectures. As we show, sparsity is particularly dominant
in critics used for image-to-image translation settings. In these cases our
approach improves upon existing methods, in less training epochs and with
smaller capacity networks, while requiring practically no computational
overhead.


  In this paper, we present SATIS, a framework to derive Web Service
specifications from end-user's requirements in order to opera-tionalise
business processes in the context of a specific application domain. The aim of
SATIS is to provide to neuroscientists, which are not familiar with computer
science, a complete solution to easily find a set of Web Services to implement
an image processing pipeline. More precisely, our framework offers the
capability to capture high-level end-user's requirements in an iterative and
incremental way and to turn them into queries to retrieve Web Services
description. The whole framework relies on reusable and combinable elements
which can be shared out by a community of users sharing some interest or
problems for a given topic. In our approach, we adopt Web semantic languages
and models as a unified framework to deal with end-user's requirements and Web
Service descriptions in order to take advantage of their reasoning and
traceability capabilities.


  This technical note studies a class of distributed nonsmooth convex consensus
optimization problem. The cost function is a summation of local cost functions
which are convex but nonsmooth. Each of the local cost functions consists of a
twice differentiable convex function and two lower semi-continuous convex
functions. We call this problem as single-smooth plus double-nonsmooth (SSDN)
problem. Under mild conditions, we propose a distributed double proximal
primal-dual optimization algorithm. The double proximal operator is designed to
deal with the difficulty caused by the unproximable property of the summation
of those two nonsmooth functions. Besides, it can also guarantee that the
proposed algorithm is locally Lipschitz continuous. An auxiliary variable in
the double proximal operator is introduced to estimate the subgradient of the
second nonsmooth function. Theoretically, we conduct the convergence analysis
by employing Lyapunov stability theory. It shows that the proposed algorithm
can make the states achieve consensus at the optimal point. In the end,
nontrivial simulations are presented and the results demonstrate the
effectiveness of the proposed algorithm.


  In this work, we perform a holographic analysis to study non local
observables associated to a uniformly \textit{boosted} strongly coupled large
$N$ thermal plasma in $d$-dimensions. In order to accomplish the holographic
analysis, the appropriate dual bulk theory turns out to be $d+1$ dimensional
\textit{boosted} AdS-Schwarzschild blackhole background. In particular, we
compute entanglement entropy of the boosted plasma at high temperature living
inside a strip geometry with entangling width $l$ in the boundary at a
particular instant of time. We also study the two-point correlators in the
boundary by following geodesic approximation method. For analyzing the effect
of boosting on the thermal plasma and correspondingly on both non local
observables, we keep the alignment of the width of region of interest both
parallel and perpendicular to the direction of the boost. We find our results
significantly modified compared to those in un-boosted plasma up to the
quadratic order of the boost velocity $v$. More interestingly, the relative
orientation of the boost and the entangling width plays a crucial role to
quantify the holographic entanglement entropy in the boundary theory. The
breaking of rotational symmetry in the boundary theory due to the boosting of
the plasma along a specific flat direction causes this interesting feature.


  We prove homological stability for two different flavours of asymptotic
monopole moduli spaces, namely moduli spaces of framed Dirac monopoles and
moduli spaces of ideal monopoles. The former are Gibbons-Manton torus bundles
over configuration spaces whereas the latter are obtained from them by
replacing each circle factor of the fibre with a monopole moduli space by the
Borel construction. They form boundary hypersurfaces in a partial
compactification of the classical monopole moduli spaces. Our results follow
from a general homological stability result for configuration spaces equipped
with non-local data.


  Use of inclusive MT2 distribution for sparticle mass determination is
discussed. We define new parameters MT2(min) and MT2mod(min), which are a kind
of minimum of sub-systerm MT2 values. Their endpoints are less affected by
initial state radiations. We demonstrate that both masses of squark and gluino
can be extracted from the endpoints of the distributions in the wide region of
parameter space expected in CMSSM. We also present a comparison with MTGen
distributions.


  Clippy lints are considered as essential tools for Rust developers, as they
can be configured as gate-keeping rules for a Rust project during continuous
integration. Despite their availability, little was known about practical
application and cost-effectiveness of the lints in reducing code quality
issues. In this study, we embark on a comprehensive analysis to unveil the true
impact of Clippy lints in the Rust development landscape. The study is
structured around three interrelated components, each contributing to the
overall effectiveness of Clippy. Firstly, we conduct a comprehensive analysis
of Clippy lints in all idiomatic crates-io Rust projects with an average
warning density of 21/KLOC. The analysis identifies the most cost-effective
lint fixes, offering valuable opportunities for optimizing code quality.
Secondly, we actively engage Rust developers through a user survey to garner
invaluable feedback on their experiences with Clippy. User insights shed light
on two crucial concerns: the prevalence of false positives in warnings and the
need for auto-fix support for most warnings. Thirdly, building upon these
findings, we engineer three innovative automated refactoring techniques to
effectively fix the four most frequent Clippy lints. As a result, the warning
density in Rosetta benchmarks has significantly decreased from 195/KLOC to an
impressive 18/KLOC, already lower than the average density of the crates-io
Rust projects. These results demonstrate tangible benefit and impact of our
efforts in enhancing the overall code quality and maintainability for Rust
developers.


  We introduce a specialization technique in order to study monomial ideals
that are generated in degree two by using our earlier results about Ferrers
ideals. It allows us to describe explicitly a cellular minimal free resolution
of various ideals including any strongly stable and any squarefree strongly
stable ideal whose minimal generators have degree two. In particular, this
shows that threshold graphs can be obtained as specializations of Ferrers
graphs, which explains their similar properties.


  We employ the formalism of vanishing cycles and perverse sheaves to introduce
and study the vanishing cohomology of complex projective hypersurfaces. As a
consequence, we give upper bounds for the Betti numbers of projective
hypersurfaces, generalizing those obtained by different methods by Dimca in the
isolated singularities case, and by Siersma-Tib\u{a}r in the case of
hypersurfaces with a $1$-dimensional singular locus. We also prove a supplement
to the Lefschetz hyperplane theorem for hypersurfaces, which takes the
dimension of the singular locus into account, and we use it to give a new proof
of a result of Kato.


  We have probed the condition of periodic oscillation in a class of two
variable nonlinear dynamical open systems modeled with
Lienard-Levinson-Smith(LLS) equation which can be a limit cycle, center or a
very slowly decaying center type oscillation. Using a variety of examples of
open systems like Glycolytic oscillator, Lotka-Volterra(L-V) model, a
generalised van der Pol oscillator and a time delayed nonlinear feedback
oscillation as a non-autonomous system, each of which contains a family of
periodic orbits, we have solved LLS systems in terms of a multi-scale
perturbation theory using Krylov-Bogoliubov(K-B) method and it is utilised to
characterise the size and shape of the limit cycle and center as well as the
approach to their steady state dynamics. We have shown the condition when the
average scaled radius of a center undergoes a power law decay with exponent
1/2.


  In airport operations, optimally using dedicated personnel for baggage
handling tasks plays a crucial role in the design of resource-efficient
processes. Teams of workers with different qualifications must be formed, and
loading or unloading tasks must be assigned to them. Each task has a time
window within which it can be started and should be finished. Violating these
temporal restrictions incurs severe financial penalties for the operator. In
practice, various components of this process are subject to uncertainties. We
consider the aforementioned problem under the assumption of stochastic travel
times across the apron. We present two binary program formulations to model the
problem at hand and solve it with a Branch-Price-Cut-and-Switch approach, in
which we dynamically switch between two master problem formulations.
Furthermore, we use an exact separation method to identify violated rank-1
Chv\'atal-Gomory cuts and utilize an efficient branching rule relying on task
finish times. We test the algorithm on instances generated based on real-world
data from a major European hub airport with a planning horizon of up to two
hours, 30 flights per hour, and three available task execution modes to choose
from. Our results indicate that our algorithm is able to significantly
outperform existing solution approaches. Moreover, an explicit consideration of
stochastic travel times allows for solutions that utilize the available
workforce more efficiently, while simultaneously guaranteeing a stable service
level for the baggage handling operator.


  Contacts between people are the absolute drivers of contagious respiratory
infections. For this reason, limiting and tracking contacts is a key strategy
for the control of the COVID-19 epidemic. Digital contact tracing has been
proposed as an automated solution to scale up traditional contact tracing.
However, the required penetration of contact tracing apps within a population
to achieve a desired target in the control of the epidemic is currently under
discussion within the research community. In order to understand the effects of
digital contact tracing, several mathematical models have been proposed. In
this article, we survey the main ones and we propose a compartmental SEIR model
with which it is possible, differently from the models in the related
literature, to derive closed-form conditions regarding the control of the
epidemic as a function of the contact tracing apps penetration and the testing
efficiency. Closed-form conditions are crucial for the understandability of
models, and thus for decision makers (including digital contact tracing
designers) to correctly assess the dependencies within the epidemic. With our
model, we find that digital contact tracing alone can rarely tame an epidemic:
for unrestrained COVID-19, this would require a testing turnaround of around 1
day and app uptake above 80% of the population, which are very difficult to
achieve in practice. However, digital contact tracing can still be effective if
complemented with other mitigation strategies, such as social distancing and
mask-wearing.


  The personalization of machine learning (ML) models to address data drift is
a significant challenge in the context of Internet of Things (IoT)
applications. Presently, most approaches focus on fine-tuning either the full
base model or its last few layers to adapt to new data, while often neglecting
energy costs. However, various types of data drift exist, and fine-tuning the
full base model or the last few layers may not result in optimal performance in
certain scenarios. We propose Target Block Fine-Tuning (TBFT), a low-energy
adaptive personalization framework designed for resource-constrained devices.
We categorize data drift and personalization into three types: input-level,
feature-level, and output-level. For each type, we fine-tune different blocks
of the model to achieve optimal performance with reduced energy costs.
Specifically, input-, feature-, and output-level correspond to fine-tuning the
front, middle, and rear blocks of the model. We evaluate TBFT on a ResNet
model, three datasets, three different training sizes, and a Raspberry Pi.
Compared with the $Block Avg$, where each block is fine-tuned individually and
their performance improvements are averaged, TBFT exhibits an improvement in
model accuracy by an average of 15.30% whilst saving 41.57% energy consumption
on average compared with full fine-tuning.


  We propose phase diagrams for an imbalanced (unequal number of atoms or Fermi
surface in two pairing hyperfine states) gas of atomic fermions near a broad
Feshbach resonance using mean field theory. Particularly, in the plane of
interaction and polarization we determine the region for a phase separation
phase composed of normal and superfluid components. We compare our prediction
of phase boundaries with the recent measurment, and find a good qualitative
agreement.


  Animal telemetry data are often analysed with discrete time movement models
assuming rotation in the movement. These models are defined with equidistant
distant time steps. However, telemetry data from marine animals are observed
irregularly. To account for irregular data, a time-irregularised
first-difference correlated random walk model with drift is introduced. The
model generalizes the commonly used first-difference correlated random walk
with regular time steps by allowing irregular time steps, including a drift
term, and by allowing different autocorrelation in the two coordinates. The
model is applied to data from a ringed seal collected through the Argos
satellite system, and is compared to related movement models through
simulations. Accounting for irregular data in the movement model results in
accurate parameter estimates and reconstruction of movement paths. Measured by
distance, the introduced model can provide more accurate movement paths than
the regular time counterpart. Extracting accurate movement paths from uncertain
telemetry data is important for evaluating space use patterns for marine
animals, which in turn is crucial for management. Further, handling irregular
data directly in the movement model allows efficient simultaneous analysis of
several animals.


  In this work, we highlight a connection between the incremental proximal
method and stochastic filters. We begin by showing that the proximal operators
coincide, and hence can be realized with, Bayes updates. We give the explicit
form of the updates for the linear regression problem and show that there is a
one-to-one correspondence between the proximal operator of the least-squares
regression and the Bayes update when the prior and the likelihood are Gaussian.
We then carry out this observation to a general sequential setting: We consider
the incremental proximal method, which is an algorithm for large-scale
optimization, and show that, for a linear-quadratic cost function, it can
naturally be realized by the Kalman filter. We then discuss the implications of
this idea for nonlinear optimization problems where proximal operators are in
general not realizable. In such settings, we argue that the extended Kalman
filter can provide a systematic way for the derivation of practical procedures.


  We propose GAN-Supervised Learning, a framework for learning discriminative
models and their GAN-generated training data jointly end-to-end. We apply our
framework to the dense visual alignment problem. Inspired by the classic
Congealing method, our GANgealing algorithm trains a Spatial Transformer to map
random samples from a GAN trained on unaligned data to a common,
jointly-learned target mode. We show results on eight datasets, all of which
demonstrate our method successfully aligns complex data and discovers dense
correspondences. GANgealing significantly outperforms past self-supervised
correspondence algorithms and performs on-par with (and sometimes exceeds)
state-of-the-art supervised correspondence algorithms on several datasets --
without making use of any correspondence supervision or data augmentation and
despite being trained exclusively on GAN-generated data. For precise
correspondence, we improve upon state-of-the-art supervised methods by as much
as $3\times$. We show applications of our method for augmented reality, image
editing and automated pre-processing of image datasets for downstream GAN
training.


  We prove Manin's conjecture for a singular cubic surface S with a singularity
of type E6. If U is the open subset of S obtained by deleting the unique line
from S, then the number of rational points in U with anticanonical height
bounded by B behaves asymptotically as cB(log B)^6, where the constant c agrees
with the one conjectured by Peyre.


  Effects of columnar defects created by 320 MeV Au irradiation on 2H-NbSe2
single crystals with a dose equivalent matching field up to 16 T were studied.
Critical temperature is found to be suppressed almost linearly at a rate of
0.07 K/T. At the same time, the lattice parameters a and c are found to be
expanded at rates of 0.016%/T and 0.030%/T, respectively. Such a lattice
expansion should work as negative pressure to affect Tc. By separating the
effect of heavy-ion irradiation on Tc suppression through lattice expansion and
disorder, it is found that Tc is suppressed more by lattice expansion rather
than by disorder.


  This is a complement to our paper arXiv:0802.1461. We study irreducibility of
spectral determinants of some one-parametric eigenvalue problems in dimension
one with polynomial potentials.


  We study a stochastic perishable inventory control problem with endogenous
(decision-dependent) uncertainty in shelf-life of units. Our primary motivation
is determining ordering policies for blood platelets. Determining optimal
ordering quantities is a challenging task due to the short maximum shelf-life
of platelets (3-5 days after testing) and high uncertainty in daily demand. We
formulate the problem as an infinite-horizon discounted Markov Decision Process
(MDP). The model captures salient features observed in our data from a network
of Canadian hospitals and allows for fixed ordering costs. We show that with
uncertainty in shelf-life, the value function of the MDP is non-convex and key
structural properties valid under deterministic shelf-life no longer hold.
Hence, we propose an Approximate Dynamic Programming (ADP) algorithm to find
approximate policies. We approximate the value function using a linear
combination of basis functions and tune the parameters using a simulation-based
policy iteration algorithm. We evaluate the performance of the proposed policy
using extensive numerical experiments in parameter regimes relevant to the
platelet inventory management problem. We further leverage the ADP algorithm to
evaluate the impact of ignoring shelf-life uncertainty. Finally, we evaluate
the out-of-sample performance of the ADP algorithm in a case study using real
data and compare it to the historical hospital performance and other
benchmarks. The ADP policy can be computed online in a few minutes and results
in more than 50% lower expiry and shortage rates compared to the historical
performance. In addition, it performs better or as well as an exact policy that
ignores uncertainty in shelf-life and becomes hard to compute for larger
instance of the problem.


  Spin-orbital-angular-momentum (SOAM) coupling has been realized in recent
experiments of Bose-Einstein condensates [Chen et al., Phys. Rev. Lett. 121,
113204 (2018) and Zhang et al., Phys. Rev. Lett. 122, 110402 (2019)], where the
orbital angular momentum imprinted upon bosons leads to quantized vortices. For
fermions, such an exotic synthetic gauge field can provide fertile ground for
fascinating pairing schemes and rich superfluid phases, which are yet to be
explored. Here we demonstrate how SOAM coupling stabilizes vortices in Fermi
superfluids through a unique mechanism that can be viewed as the angular analog
to that of the spin-orbit-coupling-induced Fulde-Ferrell state under a Fermi
surface deformation. Remarkably, the vortex size is comparable with the beam
waist of Raman lasers generating the SOAM coupling, which is typically much
larger than previously observed vortices in Fermi superfluids. With tunable
size and core structure, these giant vortex states provide unprecedented
experimental access to topological defects in Fermi superfluids.


  We present the selection of high-redshift ($z\gtrsim5.7$) radio-loud (RL)
quasi-stellar object (QSO) candidates from the combination of the radio Rapid
ASKAP Continuum Survey (RACS; at 888 MHz) and the optical/near-infrared Dark
Energy Survey (DES). In particular, we selected six candidates brighter than
$S_{\rm 888MHz}>1$ mJy beam$^{-1}$ and ${\rm mag}(z_\mathrm{{DES}})<21.3$ using
the dropout technique (in the $i$-band). From this sample, we were able to
confirm the high-$z$ nature ($z\sim6.1$) of two sources, which are now among
the highest-redshift RL QSOs currently known. Based on our Gemini-South/GMOS
observations, neither object shows a prominent Ly$\alpha$ emission line. This
suggests that both sources are likely to be weak emission-line QSOs hosting
radio jets and would therefore further strengthen the potential increase of the
fraction of weak emission-line QSOs recently found in the literature. However,
further multiwavelength observations are needed to constrain the properties of
these QSOs and of their relativistic jets. From the discovery of these two
sources, we estimated the space density of RL QSOs in the redshift range
$5.9<z<6.4$ to be 0.13$^{+0.18}_{-0.09}$ and found it to be consistent with the
expectations based on our current knowledge of the blazar population up to
$z\sim5$.


  An efficient algorithm for computing the terms appearing in the Generalised
Kolmogorov Equation (GKE) written for the indefinite plane channel flow is
presented. The algorithm, which features three distinct strategies for parallel
computing, is designed such that CPU and memory requirements are kept to a
minimum, so that high-Re wall-bounded flows can be afforded. Computational
efficiency is mainly achieved by leveraging the Parseval's theorem for the two
homogeneous directions available in the plane channel geometry. A speedup of
3-4 orders of magnitude, depending on the problem size, is reported in
comparison to a key implementation used in the literature. Validation of the
code is demonstrated by computing the residual of the GKE, and example results
are presented for channel flows at Re_{\tau}=200 and Re_{\tau}=1000, where for
the first time they are observed in the whole four-dimensional domain. It is
shown that the space and scale properties of the scale-energy fluxes change for
increasing values of the Reynolds number. Among all scale-energy fluxes, the
wall-normal flux is found to show the richest behaviour for increasing
streamwise scales.


  Differential Linear Logic enriches Linear Logic with additional logical rules
for the exponential connectives, dual to the usual rules of dereliction,
weakening and contraction. We present a proof-net syntax for Differential
Linear Logic and a categorical axiomatization of its denotational models. We
also introduce a simple categorical condition on these models under which a
general antiderivative operation becomes available. Last we briefly describe
the model of sets and relations and give a more detailed account of the model
of finiteness spaces and linear and continuous functions.


  We give a simple nonsupersymmetric example in which chronology protection
follows from unitarity and the AdS/CFT correspondence. We consider a ball of
homogeneous, rotating dust in global AdS3 whose backreaction produces a region
of Goedel space inside the ball. We solve the Israel matching conditions to
find the geometry outside of the dust ball and compute its quantum numbers in
the dual CFT. When the radius of the dust ball exceeds a certain critical
value, the spacetime will contain closed timelike curves. Our main observation
is that precisely when this critical radius is exceeded, a unitarity bound in
the dual CFT is violated, leading to a holographic argument for chronology
protection.


  This paper proposes a new graph proximity measure. This measure is a
derivative of network reliability. By analyzing its properties and comparing it
against other proximity measures through graph examples, we demonstrate that it
is more consistent with human intuition than competitors. A new deterministic
algorithm is developed to approximate this measure with practical complexity.
Empirical evaluation by two link prediction benchmarks, one in coauthorship
networks and one in Wikipedia, shows promising results. For example, a single
parameterization of this measure achieves accuracies that are 14-35% above the
best accuracy for each graph of all predictors reported in the 2007
Liben-Nowell and Kleinberg survey.


  This paper gives a rigorous interpretation of a Feynman path integral on a
Riemannian manifold M with non-positive sectional curvature. A $L^2$ Riemannian
metric $G_P$ is given on the space of piecewise geodesic paths $H_P(M)$ adapted
to the partition $P$ of $[0,1]$, whence a finite-dimensional approximation of
Wiener measure is developed. It is proved that, as $mesh(P) \to 0$, the
approximate Wiener measure converges in a $L^1$ sense to the measure
$e^{-\frac{2 + \sqrt{3}}{20\sqrt{3}} \int_0^1 Scal(\sigma(s)) ds} d\nu(\sigma)$
on the Wiener space $W(M)$ with Wiener measure $\nu$. This gives a possible
prescription for the path integral representation of the quantized Hamiltonian,
as well as yielding such a result for the natural geometric approximation
schemes originating in [L. A. Andersson and B. K. Driver, J. Funct. Anal. 165
(1999), no. 2, 430-498] and followed by [Adrian P. C. Lim, Rev. Math. Phys. 19
(2007), no. 9, 967-1044].


  Translucency is prevalent in everyday scenes. As such, perception of
transparent objects is essential for robots to perform manipulation. Compared
with texture-rich or texture-less Lambertian objects, transparency induces
significant uncertainty on object appearances. Ambiguity can be due to changes
in lighting, viewpoint, and backgrounds, each of which brings challenges to
existing object pose estimation algorithms. In this work, we propose LIT, a
two-stage method for transparent object pose estimation using light-field
sensing and photorealistic rendering. LIT employs multiple filters specific to
light-field imagery in deep networks to capture transparent material
properties, with robust depth and pose estimators based on generative sampling.
Along with the LIT algorithm, we introduce the light-field transparent object
dataset ProLIT for the tasks of recognition, localization and pose estimation.
With respect to this ProLIT dataset, we demonstrate that LIT can outperform
both state-of-the-art end-to-end pose estimation methods and a generative pose
estimator on transparent objects.


  We study models of gauge mediated SUSY breaking with more than one hidden
sector. In these models the neutralino sector of the MSSM is supplemented with
additional light neutral fermions, the nearly massless gravitino and the
massive pseudo-goldstini. For the case where the Bino is the lightest ordinary
SUSY particle, its preferred decay is to a photon and the heaviest
pseudo-goldstino, which generically cascades down to lighter pseudo-goldstini,
or to the gravitino, in association with photons. This gives rise to
multiphoton plus missing energy signatures at the LHC. We investigate in detail
simplified models where the SUSY spectrum consists of the right-handed
sleptons, a Bino-like neutralino, the pseudo-goldstini and the gravitino. We
compare against existing LHC searches and show that the sensitivity to our
models could be significantly improved by relaxing the kinematic cuts and
requiring additional final state particles. We propose inclusive searches in
the final state with at least three photons plus missing energy and in the
final state with at least two photons plus two leptons plus missing energy, the
former being sensitive to any production mode and the latter being optimized
for slepton pair production. We show that they could lead to an observation (or
strong constraints) already with the data set from LHC Run I, and present
prospects for LHC Run II.


  The effort to understand network systems in increasing detail has resulted in
a diversity of methods designed to extract their large-scale structure from
data. Unfortunately, many of these methods yield diverging descriptions of the
same network, making both the comparison and understanding of their results a
difficult challenge. A possible solution to this outstanding issue is to shift
the focus away from ad hoc methods and move towards more principled approaches
based on statistical inference of generative models. As a result, we face
instead the more well-defined task of selecting between competing generative
processes, which can be done under a unified probabilistic framework. Here, we
consider the comparison between a variety of generative models including
features such as degree correction, where nodes with arbitrary degrees can
belong to the same group, and community overlap, where nodes are allowed to
belong to more than one group. Because such model variants possess an
increasing number of parameters, they become prone to overfitting. In this
work, we present a method of model selection based on the minimum description
length criterion and posterior odds ratios that is capable of fully accounting
for the increased degrees of freedom of the larger models, and selects the best
one according to the statistical evidence available in the data. In applying
this method to many empirical unweighted networks from different fields, we
observe that community overlap is very often not supported by statistical
evidence and is selected as a better model only for a minority of them. On the
other hand, we find that degree correction tends to be almost universally
favored by the available data, implying that intrinsic node proprieties (as
opposed to group properties) are often an essential ingredient of network
formation.


  Numerically predicting the performance of heterogenous structures without
scale separation represents a significant challenge to meet the critical
requirements on computational scalability and efficiency -- adopting a mesh
fine enough to fully account for the small-scale heterogeneities leads to
prohibitive computational costs while simply ignoring these fine
heterogeneities tends to drastically over-stiffen the structure's rigidity.
  This study proposes an approach to construct new material-aware shape (basis)
functions per element on a coarse discretization of the structure with respect
to each curved bridge nodes (CBNs) defined along the elements' boundaries.
Instead of formulating their derivation by solving a nonlinear optimization
problem, the shape functions are constructed by building a map from the CBNs to
the interior nodes and are ultimately presented in an explicit matrix form as a
product of a B\'ezier interpolation transformation and a boundary-interior
transformation. The CBN shape function accomodates more flexibility in closely
capturing the coarse element's heterogeneity, overcomes the important and
challenging issues of inter-element stiffness and displacement discontinuity
across interface between coarse elements, and improves the analysis accuracy by
orders of magnitude; they also meet the basic geometric properties of shape
functions that avoid aphysical analysis results. Extensive numerical examples,
including a 3D industrial example of billions of degrees of freedom, are also
tested to demonstrate the approach's performance in comparison with results
obtained from classical approaches.


  Big data has become a great asset for many organizations, promising improved
operations and new business opportunities. However, big data has increased
access to sensitive information that when processed can directly jeopardize the
privacy of individuals and violate data protection laws. As a consequence, data
controllers and data processors may be imposed tough penalties for
non-compliance that can result even to bankruptcy. In this paper, we discuss
the current state of the legal regulations and analyze different data
protection and privacy-preserving techniques in the context of big data
analysis. In addition, we present and analyze two real-life research projects
as case studies dealing with sensitive data and actions for complying with the
data regulation laws. We show which types of information might become a privacy
risk, the employed privacy-preserving techniques in accordance with the legal
requirements, and the influence of these techniques on the data processing
phase and the research results.


  Binary code similarity detection is an important problem with applications in
areas like malware analysis, vulnerability research and plagiarism detection.
This paper proposes a novel graph neural network architecture combined with a
novel graph data representation called call graphlets. A call graphlet encodes
the neighborhood around each function in a binary executable, capturing the
local and global context through a series of statistical features. A
specialized graph neural network model is then designed to operate on this
graph representation, learning to map it to a feature vector that encodes
semantic code similarities using deep metric learning. The proposed approach is
evaluated across four distinct datasets covering different architectures,
compiler toolchains, and optimization levels. Experimental results demonstrate
that the combination of call graphlets and the novel graph neural network
architecture achieves state-of-the-art performance compared to baseline
techniques across cross-architecture, mono-architecture and zero shot tasks. In
addition, our proposed approach also performs well when evaluated against an
out-of-domain function inlining task. Overall, the work provides a general and
effective graph neural network-based solution for conducting binary code
similarity detection.


  In [Alekseevsky, Gutt, Manno, Moreno: "A general method to construct
invariant PDEs on homogeneous manifolds", Communications in Contemporary
Mathematics (2021)] the authors have developed a method for constructing
$G$-invariant PDEs imposed on hypersurfaces of an $(n+1)$-dimensional
homogeneous space $G/H$, under mild assumptions on the Lie groups $G$. In the
present paper the method is applied to the case when $G=\mathsf{PGL}(n+1)$ or
$G=\mathsf{Aff}(n+1)$ and the homogeneous space $G/H$ is the
$(n+1)$-dimensional projective $\mathbb{P}^{n+1}$ or affine $\mathbb{A}^{n+1}$
space, respectively. The paper's main result is that projectively or affinely
invariant PDEs with $n$ independent and one unknown variables are in one-to-one
correspondence with $\mathsf{CO}(d,n-d)$-invariant hypersurfaces of the space
of trace-free cubic forms in $n$ variables. Local descriptions are also
provided.


  Heavy-ion-collision measurements in combination with transport model
simulations serve as important tools for extracting the nuclear
incompressibility. However, uncertainties in transport models (or model
dependence) partly affect the reliability of the extracted result. In the
present work, by using the recently measured data of rapidity-dependent flows,
we constrain the incompressibility of nuclear matter and analyse the impact of
model uncertainties on the obtained value. The method is based on the newly
updated version of the ultrarelativistic quantum molecular dynamics (UrQMD)
model in which the Skyrme potential energy-density functional is introduced.
Three different Skyrme interactions which give different incompressibilities
varying from $K_0$=201 to 271 MeV are adopted. The incompressibility is deduced
from the comparison of the UrQMD model simulations and the FOPI data for
rapidity-dependent elliptic flow in Au+Au collisions at beam energies
0.4\emph{A} - 1.0\emph{A} GeV. The elliptic flow $v_2$ as a function of
rapidity $y_0$ can be well described by a quadratic fit $v_2=v_{20} +
v_{22}\cdot y_0^2 $. It is found that the quantity $v_{2n}$ defined by
$v_{2n}=|v_{20}|+|v_{22}|$ is quite sensitive to the incompressibility $K_0$
and the in-medium nucleon-nucleon cross section, but not sensitive to the slope
parameter $L$ of the nuclear symmetry energy. With the FU3FP4 parametrization
of the in-medium nucleon-nucleon cross section, an averaged $K_0 = 220 \pm
40$~MeV is extracted from the $v_{2n}$ of free protons and deuterons. However,
remaining systematic uncertainties, partly related to the choice of in-medium
nucleon-nucleon cross sections, are of the same magnitude ($\pm 40$~MeV).
Overall, the rapidity dependent elliptic flow supports a soft symmetric-matter
equation-of-state.


  Recently, the design study `Einstein gravitational wave Telescope' (ET) has
been funded within the European FP7 framework. The ambitious goal of this
project is to provide a conceptual design of a detector with a hundred times
better sensitivity than currently operating instruments. It is expected that
this will require the development and implementation of new technologies, which
go beyond the concepts employed for the first and second detector generations.
However, it is a very interesting and educational exercise to imagine a
Michelson interferometer in which conventional technologies have been pushed to
- or maybe beyond - their limits to reach the envisaged sensitivity for the
Einstein Telescope. In this document we present a first sketchy analysis of
what modifications and improvements are necessary to go, step-by-step, from
second generation gravitational wave detectors to the Einstein Telescope.


  We investigate potential constraints from cosmic shear on the dark matter
particle mass, assuming all dark matter is made up of light thermal relic
particles. Given the theoretical uncertainties involved in making cosmological
predictions in such warm dark matter scenarios we use analytical fits to linear
warm dark matter power spectra and compare (i) the halo model using a mass
function evaluated from these linear power spectra and (ii) an analytical fit
to the non-linear evolution of the linear power spectra. We optimistically
ignore the competing effect of baryons for this work. We find approach (ii) to
be conservative compared to approach (i). We evaluate cosmological constraints
using these methods, marginalising over four other cosmological parameters.
Using the more conservative method we find that a Euclid-like weak lensing
survey together with constraints from the Planck cosmic microwave background
mission primary anisotropies could achieve a lower limit on the particle mass
of 2.5 keV.


  Good explanations for the unusual light curve of Boyajian's Star have been
hard to find. Recent results by Montet & Simon lend strength and plausibility
to the conclusion of Schaefer that in addition to short-term dimmings, the star
also experiences large, secular decreases in brightness on decadal timescales.
This, combined with a lack of long-wavelength excess in the star's spectral
energy distribution, strongly constrains scenarios involving circumstellar
material, including hypotheses invoking a spherical cloud of artifacts. We show
that the timings of the deepest dimmings appear consistent with being randomly
distributed, and that the star's reddening and narrow sodium absorption is
consistent with the total, long-term dimming observed. Following Montet &
Simon's encouragement to generate alternative hypotheses, we attempt to
circumscribe the space of possible explanations with a range of plausibilities,
including: a cloud in the outer solar system, structure in the ISM, natural and
artificial material orbiting Boyajian's Star, an intervening object with a
large disk, and variations in Boyajian's Star itself. We find the ISM and
intervening disk models more plausible than the other natural models.


  We study the two-band Hubbard model in infinite dimensions by solving the
dynamical mean-field equations with a strong coupling continuous-time quantum
Monte Carlo method and show that an $s$-wave superconducting state can be
stabilized in the repulsively interacting case. We discuss how this
superconducting state competes with the metallic and paired Mott states. The
effects of the Hund coupling and crystalline electric field are also addressed.


  The Bernevig-Hughes-Zhang (BHZ) model, which serves as a cornerstone in the
study of the quantum spin Hall insulators, showcases robust spin-filtered
helical edge states in a nanoribbon geometry. In the presence of an in-plane
magnetic field, these (first-order) helical states gap out to be replaced by
second-order corner states under suitable open-boundary conditions. Here, we
show that the inclusion of a spin-dependent non-Hermitian balanced gain/loss
potential induces a competition between these first and second-order
topological phases. Surprisingly, the previously dormant first-order helical
edge states in the nanoribbon resurface as the non-Hermitian effect
intensifies, effectively neutralizing the role played by the magnetic field. By
employing the projected spin spectra and the spin Chern number, we conclusively
explain the resurgence of the first-order topological properties in the
time-reversal symmetry-broken BHZ model in presence of non-Hermiticity.
Finally, the biorthogonal spin-resolved Berry phase, exhibiting a non-trivial
winding, definitively establishes the topological nature of these revived edge
states, emphasizing the dominance of non-Hermiticity over the magnetic field.


  Consider a time-slotted, single-hop, wireless sensor network (WSN) consisting
of n correct devices and and t=f*n Byzantine devices where f>=0 is any
constant; that is, the Byzantine devices may outnumber the correct ones. There
exists a trusted sender Alice who wishes to deliver a message m over a single
channel to the correct devices. There also exists a malicious user Carol who
controls the t Byzantine devices and uses them to disrupt the communication
channel. For a constant k>=2, the correct and Byzantine devices each possess a
meager energy budget of O(n^{1/k}), Alice and Carol each possess a limited
budget of \tilde{O}(n^{1/k}), and sending or listening in a slot incurs unit
cost. This general setup captures the inherent challenges of guaranteeing
communication despite scarce resources and attacks on the network. Given this
Alice versus Carol scenario, we ask: Is communication of m feasible and, if so,
at what cost?
  We develop a protocol which, for an arbitrarily small constant \epsilon>0,
ensures that at least (1-\epsilon)n correct devices receive m with high
probability. Furthermore, if Carol's devices expend T energy jamming the
channel, then Alice and the correct devices each spend only
\tilde{O}(T^{1/(k+1)}). In other words, delaying the transmission of m forces a
jammer to rapidly deplete its energy supply and, consequently, cease attacks on
the network.


  We calculate the electron energy spectrum of ionization by a high energy
photon, accompanied by creation of electron-positron pair. The total cross
section of the process is also obtained. The asymptotics of the cross section
does not depend on the photon energies. At the photon energies exceeding a
certain value $\omega_0$ this appeares to to be the dominant mechanism of
formation of the ions. The dependence of $\omega_0$ on the value of nuclear
charge is obtained. Our results are consistent with experimental data.


  We analyze the Casimir-Lifshitz effect associated with the electromagnetic
field in the presence of a rectangular waveguide consisting of two distinct
dielectric materials in a (3 + 1)-dimensional spacetime. We employ the surface
mode technique to derive a generalized Lifshitz formula for this specific
geometry. Our formulation accounts for the unique dielectric properties of the
materials composing the waveguide, leading to a precise calculation of the
Casimir-Lifshitz energy. In the asymptotic limit, our results recover the
classical expressions for perfect reflecting boundaries. The behavior of the
force on the rectangular cavity walls admit us to identify the system as a
Casimir-Lifshitz anharmonic oscillator, that may vibrate due only the to vacuum
stress if we allow two parallel non-fixed walls.


  Citation count is a quantifiable measure to indicate the number of times an
article is cited by other articles. It is believed that if an article is cited
often then it must be an important or influential article; however, there is no
guarantee that the most cited articles are good in quality. In this paper, the
author suggests argumentation count, a new metric for citation analysis. The
proposed metric, argumentation count is a triplet of quantities for each
concept of an article that helps in providing a quantifiable measure about the
usefulness of an article.


  The Eocene--Oligocene transition (EOT) represents a period of global
environmental changes particularly marked in Europe and coincides with a
dramatic biotic turnover. Here, using an exceptional fossil preservation, we
document and analyse the diversity dynamics of a mammal clade, Cainotherioidea
(Artiodactyla), that survived the EOT and radiated rapidly immediately after.
We infer their diversification history from Quercy Konzentrat--Lagerst{\"a}tte
(south-west France) at the species level using Bayesian birth--death models. We
show that cainotherioid diversity fluctuated through time, with extinction
events at the EOT and in the late Oligocene, and a major speciation burst in
the early Oligocene. The latter is in line with our finding that cainotherioids
had a high morphological adaptability following environmental changes
throughout the EOT, which probably played a key role in the survival and
evolutionary success of this clade in the aftermath. Speciation is positively
associated with temperature and continental fragmentation in a time-continuous
way, while extinction seems to synchronize with environmental change in a
punctuated way. Within-clade interactions negatively affected the cainotherioid
diversification, while inter-clade competition might explain their final
decline during the late Oligocene. Our results provide a detailed dynamic
picture of the evolutionary history of a mammal clade in a context of global
change.


  Engineered $Na_{0.75}CoO_2$ is considered a prime candidate to achieve
high-efficiency thermoelectric systems to regenerate electricity from waste
heat. In this work, three elements with outmost electronic configurations, (1)
an open d shell (Ni), (2) a closed d shell (Zn), and (3) a half filled f shell
(Eu) with maximum unpaired electrons, were selected to outline the dopants'
effects on electronic and crystallographic structures of $Na_{0.75}CoO_2$.
Systematic $ab$ $initio$ density functional calculations with $DMOL^3$ package
showed that the Ni and Zn were more stable when substituting Co with formation
energy $-2.35$ eV, $2.08$ eV when Fermi level equals to the valence band
maximum. While Eu is more stable when it substitutes Na having formation energy
of $-2.64$ eV. As these results show great harmony with existing experimental
data, they provide new insights into the fundamental principle of dopant
selection for manipulating the physical properties in the development of
high-performance sodium cobaltate based multifunctional materials.


  A cornerstone of numerical homogenization is the equivalence of the
microscopic and the macroscopic energy densities, which is referred to as
Hill-Mandel condition. Among these coupling conditions, the cases of periodic,
linear displacement and constant traction conditions are most prominent in
engineering applications. While the stiffness hierarchy of these coupling
conditions is a theoretically established and numerically verified result, very
little is known about the numerical errors and convergence properties for each
of them in various norms. The present work addresses these aspects both on the
macroscale and the microscale for linear as well as quadratic finite element
shape functions. The analysis addresses aspects of (i) regularity and how its
loss affects the convergence behavior on both scales compared with the a priori
estimates, of (ii) error propagation from micro to macro and of (iii) optimal
micro-macro mesh refinement strategy. For constant traction conditions two
different approaches are compared. The performance of a recovery-type error
estimation based on superconvergence is assessed. All results of the present
work are valid for both the Finite Element Heterogeneous Multiscale Method
FE-HMM and for FE$^2$.


  PbWO4 crystal scintillators are discussed as an active shield and
light-guides in 116Cd double beta decay experiment with CdWO4 scintillators.
Scintillation properties and radioactive contamination of PbWO4 scintillators
were investigated. Energy resolution of CdWO4 detector, coupled to PbWO4
crystal as a light-guide, was tested. Efficiency of PbWO4-based active shield
to suppress background from the internal contamination of PbWO4 crystals was
calculated. Using of lead tungstate crystal scintillators as high efficiency
4-pi active shield could allow to build sensitive double beta experiment with
116CdWO4 crystal scintillators.


  Image retrieval has been a top topic in the field of both computer vision and
machine learning for a long time. Content based image retrieval, which tries to
retrieve images from a database visually similar to a query image, has
attracted much attention. Two most important issues of image retrieval are the
representation and ranking of the images. Recently, bag-of-words based method
has shown its power as a representation method. Moreover, nonnegative matrix
factorization is also a popular way to represent the data samples. In addition,
contextual similarity learning has also been studied and proven to be an
effective method for the ranking problem. However, these technologies have
never been used together. In this paper, we developed an effective image
retrieval system by representing each image using the bag-of-words method as
histograms, and then apply the nonnegative matrix factorization to factorize
the histograms, and finally learn the ranking score using the contextual
similarity learning method. The proposed novel system is evaluated on a large
scale image database and the effectiveness is shown.


  The observed anisotropies of surface vibrations for unreconstructed FCC metal
(110) surfaces are often reversed from the "common sense" expectation. The
source of these reversals is investigated by performing ab initio density
functional theory calculations to obtain the surface force constant tensors for
Ag(110), Cu(110) and Al(110). The most striking result is a large enhancement
in the coupling between the first and third layers of the relaxed surface,
which strongly reduces the amplitude of out-of-plane vibrations of atoms in the
first layer. This also provides a simple explanation for the thermal
contraction of interlayer distances. Both the anisotropies and the thermal
contraction arise primarily as a result of the bond topology, with all three
(110) surfaces showing similar behavior.


  The resistivity of disordered metallic layer sandwiched by two ferromagnetic
layers at low-temperature is investigated theoretically. It is shown that the
magnetic field acting at the interface does not affect the classical Boltzmann
resistivity but causes a dephasing among electrons in the presence of the
spin-orbit interaction, suppressing the anti-localization due to the spin-orbit
interaction. The dephasing turns out to be stronger in the case where the
magnetization of the two layers is parallel, contributing to a positive
magnetoresistance close to a switching field at low temperature.


  Generalized quasi-cyclic (GQC) codes form a wide and useful class of linear
codes that includes thoroughly quasi-cyclic codes, finite geometry (FG) low
density parity check (LDPC) codes, and Hermitian codes. Although it is known
that the systematic encoding of GQC codes is equivalent to the division
algorithm in the theory of Grobner basis of modules, there has been no
algorithm that computes Grobner basis for all types of GQC codes. In this
paper, we propose two algorithms to compute Grobner basis for GQC codes from
their parity check matrices: echelon canonical form algorithm and transpose
algorithm. Both algorithms require sufficiently small number of finite-field
operations with the order of the third power of code-length. Each algorithm has
its own characteristic; the first algorithm is composed of elementary methods,
and the second algorithm is based on a novel formula and is faster than the
first one for high-rate codes. Moreover, we show that a serial-in serial-out
encoder architecture for FG LDPC codes is composed of linear feedback shift
registers with the size of the linear order of code-length; to encode a binary
codeword of length n, it takes less than 2n adder and 2n memory elements.
Keywords: automorphism group, Buchberger's algorithm, division algorithm,
circulant matrix, finite geometry low density parity check (LDPC) codes.


  The speed of laser light pulses launched from Earth and returned by a
retro-reflector on the Moon was calculated from precision round-trip
time-of-flight measurements and modeled distances. The measured speed of light
(c) in the moving observers rest frame was found to exceed the canonical value
c = 299,792,458 m/s by 200+/-10 m/s, just the speed of the observatory along
the line-of-sight due to the rotation of the Earth during the measurements.
This is a first-order violation of local Lorentz invariance; the speed of light
seems to depend on the motion of the observer after all, as in classical wave
theory, and implies that a preferred reference frame exists for the propagation
of light. However, the present experiment cannot identify the physical system
to which such a reference frame might be tied.


  We provide a new, independent, and analytic estimate of the lowest glueball
mass, and we found it at 1661 MeV within a relativistic quantum-field model
based on analytic confinement. The conventional mesons and the weak decay
constants are described to extend the consideration. For the spectra of
two-gluon and two-quark bound states we solve the ladder Bethe-Salpeter
equation. By using a minimal set of model parameters (the quark masses, the
coupling constant, and the confinement scale) we obtain numerical results which
are in reasonable agreement with experimental evidence in the wide range of
energy scale. The model serves a reasonable framework to describe
simultaneously different sectors in low-energy particle physics.


  This paper is an edited and shortened version of Chapter 6 from the thesis of
the author. First the one dimensional orthogonal derivative will be extended to
the two-dimensional case. In the two-dimensional case we have to define the
region of integration. In this paper we treat the integration over the square
region and over the triangle region where in the last case we use biorthogonal
polynomials expressed in terms of Appell functions. Next the two-dimensional
orthogonal derivative will be extended to the two-dimensional fractional
orthogonal derivative. The results are highly dependent on the $F_1$,\ $F_2$
and $F_3$ Appell functions.


  Safely interacting with humans is a significant challenge for autonomous
driving. The performance of this interaction depends on machine learning-based
modules of an autopilot, such as perception, behavior prediction, and planning.
These modules require training datasets with high-quality labels and a diverse
range of realistic dynamic behaviors. Consequently, training such modules to
handle rare scenarios is difficult because they are, by definition, rarely
represented in real-world datasets. Hence, there is a practical need to augment
datasets with synthetic data covering these rare scenarios. In this paper, we
present a platform to model dynamic and interactive scenarios, generate the
scenarios in simulation with different modalities of labeled sensor data, and
collect this information for data augmentation. To our knowledge, this is the
first integrated platform for these tasks specialized to the autonomous driving
domain.


  As a general means of expression, audio analysis and recognition has
attracted much attentions for its wide applications in real-life world. Audio
emotion recognition (AER) attempts to understand emotional states of human with
the given utterance signals, and has been studied abroad for its further
development on friendly human-machine interfaces. Distinguish from other
existing works, the person-dependent patterns of audio emotions are conducted,
and fractal dimension features are calculated for acoustic feature extraction.
Furthermore, it is able to efficiently learn intrinsic characteristics of
auditory emotions, while the utterance features are learned from fractal
dimensions of each sub-bands. Experimental results show the proposed method is
able to provide comparative performance for audio emotion recognition.


  We consider all irreducible rank-4 hypergeometric local systems defined over
$\mathbb{Q}$ that support a rational one-dimensional variation of Hodge
structures of weight 3 and Hodge vector $(1,1,1,1)$. Up to a natural
equivalence there are only 47 cases. The first 14 cases have maximally
unipotent monodromy at one point and have been extensively studied in the
literature. We show that all 47 local systems are associated to families of
generically smooth threefolds and we analyze the geometry and arithmetic at
their conifold point.


  Gun violence is a critical security problem, and it is imperative for the
computer vision community to develop effective gun detection algorithms for
real-world scenarios, particularly in Closed Circuit Television (CCTV)
surveillance data. Despite significant progress in visual object detection,
detecting guns in real-world CCTV images remains a challenging and
under-explored task. Firearms, especially handguns, are typically very small in
size, non-salient in appearance, and often severely occluded or
indistinguishable from other small objects. Additionally, the lack of
principled benchmarks and difficulty collecting relevant datasets further
hinder algorithmic development. In this paper, we present a meticulously
crafted and annotated benchmark, called \textbf{CCTV-Gun}, which addresses the
challenges of detecting handguns in real-world CCTV images. Our contribution is
three-fold. Firstly, we carefully select and analyze real-world CCTV images
from three datasets, manually annotate handguns and their holders, and assign
each image with relevant challenge factors such as blur and occlusion.
Secondly, we propose a new cross-dataset evaluation protocol in addition to the
standard intra-dataset protocol, which is vital for gun detection in practical
settings. Finally, we comprehensively evaluate both classical and
state-of-the-art object detection algorithms, providing an in-depth analysis of
their generalizing abilities. The benchmark will facilitate further research
and development on this topic and ultimately enhance security. Code,
annotations, and trained models are available at
https://github.com/srikarym/CCTV-Gun.


  In this paper, we address the problem of sampling from a set and
reconstructing a set stored as a Bloom filter. To the best of our knowledge our
work is the first to address this question. We introduce a novel hierarchical
data structure called BloomSampleTree that helps us design efficient algorithms
to extract an almost uniform sample from the set stored in a Bloom filter and
also allows us to reconstruct the set efficiently. In the case where the hash
functions used in the Bloom filter implementation are partially invertible, in
the sense that it is easy to calculate the set of elements that map to a
particular hash value, we propose a second, more space-efficient method called
HashInvert for the reconstruction. We study the properties of these two methods
both analytically as well as experimentally. We provide bounds on run times for
both methods and sample quality for the BloomSampleTree based algorithm, and
show through an extensive experimental evaluation that our methods are
efficient and effective.


  We present the Fully cOUpled loCAl model of sUperfLuid Turbulence (FOUCAULT)
that describes the dynamics of finite temperature superfluids. The superfluid
component is described by the vortex filament method while the normal fluid is
governed by a modified Navier-Stokes equation. The superfluid vortex lines and
normal fluid components are fully coupled in a self-consistent manner by the
friction force, which induces local disturbances in the normal fluid in the
vicinity of vortex lines. The main focus of this work is the numerical scheme
for distributing the friction force to the mesh points where the normal fluid
is defined and for evaluating the velocity of the normalfluid on the Lagrangian
discretization points along the vortex lines. In particular, we show that if
this numerical scheme is not careful enough, spurious results may occur. The
new scheme which we propose to overcome these difficulties is based on physical
principles. Finally, we apply the new method to the problem of the motion of a
superfluid vortex ring in a stationary normal fluid and in a turbulent normal
fluid.


  A language $L$ over an alphabet $\Sigma$ is suffix-convex if, for any words
$x,y,z\in\Sigma^*$, whenever $z$ and $xyz$ are in $L$, then so is $yz$.
Suffix-convex languages include three special cases: left-ideal, suffix-closed,
and suffix-free languages. We examine complexity properties of these three
special classes of suffix-convex regular languages. In particular, we study the
quotient/state complexity of boolean operations, product (concatenation), star,
and reversal on these languages, as well as the size of their syntactic
semigroups, and the quotient complexity of their atoms.


  Unsteady Lifting-Line Theory (ULLT) is a low order method capable of modeling
interacting unsteady and finite wing effects at low computational cost. Most
formulations of the method assume inviscid flow and small amplitudes. Whilst
these assumptions might be suitable for small-amplitude aeroelastic problems at
high Reynolds numbers, modern engineering applications increasingly involve
lower Reynolds numbers, large amplitude kinematics and vortex structures that
lead to aerodynamic non-linearities.
  This paper establishes that ULLT still provides a good solution for low
Reynolds number, large-amplitude kinematics problems, by comparing ULLT results
against those of experimentally validated computational fluid dynamics
simulations at Re=10000. Three-dimensional (3D) effects stabilize Leading Edge
Vortex (LEV) structures, resulting in a good prediction of whole wing force
coefficients by ULLT. Whilst the inviscid spanwise force distributions are
accurate for small-amplitude kinematics, the ULLT cannot model 3D vortical
structures, and thus it cannot correctly predict the force distribution due the
LEV. It can however predict the shedding of LEVs to a limited extent via the
leading edge suction parameter criterion. This can then be used as an indicator
of the usefulness of the force distribution results.


  We study a non-Anderson disorder driven quantum phase transition in a
semi-infinite Dirac semimetal with a flat boundary. The conformally invariant
boundary conditions, which include those that are time-reversal invariant, lead
to nodal-like surface states on the boundary. In this case the boundary becomes
metallic at a critical disorder that is weaker than that for the
semimetal-diffusive metal transition in the bulk. The latter transition takes
place in the presence of a metallic surface; in the language of surface
critical phenomena this corresponds to the so-called extraordinary transition.
The lines of the surface and the extraordinary transitions meet at the special
transition point. To elucidate universal properties at different transitions on
the phase diagram, we employ renormalization group methods and compute the
corresponding surface critical exponents using $\varepsilon$-expansion.


  Colloidal particles with mobile binding molecules constitute a powerful
platform for probing the physics of self-assembly. Binding molecules are free
to diffuse and rearrange on the surface, giving rise to spontaneous control
over the number of droplet-droplet bonds, i.e., valence, as a function of the
concentration of binders. This type of valence control has been realized
experimentally by tuning the interaction strength between DNA-coated emulsion
droplets. Optimizing for valence two yields droplet polymer chains, termed
`colloidomers', which have recently been used to probe the physics of folding.
To understand the underlying self-assembly mechanisms, here we present a
coarse-grained molecular dynamics (CGMD) model to study the self-assembly of
this class of systems using explicit representations of mobile binding sites.
We explore how valence of assembled structures can be tuned through kinetic
control in the strong binding limit. More specifically, we optimize
experimental control parameters to obtain the highest yield of long linear
colloidomer chains. Subsequently tuning the dynamics of binding and unbinding
via a temperature-dependent model allows us to observe the heptamer chain
collapse into all possible rigid structures, in good agreement with recent
folding experiments. Our CGMD platform and dynamic bonding model (implemented
as an open-source custom plugin to HOOMD-Blue) reveal the molecular features
governing the binding patch size and valence control, and opens the study of
pathways in colloidomer folding. This model can therefore guide programmable
design in experiments.


  The scaling of correlations as a function of system size provides important
hints to understand critical phenomena on a variety of systems. Its study in
biological systems offers two challenges: usually they are not of infinite
size, and in the majority of cases sizes can not be varied at will. Here we
discuss how finite-size scaling can be approximated in an experimental system
of fixed and relatively small size by computing correlations inside of a
reduced field of view of various sizes (i.e., "box-scaling"). Numerical
simulations of a neuronal network are used to verify such approximation, as
well as the ferromagnetic 2D Ising model. The numerical results support the
validity of the heuristic approach, which should be useful to characterize
relevant aspects of critical phenomena in biological systems.


  Mixed-SCORE is a recent approach for mixed membership community detection
proposed by Jin et al. (2017) which is an extension of SCORE (Jin, 2015). In
the note Jin et al. (2018), the authors propose SCORE+ as an improvement of
SCORE to handle with weak signal networks. In this paper, we propose a method
called Mixed-SCORE+ designed based on the Mixed-SCORE and SCORE+, therefore
Mixed-SCORE+ inherits nice properties of both Mixed-SCORE and SCORE+. In the
proposed method, we consider K+1 eigenvectors when there are K communities to
detect weak signal networks. And we also construct vertices hunting and
membership reconstruction steps to solve the problem of mixed membership
community detection. Compared with several benchmark methods, numerical results
show that Mixed-SCORE+ provides a significant improvement on the Polblogs
network and two weak signal networks Simmons and Caltech, with error rates
54/1222, 125/1137 and 94/590, respectively. Furthermore, Mixed-SCORE+ enjoys
excellent performances on the SNAP ego-networks.


  Environment perception, including object detection and distance estimation,
is one of the most crucial tasks for autonomous driving. Many attentions have
been paid on the object detection task, but distance estimation only arouse few
interests in the computer vision community. Observing that the traditional
inverse perspective mapping algorithm performs poorly for objects far away from
the camera or on the curved road, in this paper, we address the challenging
distance estimation problem by developing the first end-to-end learning-based
model to directly predict distances for given objects in the images. Besides
the introduction of a learning-based base model, we further design an enhanced
model with a keypoint regressor, where a projection loss is defined to enforce
a better distance estimation, especially for objects close to the camera. To
facilitate the research on this task, we construct the extented KITTI and
nuScenes (mini) object detection datasets with a distance for each object. Our
experiments demonstrate that our proposed methods outperform alternative
approaches (e.g., the traditional IPM, SVR) on object-specific distance
estimation, particularly for the challenging cases that objects are on a curved
road. Moreover, the performance margin implies the effectiveness of our
enhanced method.


  We give counterexamples to the following conjecture of Auslander: given a
finitely generated module $M$ over an Artin algebra $\Lambda$, there exists a
positive integer $n_M$ such that for all finitely generated $\Lambda$-modules
$N$, if $\Ext_{\Lambda}^i(M,N)=0$ for all $i\gg 0$, then
$\Ext_{\Lambda}^i(M,N)=0$ for all $i\geq n_M$. Some of our examples moreover
yield homologically defined classes of commutative local rings strictly between
the class of local complete intersections and the class of local Gorenstein
rings.


  Analogue gravity can be used to reproduce the phenomenology of quantum field
theory in curved spacetime and in particular phenomena such as cosmological
particle creation and Hawking radiation. In black hole physics, taking into
account the backreaction of such effects on the metric requires an extension to
semiclassical gravity and leads to an apparent inconsistency in the theory: the
black hole evaporation induces a breakdown of the unitary quantum evolution
leading to the so called information loss problem. Here we show that analogue
gravity can provide an interesting perspective on the resolution of this
problem, albeit the backreaction in analogue systems is not described by
semiclassical Einstein equations. In particular, by looking at the simpler
problem of cosmological particle creation, we show, in the context of
Bose--Einstein condensates analogue gravity, that the emerging analogue
geometry and quasi-particles have correlations due to the quantum nature of the
atomic degrees of freedom underlying the emergent spacetime. The quantum
evolution is, of course, always unitary, but on the whole Hilbert space, which
cannot be exactly factorised a posteriori in geometry and quasi-particle
components. In analogy, in a black hole evaporation one should expect a
continuous process creating correlations between the Hawking quanta and the
microscopic quantum degrees of freedom of spacetime, implying so that only a
full quantum gravity treatment would be able to resolve the information loss
problem by proving the unitary evolution on the full Hilbert space.


  Inspired by the recent work of Henrard, Kvamme and van Roosmalen [17], we
prove a categorified version of higher Auslander correspondence in the context
of exact categories. We define n-Auslander exact categories and show that there
is a bijection between the equivalence classes of n-cluster tilting
subcategories of exact categories and the equivalence classes of n-Auslander
exact categories.


  As device miniaturization approaches the atomic limit, it becomes highly
desirable to exploit novel paradigms for tailoring electronic structures and
carrier dynamics in materials. Elastic strain can in principle be applied to
achieve reversible and fast control of such properties, but it remains a great
challenge to create and utilize precisely controlled inhomogeneous deformation
in semiconductors. Here, we take a combined experimental and theoretical
approach to demonstrate that elastic strain-gradient can be created
controllably and reversibly in ZnO micro/nanowires. In particular, we show that
the inhomogeneous strain distribution creates an effective field that
fundamentally alters the dynamics of the neutral excitons. As the basic
principles behind these results are quite generic and applicable to most
semiconductors, this work points to a novel route to a wide range of
applications in electronics, optoelectronics, and photochemistry.


  We demonstrate how radio pulsars can be used as random number generators.
Specifically, we focus on publicly verifiable randomness (PVR), in which the
same sequence of trusted and verifiable random numbers is obtained by multiple
parties. PVR is a critical building block for many processes and algorithms
(including cryptography, scientific trials, electoral audits and international
treaties). However, current approaches (based on number theory) may soon become
vulnerable to quantum computers, motivating a growing demand for PVR based on
natural physical phenomena. In this context, we explore pulsars as a potential
physical PVR source. We first show that bit sequences extracted from the
measured flux densities of a bright millisecond pulsar can pass standardised
tests for randomness. We then quantify three illustrative methods of
bit-extraction from pulsar flux density sequences, using simultaneous
observations of a second pulsar carried out with the Parkes telescope in
Australia and the Five-hundred-metre Aperture Spherical radio Telescope (FAST)
in China, supported by numerical simulations. We demonstrate that the same bit
sequence can indeed be obtained at both observatories, but the ubiquitous
presence of radiometer noise needs to be accounted for when determining the
expected bit error rate between two independent sequences. We discuss our
results in the context of an imaginary use-case in which two mutually
distrusting parties wish to obtain the same random bit sequence, exploring
potential methods to mitigate against a malicious participant.


  We present a novel autoencoder-based approach for designing codes that
provide unequal error protection (UEP) capabilities. The proposed design is
based on a generalization of an autoencoder loss function that accommodates
both message-wise and bit-wise UEP scenarios. In both scenarios, the
generalized loss function can be adjusted using an associated weight vector to
trade off error probabilities corresponding to different importance classes.
For message-wise UEP, we compare the proposed autoencoder-based UEP codes with
a union of random coset codes. For bit-wise UEP, the proposed codes are
compared with UEP rateless spinal codes and the superposition of random
Gaussian codes. In all cases, the autoencoder-based codes show superior
performance while providing design simplicity and flexibility in trading off
error protection among different importance classes.


  In this article, we prove the Eyring-Kramers formula for non-reversible
metastable diffusion processes that have a Gibbs invariant measure. Our result
indicates that non-reversible processes exhibit faster metastable transitions
between neighborhoods of local minima, compared to the reversible process
considered in [Bovier, Eckhoff, Gayrard, and Klein, J. Eur. Math. Soc. 6:
399-424, 2004]. Therefore, by adding non-reversibility to the model, we can
indeed accelerate the metastable transition. Our proof is based on the
potential theoretic approach to metastability through accurate estimation of
the capacity between metastable valleys. We carry out this estimation by
developing a novel method to compute the sharp asymptotics of the capacity
without relying on variational principles such as the Dirichlet principle or
the Thomson principle.


  It is shown that a class of exponentially decaying time-of-arrival
probability distributions suggested by W{\l}odarz, Marchewka and Schuss, and
Jurman and Nikoli\'c, as well as a semiclassical distribution implicit in
time-of-flight momentum measurements, do not show the expected behavior for a
Gaussian wave train. This casts doubts on the physical adequacy of these
arrival-time proposals. In contrast, the quantum flux distribution (a special
case of the Bohmian arrival-time distribution) displays the expected behavior.