  At Eurocrypt'18, Cid, Huang, Peyrin, Sasaki, and Song introduced a new tool
called Boomerang Connectivity Table (BCT) for measuring the resistance of a
block cipher against the boomerang attack (which is an important cryptanalysis
technique introduced by Wagner in 1999 against block ciphers). Next, Boura and
Canteaut introduced an important parameter (related to the BCT) for
cryptographic Sboxes called boomerang uniformity. In this context, we present a
brief state-of-the-art on the notion of boomerang uniformity of vectorial
functions (or Sboxes) and provide new results. More specifically, we present a
slightly different (and more convenient) formulation of the boomerang
uniformity and show that the row sum and the column sum of the boomerang
connectivity table can be expressed in terms of the zeros of the second-order
derivative of the permutation or its inverse. Most importantly, we specialize
our study of boomerang uniformity to quadratic permutations in even dimension
and generalize the previous results on quadratic permutation with optimal BCT
(optimal means that the maximal value in the Boomerang Connectivity Table
equals the lowest known differential uniformity). As a consequence of our
general result, we prove that the boomerang uniformity of the binomial
differentially $4$-uniform permutations presented by Bracken, Tan, and Tan
equals $4$. This result gives rise to a new family of optimal Sboxes.


  This paper examines the question for global regularity for the Boussinesq
equation with critical fractional dissipation. The main result states that the
system admits global regular solutions for all (reasonably) smooth and decaying
data, as long as $\al>2/3$. This significantly improves upon some recent works.
The main new idea is the introduction of a new, second generation
Hmidi-Keraani-Rousset type, change of variables, which further improves the
linear derivative in temperature term in the vorticity equation. This approach
is then complemented by new set of commutator estimates, which may be of
independent interest.


  Expanding edge experiments are promising to open new physics windows of
quantum Hall systems. In a static edge, the edge excitation, which is described
by free fields decoupled with the bulk dynamics, is gapless, and the dynamics
preserve conformal symmetry. When the edge expands, such properties need not be
preserved. We formulate a quantum field theory in 1+1 dimensional curved
spacetimes to analyze the edge dynamics. We propose methods to address the
following questions using edge waveforms from the expanding region: Does the
conformal symmetry survive? Is the nonlinear interaction of the edge
excitations induced by edge expansion? Do the edge excitations interact with
the bulk excitations? We additionally show that the expanding edges can be
regarded as expanding universe simulators of two-dimensional dilaton-gravity
models, including the Jackiw-Teitelboim gravity model. As an application, we
point out that our theoretical setup might simulate emission of analog Hawking
radiation with the Gibbons-Hawking temperature from the future de Sitter
horizon formed in the expanding edge region.


  We establish a necessary and sufficient condition for a heptagonal knot to be
figure-8 knot. The condition is described by a set of Radon partitions formed
by vertices of the heptagon. In addition we relate this result to the number of
nontrivial heptagonal knots in linear embeddings of the complete graph $K_7$
into $\mathbb{R}^3$.


  Automatic differentiation (AD) is an important family of algorithms which
enables derivative based optimization. We show that AD can be simply
implemented with effects and handlers by doing so in the Frank language. By
considering how our implementation behaves in Frank's operational semantics, we
show how our code performs the dynamic creation of programs during evaluation.


  The 8th edition of the European Conference on Python in Science, EuroSciPy
was held for the second time in the beautiful city of Cambridge, UK from
August, 26th to 29th, 2014. More than 200 participants, both from academia and
industry, attended the conference.
  As usual, the conference kicked off with two days of tutorials, divided into
an introductory and an advanced track. The introductory track, presented by
Joris Vankerschaver, Valerio Maggio Joris Van den Bossche, Stijn Van Hoey and
Nicolas Rougier, gave a quick but thorough overview of the SciPy stack, while
the experience track focused on different advanced topics. This second track
began with an introduction to Bokeh, by Bryan Van den Ven, followed by an image
processing tutorial with scikit-image by Emmanuelle Gouillart and Juan
Nunez-Iglesias. The afternoon continued with two tutorials on data analysis:
the first, intitulated "How 'good' is your model, and how can you make it
better?" (by Chih-Chun Chen, Dimitry Foures, Elena Chatzimichali, Giuseppe
Vettigli) focused on the challenges face while attempting model selections, and
the first day concluded with a statistics in python tutorial by Gael Varoquaux.
During the second day, the attendees tackled an in depth 4 hour tutorial on
Cython, presented by Stefan Behnel, and a crash course on "Evidence-Based
Teaching: What We Know and How to Use It", by Greg Wilson.


  Bershadsky, Cecotti, Ooguri, and Vafa constructed a real-valued invariant for
Calabi--Yau manifolds, which is now called the BCOV torsion. Based on it, a
metric-independent invariant, called the BCOV invariant, was constructed by
Fang--Lu--Yoshikawa and Eriksson--Freixas i Montplet--Mourougane. The BCOV
invariant is conjecturally related to the Gromov--Witten theory via mirror
symmetry. Based upon the previous work of the second author, we prove the
conjecture that birational Calabi--Yau manifolds have the same BCOV invariant.
We also extend the construction of the BCOV invariant to Calabi--Yau varieties
with Kawamata log terminal singularities and prove its birational invariance
for Calabi--Yau varieties with canonical singularities. We provide an
interpretation of our construction using the theory of motivic integration.


  Interacting electrons in quantum dots with large Thouless number $g$ in the
three classical random matrix symmetry classes are well-understood. When a
specific type of spin-orbit coupling known to be dominant in two dimensional
semiconductor quantum dots is introduced, we show that a new interacting
quantum critical crossover energy scale emerges and low-energy quasiparticles
generically have a decay width proportional to their energy. The low-energy
physics of this system is an example of a universal interacting crossover
regime.


  We study fountain codes transmitted over the binary-input symmetric-output
channel. For channels with small capacity, receivers needs to collects many
channel outputs to recover information bits. Since a collected channel output
yields a check node in the decoding Tanner graph, the channel with small
capacity leads to large decoding complexity. In this paper, we introduce a
novel fountain coding scheme with non-binary LDPC codes. The decoding
complexity of the proposed fountain code does not depend on the channel.
Numerical experiments show that the proposed codes exhibit better performance
than conventional fountain codes, especially for small number of information
bits.


  We examine the interplay of symmetry and topological order in $2+1$
dimensional topological phases of matter. We present a definition of the \it
topological symmetry \rm group, which characterizes the symmetry of the
emergent topological quantum numbers of a topological phase, and we describe
its relation with the microscopic symmetry of the underlying physical system.
We derive a general framework to characterize and classify symmetry
fractionalization in topological phases, including phases that are non-Abelian
and symmetries that permute the quasiparticle types and/or are anti-unitary. We
develop a theory of extrinsic defects (fluxes) associated with elements of the
symmetry group, which provides a general classification of symmetry-enriched
topological phases derived from a topological phase of matter $\mathcal{C}$
with symmetry group $G$. The algebraic theory of the defects, known as a
$G$-crossed braided tensor category $\mathcal{C}_{G}^{\times}$, allows one to
compute many properties, such as the number of topologically distinct types of
defects associated with each group element, their fusion rules, quantum
dimensions, zero modes, braiding exchange transformations, a generalized
Verlinde formula for the defects, and modular transformations of the
$G$-crossed extensions of topological phases. We also examine the promotion of
the global symmetry to a local gauge invariance, wherein the extrinsic
$G$-defects are turned into deconfined quasiparticle excitations, which results
in a different topological phase $(\mathcal{C}_{G}^{\times})^{G}$. A number of
instructive and/or physically relevant examples are studied in detail.


  It is well known that a superfluid rotates by forming an array of quantized
vortices. A relativistic formulation for superfluid vortex dynamics is required
for a range of problems in astrophysics and cosmology, from neutron star
interiors and radio pulsar glitches to possible dark matter condensates on
galactic scales. This paper develops a formalism for such systems, extending
the well-established variational approach to relativistic fluids to account for
the presence of a collection of quantized vortices. The model is firmly
anchored in the geometry of the problem (drawing on aspects from basic string
dynamics) and accounts for elastic aspects associated with a vortex array,
providing a precise foundation for applications which have so far been based on
somewhat ad hoc phenomenology.


  The growing popularity of e-mobility, heat pumps, and renewable generation
such as photovoltaics is leading to scenarios which the distribution grid was
not originally designed for. Moreover, parts of the distribution grid are only
sparsely instrumented, leaving the distribution system operator unaware of
possible bottlenecks resulting from the introduction of such loads and
renewable generation. To overcome this lack of information, we propose the use
of widely available smart home devices, such as smart plugs, for grid
monitoring. We detail the aggregation and storage of smart plug measurements
for distribution grid monitoring and examine the accuracy of the measurements.
A case study shows how the average monitoring error in a distribution grid area
decreases the more measurement devices are installed. Hence, simple smart plugs
can help with distribution grid monitoring and provide valuable information to
the DSO.


  For matrices with displacement structure, basic operations like
multiplication, inversion, and linear system solving can all be expressed in
terms of the following task: evaluate the product $\mathsf{A}\mathsf{B}$, where
$\mathsf{A}$ is a structured $n \times n$ matrix of displacement rank $\alpha$,
and $\mathsf{B}$ is an arbitrary $n\times\alpha$ matrix. Given $\mathsf{B}$ and
a so-called "generator" of $\mathsf{A}$, this product is classically computed
with a cost ranging from $O(\alpha^2 \mathscr{M}(n))$ to $O(\alpha^2
\mathscr{M}(n)\log(n))$ arithmetic operations, depending on the type of
structure of $\mathsf{A}$; here, $\mathscr{M}$ is a cost function for
polynomial multiplication. In this paper, we first generalize classical
displacement operators, based on block diagonal matrices with companion
diagonal blocks, and then design fast algorithms to perform the task above for
this extended class of structured matrices. The cost of these algorithms ranges
from $O(\alpha^{\omega-1} \mathscr{M}(n))$ to $O(\alpha^{\omega-1}
\mathscr{M}(n)\log(n))$, with $\omega$ such that two $n \times n$ matrices over
a field can be multiplied using $O(n^\omega)$ field operations. By combining
this result with classical randomized regularization techniques, we obtain
faster Las Vegas algorithms for structured inversion and linear system solving.


  A geometric approach to the standard model in terms of the Clifford algebra
$% C\ell_{7}$ is advanced. The gauge symmetries and charge assignments of the
fundamental fermions are seen to arise from a simple geometric model involving
extra space-like dimensions. The bare coupling constants are found to obey
$g_{s}/g=1$ and $g^{\prime}/g=\sqrt{3/5}$, consistent with SU(5) grand
unification but without invoking the notion of master groups. In constructing
the Lagrangian density terms, it is found that the Higgs isodoublet field
emerges in a natural manner. A matrix representation of $% C\ell_{7}$ is
included as a computational aid.


  We report on the experimental investigation of the response of a
three-dimensional Bose-Einstein condensate (BEC) in the presence of a
one-dimensional (1D) optical lattice. By means of Bragg spectroscopy we probe
the band structure of the excitation spectrum in the presence of the periodic
potential. We selectively induce elementary excitations of the BEC choosing the
transferred momentum and we observe different resonances in the energy
transfer, corresponding to the transitions to different bands. The frequency,
the width and the strength of these resonances are investigated as a function
of the amplitude of the 1D optical lattice.


  We propose an approach to saddle point optimization relying only on oracles
that solve minimization problems approximately. We analyze its convergence
property on a strongly convex--concave problem and show its linear convergence
toward the global min--max saddle point. Based on the convergence analysis, we
develop a heuristic approach to adapt the learning rate. An implementation of
the developed approach using the (1+1)-CMA-ES as the minimization oracle,
namely Adversarial-CMA-ES, is shown to outperform several existing approaches
on test problems. Numerical evaluation confirms the tightness of the
theoretical convergence rate bound as well as the efficiency of the learning
rate adaptation mechanism. As an example of real-world problems, the suggested
optimization method is applied to automatic berthing control problems under
model uncertainties, showing its usefulness in obtaining solutions robust to
uncertainty.


  Coherent oscillations of a scalar field can mimic the behavior of a perfect
fluid with an equation-of-state parameter determined by the properties of the
potential, possibly driving accelerated expansion in the early Universe
(inflation) and/or in the Universe today (dark energy) or behaving as dark
matter. We consider the growth of inhomogeneities in such a field, mapping the
problem to that of two coupled anharmonic oscillators. We provide a simple
physical argument that oscillating fields with a negative equation-of-state
parameter possess a large-scale dynamical instability to growth of
inhomogeneities. This instability renders these models unsuitable for
explaining cosmic acceleration. We then consider the gravitational instability
of oscillating fields in potentials that are close to, but not precisely,
harmonic. We use these results to show that if axions make up the dark matter,
then the small-scale cutoff in the matter power spectrum is around $10^{-15}
M_\oplus$.


  The standard model taken with a momentum space cut-off may be viewed as an
effective low energy theory. The structure of it and its known parameters can
give us hints for relations between these parameters. In the present
investigation the Higgs problem will be discussed, the possible connection of
the Higgs meson with the heavy top quark, and the geometric structure of the
quark and lepton mass matrices.


  This volume contains the papers presented at LINEARITY 2014, the Third
International Workshop on Linearity, held on July 13, 2014 in Vienna, Austria.
The workshop was a one-day satellite event of FLoC 2014, the sixth Federated
Logic Conference. It was held as part of the 2014 Vienna Summer of Logic.
  The aim of this workshop was to bring together researchers who are exploring
theory and applications of linear calculi, to foster their interaction and
provide a forum for presenting new ideas and work in progress, and enable
newcomers to learn about current activities in this area. Of interest were new
results that made a central use of linearity, ranging from foundational work to
applications in any field. This included: sub-linear logics, linear term
calculi, linear type systems, linear proof-theory, linear programming
languages, applications to concurrency, interaction-based systems, verification
of linear systems, quantum models of computation, and biological and chemical
models of computation.


  When looking through the proceedings of the recent Simulation
Interoperability Workshops, a lot of papers - some of them even awarded by the
committee - are dealing with alternative concepts outside or beyond the High
Level Architecture (HLA): Web Services, the extensible Markup Language (XML),
Java Beans, Simple Object Access Protocol (SOAP), etc. Similarly, requirements
driven by interoperability issues have resulted in the need to use meta
modeling, adaptive models, and common repositories. The use of the Unified
Modeling Language (UML) as a model description language is also rapidly
becoming a standard. All these concepts have relations to the HLA, but they are
not part of it. There seems to be the danger that HLA is overrun by respective
developments of the free market and will become irrelevant finally. ... This
paper introduces the MDA concept and shows, how the HLA can be integrated to
become a standard stub for simulation applications of legacy systems, systems
under development, and systems of the future.


  Existing e-commerce search engines typically support search only over
objective attributes, such as price and locations, leaving the more desirable
subjective attributes, such as romantic vibe and worklife balance unsearchable.
We found that this is also the case for Recruit Group, which operates a wide
range of online booking and search services, including jobs, travel, housing,
bridal, dining, beauty, and where each service is among the biggest in Japan,
if not internationally. We present our progress towards productionizing a
recent subjective search prototype (OpineDB) developed by Megagon Labs for
Recruit Group. Several components within OpineDB are enhanced to satisfy
production demands, including adding a BERT language model pre-trained on
massive hospitality domain review corpora. We also found that the challenges of
productionizing the system are beyond enhancing the components. In particular,
an important requirement in production-quality systems is to instrument a
proper way of measuring the search quality, which is extremely tricky when the
search results are subjective. This led to the creation of a high-quality
benchmark dataset from scratch, involving over 600 queries by user interviews
and a collection of more than 120,000 query-entity relevancy labels. Also, we
found that the existing search algorithms do not meet the search quality
standard required by production systems. Consequently, we enhanced the ranking
model by fine-tuning several search algorithms and combining them under a
learning-to-rank framework. The model achieves 5%-10% overall precision
improvement and 90+% precision on more than half of the benchmark testing
queries making these queries ready for AB-testing. While some enhancements can
be immediately applied to other verticals, our experience reveals that
benchmarking and fine-tuning ranking algorithms are specific to each domain and
cannot be avoided.


  Akcoglu and Suchaston proved the following result: Let $T:L^1(X,{\cf},\m)\to
L^1(X,{\cf},\m)$ be a positive contraction. Assume that for $z\in
L^1(X,{\cf},\m)$ the sequence $(T^nz)$ converges weakly in $L^1(X,{\cf},\m)$,
then either $\lim\limits_{n\to\infty}\|T^nz\|=0$ or there exists a positive
function $h\in L^1(X,{\cf},\m)$, $h\neq 0$ such that $Th=h$. In the paper we
prove an extension of this result in finite von Neumann algebra setting, and as
a consequence we obtain that if a positive contraction of a noncommutative
$L^1$-space has no non zero positive invariant element, then its mixing
property implies completely mixing property one.


  Artificially engineered biosensors are highly inefficient in accurately
measuring the concentration of biomarkers, particularly, during early diagnosis
of diseases. On the other hand, single cellular systems such as chemotactic
bacteria can sense their environment with extraordinary precision. Therefore,
one would expect that implementing the optimal cellular sensing strategies in
state-of-the-art artificial sensors can produce optimally precise biosensors.
However because of the presence of measurement noise, strategies that are
optimal in biological systems may not be optimal in artificial systems.
Therefore, mimicking biological strategies may not be the optimal path in case
of artificial sensing systems because of the presence of inherent measurement
noise.


  We model the vertical structure of magnetized accretion disks subject to
viscous and resistive heating, and irradiation by the central star. We apply
our formalism to the radial structure of magnetized accretion disks threaded by
a poloidal magnetic field dragged during the process of star formation
developed by Shu and coworkers. We consider disks around low mass protostars, T
Tauri, and FU Orionis stars. We consider two levels of disk magnetization,
$\lambda_{sys} = 4$ (strongly magnetized disks), and $\lambda_{sys} = 12$
(weakly magnetized disks). The rotation rates of strongly magnetized disks have
large deviations from Keplerian rotation. In these models, resistive heating
dominates the thermal structure for the FU Ori disk. The T Tauri disk is very
thin and cold because it is strongly compressed by magnetic pressure; it may be
too thin compared with observations. Instead, in the weakly magnetized disks,
rotation velocities are close to Keplerian, and resistive heating is always
less than 7\% of the viscous heating. In these models, the T Tauri disk has a
larger aspect ratio, consistent with that inferred from observations. All the
disks have spatially extended hot atmospheres where the irradiation flux is
absorbed, although most of the mass ($\sim 90-95$ \%) is in the disk midplane.
With the advent of ALMA one expects direct measurements of magnetic fields and
their morphology at disk scales. It will then be possible to determine the
mass-to-flux ratio of magnetized accretion disks around young stars, an
essential parameter for their structure and evolution. Our models contribute to
the understanding of the vertical structure and emission of these disks.


  The creation of specified quantum states is important for most, if not all,
applications in quantum computation and communication. The quality of the state
preparation is therefore an essential ingredient in any assessment of a
quantum-state gun. We show that the fidelity, under the standard definitions is
not sufficient to assess quantum sources, and we propose a new measure of
suitability that necessarily depends on the application for the source. We
consider the performance of single-photon guns in the context of quantum key
distribution (QKD) and linear optical quantum computation. Single-photon
sources for QKD need radically different properties than sources for quantum
computing. Furthermore, the suitability for single-photon guns is discussed
explicitly in terms of experimentally accessible criteria.


  Languages vary considerably in syntactic structure. About 40% of the world's
languages have subject-verb-object order, and about 40% have
subject-object-verb order. Extensive work has sought to explain this word order
variation across languages. However, the existing approaches are not able to
explain coherently the frequency distribution and evolution of word order in
individual languages. We propose that variation in word order reflects
different ways of balancing competing pressures of dependency locality and
information locality, whereby languages favor placing elements together when
they are syntactically related or contextually informative about each other.
Using data from 80 languages in 17 language families and phylogenetic modeling,
we demonstrate that languages evolve to balance these pressures, such that word
order change is accompanied by change in the frequency distribution of the
syntactic structures which speakers communicate to maintain overall efficiency.
Variability in word order thus reflects different ways in which languages
resolve these evolutionary pressures. We identify relevant characteristics that
result from this joint optimization, particularly the frequency with which
subjects and objects are expressed together for the same verb. Our findings
suggest that syntactic structure and usage across languages co-adapt to support
efficient communication under limited cognitive resources.


  Valley-contrast physics has gained considerable attention, particularly for
realizing photonic topological insulators (PTIs) that support reflection-free
valley-polarized edge modes (VPEMs) in the absence of inter-valley scattering.
It is an open question whether similar robust states can exist in the absence
of topological valley phase, i.e., nonvanishing Berry curvature at the valleys.
We show that a C6\u{psion}-symmetric triangular photonic crystal (PhC)
inherently exhibits uniform distribution of spatially varying phase vortices,
which support a local (limited) version of valley Hall effect (LVHE), where the
valley polarization is location defined as opposed to being fixed throughout
the bulk. We then demonstrate that defect regions with sublattice asymmetry in
otherwise a symmetric PhC lead to wave localization and splitting of photons
according to their valley index, thus enabling VPEMs along a line defect
waveguide. We fabricate our device on a silicon-on-insulator (SOI) slab and
characterize it at near-infrared frequencies showing robust transmission
through sharp bends comparable to valley PTIs. Our results present a new
perspective to creating valley edge states and outline a new waveguiding
mechanism applicable to electromagnetics as well as plasmonics, mechanics and
acoustics.


  Data-Driven Predictive Control (DDPC) has been recently proposed as an
effective alternative to traditional Model Predictive Control (MPC), in that
the same constrained optimization problem can be addressed without the need to
explicitly identify a full model of the plant. However, DDPC is built upon
input/output trajectories. Therefore, the finite sample effect of stochastic
data, due to, e.g., measurement noise, may have a detrimental impact on
closed-loop performance. Exploiting a formal statistical analysis of the
prediction error, in this paper we propose the first systematic approach to
deal with uncertainty due to finite sample effects. To this end, we introduce
two regularization strategies for which, differently from existing
regularization-based DDPC techniques, we propose a tuning rationale allowing us
to select the regularization hyper-parameters before closing the loop and
without additional experiments. Simulation results confirm the potential of the
proposed strategy when closing the loop.


  With the help of a power-domain non-orthogonal multiple access (NOMA) scheme,
satellite networks can simultaneously serve multiple users within limited
time/spectrum resource block. However, the existence of channel estimation
errors inevitably degrade the judgment on users' channel state information
(CSI) accuracy, thus affecting the user pairing processing and suppressing the
superiority of the NOMA scheme. Inspired by the advantages of machine learning
(ML) algorithms, we propose an improved support vector machine (SVM) scheme to
reduce the inappropriate user pairing risks and enhance the performance of NOMA
based satellite networks with imperfect CSI. Particularly, a genetic algorithm
(GA) is employed to optimize the regularization and kernel parameters of the
SVM, which effectively improves the classification accuracy of the proposed
scheme. Simulations are provided to demonstrate that the performance of the
proposed method is better than that with random user paring strategy,
especially in the scenario with a large number of users.


  The paper presents the algorithm of a code written for computing the cross
section for a charge transfer process involving a neutral molecule and a
monatomic ion. The entrance and exit potential energy surfaces, driving the
collision dynamics, are computed employing the Improved Lennard-Jones function
that accounts for the role of non-electrostatic forces, due to size repulsion
plus dispersion and induction attraction. In addition, electrostatic
components, affecting the entrance channels, are evaluated as sum of Coulomb
contributions, determined by the He$^+$ ion interacting with the charge
distribution on the molecular frame. The cross section is estimated by
employing the Landau-Zener-St\"uckelberg approach. The code implemented has
been employed in systems involving helium cation and a small organic molecule,
such as methanol, dimethyl ether and methyl formate.


  In this short note we improve some of recent results of Cuckovic and
Sahutoglu concerning regularity of the Berezin transform for a class of smooth
pseudoconvex domains.


  We investigate the behaviour of accelerating contact lines in an unsteady
quasi-capillary channel flow. The configuration consists of a liquid column
that moves along a vertical 2D channel, open to the atmosphere and driven by a
controlled pressure head. Both advancing and receding contact lines were
analyzed to test the validity of classic models for dynamic wetting and to
study the flow field near the interface. The operating conditions are
characterized by a large acceleration, thus dominated by inertia. The shape of
the moving meniscus was retrieved using Laser-Induced Fluorescence (LIF)-based
image processing while the flow field near was analyzed via Time-Resolved
Particle Image Velocimetry (TR-PIV). The TR-PIV measurements were enhanced in
the post-processing, using a combination of Proper Orthogonal Decomposition
(POD) and Radial Basis Functions (RBF) to achieve super-resolution of the
velocity field. Large counter-rotating vortices were observed, and their
evolution was monitored in terms of the maximum intensity of the Q-field. The
results show that classic contact angle laws based on interface velocity cannot
describe the evolution of the contact angle at a macroscopic scale. Moreover,
the impact of the interface dynamics on the flow field is considerable and
extends several capillary lengths below the interface.


  The functional analysis of the left atrium (LA) is important for evaluating
cardiac health and understanding diseases like atrial fibrillation. Cine MRI is
ideally placed for the detailed 3D characterisation of LA motion and
deformation, but it is lacking appropriate acquisition and analysis tools. In
this paper, we present Analysis for Left Atrial Displacements and Deformations
using unsupervIsed neural Networks, \textit{Aladdin}, to automatically and
reliably characterise regional LA deformations from high-resolution 3D Cine
MRI. The tool includes: an online few-shot segmentation network (Aladdin-S), an
online unsupervised image registration network (Aladdin-R), and a strain
calculations pipeline tailored to the LA. We create maps of LA Displacement
Vector Field (DVF) magnitude and LA principal strain values from images of 10
healthy volunteers and 8 patients with cardiovascular disease (CVD). We
additionally create an atlas of these biomarkers using the data from the
healthy volunteers. Aladdin is able to accurately track the LA wall across the
cardiac cycle and characterize its motion and deformation. The overall DVF
magnitude and principal strain values are significantly higher in the healthy
group vs CVD patients: $2.85 \pm 1.59~mm$ and $0.09 \pm 0.05$ vs $1.96 \pm
0.74~mm$ and $0.03 \pm 0.04$, respectively. The time course of these metrics is
also different in the two groups, with a more marked active contraction phase
observed in the healthy cohort. Finally, utilizing the LA atlas allows us to
identify regional deviations from the population distribution that may indicate
focal tissue abnormalities. The proposed tool for the quantification of novel
regional LA deformation biomarkers should have important clinical applications.
The source code, anonymized images, generated maps and atlas are publicly
available: https://github.com/cgalaz01/aladdin_cmr_la.


  Mendelian randomization uses genetic variants as instrumental variables to
make causal inferences about the effects of modifiable risk factors on diseases
from observational data. One of the major challenges in Mendelian randomization
is that many genetic variants are only modestly or even weakly associated with
the risk factor of interest, a setting known as many weak instruments. Many
existing methods, such as the popular inverse-variance weighted (IVW) method,
could be biased when the instrument strength is weak. To address this issue,
the debiased IVW (dIVW) estimator, which is shown to be robust to many weak
instruments, was recently proposed. However, this estimator still has
non-ignorable bias when the effective sample size is small. In this paper, we
propose a modified debiased IVW (mdIVW) estimator by multiplying a modification
factor to the original dIVW estimator. After this simple correction, we show
that the bias of the mdIVW estimator converges to zero at a faster rate than
that of the dIVW estimator under some regularity conditions. Moreover, the
mdIVW estimator has smaller variance than the dIVW estimator.We further extend
the proposed method to account for the presence of instrumental variable
selection and balanced horizontal pleiotropy. We demonstrate the improvement of
the mdIVW estimator over the dIVW estimator through extensive simulation
studies and real data analysis.


  Counterfeiting and piracy are a form of theft that has been steadily growing
in recent years. Banknotes and identity documents are two common objects of
counterfeiting. Aiming to detect these counterfeits, the present survey covers
a wide range of anti-counterfeiting security features, categorizing them into
three components: security substrate, security inks and security printing.
respectively. From the computer vision perspective, we present works in the
literature covering these three categories. Other topics, such as history of
counterfeiting, effects on society and document experts, counterfeiter types of
attacks, trends among others are covered. Therefore, from non-experienced to
professionals in security documents, can be introduced or deepen its knowledge
in anti-counterfeiting measures.


  Anomaly detection is used for identifying data that deviate from `normal'
data patterns. Its usage on classical data finds diverse applications in many
important areas like fraud detection, medical diagnoses, data cleaning and
surveillance. With the advent of quantum technologies, anomaly detection of
quantum data, in the form of quantum states, may become an important component
of quantum applications. Machine learning algorithms are playing pivotal roles
in anomaly detection using classical data. Two widely-used algorithms are
kernel principal component analysis and one-class support vector machine. We
find corresponding quantum algorithms to detect anomalies in quantum states. We
show that these two quantum algorithms can be performed using resources
logarithmic in the dimensionality of quantum states. For pure quantum states,
these resources can also be logarithmic in the number of quantum states used
for training the machine learning algorithm. This makes these algorithms
potentially applicable to big quantum data applications.


  We analyse non-local rotating observables in holography corresponding to
spinning bound states. To renormalize their energies and momenta we suggest and
discuss different holographic renormalization schemes motivated by the static
non-local observables. Namely the holographic renormalization and the rotating
color singlet mass subtraction scheme. In the holographic renormalization we
identify the infinite boundary terms and subtract them. In the mass subtraction
scheme we evaluate the energy of a spinning trailing string corresponding to
the color charged singlet which experiences dragging phenomena and we subtract
it from the energy of the bound state to obtain the renormalized finite energy.
Then we apply our generic framework to certain strongly coupled thermal
theories with broken rotational symmetry. We find numerical solutions
corresponding to spinning bound states with a fixed size while varying their
angular frequency. By applying numerically the renormalization schemes, we find
that there is a critical frequency where the bound state ceases to exist or
dissociates. We also note that bound states require lower angular frequencies
to dissociate when the theory has less symmetry.


  Gravitational and massless particle radiation of straight cosmic strings with
finite thickness is studied analytically. It is found that the non-linear
interaction of the radiation fields emitted by a cosmic string with the ones of
the string always makes the spacetime singular at the symmetry axis. The
singularity is not removable and is a scalar one.


  We present a novel low latency CMOS hardware accelerator for fully connected
(FC) layers in deep neural networks (DNNs). The FC accelerator, FC-ACCL, is
based on 128 8x8 or 16x16 processing elements (PEs) for matrix-vector
multiplication, and 128 multiply-accumulate (MAC) units integrated with 128
High Bandwidth Memory (HBM) units for storing the pretrained weights.
Micro-architectural details for CMOS ASIC implementations are presented and
simulated performance is compared to recent hardware accelerators for DNNs for
AlexNet and VGG 16. When comparing simulated processing latency for a 4096-1000
FC8 layer, our FC-ACCL is able to achieve 48.4 GOPS (with a 100 MHz clock)
which improves on a recent FC8 layer accelerator quoted at 28.8 GOPS with a 150
MHz clock. We have achieved this considerable improvement by fully utilizing
the HBM units for storing and reading out column-specific FClayer weights in 1
cycle with a novel colum-row-column schedule, and implementing a maximally
parallel datapath for processing these weights with the corresponding MAC and
PE units. When up-scaled to 128 16x16 PEs, for 16x16 tiles of weights, the
design can reduce latency for the large FC6 layer by 60 % in AlexNet and by 3 %
in VGG16 when compared to an alternative EIE solution which uses compression.


  The giant impact hypothesis for Moon formation successfully explains the
dynamic properties of the Earth-Moon system but remains challenged by the
similarity of isotopic fingerprints of the terrestrial and lunar mantles.
Moreover, recent geochemical evidence suggests that the Earth's mantle
preserves ancient (or "primordial") heterogeneity that predates the
Moon-forming giant impact. Using a new hydrodynamical method, we here show that
Moon-forming giant impacts lead to a stratified starting condition for the
evolution of the terrestrial mantle. The upper layer of the Earth is
compositionally similar to the disk, out of which the Moon evolves, whereas the
lower layer preserves proto-Earth characteristics. As long as this predicted
compositional stratification can at least partially be preserved over the
subsequent billions of years of Earth mantle convection, the compositional
similarity between the Moon and the accessible Earth's mantle is a natural
outcome of realistic and high-probability Moon-forming impact scenarios. The
preservation of primordial heterogeneity in the modern Earth not only
reconciles geochemical constraints but is also consistent with recent
geophysical observations. Furthermore, for significant preservation of a
proto-Earth reservoir, the bulk composition of the Earth-Moon system may be
systematically shifted towards chondritic values.


  Applications in robotics or other size-, weight- and power-constrained
autonomous systems at the edge often require real-time and low-energy solutions
to large optimization problems. Event-based and memory-integrated neuromorphic
architectures promise to solve such optimization problems with superior energy
efficiency and performance compared to conventional von Neumann architectures.
Here, we present a method to solve convex continuous optimization problems with
quadratic cost functions and linear constraints on Intel's scalable
neuromorphic research chip Loihi 2. When applied to model predictive control
(MPC) problems for the quadruped robotic platform ANYmal, this method achieves
over two orders of magnitude reduction in combined energy-delay product
compared to the state-of-the-art solver, OSQP, on (edge) CPUs and GPUs with
solution times under ten milliseconds for various problem sizes. These results
demonstrate the benefit of non-von-Neumann architectures for robotic control
applications.


  Instruction in quantum mechanics is becoming increasingly important as the
field is not only a key part of modern physics research, but is also important
for emerging technologies. However, many students regard quantum mechanics as a
particularly challenging subject, in part because it is considered very
mathematical and abstract. One potential way to help students understand and
contextualize unintuitive quantum ideas is to provide them opportunities to
work with physical apparatus demonstrating these phenomena. In order to
understand how working with quantum experiments affects students' reasoning, we
performed think-aloud lab sessions of two pairs of students as they worked
through a sequence of quantum optics experiments that demonstrated
particle-wave duality of photons. Analyzing the in-the-moment student thinking
allowed us to identify the resources students activated while reasoning through
the experimental evidence of single-photon interference, as well as student
ideas about what parts of the experiments were quantum versus classical. This
work will aid instructors in helping their students construct an understanding
of these topics from their own ideas and motivate future investigations into
the use of hands-on opportunities to facilitate student learning about quantum
mechanics.


  Eta Car, with its historical outbursts, visible ejecta and massive, variable
winds, continues to challenge both observers and modelers. In just the past
five years over 100 papers have been published on this fascinating object. We
now know it to be a massive binary system with a 5.54-year period. In January
2009, Eta Car underwent one of its periodic low-states, associated with
periastron passage of the two massive stars. This event was monitored by an
intensive multi-wavelength campaign ranging from gamma-rays to radio. A large
amount of data was collected to test a number of evolving models including 3-D
models of the massive interacting winds. August 2009 was an excellent time for
observers and theorists to come together and review the accumulated studies, as
have occurred in four meetings since 1998 devoted to Eta Car. Indeed, Eta Car
behaved both predictably and unpredictably during this most recent periastron,
spurring timely discussions. Coincidently, WR140 also passed through periastron
in early 2009. It, too, is a intensively studied massive interacting binary.
Comparison of its properties, as well as the properties of other massive stars,
with those of Eta Car is very instructive. These well-known examples of evolved
massive binary systems provide many clues as to the fate of the most massive
stars. What are the effects of the interacting winds, of individual stellar
rotation, and of the circumstellar material on what we see as
hypernovae/supernovae? We hope to learn.


  We construct a Schwarzschild-type exact external solution for a theory of
gravity admitting local Galilean invariance. In order to realize the Galilean
invariance we need to adopt a five-dimensional manifold. The solution for the
gravitational field equations obeys a Birkhoff-like theorem. Three classic
tests of general relativity are analyzed in detail: the perihelion shift of the
planet Mercury, the deflection of light by the Sun, and the gravitational
redshift of atomic spectral lines. The Galilean version of these tests exhibits
an additional parameter $b$ related to the fifth-coordinate. This constant $b$
can be estimated by a comparison with observational data. We observe that the
Galilean theory is able to reproduce the results traditionally predicted by
general relativity in the limit of negligible $b$. This shows that the tests
are not specifically Lorentz invariant.


  By means of the concept of Factorial Moments we examine DNA sequences from
Yeast to distinguish coding and non-coding regions. It is found that the FM may
be a powerful tool for analysis of DNA sequences. PACS numbers:
87.15.Cc,87.10.+e,87.14.Gg


  High-dimensional quantum information processing has emerged as a promising
avenue to transcend hardware limitations and advance the frontiers of quantum
technologies. Harnessing the untapped potential of the so-called qudits
necessitates the development of quantum protocols beyond the established qubit
methodologies. Here, we present a robust, hardware-efficient, and extensible
approach for operating multidimensional solid-state systems using
Raman-assisted two-photon interactions. To demonstrate its efficacy, we
construct a set of multi-qubit operations, realize highly entangled
multidimensional states including atomic squeezed states and Schr\"odinger cat
states, and implement programmable entanglement distribution along a qudit
array. Our work illuminates the quantum electrodynamics of strongly driven
multi-qudit systems and provides the experimental foundation for the future
development of high-dimensional quantum applications.


  An experimental study of the delayed threshold phenomenon in a Vertical
Extended Cavity Semiconductor Emitting Laser is carried out. Under modulation
of the pump power, the laser intensity exhibits a hysteresis behavior in the
vicinity of the threshold. The temporal width of this hysteresis is measured as
a function of the modulation frequency, and is proved to follow the predicted
scaling law. A model based on the rate equations is derived and used to analyze
the experimental observations. A frequency variation of the laser around the
delayed threshold and induced by the phase-amplitude coupling is predicted and
estimated.


  We present extensive numerical investigations of the structural relaxation
dynamics of a realistic model of the amorphous high-temperature ceramic
a-Si$_3$B$_3$N$_7$, probing the mean square displacement (MSD) of the atoms,
the bond survival probability (BSP), the average energy, the specific heat, and
the two-point energy average. Combining the information from these different
sources, we identify a transition temperature $T_c \approx 2000$ K below which
the system is no longer ergodic and physical quantities observed over a time
$t_{obs}$ show a systematic parametric dependence on the waiting time $t_w$, or
age, elapsed after the quench. The aging dynamics 'stiffens' as the system
becomes older, which is similar to the behavior of highly idealized models such
as Ising spin-glasses and Lennard-Jones glasses.


  We consider a Josephson-junction ladder in an external magnetic field with
half flux quantum per plaquette. When driven by external currents, periodic in
time and staggered in space, such a fully frustrated system is found to display
spatiotemporal stochastic resonance under the influence of thermal noise. Such
resonance behavior is investigated both numerically and analytically, which
reveals significant effects of anisotropy and yields rich physics.


  The gas in galaxy clusters is heated by shock compression through accretion
(outer shocks) and mergers (inner shocks). These processes additionally produce
turbulence. To analyse the relation between the thermal and turbulent energies
of the gas under the influence of non-adiabatic processes, we performed
numerical simulations of cosmic structure formation in a box of 152 Mpc
comoving size with radiative cooling, UV background, and a subgrid scale model
for numerically unresolved turbulence. By smoothing the gas velocities with an
adaptive Kalman filter, we are able to estimate bulk flows toward cluster
cores. This enables us to infer the velocity dispersion associated with the
turbulent fluctuation relative to the bulk flow. For halos with masses above
$10^{13}\,M_\odot$, we find that the turbulent velocity dispersions averaged
over the warm-hot intergalactic medium (WHIM) and the intracluster medium (ICM)
are approximately given by powers of the mean gas temperatures with exponents
around 0.5, corresponding to a roughly linear relation between turbulent and
thermal energies and transonic Mach numbers. However, turbulence is only weakly
correlated with the halo mass. Since the power-law relation is stiffer for the
WHIM, the turbulent Mach number tends to increase with the mean temperature of
the WHIM. This can be attributed to enhanced turbulence production relative to
dissipation in particularly hot and turbulent clusters.


  Traditional regression models do not generalize well when learning from small
and noisy datasets. Here we propose a novel metamodel structure to improve the
regression result. The metamodel is composed of multiple classification base
models and a regression model built upon the base models. We test this
structure on the prediction of autism spectrum disorder (ASD) severity as
measured by the ADOS communication (ADOS COMM) score from resting-state fMRI
data, using a variety of base models. The metamodel outperforms traditional
regression models as measured by the Pearson correlation coefficient between
true and predicted scores and stability. In addition, we found that the
metamodel is more flexible and more generalizable.


  In this paper, we prove the existence and uniqueness of the solution of a
coupled Mean-Field Forward-Backward SDE system with Jumps. Then, we give an
application in the field of storage problem in smart grids, studied in [4] in
the case where the production of electricity is not predictable due, for
example, to the changes in meteorological forecasts.


  We study Lipschitz-free spaces over compact and uniformly discrete metric
spaces enjoying certain high regularity properties - having group structure
with left-invariant metric. Using methods of harmonic analysis we show that,
given a compact metrizable group $G$ equipped with an arbitrary compatible
left-invariant metric $d$, the Lipschitz-free space over $G$,
$\mathcal{F}(G,d)$, satisfies the metric approximation property. We show also
that, given a finitely generated group $G$, with its word metric $d$, from a
class of groups admitting a certain special type of combing, which includes all
hyperbolic groups and Artin groups of large type, $\mathcal{F}(G,d)$ has a
Schauder basis. Examples and applications are discussed. In particular, for any
net $N$ in a real hyperbolic $n$-space $\mathbb{H}^n$, $\mathcal{F}(N)$ has a
Schauder basis.


  We present a census of circumstellar disks in the Chamaeleon I star-forming
region. Using the Infrared Array Camera and the Multiband Imaging Photometer
onboard the Spitzer Space Telescope, we have obtained images of Chamaeleon I at
3.6, 4.5, 5.8, 8.0, and 24 um. To search for new disk-bearing members of the
cluster, we have performed spectroscopy on objects that have red colors in
these data. Through this work, we have discovered four new members of
Chamaeleon I with spectral types of M4, M6, M7.5, and L0. The first three
objects are highly embedded (A_J~5) and reside near known protostars,
indicating that they may be among the youngest low-mass sources in the cluster
(<1 Myr). The L0 source is the coolest known member of Chamaeleon I. Its
luminosity implies a mass of 0.004-0.01 M_sun, making it the least massive
brown dwarf for which a circumstellar disk has been reliably detected. To
characterize the disk population in Chamaeleon I, we have classified the
infrared spectral energy distributions of the 203 known members that are
encompassed by the Spitzer images. Through these classifications, we find that
the disk fraction in Chamaeleon I is roughly constant at ~50% from 0.01 to 0.3
M_sun. These data are similar to the disk fraction of IC 348, which is a denser
cluster at the same age as Chamaeleon I. However, the disk fraction at M>1
M_sun is significantly higher in Chamaeleon I than in IC 348 (65% vs. 20%),
indicating longer disk lifetimes in Chamaeleon I for this mass range. Thus,
low-density star-forming regions like Chamaeleon I may offer more time for
planet formation around solar-type stars than denser clusters.


  Non-equilibrium time evolution in isolated many-body quantum systems
generally results in thermalization. However, the relaxation process can be
very slow, and quasi-stationary non-thermal plateaux are often observed at
intermediate times. The paradigmatic example is a quantum quench in an
integrable model with weak integrability breaking; for a long time, the state
can not escape the constraints imposed by the approximate integrability. We
unveil a new mechanism of prethermalization, based on the presence of a
symmetry of the pre-quench Hamiltonian, which is spontaneously broken at zero
temperature and is explicitly broken by the post-quench Hamiltonian. The
typical time scale of the phenomenon is proportional to the thermal correlation
length of the initial state, which diverges as the temperature is lowered. We
show that the prethermal quasi-stationary state can be approximated by a mixed
state that violates cluster decomposition property. We consider two examples:
the transverse-field Ising chain, where the full time evolution is computed
analytically, and the (non integrable) ANNNI model, which is investigated
numerically.


  Markov Decision Processes (MDPs) are a popular class of models suitable for
solving control decision problems in probabilistic reactive systems. We
consider parametric MDPs (pMDPs) that include parameters in some of the
transition probabilities to account for stochastic uncertainties of the
environment such as noise or input disturbances.
  We study pMDPs with reachability objectives where the parameter values are
unknown and impossible to measure directly during execution, but there is a
probability distribution known over the parameter values. We study for the
first time computing parameter-independent strategies that are expectation
optimal, i.e., optimize the expected reachability probability under the
probability distribution over the parameters. We present an encoding of our
problem to partially observable MDPs (POMDPs), i.e., a reduction of our problem
to computing optimal strategies in POMDPs.
  We evaluate our method experimentally on several benchmarks: a motivating
(repeated) learner model; a series of benchmarks of varying configurations of a
robot moving on a grid; and a consensus protocol.


  Context. An optically thick, geometrically thin accretion disk (AD) around a
supermassive black hole might contribute to broad-line emission in type-1
active galactic nuclei (AGN). However, emission line profiles are most often
not immediately consistent with the profiles expected from a rotating disk. The
extent to which an AD in AGN contributes to the broad Balmer lines and
high-ionization UV lines in radio-loud (RL) AGN needs to be investigated. Aims.
This work aims to address whether the AD can account for the double-peaked
profiles observed in the Balmer lines (H$\beta$, H$\alpha$), near-UV
(Mgii$\lambda$2800), and high-ionization UV lines (Civ$\lambda$1549,
Ciii]$\lambda$1909) of the extremely jetted quasar 3C 47. Methods. The low
ionization lines (LILs) (H$\beta$, H$\alpha$, and Mgii$\lambda$2800) were
analyzed using a relativistic Keplerian AD model. Fits were carried out
following Bayesian and multicomponent non-linear approaches. The profiles of
prototypical high ionization lines (HILs) were also modeled by the contribution
of the AD, along with fairly symmetric additional components. Results. The LIL
profiles of 3C 47 are in very good agreement with a relativistic Keplerian AD
model. The disk emission is constrained between $\approx$ 100 and $\approx$
10000 gravitational radii, with a viewing angle of $\approx$30 degrees.
Conclusions. The study provides convincing direct observational evidence for
the presence of an AD and explains the HIL profiles are due to disk and failed
wind contributions. The agreement between the observed profiles of the LILs and
the model is remarkable. The main alternative, a double broad line region
associated with a binary black hole, is found to be less appealing than the
disk model for the quasar 3C 47.


  Free electrons provide a powerful tool to probe material properties at
atomic-scale spatial resolution. Recent advances in ultrafast electron
microscopy enable the manipulation of free electron wavefunctions using laser
pulses. It would be of great importance if one could combine the spatial
resolution of electron probes with the ability of laser pulses to probe
coherent phenomena in quantum systems. To this end, we propose a novel
technique that leverages free electrons that are coherently-shaped by laser
pulses to measure quantum coherence in materials. Developing a quantum theory
of electron-qubit interactions in materials, we show how the energy spectrum of
laser-shaped electrons enables measuring the qubit Block-sphere state and
decoherence time (T2). Finally, we present how such shaped electrons can detect
and quantify superradiance from multiple qubits. Our scheme could be
implemented in an ultrafast transmission electron microscope (UTEM), opening
the way towards the full characterization of the state of quantum systems at
atomic-scale resolution.


  The photometric precision, monitoring baselines, and rapid, even sampling
rates required by modern satellites designed for detecting the signal of
transiting exoplanets are ideally suited to a large number of applications in
high-energy astrophysics. I will exemplify this by discussing the results for
active galactic nuclei from Kepler and summarizing other high-energy results
from Kepler/K2. These precision instruments are currently underutilized for
high-energy applications despite their great potential, due in part to complex
systematics affecting the data. I will summarize these effects and mitigation
approaches, and conclude by discussing how the recently launched Transiting
Exoplanet Survey Satellite mission will differ from Kepler/K2 in ways
significant to the high-energy community.


  It has been assumed that optical activity can be measured by illuminating
alternatively a material with left- and right- handed circular polarized light
and analyzing the differential response. This simple and intuitive approach is
in general incorrect, and has led to misleading idea that extrinsic chirality
involves optical activity.


  We construct a K-theory version of Bhatt-Morrow-Scholze's Breuil-Kisin
cohomology theory for $\sO_K$-linear idempotent-complete, small smooth proper
stable infinity-categories, where $K$ is a discretely valued extension of
$\Q_p$ with perfect residue field. As a corollary, under the assumption that
$K(1)$-local K theory satisfies the K\"unneth formula for $\sO_K$-linear
idempotent-complete, small smooth proper stable $\infty$-categories, we prove a
comparison theorem between $K(1)$-local K theory of the generic fiber and
topological cyclic periodic homology theory of the special fiber with
$\Bcry$-coefficients, and $p$-adic Galois representations of $K(1)$-local K
theory for $\sO_K$-linear idempotent-complete, small smooth proper stable
$\infty$-categories are semi-stable. We also provide an alternative
K-theoretical proof of the semi-stability of p-adic Galois representations of
the p-adic \'etale cohomology group of smooth proper varieties over $K$ with
good reduction. is a short, This is a short preliminary version of the work
that was later expanded in 2309.13654.


  We propose a cooling scheme for fermionic quantum gases, based on the
principles of the Peltier thermoelectric effect and energy filtering. The
system to be cooled is connected to another harmonically trapped gas acting as
a reservoir. The cooling is achieved by two simultaneous processes: (i) the
system is evaporatively cooled and (ii) cold fermions from deep below the Fermi
surface of the reservoir are injected below the Fermi level of the system, in
order to fill the 'holes' in the energy distribution. This is achieved by a
suitable energy dependence of the transmission coefficient connecting the
system to the reservoir. The two processes can be viewed as simultaneous
evaporative cooling of particles and holes. We show that both a significantly
lower entropy per particle and faster cooling rate can be achieved than by
using only evaporative cooling.


  The distance metric plays an important role in nearest neighbor (NN)
classification. Usually the Euclidean distance metric is assumed or a
Mahalanobis distance metric is optimized to improve the NN performance. In this
paper, we study the problem of embedding arbitrary metric spaces into a
Euclidean space with the goal to improve the accuracy of the NN classifier. We
propose a solution by appealing to the framework of regularization in a
reproducing kernel Hilbert space and prove a representer-like theorem for NN
classification. The embedding function is then determined by solving a
semidefinite program which has an interesting connection to the soft-margin
linear binary support vector machine classifier. Although the main focus of
this paper is to present a general, theoretical framework for metric embedding
in a NN setting, we demonstrate the performance of the proposed method on some
benchmark datasets and show that it performs better than the Mahalanobis metric
learning algorithm in terms of leave-one-out and generalization errors.


  Most recent network failure diagnosis systems focused on data center networks
where complex measurement systems can be deployed to derive routing information
and ensure network coverage in order to achieve accurate and fast fault
localization. In this paper, we target wide-area networks that support
data-intensive distributed applications. We first present a new multi-output
prediction model that directly maps the application level observations to
localize the system component failures. In reality, this application-centric
approach may face the missing data challenge as some input (feature) data to
the inference models may be missing due to incomplete or lost measurements in
wide area networks. We show that the presented prediction model naturally
allows the {\it multivariate} imputation to recover the missing data. We
evaluate multiple imputation algorithms and show that the prediction
performance can be improved significantly in a large-scale network. As far as
we know, this is the first study on the missing data issue and applying
imputation techniques in network failure localization.


  In this paper, we use convolutional neural networks to address the problem of
model identification for autoregressive moving average time series models. We
compare the performance of several neural network architectures, trained on
simulated time series, with likelihood based methods, in particular the Akaike
and Bayesian information criteria. We find that our neural networks can
significantly outperform these likelihood based methods in terms of accuracy
and, by orders of magnitude, in terms of speed.


  The advent of satellite-borne machine learning hardware accelerators has
enabled the on-board processing of payload data using machine learning
techniques such as convolutional neural networks (CNN). A notable example is
using a CNN to detect the presence of clouds in hyperspectral data captured on
Earth observation (EO) missions, whereby only clear sky data is downlinked to
conserve bandwidth. However, prior to deployment, new missions that employ new
sensors will not have enough representative datasets to train a CNN model,
while a model trained solely on data from previous missions will underperform
when deployed to process the data on the new missions. This underperformance
stems from the domain gap, i.e., differences in the underlying distributions of
the data generated by the different sensors in previous and future missions. In
this paper, we address the domain gap problem in the context of on-board
hyperspectral cloud detection. Our main contributions lie in formulating new
domain adaptation tasks that are motivated by a concrete EO mission, developing
a novel algorithm for bandwidth-efficient supervised domain adaptation, and
demonstrating test-time adaptation algorithms on space deployable neural
network accelerators. Our contributions enable minimal data transmission to be
invoked (e.g., only 1% of the weights in ResNet50) to achieve domain
adaptation, thereby allowing more sophisticated CNN models to be deployed and
updated on satellites without being hampered by domain gap and bandwidth
limitations.


  We formulate as an inverse problem the construction of sparse parametric
continuous curve models that fit a sequence of contour points. Our prior is
incorporated as a regularization term that encourages rotation invariance and
sparsity. We prove that an optimal solution to the inverse problem is a closed
curve with spline components. We then show how to efficiently solve the task
using B-splines as basis functions. We extend our problem formulation to curves
made of two distinct components with complementary smoothness properties and
solve it using hybrid splines. We illustrate the performance of our model on
contours of different smoothness. Our experimental results show that we can
faithfully reconstruct any general contour using few parameters, even in the
presence of imprecisions in the measurements.


  The properties of the QCD axion are strictly related to the dependence of
strong interactions on the topological parameter theta. We present a
determination of the topological properties of QCD for temperatures up to
around 600 MeV, obtained by lattice QCD simulations with 2+1 flavors and
physical quark masses. Numerical results for the topological susceptibility,
when compared to instanton gas computations, differ both in size and in the
temperature dependence. We discuss the implications of such findings for axion
phenomenology, also in comparison to similar studies in the literature, and the
prospects for future investigations.


  Recently, aspect sentiment quad prediction (ASQP) has become a popular task
in the field of aspect-level sentiment analysis. Previous work utilizes a
predefined template to paraphrase the original sentence into a structure target
sequence, which can be easily decoded as quadruplets of the form (aspect
category, aspect term, opinion term, sentiment polarity). The template involves
the four elements in a fixed order. However, we observe that this solution
contradicts with the order-free property of the ASQP task, since there is no
need to fix the template order as long as the quadruplet is extracted
correctly. Inspired by the observation, we study the effects of template orders
and find that some orders help the generative model achieve better performance.
It is hypothesized that different orders provide various views of the
quadruplet. Therefore, we propose a simple but effective method to identify the
most proper orders, and further combine multiple proper templates as data
augmentation to improve the ASQP task. Specifically, we use the pre-trained
language model to select the orders with minimal entropy. By fine-tuning the
pre-trained language model with these template orders, our approach improves
the performance of quad prediction, and outperforms state-of-the-art methods
significantly in low-resource settings.


  To study some characteristics of the interstellar medium, observational data
of pulsars with large Faraday rotation values (|RM|> 300 rad / m2) were used.
It was suggested and justified that large |RM| values can be due to the
contribution of the regions with increased electron concentration, projected on
the pulsar. Most likely these are the HII regions, dark nebulae and molecular
clouds. In these objects the magnetic field can be oriented in the direction of
a large-scale field of the Galaxy, or simply is a deformed extension of the
galactic field. It was shown that the Galactic distribution of rotation
measures of pulsars with |RM|> 300rad / m2 corresponds to the circular model of
the magnetic field of the Galaxy, with the counter-clockwise direction of the
magnetic field in the galactocentric circle 5 kpc <R <7 kpc.


  Most state-of-the-art Text-to-Speech systems use the mel-spectrogram as an
intermediate representation, to decompose the task into acoustic modelling and
waveform generation.
  A mel-spectrogram is extracted from the waveform by a simple, fast DSP
operation, but generating a high-quality waveform from a mel-spectrogram
requires computationally expensive machine learning: a neural vocoder. Our
proposed ``autovocoder'' reverses this arrangement. We use machine learning to
obtain a representation that replaces the mel-spectrogram, and that can be
inverted back to a waveform using simple, fast operations including a
differentiable implementation of the inverse STFT.
  The autovocoder generates a waveform 5 times faster than the DSP-based
Griffin-Lim algorithm, and 14 times faster than the neural vocoder HiFi-GAN. We
provide perceptual listening test results to confirm that the speech is of
comparable quality to HiFi-GAN in the copy synthesis task.


  A new Laves phase superconductor NbBe2, prototype with MgCu2, having maximum
Tc ~2.6 K has been reported very recently. Based on first-principle
calculations, we systematically study the structural, elastic, mechanical,
electronic, thermal and superconducting properties of the newly reported
superconducting intermetallic compound NbBe2. Finally, we investigate the
electron-phonon coupling constant, phonon dispersion curve and density of
states which indicates that the compound under study is a weakly coupled BCS
superconductor.


  A ferroelectric field effect in the bulk heterojunction was found when an
external electric field (EEF) was applied on the active layer of polymer solar
cells (PSCs) during the annealing process of the active layer spin-coated with
poly (3-hexylthiophene):[6,6]-phenyl-C61 butyric acid methyl ester (P3HT:PCBM).
For one direction field, the short circuit current density of PSCs was improved
from 7.2 to 8.0 mA/cm2, the power conversion efficiency increased from 2.4 to
2.8%, and the incident photon-to-current conversion efficiency increased from
42 to 49% corresponding to the different EEF magnitude. For an opposite
direction field, the applied EEF brought a minus effect on the performance
mentioned above. EEF treatment can orientate molecular ordering of the polymer,
and change the morphology of the active layer. The authors suggest a
explanation that the ferroelectric field has been built in the active layer,
and therefore it plays a key role in PSCs system. A needle-like surface
morphology of the active film was also discussed.


  Point cloud analysis has achieved outstanding performance by transferring
point cloud pre-trained models. However, existing methods for model adaptation
usually update all model parameters, i.e., full fine-tuning paradigm, which is
inefficient as it relies on high computational costs (e.g., training GPU
memory) and massive storage space. In this paper, we aim to study
parameter-efficient transfer learning for point cloud analysis with an ideal
trade-off between task performance and parameter efficiency. To achieve this
goal, we freeze the parameters of the default pre-trained models and then
propose the Dynamic Adapter, which generates a dynamic scale for each token,
considering the token significance to the downstream task. We further
seamlessly integrate Dynamic Adapter with Prompt Tuning (DAPT) by constructing
Internal Prompts, capturing the instance-specific features for interaction.
Extensive experiments conducted on five challenging datasets demonstrate that
the proposed DAPT achieves superior performance compared to the full
fine-tuning counterparts while significantly reducing the trainable parameters
and training GPU memory by 95% and 35%, respectively. Code is available at
https://github.com/LMD0311/DAPT.


  We derive numerical tight-binding hopping parameters to describe
conduction-band dispersion in arbitrary orientational phases of $A_3$C$_{60}$.
The parameters are obtained by direct Fourier inversion of the spectra from
self-consistent electronic-structure calculations for K$_3$C$_{60}$ using the
local-density approximation, including the effects of orientational dependence.
Using the new parameters, we revisit several earlier investigations of the
orientational ordering in $A_3$C$_{60}$; some of the earlier results are
substantiated, while others are slightly modified.


  We present a targeted search for narrow-band (< 5 Hz) drifting sinusoidal
radio emission from 86 stars in the Kepler field hosting confirmed or candidate
exoplanets. Radio emission less than 5 Hz in spectral extent is currently known
to only arise from artificial sources. The stars searched were chosen based on
the properties of their putative exoplanets, including stars hosting candidates
with 380 K > T_eq > 230 K, stars with 5 or more detected candidates or stars
with a super-Earth (R_p < 3 R_earth) in a > 50 day orbit. Baseband voltage data
across the entire band between 1.1 and 1.9 GHz were recorded at the Robert C.
Byrd Green Bank Telescope between Feb--Apr 2011 and subsequently searched
offline. No signals of extraterrestrial origin were found. We estimate that
fewer than ~1% of transiting exoplanet systems host technological civilizations
that are radio loud in narrow-band emission between 1-2 GHz at an equivalent
isotropically radiated power (EIRP) of ~1.5 x 10^21 erg s^-1, approximately
eight times the peak EIRP of the Arecibo Planetary Radar, and we limit the the
number of 1-2 GHz narrow-band-radio-loud Kardashev type II civilizations in the
Milky Way to be < 10^-6 M_solar^-1. Here we describe our observations, data
reduction procedures and results.


  Optimizers are essential components for successfully training deep neural
network models. In order to achieve the best performance from such models,
designers need to carefully choose the optimizer hyperparameters. However, this
can be a computationally expensive and time-consuming process. Although it is
known that all optimizer hyperparameters must be tuned for maximum performance,
there is still a lack of clarity regarding the individual influence of minor
priority hyperparameters, including the safeguard factor $\epsilon$ and
momentum factor $\beta$, in leading adaptive optimizers (specifically, those
based on the Adam optimizers). In this manuscript, we introduce a new framework
based on gradient histograms to analyze and justify important attributes of
adaptive optimizers, such as their optimal performance and the relationships
and dependencies among hyperparameters. Furthermore, we propose a novel
gradient histogram-based algorithm that automatically estimates a reduced and
accurate search space for the safeguard hyperparameter $\epsilon$, where the
optimal value can be easily found.


  Cognitive load assessment is crucial for understanding human performance in
various domains. This study investigates the impact of different task
conditions and time constraints on cognitive load using multiple measures,
including subjective evaluations, performance metrics, and physiological
eye-tracking data. Fifteen participants completed a series of primary and
secondary tasks with different time limits. The NASA-TLX questionnaire,
reaction time, inverse efficiency score, and eye-related features (blink,
saccade, and fixation frequency) were utilized to assess cognitive load. The
study results show significant differences in the level of cognitive load
required for different tasks and when under time constraints. The study also
found that there was a positive correlation (r = 0.331, p = 0.014) between how
often participants blinked their eyes and the level of cognitive load required
but a negative correlation (r = -0.290, p = 0.032) between how often
participants made quick eye movements (saccades) and the level of cognitive
load required. Additionally, the analysis revealed a significant negative
correlation (r = -0.347, p = 0.009) and (r = -0.370, p = 0.005) between
fixation and saccade frequencies under time constraints.


  We present the first non-interactive zero-knowledge argument system for QMA
with multi-theorem security. Our protocol setup constitutes an additional
improvement and is constructed in the malicious designated-verifier (MDV-NIZK)
model (Quach, Rothblum, and Wichs, EUROCRYPT 2019), where the setup consists of
a trusted part that includes only a common uniformly random string and an
untrusted part of classical public and secret verification keys, which even if
sampled maliciously by the verifier, the zero knowledge property still holds.
The security of our protocol is established under the Learning with Errors
Assumption. Our main technical contribution is showing a general transformation
that compiles any sigma protocol into a reusable MDV-NIZK protocol, using NIZK
for NP. Our technique is classical but works for quantum protocols and allows
the construction of a reusable MDV-NIZK for QMA.


  In the paper we resolve positively the conjecture on a characterization of
matrix Kummer and Wishart laws through independence property, which was posed
in [Koudou, Statist. Probab. Lett. 82 (2012), 1903--1907] . Apart from the
probabilistic result, we determine the general solution of the functional
equation associated to the characterization problem under weak assumptions.


  A general theory of vector-valued modular functions, holomorphic in the upper
half-plane, is presented for finite dimensional representations of the modular
group. This also provides a description of vector-valued modular forms of
arbitrary half-integer weight. It is shown that the space of these modular
functions is spanned, as a module over the polynomials in J, by the columns of
a matrix that satisfies an abstract hypergeometric equation, providing a simple
solution of the Riemann-Hilbert problem for representations of the modular
group. Restrictions on the coefficients of this differential equation implied
by analyticity are discussed, and an inversion formula is presented that allows
the determination of an arbitrary vector-valued modular function from its
singular behavior. Questions of rationality and positivity of expansion
coefficients are addressed. Closed expressions for the number of vector-valued
modular forms of half-integer weight are given, and the general theory is
illustrated on simple examples.


  As recent advancements in large-scale Text-to-Image (T2I) diffusion models
have yielded remarkable high-quality image generation, diverse downstream
Image-to-Image (I2I) applications have emerged. Despite the impressive results
achieved by these I2I models, their practical utility is hampered by their
large model size and the computational burden of the iterative denoising
process. In this paper, we explore the compression potential of these I2I
models in a task-oriented manner and introduce a novel method for reducing both
model size and the number of timesteps. Through extensive experiments, we
observe key insights and use our empirical knowledge to develop practical
solutions that aim for near-optimal results with minimal exploration costs. We
validate the effectiveness of our method by applying it to InstructPix2Pix for
image editing and StableSR for image restoration. Our approach achieves
satisfactory output quality with 39.2% and 56.4% reduction in model footprint
and 81.4% and 68.7% decrease in latency to InstructPix2Pix and StableSR,
respectively.


  We study the facility location problems where agents are located on a real
line and divided into groups based on criteria such as ethnicity or age. Our
aim is to design mechanisms to locate a facility to approximately minimize the
costs of groups of agents to the facility fairly while eliciting the agents'
locations truthfully. We first explore various well-motivated group fairness
cost objectives for the problems and show that many natural objectives have an
unbounded approximation ratio. We then consider minimizing the maximum total
group cost and minimizing the average group cost objectives. For these
objectives, we show that existing classical mechanisms (e.g., median) and new
group-based mechanisms provide bounded approximation ratios, where the
group-based mechanisms can achieve better ratios. We also provide lower bounds
for both objectives. To measure fairness between groups and within each group,
we study a new notion of intergroup and intragroup fairness (IIF) . We consider
two IIF objectives and provide mechanisms with tight approximation ratios.


  Observations of gamma ray bursts (GRB) afterglows in different spectral bands
provide a most valuable information about their nature, as well as about
properties of surrounding medium. Powerful infrared afterglow was observed from
the strong GRB041219. Here we explain the observed IR afterglow in the model of
a dust reradiation of the main GRB signal in the envelope surrounding the GRB
source. In this model we do not expect appearance of the prompt optical
emission which should be absorbed in the dust envelope. We estimate the
collimation angle of the gamma ray emission, and obtain restrictions on the
redshift (distance to GRB source), by fitting the model parameters to the
observational data.


  We give a complete diffeomorphism classification of 1-connected manifolds (of
dimension different from 4) whose integral homology is H(M)=Z+Z+Z.


  We construct a subset $A$ of the unit disc with the following properties. (i)
The set $A$ is the finite union of disjoint line segments. (ii) The shadow of
$A$ is arbitrarily close to the shadow of the unit disc in "most" directions.
(iii) If the line segments are considered to be mirrors reflecting light
according to the classical law of specular reflection then most light rays
hitting the set emerge on the other side of the disc moving along a parallel
line and shifted by an arbitrarily small amount.
  We also construct a set which reflects almost all light rays coming from one
direction to another direction but its shadow is arbitrarily small in other
directions, except for an arbitrarily small family of directions.


  When a massive quantum body is put into a spatial superposition, it is of
interest to consider the quantum aspects of the gravitational field sourced by
the body. We argue that in order to understand how the body may become
entangled with other massive bodies via gravitational interactions, it must be
thought of as being entangled with its own Newtonian-like gravitational field.
Thus, a Newtonian-like gravitational field must be capable of carrying quantum
information. Our analysis supports the view that table-top experiments testing
entanglement of systems interacting via gravity do probe the quantum nature of
gravity, even if no ``gravitons'' are emitted during the experiment.


  Traditional treatments of the first law of black hole thermodynamics do not
include a discussion of pressure and volume. We give an overview of recent
developments proposing a definition of volume that can be used to extend the
first law to include these appropriately. New results are also presenting
relating to the critical point and the associated second order phase transition
for a rotating black-hole in four-dimensional space-time which is
asymptotically anti-de Sitter. In line with known results for a non-rotating
charged black-hole, this phase transition is shown to be of Van der Waals type
with mean field exponents.


  We study the Minimal Supersymmetric Standard Model with Minimal Flavour
Violation for the case of a large parameter tanbeta and arbitrary values of the
supersymmetric mass parameters. We derive several resummation formulae for
tanbeta-enhanced loop corrections, which were previously only known in the
limit of supersymmetric masses far above the electroweak scale. Studying first
the renormalisation-scheme dependence of the resummation formula for the bottom
Yukawa coupling, we clarify the use of the sbottom mixing angle in the
supersymmetric loop factor Delta_b. As a new feature, we find tan-beta-enhanced
loop-induced flavour-changing neutral current (FCNC) couplings of gluinos and
neutralinos which in turn give rise to new effects in the renormalisation of
the Cabibbo-Kobayashi-Maskawa matrix and in FCNC processes of B mesons. For the
chromomagnetic Wilson coefficient C_8, these gluino-squark loops can be of the
same size as the known chargino-squark contribution. We discuss the
phenomenological consequences for the mixing-induced CP asymmetry in B_d -> phi
K_S. We further quote formulae for B_s -> mu^+ mu^- and B_s-B_s-bar mixing
valid beyond the decoupling limit and find a new contribution affecting the
phase of the B_s-B_s-bar mixing amplitude. Our resummed tan-beta-enhanced
effects are cast into Feynman rules permitting an easy implementation in
automatic calculations.


  In this paper we introduce, via a Phragmen-Lindel\"of type theorem, a maximal
plurisubharmonic function in a strongly pseudoconvex domain. We call such a
function the {\sl pluricomplex Poisson kernel} because it shares many
properties with the classical Poisson kernel of the unit disc. In particular,
we show that such a function is continuous, it is zero on the boundary except
at one boundary point where it has a non-tangential simple pole, and reproduces
pluriharmonic functions. We also use such a function to obtain a new
"intrinsic" version of the classical Julia's Lemma and
Julia-Wolff-Carath\'eodory Theorem.


  Integrating model-based machine learning methods into deep neural
architectures allows one to leverage both the expressive power of deep neural
nets and the ability of model-based methods to incorporate domain-specific
knowledge. In particular, many works have employed the expectation maximization
(EM) algorithm in the form of an unrolled layer-wise structure that is jointly
trained with a backbone neural network. However, it is difficult to
discriminatively train the backbone network by backpropagating through the EM
iterations as they are prone to the vanishing gradient problem. To address this
issue, we propose Highway Expectation Maximization Networks (HEMNet), which is
comprised of unrolled iterations of the generalized EM (GEM) algorithm based on
the Newton-Rahpson method. HEMNet features scaled skip connections, or
highways, along the depths of the unrolled architecture, resulting in improved
gradient flow during backpropagation while incurring negligible additional
computation and memory costs compared to standard unrolled EM. Furthermore,
HEMNet preserves the underlying EM procedure, thereby fully retaining the
convergence properties of the original EM algorithm. We achieve significant
improvement in performance on several semantic segmentation benchmarks and
empirically show that HEMNet effectively alleviates gradient decay.


  KOI-13 is the first known transiting system exhibiting light curve
distortions due to gravity darkening of the rapidly rotating host star. In this
paper we analyze publicly available Kepler Q2--Q3 short-cadence observations,
detecting a continuous light variation with a period of Prot = 25.43 +- 0.05
hour and a half-amplitude of 21 ppm, which is linked to stellar rotation. This
period is in exact 5:3 resonance with the orbit of KOI-13.01, which is the
first detection of a spin-orbit resonance in a host of a substellar companion.
The stellar rotation leads to stellar oblateness, which is expected to cause
secular variations in the orbital elements. We indeed detect the gradual
increment of the transit duration with a rate of (1.14 +- 0.30) * 10^{-6}
day/cycle. The confidence of this trend is 3.85-sigma, the two-sided false
alarm probability is 0.012%. We suggest that the reason for this variation is
the expected change of the impact parameter, with a rate of db/dt = -0.016 +-
0.004/yr. Assuming b~0.25, KOI-13.01 may become a nontransiting object in
75--100 years. The observed rate is compatible with the expected secular
perturbations due to the stellar oblateness yielded by the fast rotation.


  We describe a new program for determining photometric redshifts, dubbed EAZY.
The program is optimized for cases where spectroscopic redshifts are not
available, or only available for a biased subset of the galaxies. The code
combines features from various existing codes: it can fit linear combinations
of templates, it includes optional flux- and redshift-based priors, and its
user interface is modeled on the popular HYPERZ code. A novel feature is that
the default template set, as well as the default functional forms of the
priors, are not based on (usually highly biased) spectroscopic samples, but on
semi-analytical models. Furthermore, template mismatch is addressed by a novel
rest-frame template error function. This function gives different wavelength
regions different weights, and ensures that the formal redshift uncertainties
are realistic. We introduce a redshift quality parameter, Q_z, that provides a
robust estimate of the reliability of the photometric redshift estimate.
Despite the fact that EAZY is not "trained" on spectroscopic samples, the code
(with default parameters) performs very well on existing public datasets. For
K-selected samples in CDF-South and other deep fields we find a 1-sigma scatter
in dz/(1+z) of 0.034, and we provide updated photometric redshift catalogs for
the FIRES, MUSYC, and FIREWORKS surveys.


  In this paper we revisit the well-known constrained projection approximation
subspace tracking algorithm (CPAST) and derive, for the first time,
non-asymptotic error bounds. Furthermore, we introduce a novel sparse
modification of CPAST which is able to exploit sparsity in the underlying
covariance structure. We present a non-asymptotic analysis of the proposed
algorithm and study its empirical performance on simulated and real data.


  In this work I reason that in expanding space only those quantum modes
contribute to the measured vacuum energy that do not transcend the observable
volume. Since all quantised field modes have various observable consequences,
when a gravitational horizon causally confines an observer to a finite volume
quantised modes should be restricted to the observable patch to remain
consistent with gravity.
  Within the observable patch of Friedmann-Lemaitre-Robertson-Walker (FLRW)
space the vacuum expectation value of the energy-momentum tensor can be
expressed as a sum over discrete field modes. Friedmann's first equation
provides a straightforward ultraviolet cut-off allowing only a finite number of
modes in the sum. The finite volume acts as an infrared regulator and the
calculation of the vacuum energy density is tractable without regularisation
and renormalisation.
  To test the validity of this idea I quantise a scalar field on an FLRW
background and calculate its vacuum energy density in the vacuum dominated,
conformal, holographic limit. In this limit I show that the quantum vacuum
energy density scales with the square of the Hubble parameter, consistently
with gravity. In this example quantum vacuum expands space while the horizon of
the expanding space limits the energy density of the vacuum to the observed
value.


  We present a novel theory of gravity, namely, an extension of symmetric
teleparallel gravity. This is done by introducing a new class of theories where
the nonmetricity $Q$ is coupled nonminimally to the matter Lagrangian. This
nonminimal coupling entails the nonconservation of the energy-momentum tensor,
and consequently the appearance of an extra force. We also present several
cosmological applications.


  In topological semimetals such as Weyl, Dirac, and nodal-line semimetals, the
band gap closes at points or along lines in k space which are not necessarily
located at high-symmetry positions in the Brillouin zone. Therefore, it is not
straightforward to find these topological semimetals by ab initio calculations
because the band structure is usually calculated only along high-symmetry
lines. In this paper, we review recent studies on topological semimetals by ab
initio calculations. We explain theoretical frameworks which can be used for
the search for topological semimetal materials, and some numerical methods used
in the ab initio calculations.


  It is shown, that a free motion of microparticles (elementary particles) in
the gravitational field is multivariant (stochastic). This multivariance is
conditioned by multivariant physical space-time geometry. The physical geometry
is described completely by a world function. The Riemannian geometries form a
small part of possible physical geometries. The contemporary theory of
gravitation ignores existence of physical geometries. It supposes, that any
space-time geometry is a Riemannian geometry. It is a mistake. As a result the
contemporary theory of gravitation needs a revision. Besides, the Riemannian
geometry is inconsistent, and conclusions of the gravitational theory, based on
inconsistent geometry may be invalid. Free motion of macroparticles (planets),
consisting of many connected microparticles, is deterministic, because
connection of microparticles inside the macroparticle averages stochastic
motion of single macroparticles.


  We put forward a tight-binding model for rhombohedral topological insulators
materials with the space group $D^{5}_{3d}(R\bar{3}m)$. The model describes the
bulk band structure of these materials over the whole Brillouin zone. Within
this framework, we also describe the topological nature of surface states,
characterized by a Dirac cone-like dispersion and the emergence of surface
projected bulk states near to the Dirac-point in energy. We find that the
breaking of the $R_{3}$ symmetry as one moves away from the $\Gamma$ point has
an important role in the hybridization of the $p_x$, $p_y$, and $p_z$ atomic
orbitals. In our tight-binding model, the latter leads to a band mixing matrix
element ruled by a single parameter. We show that our model gives a good
description of the strategies/mechanisms proposed in the literature to
eliminate and/or energy shift the bulk states away from the Dirac point, such
as stacking faults and the introduction of an external applied electric field.


  We analyze a nonlocal diffusion operator having as special cases the
fractional Laplacian and fractional differential operators that arise in
several applications. In our analysis, a nonlocal vector calculus is exploited
to define a weak formulation of the nonlocal problem. We demonstrate that, when
sufficient conditions on certain kernel functions hold, the solution of the
nonlocal equation converges to the solution of the fractional Laplacian
equation on bounded domains as the nonlocal interactions become infinite. We
also introduce a continuous Galerkin finite element discretization of the
nonlocal weak formulation and we derive a priori error estimates. Through
several numerical examples we illustrate the theoretical results and we show
that by solving the nonlocal problem it is possible to obtain accurate
approximations of the solutions of fractional differential equations
circumventing the problem of treating infinite-volume constraints.


  Among Thurston maps (orientation-preserving, postcritically finite branched
coverings of the 2-sphere to itself), those that arise as subdivision maps of a
finite subdivision rule form a special family. For such maps, we investigate
relationships between various notions of expansion---combinatorial, dynamical,
algebraic, and coarse-geometric.


  Conditional generative adversarial networks (cGANs) have demonstrated
remarkable success due to their class-wise controllability and superior quality
for complex generation tasks. Typical cGANs solve the joint distribution
matching problem by decomposing two easier sub-problems: marginal matching and
conditional matching. From our toy experiments, we found that it is the best to
apply only conditional matching to certain samples due to the content-aware
optimization of the discriminator. This paper proposes a simple (a few lines of
code) but effective training methodology, selective focusing learning, which
enforces the discriminator and generator to learn easy samples of each class
rapidly while maintaining diversity. Our key idea is to selectively apply
conditional and joint matching for the data in each mini-batch. We conducted
experiments on recent cGAN variants in ImageNet (64x64 and 128x128), CIFAR-10,
and CIFAR-100 datasets, and improved the performance significantly (up to
35.18% in terms of FID) without sacrificing diversity.


  Dynamic scene understanding is the ability of a computer system to interpret
and make sense of the visual information present in a video of a real-world
scene. In this thesis, we present a series of frameworks for dynamic scene
understanding starting from road event detection from an autonomous driving
perspective to complex video activity detection, followed by continual learning
approaches for the life-long learning of the models. Firstly, we introduce the
ROad event Awareness Dataset (ROAD) for Autonomous Driving, to our knowledge
the first of its kind. Due to the lack of datasets equipped with formally
specified logical requirements, we also introduce the ROad event Awareness
Dataset with logical Requirements (ROAD-R), the first publicly available
dataset for autonomous driving with requirements expressed as logical
constraints, as a tool for driving neurosymbolic research in the area. Next, we
extend event detection to holistic scene understanding by proposing two complex
activity detection methods. In the first method, we present a deformable,
spatiotemporal scene graph approach, consisting of three main building blocks:
action tube detection, a 3D deformable RoI pooling layer designed for learning
the flexible, deformable geometry of the constituent action tubes, and a scene
graph constructed by considering all parts as nodes and connecting them based
on different semantics. In a second approach evolving from the first, we
propose a hybrid graph neural network that combines attention applied to a
graph encoding of the local (short-term) dynamic scene with a temporal graph
modelling the overall long-duration activity. Finally, the last part of the
thesis is about presenting a new continual semi-supervised learning (CSSL)
paradigm.