  In order to characterize very weak adhesive surfaces, we have developed a
quantitative test inspired by the Johnson, Kendall, and Roberts adhesion test
for soft adhesives, which relies on the formation and then the rupture of a
capillary bridge between the surface to be tested and a liquid bath. Both the
shape and the kinetics of breakage of the capillary bridge for various coatings
put into contact with liquids of various viscosities and surface tensions have
been studied. Several pull off regimes can be distinguished. For low pull off
velocities, a quasi-static regime is observed, well described by capillary
equations and sensitive to the hysteresis of the contact angle of the fluid on
the coating. Above a critical pull off velocity that depends on the fluid
viscosity, a dynamic regime is observed, characterized by the formation of a
flat pancake of fluid on the coating that recedes more slowly than the
capillary bridge itself. After the breakage of the capillary bridge, a small
drop can remain attached to the surface. The volume of this drop depends on the
dynamical regime and is strongly affected by very small differences between the
coatings. The aptitude of this test in characterizing very weakly adhesive
surfaces is exemplified by a comparison between three different perfluorinated
coatings.


  Deep Neural Networks (DNNs) typically require massive amount of computation
resource in inference tasks for computer vision applications. Quantization can
significantly reduce DNN computation and storage by decreasing the bitwidth of
network encodings. Recent research affirms that carefully selecting the
quantization levels for each layer can preserve the accuracy while pushing the
bitwidth below eight bits. However, without arduous manual effort, this deep
quantization can lead to significant accuracy loss, leaving it in a position of
questionable utility. As such, deep quantization opens a large hyper-parameter
space (bitwidth of the layers), the exploration of which is a major challenge.
We propose a systematic approach to tackle this problem, by automating the
process of discovering the quantization levels through an end-to-end deep
reinforcement learning framework (ReLeQ). We adapt policy optimization methods
to the problem of quantization, and focus on finding the best design decisions
in choosing the state and action spaces, network architecture and training
framework, as well as the tuning of various hyperparamters. We show how ReLeQ
can balance speed and quality, and provide an asymmetric general solution for
quantization of a large variety of deep networks (AlexNet, CIFAR-10, LeNet,
MobileNet-V1, ResNet-20, SVHN, and VGG-11) that virtually preserves the
accuracy (=< 0.3% loss) while minimizing the computation and storage cost. With
these DNNs, ReLeQ enables conventional hardware to achieve 2.2x speedup over
8-bit execution. Similarly, a custom DNN accelerator achieves 2.0x speedup and
energy reduction compared to 8-bit runs. These encouraging results mark ReLeQ
as the initial step towards automating the deep quantization of neural
networks.


  Let $J_n\subset[1,n]$, $n=1,2,\ldots$ be increasing sets of mutually coprime
numbers. Under reasonable conditions on the coefficient sequence
$\{c^j_n\}_{n,j}$, we show that $$ \lim_{T\to \infty}\frac{1}{T} \int_{0}^T
\Big| \sum_{j\in J_n} c^j_n\,j^{it}\Big| \dd t\sim \big(\frac{\pi}
{2}\sum_{j\in J_n} (c^j_n)^2\big)^{1/2} $$ as $n\to \infty$. We also show by
means of an elementary device that for all $0<\a<2$, \begin{eqnarray*}
  \lim_{T\to \infty} \Big(\frac{1}{T} \int_{0}^T \big| \sum_{n=1}^N
n^{-it}\big|^\a\dd t\Big)^{1/\a} \ge C_\a\, \frac{ N^{\frac{1}{2}}} {\big( \log
N\big)^{{\frac{1}{\a} -\frac{1}{2} }}}. \end{eqnarray*} The proof uses Ayyad,
Cochrane and Zheng estimate on the number of solutions of the equation
$x_1x_2=x_3x_4$. In the case $\a=1$, this approaches Helson's bound up to a
factor $(\log N)^{1/4}$.


  Because Full Waveform Inversion (FWI) works with a massive amount of data,
its execution requires much time and computational resources, being restricted
to large-scale computer systems such as supercomputers. Techniques such as FWI
adapt well to parallel computing and can be parallelized in shared memory
systems using the application programming interface (API) OpenMP. The
management of parallel tasks can be performed through loop schedulers contained
in OpenMP. The dynamic scheduler stands out for distributing predefined
fixed-size chunk sizes to idle processing cores at runtime. It can better adapt
to FWI, where data processing can be irregular. However, the relationship
between the size of the chunk size and the runtime is unknown. Optimization
techniques can employ meta-heuristics to explore the parameter search space,
avoiding testing all possible solutions. Here, we propose a strategy to use the
Parameter Auto Tuning for Shared Memory Algorithms (PATSMA), with Coupled
Simulated Annealing (CSA) as its optimization method, to automatically adjust
the chunk size for the dynamic scheduling of wave propagation, one of the most
expensive steps in FWI. Since testing each candidate chunk size in the complete
FWI is unpractical, our approach consists of running a PATSMA where the
objective function is the runtime of the first time iteration of the first
seismic shot of the first FWI iteration. The resulting chunk size is then
employed in all wave propagations involved in an FWI. We conducted tests to
measure the runtime of an FWI using the proposed autotuning, varying the
problem size and running on different computational environments, such as
supercomputers and cloud computing instances. The results show that applying
the proposed autotuning in an FWI reduces its runtime by up to 70.46% compared
to standard OpenMP schedulers.


  We introduce a new topological sigma model, whose fields are bundle maps from
the tangent bundle of a 2-dimensional world-sheet to a Dirac subbundle of an
exact Courant algebroid over a target manifold. It generalizes simultaneously
the (twisted) Poisson sigma model as well as the G/G-WZW model. The equations
of motion are satisfied, iff the corresponding classical field is a Lie
algebroid morphism. The Dirac Sigma Model has an inherently topological part as
well as a kinetic term which uses a metric on worldsheet and target. The latter
contribution serves as a kind of regulator for the theory, while at least
classically the gauge invariant content turns out to be independent of any
additional structure. In the (twisted) Poisson case one may drop the kinetic
term altogether, obtaining the WZ-Poisson sigma model; in general, however, it
is compulsory for establishing the morphism property.


  We consider B-type D-branes in the Gepner model consisting of two minimal
models at k=2. This Gepner model is mirror to a torus theory. We establish the
dictionary identifying the B-type D-branes of the Gepner model with A-type
Neumann and Dirichlet branes on the torus.


  This paper is dedicated to investigating the kinematic properties of the
Lupus moving group of young stars with the primary objective of deriving
individual parallaxes for each group member. We identify those stars in the
Lupus star-forming region that define the comoving association of young stars
by utilizing our new and improved convergent point search method that allows us
to derive the precise position of the convergent point of the comoving
association from the stars' proper motions. We used published proper motion
catalogs and searched the literature for radial velocities, which are needed to
compute individual parallaxes. We supplemented the radial velocity data with
new measurements from spectroscopic observations performed with the FEROS
spectrograph mounted on the MPG/ESO 2.2m telescope at La Silla. We identify a
comoving group with 109 pre-main sequence stars and candidates that define the
kinematic properties of the Lupus low-mass star-forming region. We derive
individual parallaxes for stars with known radial velocity and tentative
parallaxes for the remaining group members by assuming that all stars share the
same space motion. The convergent point method, combined with the k-NN
algorithm, makes it possible to distinguish the Lupus and Upper Centaurus Lupus
stars from the adjacent Scorpius-Centaurus association. We find significant
depth effects in this region and show that the classical T Tauri stars, located
in the close vicinity of the Lupus molecular clouds, form a background
population, while the weak-emission line T Tauri stars are dispersed not only
in angular extent but also in depth. The newly derived individual parallaxes
will be used in a forthcoming paper to refine the masses and ages of Lupus T
Tauri stars, with the aim of better constraining the lifetimes of their
circumstellar, protoplanetary disks.


  The role of neuro-inflammation in diverse, acute and chronic brain
pathologies is being increasingly recognized. Neuro-inflammation is accompanied
by increased levels of both pro- and anti-inflammatory cytokines; these have
deleterious as well as protective/reparative effects. Inflammation has varying
effects on neurogenesis and is a subject of intense contemporary interest. We
show that TNF-alpha and IFN-gamma, used concomitantly, cause apoptosis of adult
rat hippocampal progenitor/stem cells in vitro as detected by the TUNEL and MTT
assays on time scales of several hours. We have coupled Raman spectroscopy to
an optical trap to probe early changes of apoptosis in single, live neural stem
cells that have been treated with pro-inflammatory cytokines, TNF-alpha and
IFN-gamma. Changes caused by inflammation-induced denaturation of DNA are
observed in the Raman spectra that correspond to very early stages of
apoptosis, occurring on very fast time scales: as short as 10 minutes. Addition
of the anti-inflammatory cytokine IL-10 either 10-30 min before or 10-30 min
after treatment with TNF-alpha and IFN-gamma reverses the changes
substantially. Our findings imply that inflammation can induce very rapid
changes leading to cell death but that these are reversible, in the early
stages at least.


  Facing an environment increasingly complex, uncertain and changing, even in
crisis, organizations are driven to be agile in order to survive. Agility, at
the core heart of business strategy, represents the ability to grow in a
competitive environment of continuous and unpredictable changes with
information systems perceived as one of its main enablers. In other words, to
be agile, organizations must be able to rely on agile enterprise information
systems/information technology (IT/IS). Since, the agility needs are not the
same among stakeholders, the objective of this research is to develop a
conceptual model for the achievement and assessment of IT/IS agility from
balanced perspectives to support agile organizations. Several researches have
indicated that the IT balanced scorecard (BSC) approach is an appropriate
technique for evaluating IT performance. This paper provides a
balanced-scorecard based framework to evaluate the IS agility through four
perspectives: business contribution, user orientation, operation excellence and
innovation and competitiveness. The proposed framework, called IS Agility BSC,
propose a three layer structure for each of the four perspectives: mission, key
success factors, and agility evaluation criteria. According to this conceptual
model, enterprise information systems agility is measured according to 14
agility key success factors, over the four BSC Perspectives, using 42 agility
evaluation criteria that are identified based on literature survey methodology.
This paper explores agility in the broader context of the enterprise
information systems. The findings will provide, for both researchers and
practitioners, a practical approach for achieving and measuring IS agility
performance to support organizations in attempt to become agile as a new
condition of surviving in the new business world.


  Dark matter that was once in thermal equilibrium with the Standard Model is
generally prohibited from obtaining all of its mass from the electroweak phase
transition. This implies a new scale of physics and mediator particles to
facilitate dark matter annihilation. In this work, we focus on dark matter that
annihilates through a generic Higgs portal. We show how partial wave unitarity
places an upper bound on the mass of the mediator (or dark) Higgs when its mass
is increased to be the largest scale in the effective theory. For models where
the dark matter annihilates via fermion exchange, an upper bound is generated
when unitarity breaks down around 8.5 TeV. Models where the dark matter
annihilates via fermion and higgs boson exchange push the bound to 45.5 TeV. We
also show that if dark matter obtains all of its mass from a new symmetry
breaking scale that scale is also constrained. We improve these constraints by
requiring perturbativity in the Higgs sector up to each unitarity bound. In
this limit, the bounds on the dark symmetry breaking vev and the dark Higgs
mass are now 2.4 and 3 TeV, respectively, when the dark matter annihilates via
fermion exchange. When dark matter annihilates via fermion and higgs boson
exchange, the bounds are now 12 and 14.2 TeV, respectively. The available
parameter space for Higgs portal dark matter annihilation is outlined. We also
show how the bounds are improved if Higgs portal dark matter is only a fraction
of the observed relic abundance. Finally, we discuss how to apply these
arguments to other dark matter scenarios and discuss prospects for direct
detection and future collider searches. If the Higgs portal is responsible for
dark matter annihilation, planned direct detection experiments will cover
almost all the parameter space. The ILC and/or VLHC, however, is needed to
establish the Higgs portal mechanism.


  This paper introduces a nonlinear acceleration technique that accelerates the
convergence of solution of transport problems with highly forward-peaked
scattering. The technique is similar to a conventional high-order/low-order
(HOLO) acceleration scheme. The Fokker-Planck equation, which is an asymptotic
limit of the transport equation in highly forward-peaked settings, is modified
and used for acceleration; this modified equation preserves the angular flux
and moments of the (high order) transport equation. We present numerical
results using the Screened Rutherford, Exponential, and Henyey-Greenstein
scattering kernels and compare them to established acceleration methods such as
diffusion synthetic acceleration (DSA). We observe three to four orders of
magnitude speed-up in wall-clock time compared to DSA.


  We present an easily defined countable family of permutations of the natural
numbers for which explicit rearrangements (i.e., the sums induced by the
permutations) can be computed. The digamma function proves to be the key tool
for the computations found here for the alternating harmonic series. The
permutations $\phi$ under consideration are simple in a sense: they are
involutions ($\phi\circ\phi$ is the identity function). We show that the
countable set of rearrangements obtained from the simple involutions considered
below are dense in the reals.


  Real-time control of pumps can be an infeasible task in water distribution
systems (WDSs) because the calculation to find the optimal pump speeds is
resource-intensive. The computational need cannot be lowered even with the
capabilities of smart water networks when conventional optimization techniques
are used. Deep reinforcement learning (DRL) is presented here as a controller
of pumps in two WDSs. An agent based on a dueling deep q-network is trained to
maintain the pump speeds based on instantaneous nodal pressure data. General
optimization techniques (e.g., Nelder-Mead method, differential evolution)
serve as baselines. The total efficiency achieved by the DRL agent compared to
the best performing baseline is above 0.98, whereas the speedup is around 2x
compared to that. The main contribution of the presented approach is that the
agent can run the pumps in real-time because it depends only on measurement
data. If the WDS is replaced with a hydraulic simulation, the agent still
outperforms conventional techniques in search speed.


  We analyze the problem of eliminating finite-size errors from quantum Monte
Carlo (QMC) energy data. We demonstrate that both (i) adding a recently
proposed [S. Chiesa et al., Phys. Rev. Lett. 97, 076404 (2006)] finite-size
correction to the Ewald energy and (ii) using the model periodic Coulomb (MPC)
interaction [L. M. Fraser et al., Phys. Rev. B 53, 1814 (1996); P. R. C. Kent
et al., Phys. Rev. B 59, 1917 (1999); A. J. Williamson et al., Phys. Rev. B 55,
4851 (1997)] are good solutions to the problem of removing finite-size effects
from the interaction energy in cubic systems, provided the exchange-correlation
(XC) hole has converged with respect to system size. However, we find that the
MPC interaction distorts the XC hole in finite systems, implying that the Ewald
interaction should be used to generate the configuration distribution. The
finite-size correction of Chiesa et al. is shown to be incomplete in systems of
low symmetry. Beyond-leading-order corrections to the kinetic energy are found
to be necessary at intermediate and high densities, and we investigate the
effect of adding such corrections to QMC data for the homogeneous electron gas.
We analyze finite-size errors in two-dimensional systems and show that the
leading-order behavior differs from that which has hitherto been supposed. We
compare the efficiency of different twist-averaging methods for reducing
single-particle finite-size errors and we examine the performance of various
finite-size extrapolation formulas. Finally, we investigate the system-size
scaling of biases in diffusion QMC.


  The manuscript presents the $LU$ approach to matrix biorthogonal polynomials
when all the even ordered entries in the Gram matrix are zero. This arises in
case of a quadratic transformation which is briefly discussed. Further, the
main diagonal of the Gram matrix is a zero diagonal and we present the theory
that follows from this fact. Precisely, we discuss the Christoffel
transformation and matrix representations of the kernel polynomials, usually
called the ABC Theorem. Finally, we provide an illustration of our results
assuming the Gram matrix has Hankel symmetry.


  We give a characterization of nearly free plane curves in terms of their
global Tjurina numbers, similar to the characterization of free curves as
curves with a maximal Tjurina number, due to A. A. du Plessis and C.T.C. Wall.
It is also shown that an irreducible plane curve having a 1-dimensional
symmetry is nearly free. A new numerical characterization of free curves and a
simple characterization of nearly free curves in terms of their syzygies
conclude this note.


  This paper tackles the data-driven approximation of unknown dynamical systems
using Koopman-operator methods. Given a dictionary of functions, these methods
approximate the projection of the action of the operator on the
finite-dimensional subspace spanned by the dictionary. We propose the Tunable
Symmetric Subspace Decomposition algorithm to refine the dictionary, balancing
its expressiveness and accuracy. Expressiveness corresponds to the ability of
the dictionary to describe the evolution of as many observables as possible and
accuracy corresponds to the ability to correctly predict their evolution. Based
on the observation that Koopman-invariant subspaces give rise to exact
predictions, we reason that prediction accuracy is a function of the degree of
invariance of the subspace generated by the dictionary and provide a
data-driven measure to measure invariance proximity. The proposed algorithm
iteratively prunes the initial functional space to identify a refined
dictionary of functions that satisfies the desired level of accuracy while
retaining as much of the original expressiveness as possible. We provide a full
characterization of the algorithm properties and show that it generalizes both
Extended Dynamic Mode Decomposition and Symmetric Subspace Decomposition.
Simulations on planar systems show the effectiveness of the proposed methods in
producing Koopman approximations of tunable accuracy that capture relevant
information about the dynamical system.


  Formation of clusters of interstitial carbon atoms between the surface and
second atomic layers of graphite is demonstrated by means of molecular dynamics
simulations. It is shown that interstitial clusters result in the dome-like
surface features that may be associated with some of the hillocks observed by
STM on the irradiated graphite surface.


  Generating a stereophonic presentation from a monophonic audio signal is a
challenging open task, especially if the goal is to obtain a realistic spatial
imaging with a specific panning of sound elements. In this work, we propose to
convert mono to stereo by means of predicting parametric stereo (PS) parameters
using both nearest neighbor and deep network approaches. In combination with
PS, we also propose to model the task with generative approaches, allowing to
synthesize multiple and equally-plausible stereo renditions from the same mono
signal. To achieve this, we consider both autoregressive and masked token
modelling approaches. We provide evidence that the proposed PS-based models
outperform a competitive classical decorrelation baseline and that, within a PS
prediction framework, modern generative models outshine equivalent
non-generative counterparts. Overall, our work positions both PS and generative
modelling as strong and appealing methodologies for mono-to-stereo upmixing. A
discussion of the limitations of these approaches is also provided.


  We report on the development and demonstration of a MHz frequency domain
multiplexing (FDM) technology to read out arrays of cryogenic transition edge
sensor (TES) X-ray microcalorimeters. In our FDM scheme, TESs are AC-biased at
different resonant frequencies in the low MHz range through an array of
high-$Q$ LC resonators. The current signals of all TESs are summed at
superconducting quantum interference devices (SQUIDs). We have demonstrated
multiplexing for a readout of 31 pixels using room temperature electronics,
high-$Q$ LC filters and TES arrays developed at SRON, and SQUID arrays from
VTT. We repeated this on a second setup with 37 pixels. The summed X-ray
spectral resolutions $@$ 5.9 keV are $\Delta E_{\rm 31 pix ~MUX}=2.14\pm0.03$
eV and $\Delta E_{\rm 37 pix ~MUX}=2.23\pm0.03$ eV. The demonstrated results
are comparable with other multiplexing approaches. There is potential to
further improve the spectral resolution and to increase the number of
multiplexed TESs, and to open up applications for TES X-ray microcalorimeters.


  This paper presents a sandbox example of how the integration of models
borrowed from Behavioral Economic (specifically Protection-Motivation Theory)
into ML algorithms (specifically Bayesian Networks) can improve the performance
and interpretability of ML algorithms when applied to Behavioral Data. The
integration of Behavioral Economics knowledge to define the architecture of the
Bayesian Network increases the accuracy of the predictions in 11 percentage
points. Moreover, it simplifies the training process, making unnecessary
training computational efforts to identify the optimal structure of the
Bayesian Network. Finally, it improves the explicability of the algorithm,
avoiding illogical relations among variables that are not supported by previous
behavioral cybersecurity literature. Although preliminary and limited to 0ne
simple model trained with a small dataset, our results suggest that the
integration of behavioral economics and complex ML models may open a promising
strategy to improve the predictive power, training costs and explicability of
complex ML models. This integration will contribute to solve the scientific
issue of ML exhaustion problem and to create a new ML technology with relevant
scientific, technological and market implications.


  This paper is motivated by Grothendieck's splitting theorem. In the 1960s,
Gohberg generalized this to a class of Banach bundles. We consider a compact
complex manifold $X$ and a holomorphic Banach bundle $E \to X$ that is a
compact perturbation of a trivial bundle in a sense recently introduced by
Lempert. We prove that $E$ splits into the sum of a finite rank bundle and a
trivial bundle, provided $H^{1}(X, \O)=0$.


  Large scale shell model calculations, with dimensions reaching 10**9, are
carried out to describe the recently observed deformed (ND) and superdeformed
(SD) bands based on the first and second excited 0+ states of 40-Ca at 3.35-MeV
and 5.21-MeV respectively. A valence space comprising two major oscillator
shells, sd and pf, can accommodate most of the relevant degrees of freedom of
this problem. The ND band is dominated by configurations with four particles
promoted to the pf-shell (4p-4h in short). The SD band by 8p-8h configurations.
The ground state of 40-Ca is strongly correlated, but the closed shell still
amounts to 65%. The energies of the bands are very well reproduced by the
calculations. The out-band transitions connecting the SD band with other states
are very small and depend on the details of the mixing among the different
np-nh configurations, in spite of that, the calculation describes them
reasonably. For the in-band transition probabilities along the SD band, we
predict a fairly constant transition quadrupole moment Q_0(t)~170 e fm**2 up to
J=10, that decreases toward the higher spins. We submit also that the J=8
states of the deformed and superdeformed band are maximally mixed.


  Side information of items, e.g., images and text description, has shown to be
effective in contributing to accurate recommendations. Inspired by the recent
success of pre-training models on natural language and images, we propose a
pre-training strategy to learn item representations by considering both item
side information and their relationships. We relate items by common user
activities, e.g., co-purchase, and construct a homogeneous item graph. This
graph provides a unified view of item relations and their associated side
information in multimodality. We develop a novel sampling algorithm named
MCNSampling to select contextual neighbors for each item. The proposed
Pre-trained Multimodal Graph Transformer (PMGT) learns item representations
with two objectives: 1) graph structure reconstruction, and 2) masked node
feature reconstruction. Experimental results on real datasets demonstrate that
the proposed PMGT model effectively exploits the multimodality side information
to achieve better accuracies in downstream tasks including item recommendation,
item classification, and click-through ratio prediction. We also report a case
study of testing the proposed PMGT model in an online setting with 600 thousand
users.


  Over the last few years, the number of known eclipsing radio millisecond
pulsar systems in the Galactic field has dramatically increased, with many
being associated with Fermi gamma-ray sources. All are in tight binaries
(orbital period < 24 hr) with many being classical "black widows" which have
very low mass companions (companion mass Mc << 0.1 Msol) but some are
"redbacks" with low mass (Mc ~ 0.2 - 0.4Msol) companions which are probably
non-degenerate. These latter are systems where the mass transfer process may
have only temporarily halted, and so are transitional systems between low mass
X-ray binaries and ordinary binary millisecond pulsars. Here we review the new
discoveries and their multi-wavelength properties, and briefly discuss models
of shock emission, mass determinations, and evolutionary scenarios.


  Correlating neural communication in brain networks with behavior and
cognition can provide fundamental insights into the functionality of both
healthy and diseased brains. We demonstrate how communication in the brain can
be estimated from recorded neural activity using concepts from graph signal
processing. The communication is modeled as a flow signals on the edges of a
graph and naturally arises from a graph diffusion process. We apply the
diffusion model to micro-electrocorticography (ECoG) recordings from
sensorimotor cortex of two non-human primates to estimate the neural
communication flow during excitatory optogenetics. Comparisons with a baseline
model demonstrate that adding the neural flow can improve ECoG predictions.
Finally, we demonstrate how the neural flow can be decomposed into a gradient
and rotational component and show that the gradient component depends on the
location of stimulation. This technique, for the first time, offers the
opportunity to study neural communication on an unprecedented spatiotemporal
scale.


  The Maslov index in the semiclassical Bohr-Sommerfeld quantization rule is
calculated for one-dimensional power-law potentials. The result for the inverse
square root potential is compared with the recently reported exact solution.
The case of a spherically symmetric power-law potential is also considered.


  Interference of electromagnetic modes supported by subwavelength photonic
structures is one of the key concepts that underpins the subwavelength control
of light in meta-optics. It drives the whole realm of all-dielectric
Mie-resonant nanophotonics with many applications for low-loss nanoscale
optical antennas, metasurfaces, and metadevices. Specifically, interference of
the electric and toroidal dipole moments results in a very peculiar,
low-radiating optical state associated with the concept of optical anapole.
Here, we uncover the physics of multimode interferences and multipolar
interplay in nanostructures with an intriguing example of the optical anapole.
We review the recently emerged field of anapole electrodynamics explicating its
relevance to multipolar nanophotonics, including direct experimental
observations, manifestations in nonlinear optics, and rapidly expanding
applications in nanoantennas, active photonics, and metamaterials.


  More and more parts of the internet are hidden behind a login field. This
poses a barrier to any study predicated on scanning the internet. Moreover, the
authentication process itself may be a weak point. To study authentication
weaknesses at scale, automated login capabilities are needed. In this work we
introduce Shepherd, a scanning framework to automatically log in on websites.
The Shepherd framework enables us to perform large-scale scans of post-login
aspects of websites. Shepherd scans a website for login fields, attempts to
submit credentials and evaluates whether login was successful. We illustrate
Shepherd's capabilities by means of a scan for session hijacking
susceptibility. In this study, we use a set of unverified website credentials,
some of which will be invalid. Using this set, Shepherd is able to fully
automatically log in and verify that it is indeed logged in on 6,273 unknown
sites, or 12.4% of the test set. We found that from our (biased) test set,
2,579 sites, i.e., 41.4%, are vulnerable to simple session hijacking attacks.


  The running infimum of a Levy process relative to its point of issue is know
to have the same range that of the negative of a certain subordinator.
Conditioning a Levy process issued from a strictly positive value to stay
positive may therefore be seen as implicitly conditioning its descending ladder
heigh subordinator to remain in a strip. Motivated by this observation, we
consider the general problem of conditioning a subordinator to remain in a
strip. Thereafter we consider more general contexts in which subordinators
embedded in the path decompositions of Markov processes are conditioned to
remain in a strip.


  Using the Dirac constraint formalism, we examine the canonical structure of
the Einstein-Hilbert action $S_d = \frac{1}{16\pi G} \int d^dx \sqrt{-g} R$,
treating the metric $g_{\alpha\beta}$ and the symmetric affine connection
$\Gamma_{\mu\nu}^\lambda$ as independent variables. For $d > 2$ tertiary
constraints naturally arise; if these are all first class, there are $d(d-3)$
independent variables in phase space, the same number that a symmetric tensor
gauge field $\phi_{\mu\nu}$ possesses. If $d = 2$, the Hamiltonian becomes a
linear combination of first class constraints obeying an SO(2,1) algebra. These
constraints ensure that there are no independent degrees of freedom. The
transformation associated with the first class constraints is not a
diffeomorphism when $d = 2$; it is characterized by a symmetric matrix
$\xi_{\mu\nu}$. We also show that the canonical analysis is different if
$h^{\alpha\beta} = \sqrt{-g} g^{\alpha\beta}$ is used in place of
$g^{\alpha\beta}$ as a dynamical variable when $d = 2$, as in $d$ dimensions,
$\det h^{\alpha\beta} = - (\sqrt{-g})^{d-2}$. A comparison with the formalism
used in the ADM analysis of the Einstein-Hilbert action in first order form is
made by applying this approach in the two dimensional case with
$h^{\alpha\beta}$ and $\Gamma_{\mu\nu}^\lambda$ taken to be independent
variables.


  We report the discovery of two transiting Neptunes by the HATSouth survey.
The planet HATS-37Ab has a mass of 0.099 +- 0.042 M_J (31.5 +- 13.4 M_earth)
and a radius of 0.606 +- 0.016 R_J, and is on a P = 4.3315 days orbit around a
V = 12.266 mag, 0.843 M_sun star with a radius of 0.877 R_sun. We also present
evidence that the star HATS-37A has an unresolved stellar companion HATS-37B,
with a photometrically estimated mass of 0.654 M_sun.The planet HATS-38b has a
mass of 0.074 +- 0.011 M_J (23.5 +- 3.5 M_earth) and a radius of 0.614 +- 0.017
R_J, and is on a P = 4.3750 days orbit around a V = 12.411 mag, 0.890 M_sun
star with a radius of 1.105 R_sun. Both systems appear to be old, with
isochrone-based ages of 11.46 +0.79-1.45 Gyr, and 11.89 +- 0.60 Gyr,
respectively. Both HATS-37Ab and HATS-38b lie in the Neptune desert and are
thus examples of a population with a low occurrence rate. They are also among
the lowest mass planets found from ground-based wide-field surveys to date.


  Like elsewhere in the deep-sea, life in the deep Mediterranean depends on
turbulent exchange across the stable vertical density stratification for supply
of nutrients and oxygen. Commonly modelled, turbulent exchange is inversely
proportional to the stratification rate. However, this proportionality depends
on the particular turbulence type, whether it is driven by vertical current
differences (shear) or by buoyancy (convection). While shear-turbulence is well
observed in stratified seas, direct observations of convection-turbulence are
limited. In this paper, high-resolution moored temperature observations show
that Mediterranean Sea waters are not stagnant in the lower 109 m above the
seafloor at 2480 m, although variations are in the range of only 0.0001-0.001
degrC. In winter, convection-turbulence is regularly observed. Fortnightly
averaged spectra show a collapse to the inertial-subrange scaling of dominant
shear-turbulence for data from about 100 m above the seafloor, and to the
buoyancy-subrange scaling of dominant convection-turbulence at about 10 m above
the seafloor. Time-depth images reveal details of convection-turbulence driven
from below, which is considered primarily due to general geothermal heating
through the Earth crust not related to volcanic vents. When its observation is
not masked by (sub-)mesoscale eddies that advect warmer waters from above, the
geothermal heat flux matches the deep-sea turbulence dissipation rate, if in
the calculations a mixing efficiency of 0.5 is taken typical for natural
convection, integration is over 250 m above the seafloor as confirmed from
shipborne CTD, and if maximum 2-m-scale buoyancy frequency replaces its
100-m-scale mean equivalent.


  An action minimizing path between two given configurations, spatial or
planar, of the $n$-body problem is always a true -- collision-free -- solution.
Based on a remarkable idea of Christian Marchal, this theorem implies the
existence of new "simple" symmetric periodic solutions, among which the Eight
for 3 bodies, the Hip-Hop for 4 bodies and their generalizations.


  Site-dependent local spin susceptibility is investigated with $^{125}$Te
nuclear magnetic resonance in a Weyl semimetal WTe$_2$. The nuclear
spin-lattice relaxation rate $1/T_1T$ shows a dependence of the square of
temperature $T$ at high temperatures, followed by a constant behavior below 50
K. The temperature dependence features Weyl fermions appearing around the
linearly crossing bands. The Knight shift $K$ scales to the square root of
$1/T_1T$, corroborating a predominant spin contribution in low-lying
excitation. The observed dependence of $K$ and $1/T_1T$ on the four Te sites
shows the site-dependent electron correlation and density of states. The
angular profile of the NMR spectrum gives the anisotropic hyperfine coupling
tensor, consistent with $5p$ hole occupations on Te sites.


  Freestanding tubular crystals offer a general description of crystalline
order on deformable surfaces with cylindrical topology, such as single-walled
carbon nanotubes, microtubules, and recently reported colloidal assemblies.
These systems exhibit a rich interplay between the crystal's helicity on its
periodic surface, the deformable geometry of that surface, and the motions of
topological defects within the crystal. Previously, in simulations of tubular
crystals as elastic networks, we found that dislocations in nontrivial patterns
can co-stabilize with kinks in the tube shape, producing mechanical
multistability. Here, we extend that work with detailed Langevin dynamics
simulations, in order to explore defect dynamics efficiently and without the
constraints imposed by elastic network models. Along with the predicted
multistability of dislocation glide, we find a variety of irreversible defect
transformations, including vacancy formation, particle extrusions, and
"reactions" that reorient dislocation pairs. Moreover, we report spontaneous
sequences of several such defect transformations, which are unique to tubular
crystals. We demonstrate a simple method for controlling these sequences
through a time-varying external force.


  Stochastic gradient descent without replacement sampling is widely used in
practice for model training. However, the vast majority of SGD analyses assumes
data is sampled with replacement, and when the function minimized is strongly
convex, an $\mathcal{O}\left(\frac{1}{T}\right)$ rate can be established when
SGD is run for $T$ iterations. A recent line of breakthrough works on SGD
without replacement (SGDo) established an
$\mathcal{O}\left(\frac{n}{T^2}\right)$ convergence rate when the function
minimized is strongly convex and is a sum of $n$ smooth functions, and an
$\mathcal{O}\left(\frac{1}{T^2}+\frac{n^3}{T^3}\right)$ rate for sums of
quadratics. On the other hand, the tightest known lower bound postulates an
$\Omega\left(\frac{1}{T^2}+\frac{n^2}{T^3}\right)$ rate, leaving open the
possibility of better SGDo convergence rates in the general case. In this
paper, we close this gap and show that SGD without replacement achieves a rate
of $\mathcal{O}\left(\frac{1}{T^2}+\frac{n^2}{T^3}\right)$ when the sum of the
functions is a quadratic, and offer a new lower bound of
$\Omega\left(\frac{n}{T^2}\right)$ for strongly convex functions that are sums
of smooth functions.


  We discuss the Schwinger pair creation process for the system of massless
Dirac fermions in the presence of constant external magnetic and electric
fields. The pair production rate remains finite unlike the vacuum decay rate.
In the recently discovered Dirac semimetals, where the massless Dirac fermions
emerge, this pair production may be observed experimentally through the
transport properties. We estimate its contribution to the ordinary conductivity
of the semimetals.


  The Large Binocular Telescope Interferometer uses a near-infrared camera to
measure the optical path length variations between the two AO-corrected
apertures and provide high-angular resolution observations for all its science
channels (1.5-13 $\mu$m). There is however a wavelength dependent component to
the atmospheric turbulence, which can introduce optical path length errors when
observing at a wavelength different from that of the fringe sensing camera.
Water vapor in particular is highly dispersive and its effect must be taken
into account for high-precision infrared interferometric observations as
described previously for VLTI/MIDI or the Keck Interferometer Nuller. In this
paper, we describe the new sensing approach that has been developed at the LBT
to measure and monitor the optical path length fluctuations due to dry air and
water vapor separately. After reviewing the current performance of the system
for dry air seeing compensation, we present simultaneous H-, K-, and N-band
observations that illustrate the feasibility of our feedforward approach to
stabilize the path length fluctuations seen by the LBTI nuller.


  We present robust and composable device-independent quantum protocols for
oblivious transfer (OT) and bit commitment (BC) using Magic Square devices. We
assume there is no long-term quantum memory, that is, after a finite time
interval, referred to as \textbf{DELAY}, the states stored in the devices
decohere. By robustness, which is a highlight of our protocols, we mean that
the protocols are correct and secure even when devices are slightly off from
their ideal specifications (the \emph{faulty but non-malicious} regime). This
is an important property, since in the real world, devices would certainly have
small manufacturing errors and cannot be expected to be ideal. To the best of
our understanding and knowledge, none of the known DI protocols for OT and BC
in the literature are robust; they can not guarantee correctness in the faulty
but non-malicious regime. Our protocols are sequentially composable and hence,
can be used as building blocks to construct larger protocols, while still
preserving security guarantees.


  Lifelong learning without catastrophic forgetting (i.e., resiliency) remains
an open problem for deep neural networks. The prior art mostly focuses on
convolutional neural networks. With the increasing dominance of Transformers in
deep learning, it is a pressing need to study lifelong learning with
Transformers. Due to the complexity of training Transformers in practice, for
lifelong learning, a question naturally arises: Can Transformers be learned to
grow in a task aware way, that is to be dynamically transformed by introducing
lightweight learnable plastic components to the architecture, while retaining
the parameter-heavy, but stable components at streaming tasks? To that end,
motivated by the lifelong learning capability maintained by the functionality
of Hippocampi in human brain, we explore what would be, and how to implement,
Artificial Hippocampi (ArtiHippo) in Transformers. We present a method to
identify, and learn to grow, ArtiHippo in Vision Transformers (ViTs) for
resilient lifelong learning in four aspects: (i) Where to place ArtiHippo to
enable plasticity while preserving the core function of ViTs at streaming
tasks? (ii) How to represent and realize ArtiHippo to ensure expressivity and
adaptivity for tackling tasks of different nature in lifelong learning? (iii)
How to learn to grow ArtiHippo to exploit task synergies (i.e., the learned
knowledge) and overcome catastrophic forgetting? (iv) How to harness the best
of our proposed ArtiHippo and prompting-based approaches? In experiments, we
test the proposed method on the challenging Visual Domain Decathlon (VDD)
benchmark and the 5-Dataset benchmark under the task-incremental lifelong
learning setting. It obtains consistently better performance than the prior art
with sensible ArtiHippo learned continually. To our knowledge, it is the first
attempt of lifelong learning with ViTs on the challenging VDD benchmark.


  Two-level systems (TLS) are an important, if not dominant, source of loss and
noise for superconducting resonators such as those used in kinetic inductance
detectors and some quantum information science platforms. They are similarly
important for loss in photolithographically fabricated superconducting
mm-wave/THz transmission lines. For both lumped-element and transmission-line
structures, native amorphous surface oxide films are typically the sites of
such TLS in non-microstripline geometries, while loss in the (usually
amorphous) dielectric film itself usually dominates in microstriplines. We
report here on the demonstration of low TLS loss at GHz frequencies in
hydrogenated amorphous silicon (a-Si:H) films deposited by plasma-enhanced
chemical vapor deposition in superconducting lumped-element resonators using
parallel-plate capacitors (PPCs). The values we obtain from two recipes in
different deposition machines, 7$\,\times\,10^{-6}$ and 12$\,\times\,10^{-6}$,
improve on the best achieved in the literature by a factor of 2--4 for a-Si:H
and are comparable to recent measurements of amorphous germanium. Moreover, we
have taken care to extract the true zero-temperature, low-field loss tangent of
these films, accounting for temperature and field saturation effects that can
yield misleading results. Such robustly fabricated and characterized films
render the use of PPCs with deposited amorphous films a viable architecture for
superconducting resonators, and they also promise extremely low loss and high
quality factor for photolithographically fabricated superconducting mm-wave/THz
transmission lines used in planar antennas and resonant filters.


  Consider an incoming sequence of vectors, all belonging to an unknown
subspace $\operatorname{S}$, and each with many missing entries. In order to
estimate $\operatorname{S}$, it is common to partition the data into blocks and
iteratively update the estimate of $\operatorname{S}$ with each new incoming
measurement block.
  In this paper, we investigate a rather basic question: Is it possible to
identify $\operatorname{S}$ by averaging the column span of the partially
observed incoming measurement blocks on the Grassmannian?
  We show that in general the span of the incoming blocks is in fact a biased
estimator of $\operatorname{S}$ when data suffers from erasures, and we find an
upper bound for this bias. We reach this conclusion by examining the defining
optimization program for the Fr\'{e}chet expectation on the Grassmannian, and
with the aid of a sharp perturbation bound and standard large deviation
results.


  The theoretical work of V.B. Braginsky predicted that radiation pressure can
couple the mechanical, mirror-eigenmodes of a Fabry-Perot resonator to it's
optical modes, leading to a parametric oscillation instability. This regime is
characterized by regenerative mechanical oscillation of the mechanical mirror
eigenmodes. We have recently observed the excitation of mechanical modes in an
ultra-high-Q optical microcavity. Here, we present a detailed experimental
analysis of this effect and demonstrate that radiation pressure is the
excitation mechanism of the observed mechanical oscillations.


  In this work, we investigate an instance of the Heegard-Berger problem with
two sources and arbitrarily correlated side information sequences at two
decoders, in which the reconstruction sets at the decoders are degraded.
Specifically, two sources are to be encoded in a manner that one of the two is
reproduced losslessly by both decoders, and the other is reproduced to within
some prescribed distortion level at one of the two decoders. We establish a
single-letter characterization of the rate-distortion function for this model.
The investigation of this result in some special cases also sheds light on the
utility of joint compression of the two sources. Furthermore, we also
generalize our result to the setting in which the source component that is to
be recovered by both users is reconstructed in a lossy fashion, under the
requirement that all terminals (i.e., the encoder and both decoders) can share
an exact copy of the compressed version of this source component, i.e., a
common encoder-decoders reconstruction constraint. For this model as well, we
establish a single-letter characterization of the associated rate-distortion
function.


  We calculate the probability density function for the order parameter
fluctuations in the low temperature phase of the 2D-XY model of magnetism near
the line of critical points. A finite correlation length, \xi, is introduced
with a small magnetic field, h, and an accurate expression for \xi(h) is
developed by treating non-linear contributions to the field energy using a
Hartree approximation. We find analytically a series of universal non-Gaussian
distributions with a finite size scaling form and present a Gumbel-like
function that gives the PDF to an excellent approximation. We propose the
Gumbel exponent, a(h), as an indirect measure of the length scale of
correlations in a wide range of complex systems.


  A collision of orbifold planes in eleven dimensions has been proposed as an
explanation of the hot big bang. When the two planes are close to each other,
the winding membranes become the lightest modes of the theory, and can be
effectively described in terms of fundamental strings in a ten dimensional
background. Near the brane collision, the eleven-dimensional metric is an
Euclidean space times a 1+1-dimensional Milne universe. However, one may expect
small perturbations to lead into a more general Kasner background. In this
paper we extend the previous classical analysis of winding membranes to Kasner
backgrounds, and using the Hamiltonian equations, solve for the wave function
of loops with circular symmetry. The evolution across the singularity is
regular, and explained in terms of the excitement of higher oscillation modes.
We also show there is finite particle production and unitarity is preserved.


  We treat the two-particle Green's function in the Hubbard model using the
recently developed tau-CPA, a hybrid treatment that applies the
coherent-potential approximation (CPA) up to a time tau related to the inverse
of the band width, after which the system is averaged using the virtual-crystal
approximation (VCA). This model, with suitable approximations, does predict
magnetism for a modified Stoner criterion. The evaluation of the two-particle
propagator in the tau-CPA requires the solution of the pure CPA, within whose
formalism the vertex correction and the weighted Green's functions are
obtained. The dynamical susceptibility, including the vertex correction and the
weighted scattering by the residual interaction, is calculated and shows a spin
wave spectrum in the ferromagnetic regime.


  Notes of three talks given at the workshop 'Hilbert schemes, non-commutative
algebra and the McKay correspondence' CIRM-Luminy (France) October 2003. If A
is an order over a central normal affine variety X having a stability structure
such that the variety of all semi-stable A-representations is a smooth variety,
then the corresponding moduli space is a partial desingularization of X and we
have a complete classification of the remaining singularities.


  We propose a distributed algorithm, termed the Directed-Distributed Projected
Subgradient (D-DPS), to solve a constrained optimization problem over a
multi-agent network, where the goal of agents is to collectively minimize the
sum of locally known convex functions. Each agent in the network owns only its
local objective function, constrained to a commonly known convex set. We focus
on the circumstance when communications between agents are described by a
directed network. The D-DPS augments an additional variable for each agent, to
overcome the asymmetry caused by the directed communication network. The
convergence analysis shows that D-DPS converges at a rate of $O(\frac{\ln
k}{\sqrt{k}})$, where k is the number of iterations.


  As the application scenarios of mobile robots are getting more complex and
challenging, scene understanding becomes increasingly crucial. A mobile robot
that is supposed to operate autonomously in indoor environments must have
precise knowledge about what objects are present, where they are, what their
spatial extent is, and how they can be reached; i.e., information about free
space is also crucial. Panoptic mapping is a powerful instrument providing such
information. However, building 3D panoptic maps with high spatial resolution is
challenging on mobile robots, given their limited computing capabilities. In
this paper, we propose PanopticNDT - an efficient and robust panoptic mapping
approach based on occupancy normal distribution transform (NDT) mapping. We
evaluate our approach on the publicly available datasets Hypersim and
ScanNetV2. The results reveal that our approach can represent panoptic
information at a higher level of detail than other state-of-the-art approaches
while enabling real-time panoptic mapping on mobile robots. Finally, we prove
the real-world applicability of PanopticNDT with qualitative results in a
domestic application.


  End-to-End (E2E) unrolled optimization frameworks show promise for Magnetic
Resonance (MR) image recovery, but suffer from high memory usage during
training. In addition, these deterministic approaches do not offer
opportunities for sampling from the posterior distribution. In this paper, we
introduce a memory-efficient approach for E2E learning of the posterior
distribution. We represent this distribution as the combination of a
data-consistency-induced likelihood term and an energy model for the prior,
parameterized by a Convolutional Neural Network (CNN). The CNN weights are
learned from training data in an E2E fashion using maximum likelihood
optimization. The learned model enables the recovery of images from
undersampled measurements using the Maximum A Posteriori (MAP) optimization. In
addition, the posterior model can be sampled to derive uncertainty maps about
the reconstruction. Experiments on parallel MR image reconstruction show that
our approach performs comparable to the memory-intensive E2E unrolled
algorithm, performs better than its memory-efficient counterpart, and can
provide uncertainty maps. Our framework paves the way towards MR image
reconstruction in 3D and higher dimensions


  In this paper, we introduce the Dynamic Modularity-Spectral Algorithm
(DynMSA), a novel approach to identify clusters of stocks with high
intra-cluster correlations and low inter-cluster correlations by combining
Random Matrix Theory with modularity optimisation and spectral clustering. The
primary objective is to uncover hidden market structures and find diversifiers
based on return correlations, thereby achieving a more effective risk-reducing
portfolio allocation. We applied DynMSA to constituents of the S&P 500 and
compared the results to sector- and market-based benchmarks. Besides the
conception of this algorithm, our contributions further include implementing a
sector-based calibration for modularity optimisation and a correlation-based
distance function for spectral clustering. Testing revealed that DynMSA
outperforms baseline models in intra- and inter-cluster correlation
differences, particularly over medium-term correlation look-backs. It also
identifies stable clusters and detects regime changes due to exogenous shocks,
such as the COVID-19 pandemic. Portfolios constructed using our clusters showed
higher Sortino and Sharpe ratios, lower downside volatility, reduced maximum
drawdown and higher annualised returns compared to an equally weighted market
benchmark.


  Compressed sensing shows that a sparse signal can stably be recovered from
incomplete linear measurements. But, in practical applications, some signals
have additional structure, where the nonzero elements arise in some blocks. We
call such signals as block-sparse signals. In this paper, the
$\ell_2/\ell_1-\alpha\ell_2$ minimization method for the stable recovery of
block-sparse signals is investigated. Sufficient conditions based on block
mutual coherence property and associating upper bound estimations of error are
established to ensure that block-sparse signals can be stably recovered in the
presence of noise via the $\ell_2/\ell_1-\alpha\ell_2$ minimization method. For
all we know, it is the first block mutual coherence property condition of
stably reconstructing block-sparse signals by the $\ell_2/\ell_1-\alpha\ell_2$
minimization method. Additionally, the numerical experiments implemented verify
the performance of the $\ell_2/\ell_1-\alpha\ell_2$ minimization.


  Strongly disordered superconductors in a magnetic field display many
characteristic properties of type-II superconductivity--- except at low
temperatures where an anomalous linear $T$-dependence of the resistive critical
field $B_{c2}$ is routinely observed. This behavior violates the conventional
theory of superconductivity, and its origin remains a long-standing puzzle.
Here we report on systematic measurements of the critical magnetic field and
current on amorphous indium oxide films of various levels of disorder.
Surprisingly, our measurements show that the $B_{c2}$ anomaly near
zero-temperature is accompanied by a clear mean-field like scaling behavior of
the critical current. We demonstrate theoretically that these are consequences
of the vortex-glass ground state and its thermal fluctuations. This theory
further predicts the linear-$T$ anomaly to occur in films as well as bulk
superconductors with a slope that depends on the normal-state sheet
resistance---in agreement with experimental data. Thus, our combined
experimental and theoretical study reveals universal low-temperature behavior
of $B_{c2}$ in a large class of disordered superconductors.


  One of the main challenges in cosmology is to quantify how small density
fluctuations at the recombination epoch z around 1000 evolved into the galaxies
and the large-scale structure we observe in the universe today. This thesis
discusses ways of probing the intermediate epoch, focusing on the thermal
history. The main emphasis is on the role played by non-linear feedback, where
a small fraction of matter forming luminous objects can inject enough energy
into the inter-galactic medium to radically alter subsequent events. Chapter 2,
"A Cosmology Primer", provides a 60-page introduction to cosmology that may be
useful for the reader with a general physics background.


  A critical temperature for a complete signed graph of $N$ agents where
time-dependent links polarization tends towards the Heider (structural) balance
is found analytically using the heat-bath approach and the mean-field
approximation as $T^c=(N-2)/a^c$, where $a^c\approx 1.71649$. The result is in
perfect agreement with numerical simulations starting from the paradise state
where all links are positively polarized as well as with the estimation of this
temperature received earlier with much more sophisticated methods. When heating
the system, one observes a discontinuous and irreversible phase transition at
$T^c$ from a nearly balanced state when the mean link polarization is about
$x_c=0.796388$ to a disordered and unbalanced state where the polarization
vanishes. When the initial conditions for links polarization are random, then
at low temperatures a balanced bipolar state of two mutually hostile cliques
exists that decays towards the disorder and there is a discontinuous phase
transition at a temperature $T^d$ that is lower than $T^c$. The system phase
diagram corresponds to the so-called fold catastrophe when a stable solution of
the mean-field equation collides with a separatrix, and as a result a
hysteresis-like loop is observed.


  In this paper, we investigate the physical layer security over the Alternate
Rician Shadowed fading channel, which is a novel channel model for body-centric
wireless links and land mobile satellite. We derive exact closed-form
expressions for the average secrecy capacity (ASC), secrecy outage probability
(SOP), and probability of non-zero secrecy capacity (PNZ) for two cases: (i) m
is a positive real number and (ii) m is a positive integer number, where m
describes the level of fluctuation of the line-of-sight component. In the first
case, SOP is derived in terms of the Meijer's G-function, while ASC and PNZ are
derived in terms of the multivariate Fox's H-function. In the second case, ASC
is derived in terms of the Meijer's G-function, while SOP and PNZ are derived
in terms of elementary functions. In addition, we derive the asymptotic ASC,
SOP and PNZ expressions which all match well the exact ones at high values of
signal-to-noise ratio, respectively. The capacity slope of asymptotic ASC and
the secrecy diversity order of asymptotic SOP have been derived for providing
more physical insights. Finally, the accuracy of our derived expressions is
validated by Monte-Carlo simulations.


  These lectures cover the theoretical structure and phenomenology of some
basic mechanisms for inflation. A full treatment of the problem requires
`ultraviolet completion' because of the sensitivity of inflation to quantum
gravity effects, while the observables are elegantly parameterized using low
energy field theory. String theory provides novel mechanisms for inflation,
some subject to significant observational tests, with highly UV-sensitive
tensor mode measurements being a prime example. Although the ultraviolet
completion is not directly accessible experimentally, some of these mechanisms
have helped stimulate a more systematic analysis of the space of low energy
theories and signatures relevant for data analysis, including searches for
non-Gaussianity and additional structure in the power spectrum. We include a
pedagogical overview of string compactifications, with a focus on candidate
inflatons and their symmetry structure. In the last lecture we attack the
problem of thought-experimental observables in inflation, developing a
generalization of gauge-gravity duality that relies on the structure of the
scalar potential in string theory.


  We study the effect of reddening on microlensed stars towards the LMC. If
lenses are in the LMC disk, then the source stars should be at the far side or
behind the LMC disk. Thus they should experience more reddening and extinction
by dust in the LMC disk than typical stars in the immediate neighbouring lines
of sight. We simulate this effect in a variety of models for the LMC stars and
dust. We stress that the optical depth is not one number, but a function of the
reddening of the survey stars. We discuss how these effects could be used to
constrain the fraction of machos in the dark halo. The effect of patchiness of
dust can be controled by working with faint stars in the smallest patches of
the sky around the microlensed stars. This can be done most effectively with
the Hubble Space Telescope in the ultra-violet. The non-detection of the
reddening bias would be strongly in favor of machos in the Galactic halo.


  We discuss issues involving p(D)- brane charge quantization and the
normalization of effective actions, in string/M-theory. We also construct the
action of (the bosonic sector of) eleven dimensional supergravity in the
presence of two- and five- branes and discuss (perturbative) anomaly
cancellation.


  We investigate the consistency relation relating the squeezed limit of the
bispectrum to the scalar spectral index in single field models of inflation. We
give a simple integral formula for the bispectrum in the squeezed limit in
terms of the free mode mode functions of the primordial curvature perturbation,
in any Lorentz invariant single field model of inflation and without resorting
to any approximation, generalizing a recent result obtained by Ganc and Komatsu
in the case of canonical kinetic terms. We use our result to verify the
consistency relation in an exactly solvable class of models with a non-trivial
speed of sound. We then verify the consistency relation at the first
non-trivial order in the slow-varying approximation in general single field
inflation (a known result) and at second order in this approximation in
canonical single field inflation.


  In this document, we present the Technical Design Report of the Upgrade of
the T2K Near Detector ND280. The goal of this upgrade is to improve the Near
Detector performance to measure the neutrino interaction rate and to constrain
the neutrino interaction cross-sections so that the uncertainty in the number
of predicted events at Super-Kamiokande is reduced to about 4%. This will allow
to improve the physics reach of the T2K-II project. This goal is achieved by
modifying the upstream part of the detector, adding a new highly granular
scintillator detector (Super-FGD), two new TPCs (High-Angle TPC) and six TOF
planes. Details about the detector concepts, design and construction methods
are presented, as well as a first look at the test-beam data taken in Summer
2018. An update of the physics studies is also presented.


  It is shown that the influence of Kaluza scalar is to induce expansion in the
Raychaudhuri equation for two representative solutions of the Kaluza theory.


  Molecular dynamics simulation and recent theory are used to examine density
correlations in semidilute solutions of highly charged, intrinsically flexible
and hydrophilic polyelectrolytes in low salt. Quantitative comparison with no
adjustable parameters is made with recent scattering and osmometry experiments.
Agreement is found for the polymer-polymer structure factor at intermediate
wavevectors q with varying chain charge fraction f. Theory is also in agreement
with simulation and experiment for the osmotic pressure, but not with q -> 0
extrapolations of scattering data that show anomalously large intensities at
low q.


  Followership is generally defined as a strategy that evolved to solve social
coordination problems, and particularly those involved in group movement.
Followership behaviour is particularly interesting in the context of
road-crossing behaviour because it involves other principles such as
risk-taking and evaluating the value of social information. This study sought
to identify the cognitive mechanisms underlying decision-making by pedestrians
who follow another person across the road at the green or at the red light in
two different countries (France and Japan). We used agent-based modelling to
simulate the road-crossing behaviours of pedestrians. This study showed that
modelling is a reliable means to test different hypotheses and find the exact
processes underlying decision-making when crossing the road. We found that two
processes suffice to simulate pedestrian behaviours. Importantly, the study
revealed differences between the two nationalities and between sexes in the
decision to follow and cross at the green and at the red light. Japanese
pedestrians are particularly attentive to the number of already departed
pedestrians and the number of waiting pedestrians at the red light, whilst
their French counterparts only consider the number of pedestrians that have
already stepped off the kerb, thus showing the strong conformism of Japanese
people. Finally, the simulations are revealed to be similar to observations,
not only for the departure latencies but also for the number of crossing
pedestrians and the rates of illegal crossings. The conclusion suggests new
solutions for safety in transportation research.


  Here, we study the existence and the convergence of solutions for the
vanishing discount MFG problem with a quadratic Hamiltonian. We give conditions
under which the discounted problem has a unique classical solution and prove
convergence of the vanishing-discount limit to a unique solution up to
constants. Then, we establish refined asymptotics for the limit. When those
conditions do not hold, the limit problem may not have a unique solution and
its solutions may not be smooth, as we illustrate in an elementary example.
Finally, we investigate the stability of regular weak solutions and address the
selection problem. Using ideas from Aubry-Mather theory, we establish a
selection criterion for the limit.


  We study regularity of the solution $\theta$ to the Gurtin-Pipkin
integral-differential equation of the first order in time. The solution
smoothness in Sobolev spaces is proved. Also it is proved that the
'perturbation' part, namely, the difference of $\theta$ and the solution to the
corresponding wave equation is smoother than $\theta$.


  The computational complexity of the isomorphism problem for regular trees,
regular linear orders, and regular words is analyzed. A tree is regular if it
is isomorphic to the prefix order on a regular language. In case regular
languages are represented by NFAs (DFAs), the isomorphism problem for regular
trees turns out to be EXPTIME-complete (resp. P-complete). In case the input
automata are acyclic NFAs (acyclic DFAs), the corresponding trees are
(succinctly represented) finite trees, and the isomorphism problem turns out to
be PSPACE-complete (resp. P-complete). A linear order is regular if it is
isomorphic to the lexicographic order on a regular language. A polynomial time
algorithm for the isomorphism problem for regular linear orders (and even
regular words, which generalize the latter) given by DFAs is presented. This
solves an open problem by Esik and Bloom.


  Let G be a simple and simply-connected complex algebraic group, P \subset G a
parabolic subgroup. We prove an unpublished result of D. Peterson which states
that the quantum cohomology QH^*(G/P) of a flag variety is, up to localization,
a quotient of the homology H_*(Gr_G) of the affine Grassmannian \Gr_G of G. As
a consequence, all three-point genus zero Gromov-Witten invariants of $G/P$ are
identified with homology Schubert structure constants of H_*(Gr_G),
establishing the equivalence of the quantum and homology affine Schubert
calculi.
  For the case G = B, we use the Mihalcea's equivariant quantum Chevalley
formula for QH^*(G/B), together with relationships between the quantum Bruhat
graph of Brenti, Fomin and Postnikov and the Bruhat order on the affine Weyl
group. As byproducts we obtain formulae for affine Schubert homology classes in
terms of quantum Schubert polynomials. We give some applications in quantum
cohomology.
  Our main results extend to the torus-equivariant setting.


  We shall give conditions on the illuminations $\varphi_{i}$ such that the
solutions to Maxwell's equations \[ \left\{ \begin{array}{l} {\rm curl}
E^{i}=i\omega\mu H^{i}\qquad\text{in }\Omega,\\ {\rm curl}
H^{i}=-i(\omega\varepsilon+i\sigma)E^{i}\qquad\text{in }\Omega,\\
E^{i}\times\nu=\varphi_{i}\times\nu\qquad\text{on }\partial\Omega,
\end{array}\right. \] satisfy certain non-zero qualitative properties inside
the domain $\Omega$, provided that a finite number of frequencies $\omega$ are
chosen in a fixed range. The illuminations are explicitly constructed. This
theory finds applications in several hybrid imaging problems, where unknown
parameters have to be imaged from internal measurements. Some of these examples
are discussed. This paper naturally extends a previous work of the author
[Inverse Problems 29 (2013) 115007], where the Helmholtz equation was studied.


  From a general abstract system theoretical perspective, a quantum-like system
description in the spirit of a generalized Quantum Theory may appear to be
simpler and more natural than a classically inspired description. We
investigate the reasons why we nevertheless conceive ourselves embedded into a
classically structured world. Categorial, physical and pragmatic reasons are
proposed as explanations.


  We derive the three-loop order renormalization group equations that describe
the flat phase of polymerized membranes within the modified minimal subtraction
scheme, following the pioneering one-loop order computation of Aronovitz and
Lubensky [Phys. Rev. Lett. 60, 2634 (1988)] and the recent two-loop order one
of Coquand, Mouhanna and Teber [Phys. Rev. E 101, 062104 (2020)]. We analyze
the fixed points of these equations and compute the associated field anomalous
dimension $\eta$ at three-loop order. Our results display a marked proximity
with those obtained using nonperturbative techniques and reexpanded in powers
of $\epsilon=4-D$. Moreover, the three-loop order value that we get for $\eta$
at the stable fixed point, $\eta=0.8872$, in $D=2$, is compatible with known
theoretical results and within the range of accepted numerical values.


  High-purity germanium crystals approximately 12 cm in diameter were grown in
a hydrogen atmosphere using the Czochralski method. The dislocation density of
the crystals was determined to be in the range of 2000 - 4200 cm-2, which meets
a requirement for use as a radiation detector. The axial and radial
distributions of impurities in the crystals were measured and are discussed. A
planar detector was also fabricated from one of the crystals and then evaluated
for electrical and spectral performance. Measurements of gamma-ray spectra from
Cs-137 and Am-241 sources demonstrate that the detector has excellent energy
resolution.


  Fragmentation reactions induced on light target nuclei by protons and light
nuclei of energies around 1 GeV/nucleon and below are studied with the latest
Los Alamos Monte Carlo transport code MCNP6 and with its cascade-exciton model
(CEM) and Los Alamos version of the quark-gluon string model (LAQGSM) event
generators, version 03.03, used as stand-alone codes. Such reactions are
involved in different applications, like cosmic-ray-induced single event upsets
(SEU's), radiation protection, and cancer therapy with proton and ion beams,
among others; therefore, it is important that MCNP6 simulates them as well as
possible. CEM and LAQGSM assume that intermediate-energy fragmentation
reactions on light nuclei occur generally in two stages. The first stage is the
intranuclear cascade (INC), followed by the second, Fermi breakup
disintegration of light excited residual nuclei produced after INC. Both CEM
and LAQGSM account also for coalescence of light fragments (complex particles)
up to He4 from energetic nucleons emitted during INC. We investigate the
validity and performance of MCNP6, CEM, and LAQGSM in simulating fragmentation
reactions at intermediate energies and discuss possible ways of further
improving these codes.


  We consider the ``transport'' of the state of a spin across a
Heisenberg-coupled spin chain via the use of repeated SWAP gates, starting with
one of two states---one in which the leftmost spin is down and the others up,
and one in which the leftmost two spins are in a singlet state (i.e., they are
entangled), and the others are again all up. More specifically, we
``transport'' the state of the leftmost spin in the first case and the
next-to-leftmost spin in the second to the other end of the chain, and then
back again. We accomplish our SWAP operations here by increasing the exchange
coupling between the two spins that we operate on from a base value $J$ to a
larger value $J_\text{SWAP}$ for a time $t=\pi\hbar/4J_\text{SWAP}$. We
determine the fidelity of this sequence of operations in a number of
situations---one in which only nearest-neighbor coupling exists between spins
and there is no magnetic dipole-dipole coupling or noise (the most ideal case),
one in which we introduce next-nearest-neighbor coupling, but none of the other
effects, and one in which all of these effects are present. In the last case,
the noise is assumed to be quasistatic, i.e., the exchange couplings are each
drawn from a Gaussian distribution, truncated to only nonnegative values. We
plot the fidelity as a function of $J_\text{SWAP}$ to illustrate various
effects, namely crosstalk due to coupling to other spins, as well as noise,
that are detrimental to our ability to perform a SWAP operation. Our theory
should be useful to the ongoing experimental efforts in building
semiconductor-based spin quantum computer architectures.


  We investigate the induced geodesic deviation equations in the brane world
models, in which all the matter forces except gravity are confined on the
3-brane. Also, the Newtonian limit of induced geodesic deviation equation is
studied. We show that in the first Randall-Sundrum model the Bohr-Sommerfeld
quantization rule is as a result of consistency between the geodesic and
geodesic deviation equations. This indicates that the path of test particle is
made up of integral multiples of a fundamental Compton-type unit of length
$h/mc$.


  We are interested in the dynamic of a structured branching population where
the trait of each individual moves according to a Markov process. The rate of
division of each individual is a function of its trait and when a branching
event occurs, the trait of the descendants at birth depends on the trait of the
mother and on the number of descendants. In this article, we explicitly
describe the penalized Markov process, named auxiliary process, corresponding
to the dynamic of the trait along the spine by giving its associated
infinitesimal generator. We prove a Many-to-One formula and a Many-to-One
formula for forks. Furthermore, we prove that this auxiliary process
characterizes exactly the process of the trait of a uniformly sampled
individual in the large population approximation. We detail three examples of
growth-fragmentation models: the linear growth model, the exponential growth
model and the parasite infection model.


  The Schwinger - DeWitt expansion for the evolution operator kernel of the
Schrodinger equation is studied for convergence. It is established that
divergence of this expansion which is usually implied for all continuous
potentials, excluding ones of the form V(q)=aq^2+bq+c, really takes place only
if the coupling constant g is treated as independent variable. But the
expansion may be convergent for some kinds of the potentials and for some
discrete values of the charge, if the latter is considered as fixed parameter.
Class of such potentials is interesting because inside of it the property of
discreteness of the charge in the nature is reproduced in the theory in natural
way.


  Object search is a fundamental skill for household robots, yet the core
problem lies in the robot's ability to locate the target object accurately. The
dynamic nature of household environments, characterized by the arbitrary
placement of daily objects by users, makes it challenging to perform target
localization. To efficiently locate the target object, the robot needs to be
equipped with knowledge at both the object and room level. However, existing
approaches rely solely on one type of knowledge, leading to unsatisfactory
object localization performance and, consequently, inefficient object search
processes. To address this problem, we propose a commonsense scene graph-based
target localization, CSG-TL, to enhance target object search in the household
environment. Given the pre-built map with stationary items, the robot models
the room-level knowledge with object-level commonsense knowledge generated by a
large language model (LLM) to a commonsense scene graph (CSG), supporting both
types of knowledge for CSG-TL. To demonstrate the superiority of CSG-TL on
target localization, extensive experiments are performed on the real-world
ScanNet dataset and the AI2THOR simulator. Moreover, we have extended CSG-TL to
an object search framework, CSG-OS, validated in both simulated and real-world
environments. Code and videos are available at
https://sites.google.com/view/csg-os.


  High-refractive index dielectric nanoparticles may exhibit strong directional
forward light scattering at visible and near-infrared wavelengths due to
interference of simultaneously excited electric and magnetic dipole resonances.
For a spherical high-index dielectric, the so-called first Kerker's condition
can be realized, at which the backward scattering practically vanishes for some
combination of refractive index and particle size. However, Kerker's condition
for spherical particles is only possible at the tail of the scattering
resonances, when the particle scatters light weakly. Here we demonstrate that
significantly higher forward scattering can be realized if spheroidal particles
are considered instead. For each value of refractive index exists an optimum
shape of the particle, which produces minimum backscattering efficiency
together with maximum forward scattering. This effect is achieved due to the
overlapping of magnetic and electric dipole resonances of the spheroidal
particle at the resonance frequency. It permits the design of very efficient,
low-loss optical nanoantennas.


  We develop a modern extended scattering theory for CMV matrices with
asymptotically constant Verblunsky coefficients. We demonstrate that an
orthonormal system in a certain "weighted'' Hilbert space, which we call the
Fadeev-Marchenko (FM) space, behaves asymptotically as the system in the
standard (free) case. The duality between the two types of Hardy subspaces in
it plays the key role in the proof of all asymptotics involved. We show that
the traditional (Faddeev-Marchenko) condition is too restrictive to define the
class of CMV matrices for which there exists a unique scattering
representation. The main results are: 1) Szeg\"o-Blaschke class: the class of
twosided CMV matrices acting in $l^2$, whose spectral density satisfies the
Szeg\"o condition and whose point spectrum the Blaschke condition, corresponds
precisely to the class where the scattering problem can be posed and solved.
That is, to a given CMV matrix of this class, one can associate the scattering
data and related to them the FM space. The CMV matrix corresponds to the
multiplication operator in this space, and the orthonormal basis in it
(corresponding to the standard basis in $l^2$) behaves asymptotically as the
basis associated with the free system. 2) $A_2$-Carleson class: from the point
of view of the scattering problem, the most natural class of CMV matrices is
that one in which a) the scattering data determine the matrix uniquely and b)
the associated Gelfand- Levitan- Marchenko transformation operators are
bounded. Necessary and sufficient conditions for this class can be given in
terms of an $A_2$ kind condition for the density of the absolutely continuous
spectrum and a Carleson kind condition for the discrete spectrum. Similar close
to the optimal conditions are given directly in terms of the scattering data.


  In this work, we probe the stability of a $z=3$ three-dimensional Lifshitz
black hole by using scalar and spinorial perturbations. We found an analytical
expression for the quasinormal frequencies of the scalar probe field, which
perfectly agree with the behavior of the quasinormal modes obtained
numerically. The results for the numerical analysis of the spinorial
perturbations reinforce the conclusion of the scalar analysis, i.e., the model
is stable under scalar and spinor perturbations. As an application we found the
area spectrum of the Lifshitz black hole, which turns out to be equally spaced.


  Solar-like oscillations have been observed by Kepler and CoRoT in several
solar-type stars, thereby providing a way to probe the stars using
asteroseismology.
  We provide the mode frequencies of the oscillations of various stars required
to perform a comparison with those obtained from stellar modelling.
  We used a time series of nine months of data for each star. The 61 stars
observed were categorised in three groups: simple, F-like and mixed-mode. The
simple group includes stars for which the identification of the mode degree is
obvious. The F-like group includes stars for which the identification of the
degree is ambiguous. The mixed-mode group includes evolved stars for which the
modes do not follow the asymptotic relation of low-degree frequencies.
Following this categorisation, the power spectra of the 61 main sequence and
subgiant stars were analysed using both maximum likelihood estimators and
Bayesian estimators, providing individual mode characteristics such as
frequencies, linewidths, and mode heights. We developed and describe a
methodology for extracting a single set of mode frequencies from multiple sets
derived by different methods and individual scientists. We report on how one
can assess the quality of the fitted parameters using the likelihood ratio test
and the posterior probabilities.
  We provide the mode frequencies of 61 stars (with their 1-sigma error bars),
as well as their associated echelle diagrams.


  Observations of warm absorbers provided new ways to study the nuclear
environments of AGNs. I discuss basic properties of warm absorbers and early
developments with ROSAT, ASCA and HST observations. I briefly touch upon recent
advances made with Chandra and FUSE.


  This research starts with the observation that face recognition can suffer a
low impact from significant image shrinkage. To explain this fact, we proposed
the Pixel Clustering methodology. It defines regions in the image in which its
pixels are very similar to each other. We extract features from each region. We
used three face databases in the experiments. We noticed that 512 is the
maximum number of features needed for high accuracy image recognition. The
proposed method is also robust, even if only it uses a few classes from the
training set.


  We summarize recent measurements made at the Tevatron Collider using top
event candidates. Cross section and mass measurements are discussed in a
separate contribution to these Proceedings. Here we report on studies of the
top P$_T$ distribution in $\ttbar$ production and studies of single top
production. Properties of top decays examined are:
  BF($t \into Wb$)/BF($t \into Wq$), helicity amplitudes of W's from top decays
and correlations of $\ttbar$ decay products. Searches for new physics in rare
top decays and a search for a state X $\into \ttbar$ are also reported.


  Subset models provide a new semantics for justifcation logic. The main idea
of subset models is that evidence terms are interpreted as sets of possible
worlds. A term then justifies a formula if that formula is true in each world
of the interpretation of the term. In this paper, we introduce a belief
expansion operator for subset models. We study the main properties of the
resulting logic as well as the differences to a previous (symbolic) approach to
belief expansion in justification logic.


  We prove that refined analytic torsion on a manifold with boundary is an
analytic section of the determinant line bundle over the representation
variety. As a fundamental application we establish a gluing formula for refined
analytic torsion on connected components of the complex representation space
which contain a unitary point. Finally we provide a new proof of Bruening-Ma
gluing formula for the Ray-Singer torsion associated to a non-Hermitian
connection. Our proof is quite different from the one given by Bruening and Ma
and uses a temporal gauge transformation.


  We study the implications for electroweak baryogenesis (EWB) within the
minimal supersymmetric Standard Model (MSSM) of present and future searches for
the permanent electric dipole moment (EDM) of the electron, for neutralino dark
matter, and for supersymmetric particles at high energy colliders. We show that
there exist regions of the MSSM parameter space that are consistent with both
present two-loop EDM limits and the relic density and that allow for successful
EWB through resonant chargino and neutralino processes at the electroweak phase
transition. We also show that under certain conditions the lightest neutralino
may be simultaneously responsible for both the baryon asymmetry and relic
density. We give present constraints on chargino/neutralino-induced EWB implied
by the flux of energetic neutrinos from the Sun, the prospective constraints
from future neutrino telescopes and ton-sized direct detection experiments, and
the possible signatures at the Large Hadron Collider and International Linear
Collider.


  In this paper, we develop a large-$N$ field theory for a system of $N$
classical particles in one dimension at thermal equilibrium. The particles are
confined by an arbitrary external potential, $V_\text{ex} (x)$, and repel each
other via a class of pairwise interaction potentials $V_\text{int}(r)$ (where
$r$ is distance between a pair of particles) such that $ V_\text{int} \sim
|r|^{-k}$ when $r \to 0$. We consider the case where every particle is
interacting with $d$ (finite range parameter) number of particles to its left
and right. Due to the intricate interplay between external confinement,
pairwise repulsion and entropy, the density exhibits markedly distinct behavior
in three regimes $k>0$, $k \to 0$ and $k<0$. From this field theory, we compute
analytically the average density profile for large $N$ in these regimes. We
show that the contribution from interaction dominates the collective behaviour
for $k > 0$ and the entropy contribution dominates for $k<0$, and both
contributes equivalently in the $k\to 0$ limit (finite range log-gas). Given
the fact that these family of systems are of broad relevance, our analytical
findings are of paramount importance. These results are in excellent agreement
with brute-force Monte-Carlo simulations.


  In this paper, we consider a simple kinetic model of economy involving both
exchanges between agents and speculative trading. We show that the kinetic
model admits non trivial quasi-stationary states with power law tails of Pareto
type. In order to do this we consider a suitable asymptotic limit of the model
yielding a Fokker-Planck equation for the distribution of wealth among
individuals. For this equation the stationary state can be easily derived and
shows a Pareto power law tail. Numerical results confirm the previous analysis.


  Let $\cT$ be Teichm\"uller space of a closed surface of genus at least 2. For
any point $c\in \cT$, we describe an action of the circle on $\cT\times \cT$,
which limits to the earthquake flow when one of the parameters goes to a
measured lamination in the Thurston boundary of $\cT$. This circle action
shares some of the main properties of the earthquake flow, for instance it
satisfies an extension of Thurston's Earthquake Theorem and it has a complex
extension which is analogous and limits to complex earthquakes. Moreover, a
related circle action on $\cT\times \cT$ extends to the product of two copies
of the universal Teichm\"uller space.


  Let K be a Galois number field of prime degree $\ell$. Heilbronn showed that
for a given $\ell$ there are only finitely many such fields that are
norm-Euclidean. In the case of $\ell=2$ all such norm-Euclidean fields have
been identified, but for $\ell\neq 2$, little else is known. We give the first
upper bounds on the discriminants of such fields when $\ell>2$. Our methods
lead to a simple algorithm which allows one to generate a list of candidate
norm-Euclidean fields up to a given discriminant, and we provide some
computational results.


  Squeezing bubbles and impurities out of interlayer spaces by applying force
through a few-layer graphene capping layer leads to van der Waals
heterostructures with ultra-flat structure free from random electrostatic
potential arising from charged impurities. Without the graphene capping layer,
a squeezing process with an AFM tip induces applied-force-dependent charges of
n ~ 2 x 10^12 cm^-2 uN^-1, resulting in strong intensity of trions in
photoluminescence spectra of MoSe2 at low temperature. We found that a
hBN/MoSe2/hBN prepared with the "modified nano-squeezing method" shows a strong
excitonic emission with negligible trion peak, and the residual linewidth of
the exciton peak is only 2.2 meV, which is comparable to the homogeneous limit.
Furthermore, in this high-quality sample, we found that formation of biexciton
occurs even at extremely low excitation power (Phi ~ 2.3 x 10^19 cm^-2 s^-1)
due to the enhanced collisions between excitons.


  Hawking radiation is studied for arbitrary scalars, fermions, and spin-1
bosons, using a tunneling approach, to every order in $\hbar$ but ignoring
back-reaction effects. It is shown that the additional quantum terms yield no
new contribution to the Hawking temperature. Indeed, it is found that the limit
of small $\hbar$ in the standard quantum WKB approximation is replaced by the
near-horizon limit in the gravitational WKB approach.


  We present an algorithm for the approximate decomposition of diagonal
operators, focusing specifically on decompositions over the Clifford+$T$ basis,
that minimize the number of phase-rotation gates in the synthesized
approximation circuit. The equivalent $T$-count of the synthesized circuit is
bounded by $k \, C_0 \log_2(1/\varepsilon) + E(n,k)$, where $k$ is the number
of distinct phases in the diagonal $n$-qubit unitary, $\varepsilon$ is the
desired precision, $C_0$ is a quality factor of the implementation method
($1<C_0<4$), and $E(n,k)$ is the total entanglement cost (in $T$ gates). We
determine an optimal decision boundary in $(k,n,\varepsilon)$-space where our
decomposition algorithm achieves lower entanglement cost than previous
state-of-the-art techniques. Our method outperforms state-of-the-art techniques
for a practical range of $\varepsilon$ values and diagonal operators and can
reduce the number of $T$ gates exponentially in $n$ when $k << 2^n$.


  We introduce analogue black holes (BHs) based on ideal magnetohydrodynamic
equations. Similar to acoustic BHs, which trap phonons and emit Hawking
radiation (HR) at the sonic horizon where the flow speed changes from super- to
sub-sonic, in the horizon of magnetoacoustic and Alfv\'{e}nic BHs, the
magnetoacoustic and Alfve'n waves will be trapped and emit HR made of quantized
vibrations similar to phonons which we call magnephonons and Alphonons. We
proposed that magnetoacoustic and Alfve'nic BHs may be created in the
laboratory using a tube with variable cross section embedded in a uniform
magnetic field, and a super-magnetoacoustic or a super-Alfve'nic flow. We show
that the Hawking temperature for both BHs is a function of the background
magnetic field, number density of fluid, and radius of the tube. For a typical
setup, the temperature is estimated to be about 0.0266 K.


  The computer-assisted radiologic informative report has received increasing
research attention to facilitate diagnosis and treatment planning for dental
care providers. However, manual interpretation of dental images is limited,
expensive, and time-consuming. Another barrier in dental imaging is the limited
number of available images for training, which is a challenge in the era of
deep learning. This study proposes a novel self-distillation (SD) enhanced
self-supervised learning on top of the masked image modeling (SimMIM)
Transformer, called SD-SimMIM, to improve the outcome with a limited number of
dental radiographs. In addition to the prediction loss on masked patches,
SD-SimMIM computes the self-distillation loss on the visible patches. We apply
SD-SimMIM on dental panoramic X-rays for teeth numbering, detection of dental
restorations and orthodontic appliances, and instance segmentation tasks. Our
results show that SD-SimMIM outperforms other self-supervised learning methods.
Furthermore, we augment and improve the annotation of an existing dataset of
panoramic X-rays.


  We report magnetoresistance measurements through Nb films having a periodic
thickness modulation. The cylinder shaped large-thickness regions of the
sample, which form a square lattice, act as repulsive centers for the
superconducting vortices. For low driving currents along one of the axes of the
square lattice, the resistivity $\rho$ increases monotonously with increasing
magnetic field $B$ and the $\rho$--$B$ characteristics are approximately
piecewise linear. The linear $\rho$ vs $B$ segments change their slope at
matching fields where the number of vortices is an integer or a half integer
times the number of protruding cylinders in the sample. Numerical simulations
allow us to associate the different segments of linear magnetoresistance to
different vortex-flow regimes, some of which are dominated by the propagation
of discommensurations (kinks).


  This paper aims to explore the mechanical effect of a company's share
repurchase on earnings per share (EPS). In particular, while a share repurchase
scheme will reduce the overall number of shares, suggesting that the EPS may
increase, clearly the expenditure will reduce the net earnings of a company,
introducing a trade-off between these competing effects. We first of all review
accretive share repurchases, then characterise the increase in EPS as a
function of price paid by the company. Subsequently, we analyse and quantify
the estimated difference in earnings growth between a company's natural growth
in the absence of buyback scheme to that with its earnings altered as a result
of the buybacks. We conclude with an examination of the effect of share
repurchases in two cases studies in the US stock-market.


  The local electronic structure of muons (Mu) as dilute pseudo-hydrogen in
single-crystalline $\beta$-Ga$_2$O$_3$ has been studied by the muon spin
rotation/relaxation ($\mu$SR). High-precision measurements over a long time
range of $\sim$25 $\mu$s have clearly identified two distinct Mu states: a
quasi-static Mu (Mu$_1$) and fast-moving Mu (Mu$_2$). By comparing this result
with predictions from the recently established ambipolarity model, these two
states are respectively attributed to the relaxed-excited states associated
with the donor ($E^{+/0}$) and acceptor ($E^{-/0}$) levels predicted by density
functional theory (DFT) calculations for the interstitial H. Furthermore, the
local electronic structure of Mu$_1$ is found to be an OMu-bonded state with
three-coordinated oxygen. The structure is almost identical with the thermal
equilibrium state of H, and it is found to function as an electron donor. The
other Mu$_2$ is considered to be in the hydride state (Mu$^-$) from the
ambipolarity model, suggesting that it is in fast diffusion motion through the
short-lived neutral state due to the charge exchange reaction with conduction
electrons (Mu$^0+e^-\rightleftarrows$Mu$^-$).


  In this paper, we present a deep generative model based method to generate
diverse human motion interpolation results. We resort to the Conditional
Variational Auto-Encoder (CVAE) to learn human motion conditioned on a pair of
given start and end motions, by leveraging the Recurrent Neural Network (RNN)
structure for both the encoder and the decoder. Additionally, we introduce a
regularization loss to further promote sample diversity. Once trained, our
method is able to generate multiple plausible coherent motions by repetitively
sampling from the learned latent space. Experiments on the publicly available
dataset demonstrate the effectiveness of our method, in terms of sample
plausibility and diversity.