  Will Multi-Link Operation (MLO) be able to improve the latency of Wi-Fi
networks? MLO is one of the most disruptive MAC-layer techniques included in
the IEEE 802.11be amendment. It allows a device to use multiple radios
simultaneously and in a coordinated way, providing a new framework to improve
the WLAN throughput and latency. In this paper, we investigate the potential
latency benefits of MLO by using a large dataset containing 5 GHz spectrum
occupancy measurements. Experimental results show that when the channels are
symmetrically occupied, MLO can improve latency by one order of magnitude. In
contrast, in asymmetrically occupied channels, MLO can sometimes be detrimental
and increase latency. To address this case, we introduce Opportunistic
Simultaneous Transmit and Receive (STR+) channel access and study its benefits.


  We propose to combine the Floquet formalism for systems in ac fields with the
dynamical mean-field theory to study correlated electron systems periodically
driven out of equilibrium by external fields such as intense laser light. This
approach has a virtue that we can nonperturbatively include both the
correlation effects and nonlinear effects due to the driving field, which is
imperative in analyzing recent experiments for photoinduced phase transitions.
In solving the problem, we exploit a general theorem that the Hamiltonian in a
Floquet matrix form can be exactly diagonalized for single-band noninteracting
systems. As a demonstration, we have applied the method to the Falicov-Kimball
model in intense ac fields to calculate the spectral function. The result shows
that photoinduced midgap states emerge from strong ac fields, triggering an
insulator-metal transition.


  Weinberg recently pointed out a flaw in the standard argument that large
$N_c$ QCD with color-fundamental quarks [QCD(F)] cannot yield narrow tetraquark
states. In particular, he observed that the argument does not rule out narrow
tetraquarks associated with the leading-order connected diagrams; such
tetraquarks would have a width scaling as $N_c^{-1}$. It is shown here,
however, that while the standard analysis of tetraquarks does not rule them
out, a more thorough analysis rules out quantum-number exotic tetraquarks
associated with the leading-order connected diagrams. This analysis is based
entirely on conventional assumptions used in large $N_c$ physics applied to the
analytic properties of meson-meson scattering. Our result implies that one of
three possibilities must be true: i) quantum-number exotic tetraquarks do not
exist at large $N_c$; ii) quantum-number exotic tetraquarks exist, but are
associated with subleading connected diagrams and have anomalously small widths
that scale as $N_c^{-2}$ or smaller; or iii) the conventional assumptions used
in large $N_c$ analysis are inadequate.


  The R\'enyi entropy is a mathematical generalization of the concept of
entropy and it encodes the total information of a system as a funtion of its
order parameter $\alpha$. The meaning of the R\'enyi entropy in physics is not
completely enstablished: here we determined a general and explicit
representation of the R\'enyi entropy for whichever fluid of particles and
spin-statistics, in the mechanical statistics framework. This allowed us to put
physical constraints to the R\'enyi order $\alpha$, from main thermodynamical
relations and entropy bounds of the holographic theories, defining how much we
can enlarge the Boltmannian concept of entropy.


  The heavy elements (Z>30) are created in neutron-capture processes which
happen at very different nucleosynthetic sites. To study them in an environment
different from the Milky Way, we target these elements in RGB stars in the
Sculptor dwarf spheroidal galaxy. Using ESO VLT/FLAMES spectra, we measure the
chemical abundances of Y, Ba, La, Nd, and Eu, in 98 stars covering
$-2.4<\text{[Fe/H]}<-0.9$. This is the first paper in a series about the
$n$-capture elements in dwarf galaxies, and here we focus on the relative and
absolute timescales of the slow ($s$)- and rapid ($r$)-processes in Sculptor.
From the abundances of the $s$-process element Ba and the $r$-process element
Eu, it is clear that the $r$-process enrichment occurred throughout the entire
chemical evolution history of Sculptor. Furthermore, there is no evidence for
the $r$-process to have a significant time delay relative to core-collapse
supernovae. Neutron star mergers are therefore unlikely the dominant (or only)
nucleosynthetic site of the $r$-process. However, the products of the
$s$-process only become apparent at $\text{[Fe/H]}\approx-2$ in Sculptor, and
the $s$-process becomes the dominant source of Ba at $\text{[Fe/H]}\gtrsim-2$.
We test the use of [Y/Mg] and [Ba/Mg] as chemical clocks in Sculptor. Similarly
to what is observed in the Milky Way, [Y/Mg] and [Ba/Mg] increase towards
younger ages. However, there is an offset, where the abundance ratios of [Y/Mg]
in Sculptor are significantly lower than those of the Milky Way at any given
age. This is most likely caused by metallicity dependence of yields from the
$s$-process, as well as different relative contribution of the $s$-process to
core-collapse supernovae in these galaxies. Comparisons of our data with that
of the Milky Way and the Fornax dwarf spheroidal galaxy furthermore show that
these chemical clocks are both metallicity and environment dependent.


  In the Isogeometric Analysis (IGA) framework, the computational domain has
very often a multipatch representation. The multipatch domain can be obtained
by a volume segmentation of a boundary represented domain, e.g., provided by a
Computer Aided Design (CAD) model. Typically, small gap and overlapping regions
can appear at the patch interfaces of such multipatch representations. In the
current work we consider multipatch representations having only small
overlapping regions between the patches.
  We develop a Discontinuous Galerkin (DG)- IGA method which can be immediately
applied to these representations. Our method appropriately connects the fluxes
of the one face of the overlapping region with the flux of the opposite face.
We provide a theoretical justification of our approach by splitting the whole
error into two components: the first is related to the incorrect representation
of the patches (consistency error) and the second to the approximation
properties of the IGA space. We show bounds for both components of the error.
We verify the theoretical error estimates in a series of numerical examples.


  This work proposes a class of locally differentially private mechanisms for
linear queries, in particular range queries, that leverages correlated input
perturbation to simultaneously achieve unbiasedness, consistency, statistical
transparency, and control over utility requirements in terms of accuracy
targets expressed either in certain query margins or as implied by the
hierarchical database structure. The proposed Cascade Sampling algorithm
instantiates the mechanism exactly and efficiently. Our bounds show that we
obtain near-optimal utility while being empirically competitive against output
perturbation methods.


  The dynamics and thermostatistics of a classical inertial XY model,
characterized by long-range interactions, are investigated on $d$-dimensional
lattices ($d=1,2,$ and 3), through molecular dynamics. The interactions between
rotators decay with the distance $r_{ij}$ like~$1/r_{ij}^{\alpha}$ ($\alpha
\geq 0$), where $\alpha\to\infty$ and $\alpha=0$ respectively correspond to the
nearest-neighbor and infinite-range interactions. We verify that the momenta
probability distributions are Maxwellians in the short-range regime, whereas
$q$-Gaussians emerge in the long-range regime. Moreover, in this latter regime,
the individual energy probability distributions are characterized by long
tails, corresponding to $q$-exponential functions. The present investigation
strongly indicates that, in the long-range regime, central properties fall out
of the scope of Boltzmann-Gibbs statistical mechanics, depending on $d$ and
$\alpha$ through the ratio $\alpha/d$.


  This paper introduces a novel method to estimate distance fields from noisy
point clouds using Gaussian Process (GP) regression. Distance fields, or
distance functions, gained popularity for applications like point cloud
registration, odometry, SLAM, path planning, shape reconstruction, etc. A
distance field provides a continuous representation of the scene defined as the
shortest distance from any query point and the closest surface. The key concept
of the proposed method is the transformation of a GP-inferred latent scalar
field into an accurate distance field by using a reverting function related to
the kernel inverse. The latent field can be interpreted as a smooth occupancy
map. This paper provides the theoretical derivation of the proposed method as
well as a novel uncertainty proxy for the distance estimates. The improved
performance compared with existing distance fields is demonstrated with
simulated experiments. The level of accuracy of the proposed approach enables
novel applications that rely on precise distance estimation: this work presents
echolocation and mapping frameworks for ultrasonic-guided wave sensing in
metallic structures. These methods leverage the proposed distance field with a
physics-based measurement model accounting for the propagation of the
ultrasonic waves in the material. Real-world experiments are conducted to
demonstrate the soundness of these frameworks.


  We develop a minimalist compartmental model to study the impact of mobility
restrictions in Italy during the Covid-19 outbreak. We show that an early
lockdown shifts the epidemic in time, while that beyond a critical value of the
lockdown strength, the epidemic tend to restart after lifting the restrictions.
As a consequence, specific mitigation strategies must be introduced. We
characterize the relative importance of different broad strategies by
accounting for two fundamental sources of heterogeneity, i.e. geography and
demography. First, we consider Italian regions as separate administrative
entities, in which social interactions between age classs occur. Due to the
sparsity of the inter-regional mobility matrix, once started the epidemics tend
to develop independently across areas, justifying the adoption of solutions
specific to individual regions or to clusters of regions. Second, we show that
social contacts between age classes play a fundamental role and that measures
which take into account the age structure of the population can provide a
significant contribution to mitigate the rebound effects. Our model is general,
and while it does not analyze specific mitigation strategies, it highlights the
relevance of some key parameters on non-pharmaceutical mitigation mechanisms
for the epidemics.


  A semigroup $S$ is called an equational domain if any finite union of
algebraic sets over $S$ is algebraic. We prove if a completely regular
semigroup $S$ is an equational domain then $S$ is completely simple.


  The frequency-dependent exchange-correlation potential, which appears in the
usual Kohn-Sham formulation of a time-dependent linear response problem, is a
strongly nonlocal functional of the density, so that a consistent local density
approximation generally does not exist. This problem can be avoided by choosing
the current-density as the basic variable in a generalized Kohn-Sham theory.
This theory admits a local approximation which, for fixed frequency, is exact
in the limit of slowly varying densities and perturbing potentials.


  Direct detection (DD) of dark matter (DM) candidates in the $\lesssim$10 GeV
mass range is very sensitive to the tail of their velocity distribution. The
important quantity is the maximum WIMP speed in the observer's rest frame, i.e.
in average the sum of the local Galactic escape speed $v_{\rm esc}$ and of the
circular velocity of the Sun $v_c$. While the latter has been receiving
continuous attention, the former is more difficult to constrain. The RAVE
Collaboration has just released a new estimate of $v_{\rm esc}$ (Piffl {\em et
al.}, 2014 --- P14) that supersedes the previous one (Smith {\em et al.},
2007), which is of interest in the perspective of reducing the astrophysical
uncertainties in DD. Nevertheless, these new estimates cannot be used blindly
as they rely on assumptions in the dark halo modeling which induce tight
correlations between the escape speed and other local astrophysical parameters.
We make a self-consistent study of the implications of the RAVE results on DD
assuming isotropic DM velocity distributions, both Maxwellian and ergodic.
Taking as references the experimental sensitivities currently achieved by LUX,
CRESST-II, and SuperCDMS, we show that: (i) the exclusion curves associated
with the best-fit points of P14 may be more constraining by up to $\sim 40$%
with respect to standard limits, because the underlying astrophysical
correlations induce a larger local DM density; (ii) the corresponding relative
uncertainties inferred in the low WIMP mass region may be moderate, down to
10-15% below 10 GeV. We finally discuss the level of consistency of these
results with other independent astrophysical constraints. This analysis is
complementary to others based on rotation curves.


  We present DM@NLO, a Fortran 77 based program with a C++ interface dedicated
to precision calculations of dark matter (DM) (co)annihilation cross-sections
and elastic dark matter-nucleon scattering amplitudes in the Minimal
Supersymmetric (SUSY) Standard Model (MSSM) at next-to-leading order (NLO) in
perturbative (SUSY) QCD. If the annihilating initial particles carry an
electric or colour charge, the Sommerfeld enhanced cross section is included as
well and can be matched to the NLO cross section. We review these calculations
including technical details relevant for using the code. We illustrate their
impact by applying DM@NLO to an example scenario in the constrained MSSM.


  We apply a new statistics, the factorial moment correlators, to density maps
obtained from the APM survey. The resulting correlators are all proportional to
the two point correlation function, substantially amplified, with an
amplification nearly exponential with the total rank of the correlators. This
confirms the validity of the hierarchical clustering assumption on the dynamic
range examined, corresponding to $0.5 \hmpc - 50 \hmpc$ in three dimensional
space. The Kirkwood superposition with loop terms is strongly rejected. The
structure coefficients of the hierarchy are also fitted. The high quality of
the APM catalog enabled us to disentangle the various contributions from the
power spectrum, small scale nonlinear clustering, and combinatorial effects,
all of which affect the amplification of the correlators. These effects should
appear in correlations of clusters in a similar fashion.


  Flexoelectricity has garnered much attention owing to its ability to bring
electromechanical functionality to non-piezoelectric materials and its
nanoscale significance. In order to move towards a more complete understanding
of this phenomenon and improve the efficacy of flexoelectric-based devices, it
is necessary to quantify microstructural contributions to flexoelectricity.
Here we characterize the flexoelectric response of bulk centrosymmetric
LaAlO$_3$ crystals with different twin boundary microstructures. We show that
twin boundary flexoelectric contributions are comparable to intrinsic
contributions at room temperature and enhance the flexoelectric response by ~4x
at elevated temperatures. Additionally, we observe time-dependent and
non-linear flexoelectric responses associated with strain-gradient-induced twin
boundary polarization. These results are explained by considering the interplay
between twin boundary orientation, beam-bending strain fields, and pinning site
interactions.


  The goal of human stylization is to transfer full-body human photos to a
style specified by a single art character reference image. Although previous
work has succeeded in example-based stylization of faces and generic scenes,
full-body human stylization is a more complex domain. This work addresses
several unique challenges of stylizing full-body human images. We propose a
method for one-shot fine-tuning of a pose-guided human generator to preserve
the "content" (garments, face, hair, pose) of the input photo and the "style"
of the artistic reference. Since body shape deformation is an essential
component of an art character's style, we incorporate a novel skeleton
deformation module to reshape the pose of the input person and modify the DiOr
pose-guided person generator to be more robust to the rescaled poses falling
outside the distribution of the realistic poses that the generator is
originally trained on. Several human studies verify the effectiveness of our
approach.


  The building blocks of a quantum theory of general relativity are expected to
be discrete structures. Loop quantum gravity is formulated using a basis of
spin networks (wave functions over oriented graphs with coloured edges), thus
realizing the aforementioned expectation. Semiclassical states should, however,
reproduce the classical smooth geometry in the appropriate limits. The question
of how to recover a continuous geometry from these discrete structures is,
therefore, relevant in this context. Following previous works by Bombelli et
al. we explore this problem from a rather general mathematical perspective
using, in particular, properties of Voronoi graphs to search for their
compatible continuous geometries. We test the previously proposed methods for
computing the curvature associated to such graphs and analyse the framework in
detail, in the light of the results obtained.


  Massless superparticle model is considered on the OSp(4|6)/(SO(1,3) x U(3))
supercoset manifold and in the AdS_4 x CP^3 superspace. In the former case
integrability of the equations of motion is rather obvious, while for the AdS_4
x CP^3 superparticle we prove integrability in the partial kappa-symmetry gauge
for which 4 anticommuting coordinates related to the broken conformal
supersymmetry are set to zero. This allows us to propose expression for the Lax
pair that may encode complete equations of motion for the AdS_4 x CP^3
superparticle.


  Since its inception, the choice modelling field has been dominated by
theory-driven modelling approaches. Machine learning offers an alternative
data-driven approach for modelling choice behaviour and is increasingly drawing
interest in our field. Cross-pollination of machine learning models, techniques
and practices could help overcome problems and limitations encountered in the
current theory-driven modelling paradigm, such as subjective labour-intensive
search processes for model selection, and the inability to work with text and
image data. However, despite the potential benefits of using the advances of
machine learning to improve choice modelling practices, the choice modelling
field has been hesitant to embrace machine learning. This discussion paper aims
to consolidate knowledge on the use of machine learning models, techniques and
practices for choice modelling, and discuss their potential. Thereby, we hope
not only to make the case that further integration of machine learning in
choice modelling is beneficial, but also to further facilitate it. To this end,
we clarify the similarities and differences between the two modelling
paradigms; we review the use of machine learning for choice modelling; and we
explore areas of opportunities for embracing machine learning models and
techniques to improve our practices. To conclude this discussion paper, we put
forward a set of research questions which must be addressed to better
understand if and how machine learning can benefit choice modelling.


  We study the interplay between an on-site Hubbard repulsion and quasiperiodic
potential in one-dimensional fermion chains using the density matrix
renormalization group. We find that, at half-filling, the quasiperiodic
potential can destroy the Mott gap, leading to a metallic Luttinger liquid
phase between the gapped Mott insulator at strong repulsion and localized
gapless Aubry- Andr\'e insulator at strong quasiperiodic potential. Away from
half-filing, the metallic phase of the interacting model persists to larger
critical strengths of the potential than in the non-interacting case,
suggesting interaction-stabilized delocalization at finite doping. We
characterize the Luttinger liquid through its charge and spin correlations,
structure factors, and entanglement entropy.


  A coherent microwave radiation, concomitant with experiments of stimulated
Raman scattering (SRS) in weakly compressed hydrogen, had been observed;
qualitative and quantitative results had been obtained, and are given in this
article. .An attempt to interpret this emission on a phenomenological basis is
presented, on the basis of a model in which the influence of hydrogen molecule
anharmonicity is taken into account, leading to two main effects:1) possible
existence of an intermediate state involving a vibrationally excited
quasiparticle system, the lifetime of which should be very short (0.1-1 ps); 2)
nonlinear coupling between the Stokes light wave and the longitudinal
propagating waves associated to the quasiparticle system. Moreover, several
processes are also to be taken into account; existence of a macroscopic
longitudinal electric field, due to coulombian interactions; electrostriction
effect giving rise to a "piezoelectric" character for the quasiparticle system;
Cherenkov-type and anomalous relativistic Doppler effects. Then, the source of
the coherent microwave radiation might be the in phase quadrature part of the
longitudinal polarisation associated to the quasiparticle waves, and its
emission should appear like parametric instabilities. Concerning the SRS
process itself, an additional contribution to the Raman gain should arise from
the laser induced electro-optic effect.


  This paper gives an Indic-to-Indic (IL-IL) MNMT baseline model for 11 ILs
implemented on the Samanantar corpus and analyzed on the Flores-200 corpus. All
the models are evaluated using the BLEU score. In addition, the languages are
classified under three groups namely East Indo- Aryan (EI), Dravidian (DR), and
West Indo-Aryan (WI). The effect of language relatedness on MNMT model
efficiency is studied. Owing to the presence of large corpora from English (EN)
to ILs, MNMT IL-IL models using EN as a pivot are also built and examined. To
achieve this, English- Indic (EN-IL) models are also developed, with and
without the usage of related languages. Results reveal that using related
languages is beneficial for the WI group only, while it is detrimental for the
EI group and shows an inconclusive effect on the DR group, but it is useful for
EN-IL models. Thus, related language groups are used to develop pivot MNMT
models. Furthermore, the IL corpora are transliterated from the corresponding
scripts to a modified ITRANS script, and the best MNMT models from the previous
approaches are built on the transliterated corpus. It is observed that the
usage of pivot models greatly improves MNMT baselines with AS-TA achieving the
minimum BLEU score and PA-HI achieving the maximum score. Among languages, AS,
ML, and TA achieve the lowest BLEU score, whereas HI, PA, and GU perform the
best. Transliteration also helps the models with few exceptions. The best
increment of scores is observed in ML, TA, and BN and the worst average
increment is observed in KN, HI, and PA, across all languages. The best model
obtained is the PA-HI language pair trained on PAWI transliterated corpus which
gives 24.29 BLEU.


  Direct CP violation can exist in untagged, neutral B-meson decays to certain
self-conjugate, hadronic final states. It can occur if the resonances which
appear therein permit the identification of distinct, CP-conjugate states -- in
analogy to stereochemistry, we term such states ``CP-enantiomers.'' These
states permit the construction of a CP-odd amplitude combination in the
untagged decay rate, which is non-zero if direct CP violation is present. The
decay B\to \pi^+\pi^-\pi^0, containing the distinct CP-conjugate states \rho^+
\pi^- and \rho^- \pi^+, provides one such example of a CP-enantiomeric pair. We
illustrate the possibilities in various multi-particle final states.


  Coordinate-MLPs are emerging as an effective tool for modeling
multidimensional continuous signals, overcoming many drawbacks associated with
discrete grid-based approximations. However, coordinate-MLPs with ReLU
activations, in their rudimentary form, demonstrate poor performance in
representing signals with high fidelity, promoting the need for positional
embedding layers. Recently, Sitzmann et al. proposed a sinusoidal activation
function that has the capacity to omit positional embedding from
coordinate-MLPs while still preserving high signal fidelity. Despite its
potential, ReLUs are still dominating the space of coordinate-MLPs; we
speculate that this is due to the hyper-sensitivity of networks -- that employ
such sinusoidal activations -- to the initialization schemes. In this paper, we
attempt to broaden the current understanding of the effect of activations in
coordinate-MLPs, and show that there exists a broader class of activations that
are suitable for encoding signals. We affirm that sinusoidal activations are
only a single example in this class, and propose several non-periodic functions
that empirically demonstrate more robust performance against random
initializations than sinusoids. Finally, we advocate for a shift towards
coordinate-MLPs that employ these non-traditional activation functions due to
their high performance and simplicity.


  We report a crucial experimental test of the present models of the peak
effect in weakly disordered type-II superconductors. Our results favor the
scenario in which the peak effect arises from a crossover between the Larkin
pinning length and a rapidly falling elastic length in a vortex phase populated
with thermally excited topological defects. A thickness dependence study of the
onset of the peak effect at varying driving currents suggests that both screw
and edge dislocations are involved in the vortex lattice disordering. The
driven dynamics in 3D samples are drastically different from those in 2D
samples. We suggest that this may be a consequence of the absence of a Peierls
potential for screw dislocations in a vortex line lattice.


  We confront the most common version of the CP-conserving 2HDM with LHC data,
taking into account all previously available experimental data. We also discuss
the scenario where the 125 GeV Higgs discovered at the LHC is the lightest
neutral scalar of a particular CP-violating 2HDM. In this scenario we focus on
what data can already tell us about the amount of mixing between CP-even and
CP-odd states.


  We revisit the Bogoliubov theory of quantum droplets proposed by Petrov
{[}Phys. Rev. Lett. \textbf{115}, 155302 (2015){]} for an ultracold Bose-Bose
mixture, where the mean-field collapse is stabilized by the Lee-Huang-Yang
quantum fluctuations. We show that a loophole in Petrov's theory, i.e., the
ignorance of the softening complex Bogoliubov spectrum, can be naturally
removed by the introduction of bosonic pairing. The pairing leads to weaker
mean-field attractions, and also stronger Lee-Huang-Yang term in the case of
unequal intraspecies interactions. As a result, the equilibrium density for the
formation of self-bound droplets significantly decrease in the deep droplet
regime, in agreement with a recent observation from diffusion Monte Carlo
simulations. Our construction of a consistent Bogoliubov theory paves the way
to understand the puzzling low critical number of small quantum droplets
observed in the experiment {[}Science \textbf{359}, 301 (2018){]}.


  This study investigates the dynamical response of dark matter (DM) halos to
recurrent starbursts in forming less-massive galaxies to solve the core-cusp
problem. The gas, which is heated by supernova feedback after a starburst,
expands and the star formation then terminates. This expanding gas loses energy
by radiative cooling and then falls back toward the galactic center.
Subsequently, the starburst is enhanced again. This cycle of expansion and
contraction of the interstellar gas leads to a repetitive change in the
gravitational potential of the gas. The resonance between DM particles and the
density wave excited by the oscillating potential plays a key role in
understanding the physical mechanism of the cusp-core transition of DM halos.
DM halos effectively gain kinetic energy from the baryon potential through the
energy transfer driven by the resonance between the particles and density
waves. We determine that the critical condition for the cusp-core transition is
such that the oscillation period of the gas potential is approximately the same
as the local dynamical time of DM halos. We present the resultant core radius
of a DM halo after the cusp-core transition induced by the resonance by using
the conventional mass density profile predicted by the cold dark matter models.
Moreover, we verify the analytical model by using $N$-body simulations, and the
results validate the resonance model.


  We show that perturbations of massless fields in the Kerr black hole
background enjoy a hidden $SL(2,\mathbb{R})\times {U}(1)$ ("Love") symmetry in
the properly defined near zone approximation. Love symmetry mixes IR and UV
modes. Still, this approximate symmetry allows us to derive exact results about
static tidal responses. Generators of the Love symmetry are globally well
defined and have a smooth Schwarzschild limit. Generic regular solutions of the
near zone Teukolsky equation form infinite-dimensional $SL(2,\mathbb{R})$
representations. In some special cases ($\hat{\ell}$ parameter is an integer),
these are highest weight representations. This is the situation that
corresponds to vanishing Love numbers. In particular, static perturbations of
four-dimensional Schwarzschild black holes belong to finite-dimensional
representations. Other known facts about static Love numbers also acquire an
elegant explanation in terms of the $SL(2,\mathbb{R})$ representation theory.


  Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a minimally
invasive imaging technique which can be used for characterizing tumor biology
and tumor response to radiotherapy. Pharmacokinetic (PK) estimation is widely
used for DCE-MRI data analysis to extract quantitative parameters relating to
microvascu- lature characteristics of the cancerous tissues. Unavoidable noise
corruption during DCE-MRI data acquisition has a large effect on the accuracy
of PK estimation. In this paper, we propose a general denoising paradigm called
gather- noise attenuation and reduce (GNR) and a novel temporal-spatial
collaborative filtering (TSCF) denoising technique for DCE-MRI data. TSCF takes
advantage of temporal correlation in DCE-MRI, as well as anatomical spatial
similar- ity to collaboratively filter noisy DCE-MRI data. The proposed TSCF
denoising algorithm decreases the PK parameter normalized estimation error by
57% and improves the structural similarity of PK parameter estimation by 86%
com- pared to baseline without denoising, while being an order of magnitude
faster than state-of-the-art denoising methods. TSCF improves the univariate
linear regression (ULR) c-statistic value for early prediction of pathologic
response up to 18%, and shows complete separation of pathologic complete
response (pCR) and non-pCR groups on a challenge dataset.


  We present results from an imaging and spectroscopic study of four strong
MgII absorbers of W(2796) >~ 1 Ang revealed by the afterglow of GRB060418 at
z_GRB=1.491. These absorbers, at z=0.603,0.656,1.107 and z_GRB, exhibit large
ion abundances that suggest neutral gas column densities characteristic of
damped Lya systems. The imaging data include optical images obtained using LRIS
on the Keck I telescope and using ACS on board HST, and near-infrared H-band
images obtained using PANIC on the Magellan Baade Telescope and K'-band images
obtained using NIRC2 with LGSAO on the Keck II telescope. These images reveal
six distinct objects at <~ 3.5'' of the afterglow's position, two of which
exhibit well-resolved mature disk morphology, one shows red colors, and three
are blue compact sources. Follow-up spectroscopic observations using LRIS
confirm that one of the disk galaxies coincides with the MgII absorber at
z=0.656. The observed broad-band spectral energy distributions of the second
disk galaxy and the red source indicate that they are associated with the
absorbers at z=0.603 and z=1.107, respectively. These results show that strong
MgII absorbers identified in GRB afterglow spectra are associated with typical
galaxies of luminosity ~ (0.1-1) L* at impact parameter <~ 10 h^-1 kpc. The
close angular separation would preclude easy detections toward a bright quasar.
Finally, we associate the remaining three blue compact sources with the GRB
host galaxy, noting that they are likely star-forming knots located at
projected distances 2-12 h^-1 kpc from the afterglow. At the afterglow's
position, we derive a 2-sigma upper limit to the underlying SFR intensity of
0.0074 M_sun yr^-1 kpc^-2.


  5th Generation Mobile Network Systems(5G systems) operating in milli-meter
Wave (mmW) bands will employ base stations with integrated Active Antenna
Systems (AASs) capable of beam-tracking using narrow beams obtained from array
antennas encapsulated in a chip-like device. Regulatory limits for unwanted
Radio Frequency (RF) emissions are currently set in terms of Total Radiated
Power (TRP). As measurements at the antenna connectors are not possible,
Over-The-Air (OTA) methods for TRP of unwanted emissions are needed. The method
investigated here uses power density measurements on a spherical surface in an
anechoic chamber. Two major challenges with such a method are: need for large
number of angular points, and search for worst case antenna configuration per
frequency for electrically large devices at high frequencies. These challenges
are addressed by investigating the impact of correlation, sparse sampling, and
use of beam sweeping on the TRP estimate. Finally, it is investigated how and
in which spatial regions near-field tangential electric field measurements can
be used to assess TRP.


  We establish a 3-manifold invariant for each finite-dimensional, involutory
Hopf algebra. If the Hopf algebra is the group algebra of a group $G$, the
invariant counts homomorphisms from the fundamental group of the manifold to
$G$. The invariant can be viewed as a state model on a Heegaard diagram or a
triangulation of the manifold. The computation of the invariant involves tensor
products and contractions of the structure tensors of the algebra. We show that
every formal expression involving these tensors corresponds to a unique
3-manifold modulo a well-understood equivalence. This raises the possibility of
an algorithm which can determine whether two given 3-manifolds are
homeomorphic.


  Recently a number of empirical "universal" scaling law papers have been
published, most notably by OpenAI. `Scaling laws' refers to power-law decreases
of training or test error w.r.t. more data, larger neural networks, and/or more
compute. In this work we focus on scaling w.r.t. data size $n$. Theoretical
understanding of this phenomenon is largely lacking, except in
finite-dimensional models for which error typically decreases with $n^{-1/2}$
or $n^{-1}$, where $n$ is the sample size. We develop and theoretically analyse
the simplest possible (toy) model that can exhibit $n^{-\beta}$ learning curves
for arbitrary power $\beta>0$, and determine whether power laws are universal
or depend on the data distribution.


  In this communication we introduce a new design of the magnifying superlens
and demonstrate it in the experiment.


  We associate to any holomorphic vertex algebra a collection of
Teichm\"{u}ller modular forms, one in each genus. In genus one we obtain the
character of the vertex algebra, and we thus reprove Zhu's modularity result.
In higher genus, we prove that these forms have an expansion in terms of the
correlation functions of the vertex algebra. We propose applications to the
Schottky problem, to the study of the slope of the effective cone of the moduli
space of curves, and to the classification of holomorphic vertex algebras. In
particular, we prove a uniqueness result for high genera partition functions of
the moonshine vertex algebra.


  Restaking protocols have aggregated billions of dollars of security by
utilizing token incentives and payments. A natural question to ask is: How much
security do restaked services \emph{really} need to purchase? To answer this
question, we expand a model of Durvasula and Roughgarden [DR24] that includes
incentives and an expanded threat model consisting of strategic attackers and
users. Our model shows that an adversary with a strictly submodular profit
combined with strategic node operators who respond to incentives can avoid the
large-scale cascading failures of~[DR24]. We utilize our model to construct an
approximation algorithm for choosing token-based incentives that achieve a
given security level against adversaries who are bounded in the number of
services they can simultaneously attack. Our results suggest that incentivized
restaking protocols can be secure with proper incentive management.


  Census counts are subject to different types of nonsampling errors. One of
these main errors is coverage error. Undercount and overcount are two types of
coverage error. Undercount usually occurs more than the other, thus net
undercount estimation is important. There are various methods for estimating
the coverage error in censuses. One of these methods is dual system (DS) that
usually uses data from the census and a post-enumeration survey (PES). In this
paper, the coverage error and necessity of its evaluation, PES design and DS
method are explained. Then PES associated approaches and their effects on DS
estimation are illustrated and these approaches are compared. Finally, we
explain the Statistical Center of Iran method of estimating net undercount in
Iran 2006 population and dwelling census and a suggestion will be given for
improving net undercount estimation in population and dwelling censuses of
Iran.


  This research introduces a revolutionary paradigm for HetNet management,
presenting an innovative algorithmic framework that transcends traditional
notions of network capacity enhancement. Our exploration delves into the
intricate interplay among distinct components, weaving together metaheuristic
algorithms, Neural Networks optimization, and Federated Learning approaches.
The primary focus is on optimizing capacity in IoT-based heterogeneous networks
while ensuring impeccable coverage and data reliability. Employing multi-layer
optimization methods, we propose a dynamic model for optimal transmission
strategy, strategically allocating replicas within cloud computing environments
to curtail data access costs. Our algorithm not only discerns optimal data
replication locations but also navigates the delicate balance between spectral
efficiency and ergodic capacity in cellular IoT networks with small cells using
on/off control. The orchestrated interplay between metaheuristic algorithms,
Neural Networks optimization, and Federated Learning orchestrates resource
reallocation, attaining an optimal balance between spectral efficiency, power
utility, and ergodic capacity based on Quality of Service (QoS) requirements.
Simulation results corroborate the efficacy of our approach, showcasing
enhanced tradeoffs between spectral efficiency and total ergodic capacity with
diminished outage probability compared to prevailing algorithms across diverse
scenarios.


  We report on a lattice investigation of improved quenched Wilson fermions
above and below the confinement-deconfinement phase transition. Results on
meson screening masses as well as spatial wave functions are presented.
Moreover, the meson dispersion relation is studied. Below the critical
temperature we do not observe any significant temperature effect while above
$T_c$ the data are consistent with a leading free quark behavior.


  For a real or complex one-dimensional map satisfying a weak hyperbolicity
assumption, we study the existence and statistical properties of physical
measures, with respect to geometric reference measures. We also study geometric
properties of these measures.


  In an earlier paper [4], we derived asymptotic formulas for the number of
representations of zero and of large positive integers by the cubic forms in
seven variables which can be written as $L_1(x_1,x_2,x_3) Q_1(x_1,x_2,x_3)+
L_2(x_4,x_5,x_6) Q_2(x_4,x_5,x_6) + a_7 x_7^3$ where $L_1$ and $L_2$ are linear
forms, $Q_1$ and $Q_2$ are quadratic forms and $a_7$ is a non-zero integer and
for which certain quantities related to $L_1Q_1$ and $L_2Q_2$ were non-zero. In
this paper, we consider the case when one or both of these quantities is zero
but $L_1Q_1$ and $L_2Q_2$ are still nondegenerate cubic forms in three
variables.


  The category of pruned trees has been defined by M. Batanin with the aim of
understanding the cell structure of certain E_n-operads in categorical terms.
The objects of this category are planar trees with n levels so that all leaves
are at the top level of the tree. The goal of this article is to prove that the
category of pruned trees is Koszul. This result gives us a minimal differential
graded model of this category, small complexes to compute Tor and Ext functors
in associated categories of diagrams, and allows us to generalize a recent
result of M. Livernet and B. Richter about the interpretation of E_n-homology
in terms of categorical Tor functors.


  We prove existence, regularity and a Feynman-Ka\v{c} representation formula
of the strong solution to the free boundary problem arising in the financial
problem of the pricing of the American Asian option with arithmetic average.


  Previous work on bridging anaphora recognition (Hou et al., 2013a) casts the
problem as a subtask of learning fine-grained information status (IS). However,
these systems heavily depend on many hand-crafted linguistic features. In this
paper, we propose a discourse context-aware self-attention neural network model
for fine-grained IS classification. On the ISNotes corpus (Markert et al.,
2012), our model with the contextually-encoded word representations (BERT)
(Devlin et al., 2018) achieves new state-of-the-art performances on
fine-grained IS classification, obtaining a 4.1% absolute overall accuracy
improvement compared to Hou et al. (2013a). More importantly, we also show an
improvement of 3.9% F1 for bridging anaphora recognition without using any
complex hand-crafted semantic features designed for capturing the bridging
phenomenon.


  We present R-ANODE, a new method for data-driven, model-agnostic resonant
anomaly detection that raises the bar for both performance and
interpretability. The key to R-ANODE is to enhance the inductive bias of the
anomaly detection task by fitting a normalizing flow directly to the small and
unknown signal component, while holding fixed a background model (also a
normalizing flow) learned from sidebands. In doing so, R-ANODE is able to
outperform all classifier-based, weakly-supervised approaches, as well as the
previous ANODE method which fit a density estimator to all of the data in the
signal region instead of just the signal. We show that the method works equally
well whether the unknown signal fraction is learned or fixed, and is even
robust to signal fraction misspecification. Finally, with the learned signal
model we can sample and gain qualitative insights into the underlying anomaly,
which greatly enhances the interpretability of resonant anomaly detection and
offers the possibility of simultaneously discovering and characterizing the new
physics that could be hiding in the data.


  We propose and experimentally demonstrate a new cooling mechanism leading to
purification of a spinor Bose-Einstein Condensate (BEC). Our scheme starts with
a BEC polarized in the lowest energy spin state. Spin excited states are
thermally populated by lowering the single particle energy gap set by the
magnetic field. Then these spin-excited thermal components are filtered out,
which leads to an increase of the BEC fraction. We experimentally demonstrate
such cooling for a spin 3 52Cr dipolar BEC. Our scheme should be applicable to
Na or Rb, with perspective to reach temperatures below 1 nK.


  After new publications of H1 data for the diffractive photoproduction of
dijets, which overlap with the earlier published H1 data and the recently
published data of the ZEUS collaboration, have appeared, we have recalculated
the cross sections for this process in next-to-leading order (NLO) of
perturbative QCD to see whether they can be interpreted consistently. The
results of these calculations are compared to the data of both collaborations.
We find that the NLO cross sections disagree with the data, showing that
factorization breaking occurs at that order. If direct and resolved
contributions are both suppressed by the same amount, the global suppression
factor depends on the transverse-energy cut. However, by suppressing only the
resolved contribution, also reasonably good agreement with all the data is
found with a suppression factor independent of the transverse-energy cut.


  In this paper we propose a multivariate ordinal regression model which allows
the joint modeling of three-dimensional panel data containing both repeated and
multiple measurements for a collection of subjects. This is achieved by a
multivariate autoregressive structure on the errors of the latent variables
underlying the ordinal responses, where we distinguish between the correlations
at a single point in time and the persistence over time. The error distribution
is assumed to be normal or Student t distributed. The estimation is performed
using composite likelihood methods. We perform several simulation exercises to
investigate the quality of the estimates in different settings as well as in
comparison with a Bayesian approach. The simulation study confirms that the
estimation procedure is able to recover the model parameters well and is
competitive in terms of computation time. We also introduce R package mvordflex
and illustrate how this implementation can be used to estimate the proposed
model in a user-friendly, convenient way. Finally, we illustrate the framework
on a data set containing firm failure and credit ratings information from the
rating agencies S&P and Moody's for US listed companies.


  The effect of surface degradation of the thermolectric cobaltite on Raman
spectra is discussed and compared to experimental results from Co3O4 single
crystals. We conclude that on NaCl flux grown NaxCoO2 crystals a surface layer
of Co3O4 easily forms that leads to the observation of an intense phonon around
700 cm-1 [Phys. Rev. B 70, 052502 (2004)]. Raman spectra on freshly cleaved
crystals from optical floating zone ovens do not show such effects and have a
high frequency phonon cut-off at approximately 600 cm -1 [Phys. Rev. Lett 96,
167204 (2006)]. We discuss the relation of structural dimensionality,
electronic correlations and the high frequency phonon cut-off of the
thermolectric cobaltite.


  While backpropagation (BP) is the mainstream approach for gradient
computation in neural network training, its heavy reliance on the chain rule of
differentiation constrains the designing flexibility of network architecture
and training pipelines. We avoid the recursive computation in BP and develop a
unified likelihood ratio (ULR) method for gradient estimation with just one
forward propagation. Not only can ULR be extended to train a wide variety of
neural network architectures, but the computation flow in BP can also be
rearranged by ULR for better device adaptation. Moreover, we propose several
variance reduction techniques to further accelerate the training process. Our
experiments offer numerical results across diverse aspects, including various
neural network training scenarios, computation flow rearrangement, and
fine-tuning of pre-trained models. All findings demonstrate that ULR
effectively enhances the flexibility of neural network training by permitting
localized module training without compromising the global objective and
significantly boosts the network robustness.


  In ultrafast experiments, an optical pump pulse often generates transient
domain walls of the order parameter in materials with spontaneous symmetry
breaking, due to either a finite penetration depth of the light on a
three-dimensional (3D) material, or a finite spot size on a two-dimensional
(2D) material. We clarify the decaying process of such a domain wall that is
caused by fluctuations of the order parameters. We study a generic system with
$U(1)$-symmetric order, and those with an additional weak $Z_2$
($U(1)$-symmetry-breaking) term, representing the charge-density-wave (CDW)
orders in recent experiments. The decay process comprises two non-trivial
stages. During the first stage, exponentially growing thermal fluctuations
convert the domain wall into an interface with randomly distributed topological
defects. In the second stage, the topological defects undergo a coarsening
dynamics within the interface. For a 2D interface in the 3D system, the
coarsening dynamics leads to a diffusive growth of the correlation length. For
a one-dimensional (1D) interface in the 2D system with the weak $Z_2$ term, the
correlation-length growth shows a crossover from diffusive to sub-diffusive
behavior. Our theory provides a fundamental physical picture for the dynamics
of pump-induced domain walls in ultrafast experiments.


  Automatic and consistent meningioma segmentation in T1-weighted MRI volumes
and corresponding volumetric assessment is of use for diagnosis, treatment
planning, and tumor growth evaluation. In this paper, we optimized the
segmentation and processing speed performances using a large number of both
surgically treated meningiomas and untreated meningiomas followed at the
outpatient clinic. We studied two different 3D neural network architectures:
(i) a simple encoder-decoder similar to a 3D U-Net, and (ii) a lightweight
multi-scale architecture (PLS-Net). In addition, we studied the impact of
different training schemes. For the validation studies, we used 698 T1-weighted
MR volumes from St. Olav University Hospital, Trondheim, Norway. The models
were evaluated in terms of detection accuracy, segmentation accuracy and
training/inference speed. While both architectures reached a similar Dice score
of 70% on average, the PLS-Net was more accurate with an F1-score of up to 88%.
The highest accuracy was achieved for the largest meningiomas. Speed-wise, the
PLS-Net architecture tended to converge in about 50 hours while 130 hours were
necessary for U-Net. Inference with PLS-Net takes less than a second on GPU and
about 15 seconds on CPU. Overall, with the use of mixed precision training, it
was possible to train competitive segmentation models in a relatively short
amount of time using the lightweight PLS-Net architecture. In the future, the
focus should be brought toward the segmentation of small meningiomas (less than
2ml) to improve clinical relevance for automatic and early diagnosis as well as
speed of growth estimates.


  $PSP$ and $SolO$ data are utilized to investigate magnetic field
intermittency in the solar wind (SW). Small-scale intermittency $(20-100d_{i})$
is observed to radially strengthen when methods relying on higher-order moments
are considered ($SF_q$, $SDK$), but no clear trend is observed at larger
scales. However, lower-order moment-based methods (e.g., PVI) are deemed more
appropriate for examining the evolution of the bulk of Coherent Structures
(CSs), $PVI \ge 3$. Using PVI, we observe a scale-dependent evolution in the
fraction of the dataset occupied by CSs, $f_{PVI \ge 3}$. Specifically,
regardless of the SW speed, a subtle increase is found in $f_{PVI\ge3}$ for
$\ell =20 d_i$, in contrast to a more pronounced radial increase in CSs
observed at larger scales. Intermittency is investigated in relation to plasma
parameters. Though, slower SW speed intervals exhibit higher $f_{PVI \geq 6}$
and higher kurtosis maxima, no statistical differences are observed for $f_{PVI
\geq 3}$. Highly Alfv\'enic intervals, display lower levels of intermittency.
The anisotropy with respect to the angle between the magnetic field and SW
flow, $\Theta_{VB}$ is investigated. Intermittency is weaker at $\Theta_{VB}
\approx 0^{\circ}$ and is strengthened at larger angles. Considering the
evolution at a constant alignment angle, a weakening of intermittency is
observed with increasing advection time of the SW. Our results indicate that
the strengthening of intermittency in the inner heliosphere is driven by the
increase in comparatively highly intermittent perpendicular intervals sampled
by the probes with increasing distance, an effect related directly to the
evolution of the Parker spiral.


  Georeferenced compositional data are prominent in many scientific fields and
in spatial statistics. This work addresses the problem of proposing models and
methods to analyze and predict, through kriging, this type of data. To this
purpose, a novel class of transformations, named the Isometric
$\alpha$-transformation ($\alpha$-IT), is proposed, which encompasses the
traditional Isometric Log-Ratio (ILR) transformation. It is shown that the ILR
is the limit case of the $\alpha$-IT as $\alpha$ tends to 0 and that $\alpha=1$
corresponds to a linear transformation of the data. Unlike the ILR, the
proposed transformation accepts 0s in the compositions when $\alpha>0$. Maximum
likelihood estimation of the parameter $\alpha$ is established. Prediction
using kriging on $\alpha$-IT transformed data is validated on synthetic spatial
compositional data, using prediction scores computed either in the geometry
induced by the $\alpha$-IT, or in the simplex. Application to land cover data
shows that the relative superiority of the various approaches w.r.t. a
prediction objective depends on whether the compositions contained any zero
component. When all components are positive, the limit cases (ILR or linear
transformations) are optimal for none of the considered metrics. An
intermediate geometry, corresponding to the $\alpha$-IT with maximum likelihood
estimate, better describes the dataset in a geostatistical setting. When the
amount of compositions with 0s is not negligible, some side-effects of the
transformation gets amplified as $\alpha$ decreases, entailing poor kriging
performances both within the $\alpha$-IT geometry and for metrics in the
simplex.


  We present a fully analytical, time-dependent leptonic one-zone model that
describes a simplified radiation process of multiple interacting
ultrarelativistic electron populations, accounting for the flaring of GeV
blazars. In this model, several mono-energetic, ultrarelativistic electron
populations are successively and instantaneously injected into the emission
region, i.e., a magnetized plasmoid propagating along the blazar jet, and
subjected to linear, time-independent synchrotron radiative losses, which are
caused by a constant magnetic field, and nonlinear, time-dependent synchrotron
self-Compton radiative losses in the Thomson limit. Considering a general
multiple-injection scenario is, from a physical point of view, more realistic
than the usual single-injection scenario invoked in common blazar models, as
blazar jets may extend over tens of kiloparsecs and, thus, most likely pick up
several particle populations from intermediate clouds. We analytically compute
the electron number density by solving a kinetic equation using Laplace
transformations and the method of matched asymptotic expansions. Moreover, we
explicitly calculate the optically thin synchrotron intensity, the synchrotron
self-Compton intensity in the Thomson limit, as well as the associated total
fluences. In order to mimic injections of finite duration times and radiative
transport, we model flares by sequences of these instantaneous injections,
suitably distributed over the entire emission region. Finally, we present a
parameter study for the total synchrotron and synchrotron self-Compton fluence
spectral energy distributions for a generic three-injection scenario, varying
the magnetic field strength, the Doppler factor, and the initial electron
energy of the first injection in realistic parameter domains, demonstrating
that our model can reproduce the typical broad-band behavior seen in
observational data.


  The maser, older sibling of the laser, has been confined to relative
obscurity due to its reliance on cryogenic refrigeration and high-vacuum
systems. Despite this it has found application in deep-space communications and
radio astronomy due to its unparalleled performance as a low-noise amplifier
and oscillator. The recent demonstration of a room-temperature solid- state
maser exploiting photo-excited triplet states in organic pentacene molecules
paves the way for a new class of maser that could find applications in
medicine, security and sensing, taking advantage of its sensitivity and low
noise. However, to date, only pulsed operation has been observed in this
system. Furthermore, organic maser molecules have poor thermal and mechanical
properties, and their triplet sub-level decay rates make continuous emission
challenging: alternative materials are therefore required. Therefore, inorganic
materials containing spin-defects such as diamond and silicon carbide have been
proposed. Here we report a continuous-wave (CW) room-temperature maser
oscillator using optically pumped charged nitrogen-vacancy (NV) defect centres
in diamond. This demonstration unlocks the potential of room-temperature
solid-state masers for use in a new generation of microwave devices.


  A colored space is the pair $(X,r)$ of a set $X$ and a function $r$ whose
domain is $\binom{X}{2}$. Let $(X,r)$ be a finite colored space and
$Y,Z\subseteq X$. We shall write $Y\simeq_r Z$ if there exists a bijection
$f:Y\to Z$ such that $r(U)=r(f(U))$ for each $U\in\binom{Y}{2}$. We denote the
numbers of equivalence classes with respect to $\simeq_r$ contained in
$\binom{X}{2}$ and $\binom{X}{3}$ by $a_2(r)$ and $a_3(r)$, respectively. In
this paper we prove that $a_2(r)\leq a_3(r)$ when $5\leq |X|$, and show what
happens when the equality holds.


  We consider the six-sphere S^6=G_2/SU(3) and its twistor space Z=G_2/U(2)
associated with the SU(3)-structure on S^6. It is shown that a Hermitian
Yang-Mills connection (instanton) on a smooth vector bundle over S^6 is
equivalent to a flat partial connection on a vector bundle over the twistor
space Z. The relation with Tian's tangent instantons on R^7 and their twistor
description are briefly discussed.


  The Internet of Energy (IoE) is a distributed paradigm that leverages smart
networks and distributed system technologies to enable decentralized energy
systems. In contrast to the traditional centralized energy systems, distributed
Energy Internet systems comprise multiple components and communication
requirements that demand innovative technologies for decentralization,
reliability, efficiency, and security. Recent advances in blockchain
architectures, smart contracts, and distributed federated learning technologies
have opened up new opportunities for realizing decentralized Energy Internet
services. In this paper, we present a comprehensive analysis and classification
of state-of-the-art solutions that employ blockchain, smart contracts, and
federated learning for the IoE domains. Specifically, we identify four
representative system models and discuss their key aspects. These models
demonstrate the diverse ways in which blockchain, smart contracts, and
federated learning can be integrated to support the main domains of IoE, namely
distributed energy trading and sharing, smart microgrid energy networks, and
electric and connected vehicle management. Furthermore, we provide a detailed
comparison of the different levels of decentralization, the advantages of
federated learning, and the benefits of using blockchain for the IoE systems.
Additionally, we identify open issues and areas for future research for
integrating federated learning and blockchain in the Internet of Energy
domains.


  We report measurements of the microwave complex resistivity at 48 GHz in
SmBaCuO thin films. Measurements are performed with a moderate magnetic field,
$\mu_{0}H<$ 0.8T, applied along the c-axis. We find that the complex
resistivity presents clear sublinear field dependences, and that the imaginary
part is remarkably sensitive to the moderate magnetic field. Interpretation
considering an unusually strong pinning leads to very anomalous field
dependences of the single-vortex viscosity and of the pinning constant. By
contrast, allowing for a significant effect of the magnetic field on the
depletion of the condensate, the data are quantitatively described by simple
free-flux flow model, supplemented with two-fluid conductivity. In this frame,
we obtain the vortex viscosity from the data. We compare vortex viscosity in
SmBaCuO and in YBaCuO.


  We study the operator product expansion of the plaquette (gluon condensate)
and the self-energy of an infinitely heavy quark. We first compute their
perturbative expansions to order $\alpha^{35}$ and $\alpha^{20}$, respectively,
in the lattice scheme. In both cases we reach the asymptotic regime where the
renormalon behavior sets in. Subtracting the perturbative series, we obtain the
leading non-perturbative corrections of their respective operator product
expansions. In the first case we obtain the gluon condensate and in the second
the binding energy of the heavy quark in the infinite mass limit. The results
are fully consistent with the expectations from renormalons and the operator
product expansion.


  We introduce adaptive input representations for neural language modeling
which extend the adaptive softmax of Grave et al. (2017) to input
representations of variable capacity. There are several choices on how to
factorize the input and output layers, and whether to model words, characters
or sub-word units. We perform a systematic comparison of popular choices for a
self-attentional architecture. Our experiments show that models equipped with
adaptive embeddings are more than twice as fast to train than the popular
character input CNN while having a lower number of parameters. On the
WikiText-103 benchmark we achieve 18.7 perplexity, an improvement of 10.5
perplexity compared to the previously best published result and on the Billion
Word benchmark, we achieve 23.02 perplexity.


  We examine the prospects for using lead as a supernova-neutrino detector by
considering the spectrum of electrons produced, and the number of one- and
two-neutron events. We show that the electron energy spectrum from
charged-current reactions can be used to extract information about the
high-temperature component of the neutrino spectrum. Some degree of electron
neutrino oscillation is expected in the supernova envelope. We examine the
prospects for untangling the signatures of various oscillation scenarios,
including, e.g. normal or inverted hierarchies, and different values for the
small mixing angle, theta_13.


  In Score based Generative Modeling (SGMs), the state-of-the-art in generative
modeling, stochastic reverse processes are known to perform better than their
deterministic counterparts. This paper delves into the heart of this
phenomenon, comparing neural ordinary differential equations (ODEs) and neural
stochastic differential equations (SDEs) as reverse processes. We use a control
theoretic perspective by posing the approximation of the reverse process as a
trajectory tracking problem. We analyze the ability of neural SDEs to
approximate trajectories of the Fokker-Planck equation, revealing the
advantages of stochasticity. First, neural SDEs exhibit a powerful regularizing
effect, enabling $L^2$ norm trajectory approximation surpassing the Wasserstein
metric approximation achieved by neural ODEs under similar conditions, even
when the reference vector field or score function is not Lipschitz. Applying
this result, we establish the class of distributions that can be sampled using
score matching in SGMs, relaxing the Lipschitz requirement on the gradient of
the data distribution in existing literature. Second, we show that this
approximation property is preserved when network width is limited to the input
dimension of the network. In this limited width case, the weights act as
control inputs, framing our analysis as a controllability problem for neural
SDEs in probability density space. This sheds light on how noise helps to steer
the system towards the desired solution and illuminates the empirical success
of stochasticity in generative modeling.


  The rising demand for networked embedded systems with machine intelligence
has been a catalyst for sustained attempts by the research community to
implement Convolutional Neural Networks (CNN) based inferencing on embedded
resource-limited devices. Redesigning a CNN by removing costly multiplication
operations has already shown promising results in terms of reducing inference
energy usage. This paper proposes a new method for replacing multiplications in
a CNN by table look-ups. Unlike existing methods that completely modify the CNN
operations, the proposed methodology preserves the semantics of the major CNN
operations. Conforming to the existing mechanism of the CNN layer operations
ensures that the reliability of a standard CNN is preserved. It is shown that
the proposed multiplication-free CNN, based on a single activation codebook,
can achieve 4.7x, 5.6x, and 3.5x reduction in energy per inference in an FPGA
implementation of MNIST-LeNet-5, CIFAR10-VGG-11, and Tiny ImageNet-ResNet-18
respectively. Our results show that the DietCNN approach significantly improves
the resource consumption and latency of deep inference for smaller models,
often used in embedded systems. Our code is available at:
https://github.com/swadeykgp/DietCNN


  Rheological measurements of model colloidal gels reveal that large variations
in the shear moduli as colloidal volume-fraction changes are not reflected by
simple structural parameters such as the coordination number, which remains
almost a constant. We resolve this apparent contradiction by conducting a
normal mode analysis of experimentally measured bond networks of the gels. We
find that structural heterogeneity of the gels, which leads to floppy modes and
a nonaffine-affine crossover as frequency increases, evolves as a function of
the volume fraction and is key to understand the frequency dependent
elasticity. Without any free parameters, we achieve good qualitative agreement
with the measured mechanical response. Furthermore, we achieve universal
collapse of the shear moduli through a phenomenological spring-dashpot model
that accounts for the interplay between fluid viscosity, particle dissipation,
and contributions from the affine and non-affine network deformation.


  We present an asymptotic analysis of Viterbi Training (VT) and contrast it
with a more conventional Maximum Likelihood (ML) approach to parameter
estimation in Hidden Markov Models. While ML estimator works by (locally)
maximizing the likelihood of the observed data, VT seeks to maximize the
probability of the most likely hidden state sequence. We develop an analytical
framework based on a generating function formalism and illustrate it on an
exactly solvable model of HMM with one unambiguous symbol. For this particular
model the ML objective function is continuously degenerate. VT objective, in
contrast, is shown to have only finite degeneracy. Furthermore, VT converges
faster and results in sparser (simpler) models, thus realizing an automatic
Occam's razor for HMM learning. For more general scenario VT can be worse
compared to ML but still capable of correctly recovering most of the
parameters.


  The main objective of the present paper is to investigate the curvature
properties of generalized pp-wave metric. It is shown that generalized pp-wave
spacetime is Ricci generalized pseudosymmetric, 2-quasi-Einstein and
generalized quasi-Einstein in the sense of Chaki. As a special case it is shown
that pp-wave spacetime is semisymmetric, semisymmetric due to conformal and
projective curvature tensors, $R$-space by Venzi and satisfies the
pseudosymmetric type condition $P\cdot P = -\frac{1}{3}Q(S, P)$. Again we
investigate the sufficient condition for which a generalized pp-wave spacetime
turns into pp-wave spacetime, pure radiation spacetime, locally symmetric and
recurrent. Finally, it is shown that the energy-momentum tensor of pp-wave
spacetime is parallel if and only if it is cyclic parallel. And the energy
momentum tensor is Codazzi type if it is cyclic parallel but the converse is
not true as shown by an example. Finally we make a comparison between the
curvature properties of the Robinson-Trautman metric and generalized pp-wave
metric.


  We give an introduction to adelic mixing and its applications for
mathematicians knowing about the mixing of the geodesic flow on hyperbolic
surfaces. We focus on the example of the Hecke trees in the modular surface.


  Understanding the reasons associated with successful proposals is of
paramount importance to improve evaluation processes. In this context, we
analyzed whether bibliometric features are able to predict the success of
research grants. We extracted features aiming at characterizing the academic
history of Brazilian researchers, including research topics, affiliations,
number of publications and visibility. The extracted features were then used to
predict grants productivity via machine learning in three major research areas,
namely Medicine, Dentistry and Veterinary Medicine. We found that research
subject and publication history play a role in predicting productivity. In
addition, institution-based features turned out to be relevant when combined
with other features. While the best results outperformed text-based attributes,
the evaluated features were not highly discriminative. Our findings indicate
that predicting grants success, at least with the considered set of
bibliometric features, is not a trivial task.


  In order to explore any change caused by the G2 cloud approaching, we have
monitored the flux density of Sgr A* at 22 GHz from Feb. 2013 to Aug. 2014 with
a sub-array of Japanese VLBI Network . The observation period included the
expected periastron dates. The number of observation epochs was 283 days. We
have observed no significant microwave enhancement of Sgr A* in the whole
observation period. The average flux density in the period is $S=1.23+/-0.33$
Jy. The average is consistent with the usually observed flux density range of
Sgr A* at 22 GHz.


  Nanoscale chiral skyrmions in noncentrosymmetric helimagnets are promising
binary state variables in high-density, low-energy nonvolatile memory.
Skyrmions are ubiquitous as an ordered, single-domain lattice phase, which
makes it difficult to write information unless they are spatially broken up
into smaller units, each representing a bit. Thus, the formation and
manipulation of skyrmion lattice domains is a prerequisite for memory
applications. Here, using an imaging technique based on resonant magnetic x-ray
diffraction, we demonstrate the mapping and manipulation of skyrmion lattice
domains in Cu2OSeO3. The material is particularly interesting for applications
owing to its insulating nature, allowing for electric field-driven domain
manipulation.


  We present Far Ultraviolet Spectroscopic Explorer (FUSE) and Space Telescope
Imaging Spectrograph (STIS E140M) observations of the post-asymptotic giant
branch star ZNG 1 in the globular cluster Messier 5 (l=3.9, b=+47.7; d=7.5 kpc,
z=+5.3 kpc). High velocity absorption is seen in C IV, Si IV, O VI, and lower
ionization species at LSR velocities of -140 and -110 km/s. We conclude that
this gas is not circumstellar on the basis of photoionization models and path
length arguments. Thus, the high velocity gas along the ZNG 1 sight line is the
first evidence that highly-ionized HVCs can be found near the Galactic disk. We
measure the metallicity of these HVCs to be [O/H]=+0.22\pm0.10, the highest of
any known HVC. Given the clouds' metallicity and distance constraints, we
conclude that these HVCs have a Galactic origin. This sight line probes gas
toward the inner Galaxy, and we discuss the possibility that these HVCs may be
related to a Galactic nuclear wind or Galactic fountain circulation in the
inner regions of the Milky Way.


  Reachable set computation is an important technique for the verification of
safety properties of dynamical systems. In this paper, we investigate reachable
set computation for discrete nonlinear systems based on parallelotope bundles.
The algorithm relies on computing an upper bound on the supremum of a nonlinear
function over a rectangular domain, which has been traditionally done using
Bernstein polynomials. We strive to remove the manual step of parallelotope
template selection to make the method fully automatic. Furthermore, we show
that changing templates dynamically during computations cans improve accuracy.
To this end, we investigate two techniques for generating the template
directions. The first technique approximates the dynamics as a linear
transformation and generates templates using this linear transformation. The
second technique uses Principal Component Analysis (PCA) of sample trajectories
for generating templates. We have implemented our approach in a Python-based
tool called Kaa and improve its performance by two main enhancements. The tool
is modular and use two types of global optimization solvers, the first using
Bernstein polynomials and the second using NASA's Kodiak nonlinear optimization
library. Second, we leverage the natural parallelism of the reachability
algorithm and parallelize the Kaa implementation. We demonstrate the improved
accuracy of our approach on several standard nonlinear benchmark systems.


  We investigate the physical properties of a sample of 52 Ly{\alpha} emitting
galaxies in the VANDELS survey, with particular focus on the role of kinematics
and neutral hydrogen column density in the escape and spatial distribution of
Ly{\alpha} photons. For these galaxies, we derive different physical properties
and characterize the Ly{\alpha} emission in terms of kinematics, EW, FWHM and
spatial extension and then estimate the velocity of the neutral outflowing gas.
  We reproduce some of the well known correlations between Ly{\alpha} EW and
stellar mass, dust extinction and UV \b{eta} slope, in the sense that the
emission line appears brighter in lower mass, less dusty and bluer galaxies. We
do not find any correlation with the SED-derived star formation rate, while we
find that galaxies with brighter Ly{\alpha} tend to be more compact both in UV
and in Ly{\alpha}. Our data reveal a new interesting correlation between the
Ly{\alpha} velocity and the offset of the inter-stellar absorption lines with
respect to the systemic redshift, in the sense that galaxies with larger
inter-stellar medium (ISM) out-flow velocities show smaller Ly{\alpha} velocity
shifts. We interpret this relation in the context of the shell-model scenario,
where the velocity of the ISM and the HI column density contribute together in
determining the Ly{\alpha} kinematics. In support to our interpretation, we
observe that galaxies with high HI column densities have much more extended
Ly{\alpha} spatial profiles, a sign of increased scattering. However, we do not
find any evidence that the HI column density is related to any other physical
properties of the galaxies, although this might be due in part to the limited
range of parameters that our sample spans.


  We present findings from uniaxial (oedometric) compression tests on two
cohesive industrially relevant granular materials (cocoa and limestone powder).
Experimental results are presented for the compressibility, tested with two
devices -- the FT4 Powder Rheometer and the custom made lambdameter. We focus
on the stress response and the slow relaxation behavior of the cohesive samples
tested. After compression ends, at constant volume, the ongoing stress
relaxation is found to follow a power law consistently for both cohesive
powders and for the different testing equipments. A simple (incremental
algebraic evolution) model is proposed for the stress relaxation in cohesive
powders, which includes a response timescale along with a second, dimensionless
relaxation parameter. The reported observations are useful for both the
improvement of discrete element simulations and constitutive macroscopic models
for cohesive granular materials.


  In image processing, Total Variation (TV) regularization models are commonly
used to recover blurred images. One of the most efficient and popular methods
to solve the convex TV problem is the Alternating Direction Method of
Multipliers (ADMM) algorithm, recently extended using the inertial proximal
point method. Although all the classical studies focus on only a convex
formulation, recent articles are paying increasing attention to the nonconvex
methodology due to its good numerical performance and properties. In this
paper, we propose to extend the classical formulation with a novel nonconvex
Alternating Direction Method of Multipliers with the Inertial technique
(IADMM). Under certain assumptions on the parameters, we prove the convergence
of the algorithm with the help of the Kurdyka-{\L}ojasiewicz property. We also
present numerical simulations on classical TV image reconstruction problems to
illustrate the efficiency of the new algorithm and its behavior compared with
the well established ADMM method.


  Comparison of structure functions measured in neutrino and charged lepton
deep inelastic scattering can be used to test basic properties of parton
distribution functions such as the validity of charge symmetry. Recent
experiments indicate a substantial discrepancy between $F_2^\nu$ and $F_2^\mu$
in the region of small Bjorken-x. We discuss nuclear corrections and strange
and anti strange quark effects and show that none of them can account for the
observed discrepancy. These results suggest surprisingly large CSV effects in
nucleon sea distributions.


  In downlink multiuser multiple-input multiple-output (MIMO) systems, block
diagonalization (BD) is a practical linear precoding scheme which achieves the
same degrees of freedom (DoF) as the optimal linear/nonlinear precoding
schemes. However, its sum-rate performance is rather poor in the practical SNR
regime due to the transmit power boost problem. In this paper, we propose an
improved linear precoding scheme over BD with a so-called
"effective-SNR-enhancement" technique. The transmit covariance matrices are
obtained by firstly solving a power minimization problem subject to the minimum
rate constraint achieved by BD, and then properly scaling the solution to
satisfy the power constraints. It is proved that such approach equivalently
enhances the system SNR, and hence compensates the transmit power boost problem
associated with BD. The power minimization problem is in general non-convex. We
therefore propose an efficient algorithm that solves the problem heuristically.
Simulation results show significant sum rate gains over the optimal BD and the
existing minimum mean square error (MMSE) based precoding schemes.


  PURPOSE: Subarachnoid hemorrhage (SAH) entails high morbidity and mortality
rates. Convolutional neural networks (CNN), a form of deep learning, are
capable of generating highly accurate predictions from imaging data. Our
objective was to predict mortality in SAH patients by processing the initial CT
scan on a CNN based algorithm.
  METHODS: Retrospective multicentric study of a consecutive cohort of patients
with SAH between 2011-2022. Demographic, clinical and radiological variables
were analyzed. Pre-processed baseline CT scan images were used as the input for
training a CNN using AUCMEDI Framework. Our model's architecture leverages the
DenseNet-121 structure, employing transfer learning principles. The output
variable was mortality in the first three months. Performance of the model was
evaluated by statistical parameters conventionally used in studies involving
artificial intelligence methods.
  RESULTS: Images from 219 patients were processed, 175 for training and
validation of the CNN and 44 for its evaluation. 52%(115/219) of patients were
female, and the median age was 58(SD=13.06) years. 18.5%(39/219) were
idiopathic SAH. Mortality rate was 28.5%(63/219). The model showed good
accuracy at predicting mortality in SAH patients exclusively using the images
of the initial CT scan (Accuracy=74%, F1=75% and AUC=82%). CONCLUSION: Modern
image processing techniques based on AI and CNN make possible to predict
mortality in SAH patients with high accuracy using CT scan images as the only
input. These models might be optimized by including more data and patients
resulting in better training, development and performance on tasks which are
beyond the skills of conventional clinical knowledge.


  We consider moduli fields interacting with thermalized relativistic matter.
We determine the temperature dependence of their damping rate and find it is
dominated by thermal effects in the high temperature regime, i.e. for
temperatures larger than their mass. For a simple scalar model the damping rate
is expressed through the known matter bulk viscosity. The high temperature
damping rate is always smaller than the Hubble rate, so that thermal effects
are not sufficient for solving the cosmological moduli problem.


  This paper presents a conceptual approach to phase modulation of the cavity
field in storage ring RF systems. An implementation of the concept on Dimtel
low-level RF controllers is also presented. The method is illustrated with the
test results from a cavity simulator, as well as an electron storage ring KARA.


  We study the four-body exclusive semileptonic baryonic $\bar B$ decays of
$B^-\to p\bar p \ell^- \bar\nu_{\ell}$ ($\ell=e,\mu,\tau$) in the standard
model. We find that their decay branching ratios are about $(1.0,
1.0,0.5)\times 10^{-4}$, respectively. In particular, the electron mode is
close to the corresponding CLEO's upper limit of $5.2\times 10^{-3}$, while all
results are about one or two orders of magnitude larger than the previous
estimated values for the inclusive modes of $\bar B\to {\bf B\bar B'}\ell \bar
\nu$. Clearly, both B-factories of Belle and BaBar should be able to observe
these exclusive four-body modes.


  We explore the effect of poly (ethylene glycol) (PEG) molar mass on the
intrinsic permeability and structural characteristics of poly (ethylene glycol)
diacrylate PEGDA/PEG composite hydrogel membranes. We observe that by varying
the PEG content and molar mass, we can finely adjust the water intrinsic
permeability over several orders of magnitude. Notably, we show the existence
of a maximum water intrinsic permeability, already identified in a previous
study to be located at the critical overlap concentration C^* of PEG chains,
for the highest PEG molar mass studied. Furthermore, we note that the maximum
intrinsic permeability follows a non-monotonic evolution with respect to the
PEG molar mass and reaches its peak at 35 000 g.mol-1. Besides our results show
that a significant fraction of PEG chains is irreversibly trapped within the
PEGDA matrix even for the shortest molar masses down to 600 g.mol-1. This
observation suggests the possibility of covalent grafting of PEG chains to the
PEGDA matrix. CryoSEM and AFM measurements demonstrate the presence of large
micron-sized cavities separated by PEGDA-rich walls whose nanometric structure
strongly depends on the PEG content. By combining our permeability and
structural measurements, we suggest that the PEG chains trapped inside the
PEGDA rich walls induce nanoscale defects in the cross linking density,
resulting in an increased permeability below C^*. Conversely, above C^*, we
speculate that partially-trapped PEG chains may form a brush-like arrangement
on the surface of the PEGDA-rich walls, leading to a reduction in permeability.
These two opposing effects are anticipated to exhibit molar-mass-dependent
trends, contributing to the non-monotonic variation of the maximum intrinsic
permeability at C^*. Overall, our results demonstrate the potential to
fine-tune the properties of hydrogel membranes, offering new opportunities in
separation applications.


  Within the framework of pure BCS(i.e. when the critical temperature is
proportional to the Debye temperature), we show that the isotope coefficient is
always less than 1/2 and could be extremely small in polyatomic superconductors
depending on the chemical formula unit. This finding leads to quantitative
explanation of observed values(correct orders of magnitude and sometimes exact
numerical values) in optimally doped cuprates, magnesium diboride and
alkali-doped fullerenes. Consequently, the smallness of the isotope coefficient
is not only compatible with pure electron-phonon interaction, but this is
perhaps the rule in polyatomic systems.


  In [1] the author gives a description of Poisson brackets on some algebras of
quantum polynomials $\mathcal{O}_q$, which is called\textit{ general algebra of
quantum polynomials}. The main of this paper is to present a generalization of
[1] through a description of Poisson brackets on some skew PBW extensions of a
ring $A$ by the extensions $\mathcal{O}^{r,n}_{q,\delta}$, which are a
generalization of $\mathcal{O}_q$ and show some examples of skew PBW extension
where we can apply this description.


  We complete the kinetic theory of inhomogeneous systems with long-range
interactions initiated in previous works. We use a simpler and more physical
formalism. We consider a system of particles submitted to a small external
stochastic perturbation and determine the response of the system to the
perturbation. We derive the diffusion tensor and the friction by polarization
of a test particle. We introduce a general Fokker-Planck equation involving a
diffusion term and a friction term. When the friction by polarization can be
neglected, we obtain a secular dressed diffusion (SDD) equation sourced by the
external noise. When the external perturbation is created by a discrete
collection of $N$ field particles, we obtain the inhomogeneous Lenard-Balescu
kinetic equation reducing to the inhomogeneous Landau kinetic equation when
collective effects are neglected. We consider a multi-species system of
particles. When the field particles are at statistical equilibrium (thermal
bath), we establish the proper expression of the fluctuation-dissipation
theorem for systems with long-range interactions relating the power spectrum of
the fluctuations to the response function of the system. In that case, the
friction and diffusion coefficients satisfy the Einstein relation and the
Fokker-Planck equation reduces to the inhomogeneous Kramers equation. We also
consider a gas of Brownian particles with long-range interactions described by
$N$ coupled stochastic Langevin equations and determine its mean and mesoscopic
evolution. We discuss the notion of stochastic kinetic equations and the role
of fluctuations possibly triggering random transitions from one equilibrium
state to the other. Our presentation parallels the one given for
two-dimensional point vortices in a previous paper [P.H. Chavanis, Eur. Phys.
J. Plus 138, 136 (2023)].


  Data saving capability of "Compressed sensing (sampling)" in signal
discretization is disputed and found to be far below the theoretical upper
bound defined by the signal sparsity. On a simple and intuitive example, it is
demonstrated that, in a realistic scenario for signals that are believed to be
sparse, one can achieve a substantially larger saving than compressing sensing
can. It is also shown that frequent assertions in the literature that
"Compressed sensing" can beat the Nyquist sampling approach are misleading
substitution of terms and are rooted in misinterpretation of the sampling
theory.


  The van de Waals interaction between two graphene sheets is studied at finite
temperatures. Graphene's thermal length $(\xi_T = \hbar v / k_B T)$ controls
the force versus distance $(z)$ as a crossover from the zero temperature
results for $z\ll \xi_T$, to a linear-in-temperature, universal regime for
$z\gg \xi_T$. The large separation regime is shown to be a consequence of the
classical behavior of graphene's plasmons at finite temperature. Retardation
effects are largely irrelevant, both in the zero and finite temperature
regimes. Thermal effects should be noticeable in the van de Waals interaction
already for distances of tens of nanometers at room temperature.


  Results are presented on neutral kaon, hyperon and antihyperon production in
Pb-Pb and p-Be interactions at 40 GeV/c per nucleon. The enhancement pattern
follows the same hierarchy as seen in the higher energy data - the enhancement
increases with the strangeness content of the hyperons and with the centrality
of collision. The centrality dependence of the Pb-Pb yields and enhancements is
steeper at 40 than at 158 A GeV/c. The energy dependence of strangeness
enhancements at mid-rapidity is discussed.


  We present experimental results regarding the electrotransfer of plasmid DNA
into phosphatidylcholine giant unilamellar vesicles (GUVs). Our observations
indicate that a direct entry is the predominant mechanism of electrotransfer. A
quantitative analysis of the DNA concentration increments inside the GUVs is
also performed, and we find that our experimental data are very well described
by a simple theoretical model in which DNA entry is mostly driven by
electrophoresis. Our theoretical framework allows for the prediction of the
amount of transfered DNA as a function of the electric field parameters, and
thus paves the way towards a novel method for encapsulating with high
efficiency not only DNA, but any negatively charged macromolecule into GUVs.


  Bribery in elections is an important problem in computational social choice
theory. However, bribery with money is often illegal in elections. Motivated by
this, we introduce the notion of frugal bribery and formulate two new pertinent
computational problems which we call Frugal-bribery and Frugal- $bribery to
capture bribery without money in elections. In the proposed model, the briber
is frugal in nature and this is captured by her inability to bribe votes of a
certain kind, namely, non-vulnerable votes. In the Frugal-bribery problem, the
goal is to make a certain candidate win the election by changing only
vulnerable votes. In the Frugal-{dollar}bribery problem, the vulnerable votes
have prices and the goal is to make a certain candidate win the election by
changing only vulnerable votes, subject to a budget constraint of the briber.
We further formulate two natural variants of the Frugal-{dollar}bribery problem
namely Uniform-frugal-{dollar}bribery and Nonuniform-frugal-{dollar}bribery
where the prices of the vulnerable votes are, respectively, all the same or
different.
  We study the computational complexity of the above problems for unweighted
and weighted elections for several commonly used voting rules. We observe that,
even if we have only a small number of candidates, the problems are intractable
for all voting rules studied here for weighted elections, with the sole
exception of the Frugal-bribery problem for the plurality voting rule. In
contrast, we have polynomial time algorithms for the Frugal-bribery problem for
plurality, veto, k-approval, k-veto, and plurality with runoff voting rules for
unweighted elections. However, the Frugal-{dollar}bribery problem is
intractable for all the voting rules studied here barring the plurality and the
veto voting rules for unweighted elections.


  We have examined solutions of tetrahedron equations from the elliptic free
fermion model by using Korepanov mechanism based on tetrahedral Zamolodchikov
algebras. As a byproduct, we have found a new integrable 2-dim. lattice model.
We have also studied the relation between tetrahedral Zamolodchikov algebras
and tetrahedron equations.


  A novel technique to determine invariant curves in nonlinear beam dynamics
based on the method of formal series has been developed. It is first shown how
the solution of the Hamilton equations of motion describing nonlinear betatron
oscillations in the presence of a single sextupole can be represented in a
nonperturbative form. Further, the solution of the Hamilton-Jacobi equation is
obtained in a closed symbolic form as a ratio of two series in the perturbation
parameter (and the nonlinear action invariant), rather than a conventional
power series according to canonical perturbation theory. It is well behaved
even for large values of the perturbation parameter close to strong structural
resonances. The relationship between existing invariant curves and the
so-called scattering orbits in classical scattering theory has been revealed.


  Game analytics supports game development by providing direct quantitative
feedback about player experience. Player retention and monetization in
particular have become central business statistics in free-to-play game
development. Many metrics have been used for this purpose. However, game
developers often want to perform analytics in a timely manner before all users
have churned from the game. This causes data censoring which makes many metrics
biased. In this work, we introduce how the Mean Cumulative Function (MCF) can
be used to generalize many academic metrics to censored data. The MCF allows us
to estimate the expected value of a metric over time, which for example may be
the number of game sessions, number of purchases, total playtime and lifetime
value. Furthermore, the popular retention rate metric is the derivative of this
estimate applied to the expected number of distinct days played. Statistical
tools based on the MCF allow game developers to determine whether a given
change improves a game, or whether a game is yet good enough for public
release. The advantages of this approach are demonstrated on a real
in-development free-to-play mobile game, the Hipster Sheep.


  We present the interactive Java-based open-source traffic simulator available
at www.traffic-simulation.de. In contrast to most closed-source commercial
simulators, the focus is on investigating fundamental issues of traffic
dynamics rather than simulating specific road networks. This includes testing
theories for the spatiotemporal evolution of traffic jams, comparing and
testing different microscopic traffic models, modeling the effects of driving
styles and traffic rules on the efficiency and stability of traffic flow, and
investigating novel ITS technologies such as adaptive cruise control,
inter-vehicle and vehicle-infrastructure communication.


  Learning with noisy labels (LNL) has been extensively studied, with existing
approaches typically following a framework that alternates between clean sample
selection and semi-supervised learning (SSL). However, this approach has a
limitation: the clean set selected by the Deep Neural Network (DNN) classifier,
trained through self-training, inevitably contains noisy samples. This mixture
of clean and noisy samples leads to misguidance in DNN training during SSL,
resulting in impaired generalization performance due to confirmation bias
caused by error accumulation in sample selection. To address this issue, we
propose a method called Collaborative Sample Selection (CSS), which leverages
the large-scale pre-trained model CLIP. CSS aims to remove the mixed noisy
samples from the identified clean set. We achieve this by training a
2-Dimensional Gaussian Mixture Model (2D-GMM) that combines the probabilities
from CLIP with the predictions from the DNN classifier. To further enhance the
adaptation of CLIP to LNL, we introduce a co-training mechanism with a
contrastive loss in semi-supervised learning. This allows us to jointly train
the prompt of CLIP and the DNN classifier, resulting in improved feature
representation, boosted classification performance of DNNs, and reciprocal
benefits to our Collaborative Sample Selection. By incorporating auxiliary
information from CLIP and utilizing prompt fine-tuning, we effectively
eliminate noisy samples from the clean set and mitigate confirmation bias
during training. Experimental results on multiple benchmark datasets
demonstrate the effectiveness of our proposed method in comparison with the
state-of-the-art approaches.


  Semiconductor lasers subject to optical feedback can behave chaotically,
which can be used as a source of randomness. The optical feedback, provided by
mirrors at a distance, determines the characteristics of the chaos and thus the
quality of the randomness. However, this fixed distance also shows itself in
the intensity, an unwanted feature called the Time Delay Signature (TDS). One
promising solution to optimize the chaotic behavior is using double optical
feedback, which we study here. In particular, we focus on the impact of the
feedback phase, a small sub-wavelength change in the position of the mirrors,
on the TDS and complexity of the system. We show that by optimizing the
feedback parameters, including the feedback phases, the TDS can be suppressed,
and that in some cases feedback phase control is necessary rather than
optional. With control of all feedback parameters, it is possible to suppress
the TDS without loss of the chaotic bandwidth. Further, we show that the system
can restabilize at strong feedback rates, and one can switch between a chaotic
and steady state by changing the feedback phase. Finally, we relate the
feedback phase sensitivity to interference between the two delayed signals.
This system is promising as one can either suppress the TDS without loss of the
chaotic bandwidth or significantly increase the chaotic bandwidth.