  We proposed a viable and predictive model based on the $SU(3)_C \times
SU(3)_L \times U(1)_X$ gauge symmetry, supplemented by the global $U(1)_{Lg}$
symmetry, the $S_4$ family symmetry and several auxiliary cyclic symmetries,
which successfully reproduces the experimentally observed SM fermion mass and
mixing pattern. The tiny active neutrino masses are generated through an
inverse seesaw mechanism mediated by right-handed Majorana neutrinos. The model
is consistent with the SM fermion masses and mixings and successfully
accommodates the current Higgs diphoton decay rate constraints as well as the
constraints arising from oblique $S$, $T$ and $U$ parameters and we studied the
meson mixing due to flavor changing neutral currents mediated by heavy scalars,
finding parameter space consistent with experimental constraints.


  Calculating the abundance of thermally produced dark matter particles has
become a standard procedure, with sophisticated methods guaranteeing a
precision that matches the percent-level accuracy in the observational
determination of the dark matter density. Here, we point out that one of the
main assumptions in the commonly adopted formalism, namely local thermal
equilibrium during the freeze-out of annihilating dark matter particles, does
not have to be satisfied in general. We present two methods for how to deal
with such situations, in which the kinetic decoupling of dark matter happens so
early that it interferes with the chemical decoupling process: i) an
approximate treatment in terms of a coupled system of differential equations
for the leading momentum moments of the dark matter distribution, and ii) a
full numerical solution of the Boltzmann equation in phase-space. For
illustration, we apply these methods to the case of Scalar Singlet dark matter.
We explicitly show that even in this simple model the prediction for the dark
matter abundance can be affected by up to one order of magnitude compared to
the traditional treatment.


  The present study reports magnetic and optical properties of silica mediated
lauric acid stabilized magnetic fluids. The tunable birefringence ({\Delta}n)
and other properties are investigated as a function of (i) concentrations of
silica suspension, and (ii) saturation magnetization (MS) 0.5099 kA/m (FN30)
and 1.2855 kA/m (F30) of magnetite magnetic fluid (MF). The study reveals that
{\Delta}n suppresses on addition of silica in FN30, whereas enhances (up to
critical concentrations of silica) in F30. The magnetic field induced chain
observed in the FN30 based fluids are long, thick and scattered, while short,
thin and dense chains emerges in F30 based fluid. The magnetic field induced
assembly and the magnetic parameters correlates with the results of {\Delta}n.
The particle size analysis indicates increment of particle size on addition of
silica nanoparticles. The thermogravimetry analysis confirms the direct
interaction of silica nanoparticles and the lauric acid coated magnetite
particles. This is the first report of direct interaction of silica - magnetite
magnetic fluids, and its subsequent effect on tunable birefringence and other
properties.


  We propose a robust photonic platform for encapsulation and addressing of
optically active 2D- and nano-materials. Our implementation utilises a
monolayer of MoS$_2$ transition metal dichalcogenide embedded in an elastomeric
waveguide chip. The structure is manufactured from PDMS using soft-lithography
and capable of sustaining a single mode of guided light. We prove that this
setup facilitates addressing of the 2D material flake by pumping it with
polarised laser light and gathering polarisation-resolved photoluminescence
spectra with the extinction ratio of 31, which highlights the potential for
selection-rule dependent measurements. Our results demonstrate improved
handling of the material and experimental simplification compared to other
techniques. Furthermore, inherent elasticity of the host provides an avenue for
direct mechanical coupling to embedded materials.


  In this article we obtain a rigid isotopy classification of generic pointed
quartic curves $(A,p)$ in $\mathbb{R}\mathbb{P}^{2}$ by studying the
combinatorial properties of dessins. The dessins are real versions, proposed by
S. Orevkov, of Grothendieck's dessins d'enfants.
  This classification contains 20 classes determined by the number of ovals of
$A$, the parity of the oval containing the marked point $p$, the number of
ovals that the tangent line $T_p A$ intersects, the nature of connected
components of $A\setminus T_p A$ adjacent to $p$, and in the maximal case, on
the convexity of the position of the connected components of $A\setminus T_p
A$.
  We study the combinatorial properties and decompositions of dessins
corresponding to real uninodal trigonal curves in real ruled surfaces. Uninodal
dessins in any surface with non-empty boundary can be decomposed in blocks
corresponding to cubic dessins in the disk $\mathbf{D}^2$, which produces a
classification of these dessins. The classification of dessins under
consideration leads to a rigid isotopy classification of generic pointed
quartic curves in $\mathbb{R}\mathbb{P}^{2}$. This classification was first
obtained by S. Rieken based on the relation between quartic curves and del
Pezzo surfaces.


  We prove the existence of minimizers of causal variational principles on
second countable, locally compact Hausdorff spaces. Moreover, the corresponding
Euler-Lagrange equations are derived. The method is to first prove the
existence of minimizers of the causal variational principle restricted to
compact subsets for a lower semi-continuous Lagrangian. Exhausting the
underlying topological space by compact subsets and rescaling the corresponding
minimizers, we obtain a sequence which converges vaguely to a regular Borel
measure of possibly infinite total volume. It is shown that, for continuous
Lagrangians of compact range, this measure solves the Euler-Lagrange equations.
Furthermore, we prove that the constructed measure is a minimizer under
variations of compact support. Under additional assumptions, it is proven that
this measure is a minimizer under variations of finite volume. We finally
extend our results to continuous Lagrangians decaying in entropy.


  Optomechanical systems attract a lot of attention because they provide a
novel platform for quantum measurements, transduction, hybrid systems, and
fundamental studies of quantum physics. Their classical nonlinear dynamics is
surprisingly rich and so far remains underexplored. Works devoted to this
subject have typically focussed on dissipation constants which are
substantially larger than those encountered in current experiments, such that
the nonlinear dynamics of weakly dissipative optomechanical systems is almost
uncharted waters. In this work, we fill this gap and investigate the regular
and chaotic dynamics in this important regime. To analyze the dynamical
attractors, we have extended the "Generalized Alignment Index" method to
dissipative systems. We show that, even when chaotic motion is absent, the
dynamics in the weakly dissipative regime is extremely sensitive to initial
conditions. We argue that reducing dissipation allows chaotic dynamics to
appear at a substantially smaller driving strength and enables various routes
to chaos. We identify three generic features in weakly dissipative classical
optomechanical nonlinear dynamics: the Neimark-Sacker bifurcation between limit
cycles and limit tori (leading to a comb of sidebands in the spectrum), the
quasiperiodic route to chaos, and the existence of transient chaos.


  Computed Tomography (CT) plays an essential role in clinical diagnosis. Due
to the adverse effects of radiation on patients, the radiation dose is expected
to be reduced as low as possible. Sparse sampling is an effective way, but it
will lead to severe artifacts on the reconstructed CT image, thus sparse-view
CT image reconstruction has been a prevailing and challenging research area.
With the popularity of mobile devices, the requirements for lightweight and
real-time networks are increasing rapidly. In this paper, we design a novel
lightweight network called CAGAN, and propose a dual-domain reconstruction
pipeline for parallel beam sparse-view CT. CAGAN is an adversarial
auto-encoder, combining the Coordinate Attention unit, which preserves the
spatial information of features. Also, the application of Shuffle Blocks
reduces the parameters by a quarter without sacrificing its performance. In the
Radon domain, the CAGAN learns the mapping between the interpolated data and
fringe-free projection data. After the restored Radon data is reconstructed to
an image, the image is sent into the second CAGAN trained for recovering the
details, so that a high-quality image is obtained. Experiments indicate that
the CAGAN strikes an excellent balance between model complexity and
performance, and our pipeline outperforms the DD-Net and the DuDoNet.


  In a landmark paper, P\v{a}tra\c{s}cu demonstrated how a single lower bound
for the static data structure problem of reachability in the butterfly graph,
could be used to derive a wealth of new and previous lower bounds via
reductions. These lower bounds are tight for numerous static data structure
problems. Moreover, he also showed that reachability in the butterfly graph
reduces to dynamic marked ancestor, a classic problem used to prove lower
bounds for dynamic data structures. Unfortunately, P\v{a}tra\c{s}cu's reduction
to marked ancestor loses a $\lg \lg n$ factor and therefore falls short of
fully recovering all the previous dynamic data structure lower bounds that
follow from marked ancestor. In this paper, we revisit P\v{a}tra\c{s}cu's work
and give a new lossless reduction to dynamic marked ancestor, thereby
establishing reachability in the butterfly graph as a single seed problem from
which a range of tight static and dynamic data structure lower bounds follow.


  Quantum machine learning aims to improve learning methods through the use of
quantum computers. If it is to ever realize its potential, many obstacles need
to be overcome. A particularly pressing one arises at the prediction stage
because the outputs of quantum learning models are inherently random. This
creates an often considerable overhead, as many executions of a quantum
learning model have to be aggregated to obtain an actual prediction. In this
work, we analyze when quantum learning models can evade this issue and produce
predictions in a near-deterministic way -- paving the way to single-shot
quantum machine learning. We give a rigorous definition of single-shotness in
quantum classifiers and show that the degree to which a quantum learning model
is near-deterministic is constrained by the distinguishability of the embedded
quantum states used in the model. Opening the black box of the embedding, we
show that if the embedding is realized by quantum circuits, a certain depth is
necessary for single-shotness to be even possible. We conclude by showing that
quantum learning models cannot be single-shot in a generic way and trainable at
the same time.


  Parity violation and its effects for neutrinos in astrophysical contexts have
been considered earlier in pioneering papers of Hawking and Vilenkin. But
because even the largest magnetic moments predicted by physics beyond the
Standard Model are some twelve orders of magnitude smaller than the Bohr
magneton, their implications for magnetic field generation and neutrino
oscillations are generally considered insignificant. Here we show that since in
astrophysical scenarios a huge number of neutrinos may be emitted, the
smallness of the magnetic moment, when coupled with parity violation, is
compensated by the sheer number of neutrinos. The merger of neutron stars would
leave behind a short pulse of electromagnetic synchrotron radiation even if the
neutrino jet in the merger points away from the neutrino detectors. We show
that the magnetic field can be as large as $10^6\,\mbox{Gauss}$ and comment on
the possibility of direct detection. Observation of such a pulse would lend
strong support for neutrino magnetic moments and resolve the missing neutrino
problem in neutron star mergers.


  The microhexcavity plasma panel detector is a type of gaseous particle
detector consisting of a close-packed array of millimeter-size hexagonal cells.
The cells are biased to operate in Geiger mode where each cell functions as an
independent detection unit. The response of the detector to ionizing radiation
was investigated using low-energy radioactive $ \beta $ sources and cosmic ray
muons. Efficiency measurements were conducted with cosmic ray muons in
conjunction with a scintillator hodoscope. The rate response and signals
obtained from the microhexcavity detector filled with Penning gas mixture at
atmospheric pressure are herein described. The relative pixel efficiency, after
allowing for ion-pair formation in the gas volume, is 96.8 $ \pm $ 4.4$ \% $
for operation of the detector above an applied high voltage of 1000 V.


  Heavy flavour pairs produced in hadronic reactions provide a valuable
laboratory for the study of strong interactions. Due to their relatively large
mass, the production of heavy quarks should be reliably calculable in the
perturbative approach. Charm and beauty quarks once produced in a heavy ion
collision have to propagate through the surrounding quark-gluon matter. Heavy
quark states are then a sensitive probe of the properties of the dense medium.
ALICE is a general-purpose experiment equipped to reconstruct, among other
signals, leptons from open charm and beauty via their leptonic decays in p-p,
p-A and A-A collisions. In these proceedings, we present feasibility studies
for ALICE measurements of beauty production in central Pb-Pb collisions at
s_{NN}^1/2=5.5TeV using semileptonic decays.


  Time series forecasting based on deep architectures has been gaining
popularity in recent years due to their ability to model complex non-linear
temporal dynamics. The recurrent neural network is one such model capable of
handling variable-length input and output. In this paper, we leverage recent
advances in deep generative models and the concept of state space models to
propose a stochastic adaptation of the recurrent neural network for
multistep-ahead time series forecasting, which is trained with stochastic
gradient variational Bayes. In our model design, the transition function of the
recurrent neural network, which determines the evolution of the hidden states,
is stochastic rather than deterministic as in a regular recurrent neural
network; this is achieved by incorporating a latent random variable into the
transition process which captures the stochasticity of the temporal dynamics.
Our model preserves the architectural workings of a recurrent neural network
for which all relevant information is encapsulated in its hidden states, and
this flexibility allows our model to be easily integrated into any deep
architecture for sequential modelling. We test our model on a wide range of
datasets from finance to healthcare; results show that the stochastic recurrent
neural network consistently outperforms its deterministic counterpart.


  New experimental data from HERA explore the region of very small x and small
and moderate $Q^2$. We study the role of unitarity corrections in the
description of these data. For that, we propose a model separating the small
and large components of the $q\bar q$ fluctuation. This model gives a unified
description of total and diffractive production in $\gamma^*p$ interactions. In
this model unitarity corrections are controlled by diffraction data.


  Black hole low mass X-ray binaries (BH LMXBs) can launch powerful outflows in
the form of discrete ejecta. Observing the entire trajectory of these ejecta
allows us to model their motion with great accuracy, and this is essential for
measuring their physical properties. In particular, observing the final
deceleration phase, often poorly sampled, is fundamental to obtain a reliable
estimate of the jet's energy. During its 2019/2020 outburst, the BH LMXB MAXI
J1348$-$630 launched a single-sided radio-emitting jet that was detected at
large scales after a strong deceleration due to the interaction with the
interstellar medium (ISM). We successfully modelled the jet motion with a
dynamical external shock model, which allowed us to constrain the jet initial
Lorentz factor $\Gamma_0 = 1.85^{+0.15}_{-0.12}$, inclination angle $\theta =
29.3_{-3.2}^{+2.7}$ deg and ejection date $t_{\rm ej} = 21.5_{-3.0}^{+1.8}$
(MJD $-$ $58500$). Under simple assumptions on the jet opening angle and on the
external ISM density, we find that the jet has a large initial kinetic energy
$E_0 = 4.6^{+20.0}_{-3.4} \times 10^{46}$ erg, far greater than what commonly
measured for LMXBs from the jet's synchrotron emission. This implies that
discrete ejecta radiate away only a small fraction of their total energy, which
is instead transferred to the environment. The jet power estimate is larger
than the simultaneous available accretion power, and we present several options
to mitigate this discrepancy. We infer that MAXI J1348$-$630 is likely embedded
in an ISM cavity with internal density $n = 0.0010^{+0.0005}_{-0.0003}$
cm$^{-3}$ and radius $R_{\rm c} = 0.61^{+0.11}_{-0.09}$ pc, which could have
been produced by the system's previous activity, as proposed for other BH
LMXBs.


  We investigate the thermodynamics of a spin ladder model which possesses a
free parameter besides the rung and leg couplings. The model is exactly solved
by the Bethe Ansatz and exhibits a phase transition between a gapped and a
gapless spin excitation spectrum. The magnetic susceptibility is obtained
numerically and its dependence on the anisotropy parameter is determined. A
connection with the compounds KCuCl3, Cu2(C5H12N2)2Cl4 and (C5H12N)2CuBr4 in
the strong coupling regime is made and our results for the magnetic
susceptibility fit the experimental data remarkably well.


  In the context of the `selfish-mine' strategy proposed by Eyal and Sirer, we
study the effect of propagation delay on the evolution of the Bitcoin
blockchain. First, we use a simplified Markov model that tracks the contrasting
states of belief about the blockchain of a small pool of miners and the `rest
of the community' to establish that the use of block-hiding strategies, such as
selfish-mine, causes the rate of production of orphan blocks to increase. Then
we use a spatial Poisson process model to study values of Eyal and Sirer's
parameter $\gamma$, which denotes the proportion of the honest community that
mine on a previously-secret block released by the pool in response to the
mining of a block by the honest community. Finally, we use discrete-event
simulation to study the behaviour of a network of Bitcoin miners, a proportion
of which is colluding in using the selfish-mine strategy, under the assumption
that there is a propagation delay in the communication of information between
miners.


  In this work we show how to engineer bilinear and quadratic Hamiltonians in
cavity quantum electrodynamics (QED) through the interaction of a single driven
two-level atom with cavity modes. The validity of the engineered Hamiltonians
is numerically analyzed even considering the effects of both dissipative
mechanisms, the cavity field and the atom. The present scheme can be used, in
both optical and microwave regimes, for quantum state preparation, the
implementation of quantum logical operations, and fundamental tests of quantum
theory.


  The process of diffusive shock acceleration relies on the efficacy with which
hydromagnetic waves can scatter charged particles in the precursor of a shock.
The growth of self-generated waves is driven by both resonant and non-resonant
processes. We perform high-resolution magnetohydrodynamic simulations of the
non-resonant cosmic-ray driven instability, in which the unstable waves are
excited beyond the linear regime. In a snapshot of the resultant field,
particle transport simulations are carried out. The use of a static snapshot of
the field is reasonable given that the Larmor period for particles is typically
very short relative to the instability growth time. The diffusion rate is found
to be close to, or below, the Bohm limit for a range of energies. This provides
the first explicit demonstration that self-excited turbulence reduces the
diffusion coefficient and has important implications for cosmic ray transport
and acceleration in supernova remnants.


  For a connected graph $G$ on $n$ vertices, recall that the distance signless
Laplacian matrix of $G$ is defined to be $\mathcal{Q}(G)=Tr(G)+\mathcal{D}(G)$,
where $\mathcal{D}(G)$ is the distance matrix, $Tr(G)=diag(D_1, D_2, \ldots,
D_n)$ and $D_{i}$ is the row sum of $\mathcal{D}(G)$ corresponding to vertex
$v_{i}$. Denote by $\rho^{\mathcal{D}}(G),$ $\rho_{min}^{\mathcal{D}}(G)$ the
largest eigenvalue and the least eigenvalue of $\mathcal{D}(G)$, respectively.
And denote by $q^{\mathcal{D}}(G)$, $q_{min}^{\mathcal{D}}(G)$ the largest
eigenvalue and the least eigenvalue of $\mathcal{Q}(G)$, respectively. The
distance spread of a graph $G$ is defined as
$S_{\mathcal{D}}(G)=\rho^{\mathcal{D}}(G)- \rho_{min}^{\mathcal{D}}(G)$, and
the distance signless Laplacian spread of a graph $G$ is defined as
$S_{\mathcal{Q}}(G)=q^{\mathcal{D}}(G)-q_{min}^{\mathcal{D}}(G)$. In this
paper, we point out an error in the result of Theorem 2.4 in "Distance spectral
spread of a graph" [G.L. Yu, et al, Discrete Applied Mathematics. 160 (2012)
2474--2478] and rectify it. As well, we obtain some lower bounds on ddistance
signless Laplacian spread of a graph.


  We examine the production of energetic neutral atoms (ENAs) in solar flares
and CME-driven shocks and their subsequent propagation to 1 au. Time profiles
and fluence spectra of solar ENAs at 1 au are computed for two scenarios: 1)
ENAs are produced downstream at CME-driven shocks, and 2) ENAs are produced at
large-scale post-flare loops in solar flares. Both the time profiles and
fluence spectra for these two scenarios are vastly different. Our calculations
indicate that we can use solar ENAs as a new probe to examine the underlying
acceleration process of solar energetic particles (SEPs) and to differentiate
the two accelertion sites: large loops in solar flares and downstream of
CME-driven shocks, in large SEP events.


  Modern SAT and SMT solvers are designed to handle problems expressed in
Conjunctive Normal Form (CNF) so that non-CNF problems must be CNF-ized
upfront, typically by using variants of either Tseitin or Plaisted and
Greenbaum transformations. When passing from solving to enumeration, however,
the capability of producing partial satisfying assignments that are as small as
possible becomes crucial, which raises the question of whether such CNF
encodings are also effective for enumeration.
  In this paper, we investigate both theoretically and empirically the
effectiveness of CNF conversions for SAT and SMT enumeration. On the negative
side, we show that: (i) Tseitin transformation prevents the solver from
producing short partial assignments, thus seriously affecting the effectiveness
of enumeration; (ii) Plaisted and Greenbaum transformation overcomes this
problem only in part. On the positive side, we prove theoretically and we show
empirically that combining Plaisted and Greenbaum transformation with NNF
preprocessing upfront -- which is typically not used in solving -- can fully
overcome the problem and can drastically reduce both the number of partial
assignments and the execution time.


  Deep priors have emerged as potent methods in hyperspectral image (HSI)
reconstruction. While most methods emphasize space-domain learning using image
space priors like non-local similarity, frequency-domain learning using image
frequency priors remains neglected, limiting the reconstruction capability of
networks. In this paper, we first propose a Hyperspectral Frequency Correlation
(HFC) prior rooted in in-depth statistical frequency analyses of existent HSI
datasets. Leveraging the HFC prior, we subsequently establish the frequency
domain learning composed of a Spectral-wise self-Attention of Frequency (SAF)
and a Spectral-spatial Interaction of Frequency (SIF) targeting low-frequency
and high-frequency components, respectively. The outputs of SAF and SIF are
adaptively merged by a learnable gating filter, thus achieving a thorough
exploitation of image frequency priors. Integrating the frequency domain
learning and the existing space domain learning, we finally develop the
Correlation-driven Mixing Domains Transformer (CMDT) for HSI reconstruction.
Extensive experiments highlight that our method surpasses various
state-of-the-art (SOTA) methods in reconstruction quality and computational
efficiency.


  Integrated quantum photonics devices in diamond have tremendous potential for
many quantum applications, including long-distance quantum communication,
quantum information processing, and quantum sensing. These devices benefit from
diamond's combination of exceptional thermal, optical, and mechanical
properties. Its wide electronic bandgap makes diamond an ideal host for a
variety of optical active spin qubits that are key building blocks for quantum
technologies. In landmark experiments, diamond spin qubits have enabled
demonstrations of remote entanglement, memory-enhanced quantum communication,
and multi-qubit spin registers with fault-tolerant quantum error correction,
leading to the realization of multinode quantum networks. These advancements
put diamond at the forefront of solid-state material platforms for quantum
information processing. Recent developments in diamond nanofabrication
techniques provide a promising route to further scaling of these landmark
experiments towards real-life quantum technologies. In this paper, we focus on
the recent progress in creating integrated diamond quantum photonic devices,
with particular emphasis on spin-photon interfaces, cavity optomechanical
devices, and spin-phonon transduction. Finally, we discuss prospects and
remaining challenges for the use of diamond in scalable quantum technologies.


  The nuclear spins of low-density implanted Ga atoms in Ge are interesting
candidates for solid state-based qubits. To date, activation studies of
implanted Ga in Ge have focused on high densities. Here we extend activation
studies into the low-density regime. We use spreading resistance profiling and
secondary ion mass spectrometry to derive electrical activation of Ga ions
implanted into Ge as a function of rapid thermal anneal temperature and implant
density. We show that for our implant conditions the activation is best for
anneal temperatures between 400 and 650 $^\circ$C, with a maximum activation of
64% at the highest fluence. Below 400 $^\circ$C, remaining implant damage
results in defects that act as superfluous carriers, and above 650 $^\circ$C,
surface roughening and loss of Ga ions are observed. The activation increased
monotonically from 10% to 64% as the implant fluence increased from
$6\times10^{10}$ to $6\times10^{12}$ cm$^{-2}$. The results provide thermal
anneal conditions to be used for initial studies of using low-density Ga atoms
in Ge as nuclear spin qubits.


  The effective quantum pseudospin-1/2 model for interacting rare-earth
magnetic moments, which are locally described with atomic doublets, is studied
theoretically for magnetic pyrochlore oxides. It is derived microscopically for
localized Pr^{3+} 4f moments in Pr_2TM_2O_7 (TM = Zr, Sn, Hf, and Ir) by
starting from the atomic non-Kramers magnetic doublets and performing the
strong-coupling perturbation expansion of the virtual electron transfer between
the Pr 4f and O 2p electrons. The most generic form of the nearest-neighbor
anisotropic superexchange pseudospin-1/2 Hamiltonian is also constructed from
the symmetry properties, which is applicable to Kramers ions Nd^{3+}, Sm^{3+},
and Yb^{3+} potentially showing large quantum effects. The effective model is
then studied by means of a classical mean-field theory and the exact
diagonalization on a single tetrahedron and on a 16-site cluster. These
calculations reveal appreciable quantum fluctuations leading to quantum phase
transitions to a quadrupolar state as a melting of spin ice for the Pr^{3+}
case. The model also shows a formation of cooperative quadrupole moment and
pseudospin chirality on tetrahedrons. A sign of a singlet quantum spin ice is
also found in a finite region in the space of coupling constants. The relevance
to the experiments is discussed.


  New thermoelectric power (TEP) measurements on prototype heavy-fermion
compounds close to magnetic quantum criticality are presented. The highly
sensitive technique of TEP is an unique tool to reveal Fermi surface
instabilities, referred here as Lifshitz transitions. The first focus is on the
Ising CeRu2Si2 series. Doping CeRu2Si2 with Rh produces a decoupling between
the first order metamagnetic transition and the pseudo-metamagnetism observed
in the pure compound. Comparison is made with the case of YbRh2Si2 which is
often considered as the archetype of local quantum criticality by contrast to
CeRu2Si2, taken as an example of spin-density wave criticality. Up to now for
ferromagnetic materials showing ferromagnetic wings, no simple case appears
where the Fermi surface is preserved between the ferromagnetic and paramagnetic
phases. An open issue is the consequence of Lifshitz transitions on
superconductivity in these multiband systems.


  In this article, we find the full Fourier expansion for the generalized
non-holomorphic Eisenstein series for certain values of parameters. We give a
connection of the boundary condition on such Fourier series with convolution
formulas on the divisor functions. Additionally, we discuss a possible relation
with the differential Galois theory.


  In this paper, we first study nonsmooth steepest descent method for nonsmooth
functions defined on Hilbert space and establish the corresponding algorithm by
proximal subgradients. Then, we use this algorithm to find stationary points
for those functions satisfying prox-regularity and Lipschitzian continuity. As
one application, the established algorithm is used to search the minimizer of
lower semicontinuous and convex functions on finite-dimensional space. The
convergent theorem, as one extension and improvement of the existing converging
result for twice continuously differentiable convex functions, is also
presented therein.


  We suggest an approach for simulating theories with a sign problem that
relies on optimisation of complex integration contours that are not restricted
to lie along Lefschetz thimbles. To that end we consider the toy model of a
one-dimensional Bose gas with chemical potential. We identify the main
contribution to the sign problem in this case as coming from a nearest
neighbour interaction and approximately cancel it by an explicit deformation of
the integration contour. We extend the obtained expressions to more general
ones, depending on a small set of parameters. We find the optimal values of
these parameters on a small lattice and study their range of validity. We also
identify precursors for the onset of the sign problem. A fast method of
evaluating the Jacobian related to the contour deformation is proposed and its
numerical stability is examined. For a particular choice of lattice parameters,
we find that our approach increases the lattice size at which the sign problem
becomes serious from $L \approx 32$ to $L \approx 700$. The efficient
evaluation of the Jacobian ($O(L)$ for a sweep) results in running times that
are of the order of a few minutes on a standard laptop.


  Over all non-prime finite fields, we construct some recursive towers of
function fields with many rational places. Thus we obtain a substantial
improvement on all known lower bounds for Ihara's quantity $A(\ell)$, for $\ell
= p^n$ with $p$ prime and $n>3$ odd. We relate the explicit equations to
Drinfeld modular varieties.


  We introduce dynamic nested sampling: a generalisation of the nested sampling
algorithm in which the number of "live points" varies to allocate samples more
efficiently. In empirical tests the new method significantly improves
calculation accuracy compared to standard nested sampling with the same number
of samples; this increase in accuracy is equivalent to speeding up the
computation by factors of up to ~72 for parameter estimation and ~7 for
evidence calculations. We also show that the accuracy of both parameter
estimation and evidence calculations can be improved simultaneously. In
addition, unlike in standard nested sampling, more accurate results can be
obtained by continuing the calculation for longer. Popular standard nested
sampling implementations can be easily adapted to perform dynamic nested
sampling, and several dynamic nested sampling software packages are now
publicly available.


  For all the irreducible dual pairs of type I $(G,G')$, we analyze the
restriction of the oscillator representation as a $(\mathfrak{g}', K')$-module,
when $G'$ is the smaller group. For all $(G,G')$ in the stable range, as well
as one more case, the modules obtained are projective. We use the duality
correspondence introduced by Howe to analyze these restrictions.


  Multivariate orthogonal polynomials can be introduced by using a moment
functional defined on the linear space of polynomials in several variables with
real coefficients. We study the so-called Uvarov and Christoffel modifications
obtained by adding to the moment functional a finite set of mass points, or by
multiplying it times a polynomial of total degree 2, respectively. Orthogonal
polynomials associated with modified moment functionals will be studied, as
well as the impact of the modification in useful properties of the orthogonal
polynomials. Finally, some illustrative examples will be given.


  We investigate prospects of using counter-rotating vortex superposition
states in non-equilibrium exciton-polariton Bose-Einstein condensates for the
purposes of Sagnac interferometry. We first investigate the stability of
vortex-antivortex superposition states, and show that they survive at
steady-state in a variety of configurations. Counter-rotating vortex
superpositions are of potential interest to gyroscope and seismometer
applications for detecting rotations. Methods of improving the sensitivity are
investigated by targeting high momentum states via metastable condensation, and
the application of periodic lattices. The sensitivity of the polariton
gyroscope is compared to its optical and atomic counterparts. Due to the large
interferometer areas in optical systems and small de Broglie wavelengths for
atomic BECs, the sensitivity per detected photon is found to be considerably
less for the polariton gyroscope than with competing methods. However,
polariton gyroscopes have an advantage over atomic BECs in a high
signal-to-noise ratio, and have other practical advantages such as
room-temperature operation and robust design. We estimate that the final
sensitivities including signal-to-noise aspects are competitive with existing
methods.


  The Expectation--Maximization (EM) algorithm is a simple meta-algorithm that
has been used for many years as a methodology for statistical inference when
there are missing measurements in the observed data or when the data is
composed of observables and unobservables. Its general properties are well
studied, and also, there are countless ways to apply it to individual problems.
In this paper, we introduce the $em$ algorithm, an information geometric
formulation of the EM algorithm, and its extensions and applications to various
problems. Specifically, we will see that it is possible to formulate an
outlier-robust inference algorithm, an algorithm for calculating channel
capacity, parameter estimation methods on probability simplex, particular
multivariate analysis methods such as principal component analysis in a space
of probability models and modal regression, matrix factorization, and learning
generative models, which have recently attracted attention in deep learning,
from the geometric perspective.


  We work entirely in the smooth category. An embedding $f:(S^2\times
S^1)\sqcup S^3\rightarrow {\mathbb R}^6$ is {\it Brunnian}, if the restriction
of $f$ to each component is isotopic to the standard embedding. For each triple
of integers $k,m,n$ such that $m\equiv n \pmod{2}$, we explicitly construct a
Brunnian embedding $f_{k,m,n}:(S^2\times S^1)\sqcup S^3 \rightarrow {\mathbb
R}^6$ such that the following theorem holds.
  Theorem: Any Brunnian embedding $f:(S^2\times S^1)\sqcup S^3\rightarrow
{\mathbb R}^6$ is isotopic to $f_{k,m,n}$ for some integers $k,m,n$ such that
$m\equiv n \pmod{2}$. Two embeddings $f_{k,m,n}$ and $f_{k',m',n'}$ are
isotopic if and only if $k=k'$, $m\equiv m' \pmod{2k}$ and $n\equiv n'
\pmod{2k}$.
  We use Haefliger's classification of embeddings $S^3\sqcup S^3\rightarrow
{\mathbb R}^6$ in our proof. The following corollary shows that the relation
between the embeddings $(S^2\times S^1)\sqcup S^3\rightarrow {\mathbb R}^6$ and
$S^3\sqcup S^3\rightarrow {\mathbb R}^6$ is not trivial.
  Corollary: There exist embeddings $f:(S^2\times S^1)\sqcup S^3\rightarrow
{\mathbb R}^6$ and $g,g':S^3\sqcup S^3\rightarrow {\mathbb R}^6$ such that the
componentwise embedded connected sum $f\#g$ is isotopic to $f\#g'$ but $g$ is
not isotopic to $g'$.


  Recently Nikodem, Rajba and W\k{a}sowicz compared the classes of
n-Wright-convex functions and n-Jensen-convex functions by showing that the
first one is a proper subclass of the latter one, whenever n is an odd natural
number. Till now the case of even n was an open problem. In this paper the
complete solution is given: it is shown that the inclusion is proper for any
natural n. The classes of strongly n-Wright-convex and strongly n-Jensen-convex
functions are also compared (with the same assertion).


  New family of extended Cauchy type identities is found and related Fermat
type matrices are provided ready for applications in extended scope. This is
achieved due to the use specifically non-commuting variables of extended finite
operator calculus introduced by the author few years ago.


  In Part I of this paper a modified BCS mechanism of Cooper pair formation was
proposed. The present Part III gives a physical interpretation of this
mechanism in terms of spin-flipping processes in superconducting bands.


  We show that any adjoint absolutely simple linear algebraic group over a
field of characteristic zero is the automorphism group of some projector on a
central simple algebra. Projective homogeneous varieties can be described in
these terms; in particular, we reproduce quadratic equations by Lichtenstein
defining them.


  Analogues of the key results of Wiman-Valiron theory are proved for a class
of functions meromorphic in the unit disc, based on an approach developed by
Bergweiler, Rippon and Stallard for the plane setting. The results give local
approximations for the function and its logarithmic derivative and, in the case
of positive order of growth, for higher order logarithmic derivatives as well.


  Background: Prostate cancer (PC) MRI-based risk calculators are commonly
based on biological (e.g. PSA), MRI markers (e.g. volume), and patient age.
Whilst patient age measures the amount of years an individual has existed,
biological age (BA) might better reflect the physiology of an individual.
However, surrogates from prostate MRI and linkage with clinically significant
PC (csPC) remain to be explored. Purpose: To obtain and evaluate Prostate Age
Gap (PAG) as an MRI marker tool for csPC risk. Study type: Retrospective.
Population: A total of 7243 prostate MRI slices from 468 participants who had
undergone prostate biopsies. A deep learning model was trained on 3223 MRI
slices cropped around the gland from 81 low-grade PC (ncsPC, Gleason score <=6)
and 131 negative cases and tested on the remaining 256 participants.
Assessment: Chronological age was defined as the age of the participant at the
time of the visit and used to train the deep learning model to predict the age
of the patient. Following, we obtained PAG, defined as the model predicted age
minus the patient's chronological age. Multivariate logistic regression models
were used to estimate the association through odds ratio (OR) and predictive
value of PAG and compared against PSA levels and PI-RADS>=3. Statistical tests:
T-test, Mann-Whitney U test, Permutation test and ROC curve analysis. Results:
The multivariate adjusted model showed a significant difference in the odds of
clinically significant PC (csPC, Gleason score >=7) (OR =3.78, 95% confidence
interval (CI):2.32-6.16, P <.001). PAG showed a better predictive ability when
compared to PI-RADS>=3 and adjusted by other risk factors, including PSA
levels: AUC =0.981 vs AUC =0.704, p<.001. Conclusion: PAG was significantly
associated with the risk of clinically significant PC and outperformed other
well-established PC risk factors.


  Massive star winds are important contributors to the energy, momentum and
chemical enrichment of the interstellar medium. Strong, organized and
predominantly dipolar magnetic fields have been firmly detected in a small
subset of massive O-type stars. Magnetic massive stars are known to exhibit
phase-locked variability of numerous observable quantities that is hypothesized
to arise due to the presence of an obliquely rotating magnetosphere formed via
the magnetic confinement of their strong outflowing winds. Analyzing the
observed modulations of magnetic O-type stars is thus a key step towards the
better understanding of the physical processes that occur within their
magnetospheres. The dynamical processes that lead to the formation of a
magnetosphere are formally solved utilizing complex MHD simulations. Recently,
an Analytic Dynamical Magnetosphere (ADM) model has been developed that can
quickly be employed to compute the time-averaged density, temperature and
velocity gradients within a dynamical magnetosphere. Here, we exploit the ADM
model to compute photometric and polarimetric observables of magnetic Of?p
stars, to test geometric models inferred from magnetometry. We showcase
important results on the prototypical Of?p-type star HD 191612, that lead to a
better characterization of massive star wind and magnetic properties.


  Despite the high overlap with the exact Coulomb ground state, the so-called
Gaffnian state fails to describe the incompressibility at the 2/5 quantum Hall
filling factor and consequently it was conjectured to be a quantum critical
state. To achieve a gapped state starting from the Gaffnian wavefunction, which
we interpret as the inter-flavor pairing of the composite fermions, we propose
a minimally `modified Gaffnian' wavefunction keeping the pairing intact. We
find that a suitable hybridization of these two wavefunctions is an excellent
description of the 2/5 quantum Hall state. It has a very high overlap with the
exact Coulomb state and their entanglement spectra match up to reasonably
higher levels. Interestingly, this hybridized wavefunction being a
representative of a paired state suggests an exotic possibility of non-Abelian
quasiparticle excitations at 2/5 filling.


  We study the general dynamics of the spherically symmetric gravitational
collapse of a massless scalar field. We apply the Galerkin projection method to
transform a system of partial differential equations into a set of ordinary
differential equations for modal coefficients, after a convenient truncation
procedure, largely applied to problems of turbulence. In the present case, we
have generated a finite dynamical system that reproduces the essential features
of the dynamics of the gravitational collapse, even for a lower order of
truncation. Each initial condition in the space of modal coefficients
corresponds to a well definite spatial distribution of scalar field. Numerical
experiments with the dynamical system show that depending on the strength of
the scalar field packet, the formation of black-holes or the dispersion of the
scalar field leaving behind flat spacetime are the two main outcomes. We also
found numerical evidence that between both asymptotic states, there is a
critical solution represented by a limit cycle in the modal space with period
$\Delta u \approx 3.55$.


  Piezoresponse force microscopy (PFM) is a powerful tool for probing
nanometer-scale ferroelectric and piezoelectric properties. Hysteretic
switching of the phase and amplitude of the PFM response are believed to be the
hallmark of ferroelectric and piezoelectric behavior respectively. However, the
application of PFM is limited by the fact that similar hysteretic effects may
also arise from mechanisms not related to ferroelectricity or piezoelectricity.
In this paper we report our studies on regular glass slides that show
ferroelectric-like signal without being ferroelectric and frequently used as a
substrate in PFM experiments. We demonstrate how the substrates and other
environmental factors like relative humidity and experimental conditions may
influence the PFM results on novel materials.


  It has been proposed that the effective Hamiltonian describing high T_c
superconductivity in cuprate materials has an approximate SO(5) symmetry
relating the superconducting (SC) and antiferromagnetic (AF) phases of these
systems. We show that robust consequences of this proposal are potentially
large optical conductivities and Raman scattering rates in the AF phase, due to
the electromagnetic response of the doubly-charged pseudo Goldstone bosons
which must exist there. This provides strong constraints on the properties of
the bosons, such as their mass gap and velocity.


  Using mostly elementary results and functions from probability, we prove
Wallis's formula for pi: pi/2 = prod_n (2n * 2n) / ((2n-1) * (2n+1)). The proof
involves normalization constants and the Gamma function, Standard normal, and
the Student t-Distribution.


  Predicting the start-ups that will eventually succeed is essentially
important for the venture capital business and worldwide policy makers,
especially at an early stage such that rewards can possibly be exponential.
  Though various empirical studies and data-driven modeling work have been
done, the predictive power of the complex networks of stakeholders including
venture capital investors, start-ups, and start-ups' managing members has not
been thoroughly explored. We design an incremental representation learning
mechanism and a sequential learning model, utilizing the network structure
together with the rich attributes of the nodes. In general, our method achieves
the state-of-the-art prediction performance on a comprehensive dataset of
global venture capital investments and surpasses human investors by large
margins. Specifically, it excels at predicting the outcomes for start-ups in
industries such as healthcare and IT. Meanwhile, we shed light on impacts on
start-up success from observable factors including gender, education, and
networking, which can be of value for practitioners as well as policy makers
when they screen ventures of high growth potentials.


  We estimate the wave speed in the acoustic wave equation from boundary
measurements by constructing a reduced-order model (ROM) matching discrete
time-domain data. The state-variable representation of the ROM can be
equivalently viewed as a Galerkin projection onto the Krylov subspace spanned
by the snapshots of the time-domain solution. The success of our algorithm
hinges on the data-driven Gram--Schmidt orthogonalization of the snapshots that
suppresses multiple reflections and can be viewed as a discrete form of the
Marchenko--Gel'fand--Levitan--Krein algorithm. In particular, the
orthogonalized snapshots are localized functions, the (squared) norms of which
are essentially weighted averages of the wave speed. The centers of mass of the
squared orthogonalized snapshots provide us with the grid on which we
reconstruct the velocity. This grid is weakly dependent on the wave speed in
traveltime coordinates, so the grid points may be approximated by the centers
of mass of the analogous set of squared orthogonalized snapshots generated by a
known reference velocity. We present results of inversion experiments for one-
and two-dimensional synthetic models.


  Satellite-terrestrial integrated networks (STIN) are envisioned as a
promising architecture for ubiquitous network connections to support
diversified services. In this paper, we propose a novel resource slicing scheme
with cross-cell coordination in STIN to satisfy distinct service delay
requirements and efficient resource usage. To address the challenges posed by
spatiotemporal dynamics in service demands and satellite mobility, we formulate
the resource slicing problem into a long-term optimization problem and propose
a distributed resource slicing (DRS) scheme for scalable and flexible resource
management across different cells. Specifically, a hybrid data-model co-driven
approach is developed, including an asynchronous multi-agent reinforcement
learning-based algorithm to determine the optimal satellite set serving each
cell and a distributed optimization-based algorithm to make the resource
reservation decisions for each slice. Simulation results demonstrate that the
proposed scheme outperforms benchmark methods in terms of resource usage and
delay performance.


  Existing large language model-based code generation pipelines typically use
beam search or sampling algorithms during the decoding process. Although the
programs they generate achieve high token-matching-based scores, they often
fail to compile or generate incorrect outputs. The main reason is that
conventional Transformer decoding algorithms may not be the best choice for
code generation. In this work, we propose a novel Transformer decoding
algorithm, Planning-Guided Transformer Decoding (PG-TD), that uses a planning
algorithm to do lookahead search and guide the Transformer to generate better
programs. Specifically, instead of simply optimizing the likelihood of the
generated sequences, the Transformer makes use of a planner to generate
candidate programs and test them on public test cases. The Transformer can
therefore make more informed decisions and generate tokens that will eventually
lead to higher-quality programs. We also design a mechanism that shares
information between the Transformer and the planner to make our algorithm
computationally efficient. We empirically evaluate our framework with several
large language models as backbones on public coding challenge benchmarks,
showing that 1) it can generate programs that consistently achieve higher
performance compared with competing baseline methods; 2) it enables
controllable code generation, such as concise codes and highly-commented codes
by optimizing modified objective.


  Non-resonant radio frequency absorption (NRRA) of superconducting MgB2 pellet
is investigated as a function of frequency, temperature, pressure, rf power,
magnetic field, sample size and grain size. The NRRA signals of granular MgB2
samples show three separate and distinguish temperature dependent phase
reversals, one of them is similar to the phase reversal occurs very often in
high Tc superconductors at temperature few degrees below Tc, the other two are
anomalous and occur at lower temperatures. Interpretation based on the
assumptions in the line shape model indicates that the Josephson junctions are
weakly coupled in MgB2 than other high temperature superconductors like
YBa2Cu3O7, La1.9Sr0.1CuO4 and Bi2Sr2CaCu2O8. The samples show a significant
magnetic field sweep hysteresis, which is a signature of trapped flux. The
hysteresis behavior suggests that the vortices in the MgB2 are rather rigid.
The effects of passing dc current through the sample on the NRRA signal
patterns are discussed. It is seen that the transition temperature Tc measured
using the NRRA technique decreases with decreasing sample size, whereas the Tc
measured using ac susceptibility and temperature dependent resistivity is
independent of the sample size. The results are discussed in terms of
resistively shunted junction, flux flow and bond percolation models. Effect of
MgO doping is analyzed and discussed. The intergranular coupling energy and the
activation energy of MgB2 are calculated and compared with other high
temperature superconductors. The effects of sintering on the weak links in MgB2
polycrystalline pellet are discussed.


  We present new Chandra observations of a high redshift (z~1) galaxy cluster
discovered in the Red-Sequence Cluster Survey (RCS): RCS043938-2904.7. X-ray
luminosity measurements and mass estimates are consistent with L_X-T_X and
M_delta-T_X relationships obtained from low-redshift data. Assuming a single
cluster, X-ray mass estimates are a factor of ~10-100 below the red-sequence
optical richness mass estimate. Optical spectroscopy reveals that this cluster
comprises two components which are close enough to perhaps be physically
associated. We present simple modeling of this two-component system which then
yields an X-ray mass and optical richness consistent with expectations from
statistical samples of lower redshift clusters. An unexpectedly high gas mass
fraction is measured assuming a single cluster, which independently supports
this interpretation. Additional observations will be necessary to confirm the
excess gas mass fraction and to constrain the mass distribution.


  Given a positive integer $M$ and a real number $q>1$, we consider the
univoque set $\mathcal{U}_q$ of reals which have a unique $q$-expansion over
the alphabet $\set{0,1,\cdots,M}$. In this paper we show that for any
$x\in\mathcal{U}_q$ and all sufficiently small $\varepsilon>0$ the Hausdorff
dimension $\dim_H\mathcal{U}_q\cap(x-\varepsilon, x+\varepsilon)$ equals either
$\dim_H\mathcal{U}_q$ {or} zero.
  Moreover, we give a complete description of the typical points
$x\in\mathcal{U}_q$ which satisfy \[ \dim_H\mathcal{U}_q\cap(x-\varepsilon,
x+\varepsilon)=\dim_H\mathcal{U}_q\quad\textrm{for any}\quad \varepsilon>0, \]
and prove that the set of typical points of $\mathcal{U}_q$ has full Hausdorff
dimension. In particular, we show that if $\mathcal{U}_q$ is a Cantor set, then
all points of $\mathcal{U}_q$ are typical points. This strengthen a result of
de Vries and Komornik (Adv. Math., 2009).


  As the parameters of a map are varied an attractor may vary continuously in
the Hausdorff metric. The purpose of this paper is to explore the continuation
of chaotic attractors. We argue that this is not a helpful concept for smooth
unimodal maps for which periodic windows fill parameter space densely, but that
for piecewise-smooth maps it provides a way to delineate structure within
parameter regions of robust chaos and form a stronger notion of robustness. We
obtain conditions for the continuity of an attractor and demonstrate the
results with coupled skew tent maps, the Lozi map, and the border-collision
normal form.


  This paper provides a block coordinate descent algorithm to solve
unconstrained optimization problems. In our algorithm, computation of function
values or gradients is not required. Instead, pairwise comparison of function
values is used. Our algorithm consists of two steps; one is the direction
estimate step and the other is the search step. Both steps require only
pairwise comparison of function values, which tells us only the order of
function values over two points. In the direction estimate step, a Newton type
search direction is estimated. A computation method like block coordinate
descent methods is used with the pairwise comparison. In the search step, a
numerical solution is updated along the estimated direction. The computation in
the direction estimate step can be easily parallelized, and thus, the algorithm
works efficiently to find the minimizer of the objective function. Also, we
show an upper bound of the convergence rate. In numerical experiments, we show
that our method efficiently finds the optimal solution compared to some
existing methods based on the pairwise comparison.


  The form factor for gamma pi^+ -> pi^+ pi^0 was calculated in a simple-minded
constituent model with a constant quark mass parameter, as well as in the
Schwinger-Dyson approach. The comparative discussion of these and various other
theoretical results on this anomalous process, as well as the scarce already
available data (hopefully to be supplemented by more accurate CEBAF data soon),
seem to favor Schwinger-Dyson modeling which would yield relatively small
low-momentum values of the constituent (dynamically dressed) quark mass
function.


  Let $M=X/\Gamma$ be a geometrically finite negatively curved manifold with
fundamental group $\Gamma$ acting on $X$ by isometries. The purpose of this
paper is to study the mixing property of the geodesic flow on $T^1M$, the
asymptotic equivalent as $R\longrightarrow+\infty$ of the number of closed
geodesics on $M$ of length less than $R$ and of the orbital counting function
$\sharp\{\gamma\in\Gamma\ |\ d(o,\gamma.o)\le R\}$.
  These properties are well known when the Bowen-Margulis measure on $T^1M$ is
finite. We consider here divergent Schottky groups whose Bowen-Margulis measure
is infinite and ergodic, and we precise these ergodic properties using a
suitable symbolic coding.


  We consider a system of N fermions in the mean-field regime interacting
though an inverse power law potential $V(x)=1/|x|^{\alpha}$, for
$\alpha\in(0,1]$. We prove the convergence of a solution of the many-body
Schr\"{o}dinger equation to a solution of the time-dependent Hartree-Fock
equation in the sense of reduced density matrices. We stress the dependence on
the singularity of the potential in the regularity of the initial data. The
proof is an adaptation of [22], where the case $\alpha=1$ is treated.


  Quantum Computers, one fully realized, can represent an exponential boost in
computing power. However, the computational power of the current quantum
computers, referred to as Noisy Internediate Scale Quantum, or NISQ, is
severely limited because of environmental and intrinsic noise, as well as the
very low connectivity between qubits compared to their total amount. We propose
a virtual quantum processor that emulates a generic hybrid quantum machine
which can serve as a logical version of quantum computing hardware. This hybrid
classical quantum machine powers quantum-logical computations which are
substitutable by future native quantum processors.


  We present the results of ASCA and RXTE observations of the early X-ray
emission from the classical nova V382 Velorum. Its ASCA spectrum was hard
(kT~10 keV) with a strong (10**23 cm**-2) intrinsic absorption. In the
subsequent RXTE data, the spectra became softer both due to a declining
temperature and a diminishing column. We argue that this places the X-ray
emission interior to the outermost ejecta produced by V382 Vel in 1999, and
therefore must have been the result of a shock internal to the nova ejecta. The
weakness of the Fe K alpha lines probably indicates that the X-ray emitting
plasmas are not in ionization equilibrium.


  A general method for rotational microwave spectroscopy and control of polar
molecular ions via direct microwave addressing is considered. Our method makes
use of spatially varying AC Stark shifts, induced by far off-resonant, focused
laser beams to achieve an effective coupling between the rotational state of a
molecular ion and the electronic state of an atomic ion. In this setting, the
atomic ion is used for read-out of the molecular ion state, in a manner
analogous to quantum logic spectroscopy based on Raman transitions. In addition
to high-precision spectroscopy, this setting allows for rotational ground state
cooling, and can be considered as a candidate for the quantum information
processing with polar molecular ions. All elements of our proposal can be
realized with currently available technology.


  We study the screening length of a heavy quark-antiquark pair in strongly
coupled gauge theory plasmas flowing at velocity v following a proposal by Liu,
Rajagopal, and Wiedemann. We analyze the screening length as the direction of
the plasma winds vary. To leading order in v, this angle-dependence can be
studied analytically for many theories by extending our previous formalism. We
show that the screening length is locally a minimum (maximum) when the pair is
perpendicular (parallel) to the plasma winds, which has been observed for the
N=4 plasma. Also, we compare AdS/CFT results with weak coupling ones, and we
discuss the subleading dependence on v for the Dp-brane.


  We show that reductions of KP hierarchies related to the loop algebra of
$SL_n$ with homogeneous gradation give solutions of the Darboux-Egoroff system
of PDE's. Using explicit dressing matrices of the Riemann-Hilbert problem
generalized to include a set of commuting additional symmetries, we construct
solutions of the Witten--Dijkgraaf--E. Verlinde--H. Verlinde equations.


  In this paper, we prove the stability of viscosity solutions of the
Hamilton--Jacobi equations for a sequence of networks embedded in Euclidean
space. The network considered in this paper is not merely a graph -- it
comprises a collection of line segments. We investigate the conditions under
which the stability of viscosity solutions holds if the sequence of networks
converges to some compact set in the Hausdorff sense. As a corollary, a
characterization of the limit of a sequence of networks on which viscosity
solutions can be considered, is obtained. In consideration of this problem, we
adopt the concept of viscosity solutions as presented in the sense of Gangbo
and \'{S}wi\k{e}ch.


  In this paper we obtain the average sensitivity of the laced Boolean
functions. This confirms a conjecture of Shparlinski. We also compute the
weights of the laced Boolean functions and show that they are almost balanced.


  Historically, amyloid fibers (AF) in research has always been linked to
degenerative diseases. However, HET-s AF, by their morphology and function,
have only little in common to pathogenic amyloid fibers such as
{\alpha}-synuclein or a\b{eta} and they have appeared as promising candidate
for biocoating since few years. Here we have shown than HET-s amyloid fibers
hydrogel is an extremely polyvalent coating material for the in vitro culture
of primary hippocampal neurons. First, the non-cytotoxicity was demonstrated in
vitro using standardized ISO protocols. Then, it is shown that in vitro culture
of primary hippocampal neurons on HET-s AF hydrogels could last more than 45
days with clear signatures of spontaneous network activity, with which is a
feat that not many other coatings have achieved yet. Finally, interactions
between the cells, the dendrites and the hydrogels are highlighted, showing
that dendrites might be able to penetrate the hydrogels in depth, therefore
allowing recordings even within micrometer-thick hydrogels. In the end, those
properties combined with group functionalization using standard biochemistry
techniques, makes HET-s hydrogels ideal candidates to be used for the long-term
growth of neurons as well as other types of cells. This versatility and
easiness to use are definitely still unheard, especially for protein material.
Due to its ability to transform from dry films to hydrogel when in contact with
the extracellular matrix (ECM), it could also be used for in vivo implants,
solving the issue of hydrogel damaging during the implant surgery.


  In this paper we study the possibility of utilizing a loaded wire medium as
an artificial material for beam shaping element in base station antenna
applications. The wires in the lattice are loaded with a proper capacitive
impedance to come up with a structure effectively operating as a dielectric
lens at the UMTS (Universal Mobile Telecommunications System) frequency range.
It is proven with full-wave simulations that the interpretation of a finite
size wire lattice as a dielectric lens is physically sound even with a moderate
number of lattice periods. A prototype having a mechanically reconfigurable
beam width is constructed and measured. It is shown that very promising
performance can be achieved with a rather simple structure and a cheap
manufacturing process.


  We estimate the number of possible types degree patterns of $k$-lacunary
polynomials of degree $t < p$ which split completely modulo $p$. The result is
based on a combination of a bound on the number of zeros of lacunary
polynomials with some graph theory arguments.


  Stochastic versions of a classical model for natural ventilation are proposed
and investigated to demonstrate the effect of random fluctuations on stability
and predictability. In a stochastic context, the well-known deterministic
result that ventilation driven by the competing effects of buoyancy and wind
admits multiple steady states can be misleading. With fluctuations in the
buoyancy exchanged with an external environment modelled as a Wiener process,
such systems tend to reside in the vicinity of global minima of their
potential, rather than states associated with metastable equilibria. For a
heated space with a leeward low-level and windward high-level opening,
sustained buoyancy-driven flow opposing the wind direction is unlikely for wind
strengths exceeding a statistically critical value, which is slightly larger
than the critical value of the wind strength at which bifurcation in the
deterministic system occurs. When fluctuations in the applied wind strength are
modelled as an Ornstein-Uhlenbeck process, the topology of the system's
potential is effectively modified due to the nonlinear role that wind strength
has in the equation for buoyancy conservation. Consequently, large fluctuations
in the wind of sufficient duration rule out the possibility of sustained
ventilation opposing the wind direction at large base wind strengths.


  Given an automorphism of a smooth complex algebraic curve, there is an
induced action on the moduli space of semi-stable rank 2 holomorphic bundles
with fixed determinant. We give a complete description of the fixed variety in
terms of moduli spaces of parabolic bundles on the quotient curve.


  A sheet of glassy polymers placed in a solvent shows swelling behaviors quite
different from that of soft polymers (rubbers and gels). (1) Non-Fickian
diffusion (called case II diffusion): As solvent permeates into the sample, a
sharp front is created between the swollen part and the glassy part, and it
moves toward the center at constant speed. (2) Nonmonotonous swelling: The
thickness of the sample first increases and then decreases toward the
equilibrium value. Here we propose a theory to explain such anomalous behavior
by extending the previous theory for swelling of soft gels. We regard the
material as a continuum mixture of a glassy polymer network and solvent. We
assume that the polymer network is a viscoelastic gel of glassy polymers, and
its relaxation time depends strongly on solvent concentration. We show that
this theory explains the above two characteristics of glassy polymers in a
simple and unified framework. The theory predicts how the permeation speed of
the solvent and the characteristic times of the swelling process depend on
material parameters and experimental conditions, which can be checked
experimentally.


  Accurate lung nodule segmentation is crucial for early-stage lung cancer
diagnosis, as it can substantially enhance patient survival rates. Computed
tomography (CT) images are widely employed for early diagnosis in lung nodule
analysis. However, the heterogeneity of lung nodules, size diversity, and the
complexity of the surrounding environment pose challenges for developing robust
nodule segmentation methods. In this study, we propose an efficient end-to-end
framework, the multi-encoder-based self-adaptive hard attention network
(MESAHA-Net), for precise lung nodule segmentation in CT scans. MESAHA-Net
comprises three encoding paths, an attention block, and a decoder block,
facilitating the integration of three types of inputs: CT slice patches,
forward and backward maximum intensity projection (MIP) images, and region of
interest (ROI) masks encompassing the nodule. By employing a novel adaptive
hard attention mechanism, MESAHA-Net iteratively performs slice-by-slice 2D
segmentation of lung nodules, focusing on the nodule region in each slice to
generate 3D volumetric segmentation of lung nodules. The proposed framework has
been comprehensively evaluated on the LIDC-IDRI dataset, the largest publicly
available dataset for lung nodule segmentation. The results demonstrate that
our approach is highly robust for various lung nodule types, outperforming
previous state-of-the-art techniques in terms of segmentation accuracy and
computational complexity, rendering it suitable for real-time clinical
implementation.


  This short paper is an introduction of the memoir recently written by the two
authors (see [D.Bresch., P.--E. Jabin, arXiv:1507.04629, (2015)]) which
concerns the resolution of two longstanding problems: Global existence of weak
solutions for compressible Navier-Stokes equations with thermodynamically
unstable pressure and with anisotropic stress tensor. We focus here on a
Stokes-like system which can for instance model flows in a compressible tissue
in biology or in a compressible porous media in petroleum engineering. This
allows us to explain, on a simpler but still relevant and important system, the
tools recently introduced by the authors and to discuss the important results
that have been obtained on the compressible Navier--Stokes equations. It is
finally a real pleasure to dedicate this paper to G. Metivier for his 65's
Birthday. The article will appear in the corresponding special issue, Springer
INdAM-Series, Eds F. Colombini, D. Del Santos, D. Lannes (2016).


  A wave-packet time evolution method, based on the split-operator technique,
is developed to investigate the scattering of quasi-particles at a
normal-superconductor interface of arbitrary profile and shape. As a practical
application, we consider a system where low energy electrons can be described
as Dirac particles, which is the case for most two-dimensional materials, such
as graphene and transition metal dichalcogenides. However the method is easily
adapted for other cases such as electrons in few layer black phosphorus, or any
Schr\"odinger quasi-particles within the effective mass approximation in
semiconductors. We employ the method to revisit Andreev reflection in graphene,
where specular and retro reflection cases are observed for electrons scattered
by a step-like superconducting region. The effect of opening a zero-gap channel
across the superconducting region on the electron and hole scattering is also
addressed, as an example of the versatility of the technique proposed here.


  Multiple Coulomb Scattering (MCS) is a well known phenomenon occurring when
charged particles traverse materials. Measurements of muons traversing low $Z$
materials made in the MuScat experiment showed that theoretical models and
simulation codes, such as GEANT4 (v7.0), over-estimated the scattering. The
Muon Ionization Cooling Experiment (MICE) measured the cooling of a muon beam
traversing a liquid hydrogen or lithium hydride (LiH) energy absorber as part
of a programme to develop muon accelerator facilities, such as a Neutrino
Factory or a Muon Collider. The energy loss and MCS that occur in the absorber
material are competing effects that alter the performance of the cooling
channel. Therefore measurements of MCS are required in order to validate the
simulations used to predict the cooling performance in future accelerator
facilities. We report measurements made in the MICE apparatus of MCS using a
LiH absorber and muons within the momentum range 160 to 245 MeV/c. The measured
RMS scattering width is about 9% smaller than that predicted by the approximate
formula proposed by the Particle Data Group. Data at 172, 200 and 240 MeV/c are
compared to the GEANT4 (v9.6) default scattering model. These measurements show
agreement with this more recent GEANT4 (v9.6) version over the range of
incident muon momenta.


  The article is devoted to the development of the method of expansion and
mean-square approximation of iterated Ito stochastic integrals based on
generalized multiple Fourier series converging in the mean. We adapt this
method for iterated Stratonovich stochastic integrals of multiplicity 3 from
the Taylor-Stratonovich expansion. The main result of the article has been
derived with using the triple Fourier-Legendre series and triple trigonometric
Fourier series for the general case of series summation. Some recent results on
the expansion of iterated Stratonovich stochastic integrals of multiplicities 3
to 6 are given. The results of the article can be applied to the numerical
integration of Ito stochastic differential equations in accordance with the
strong criterion of convergence.


  Trustworthy environment perception is the fundamental basis for the safe
deployment of automated agents such as self-driving vehicles or intelligent
robots. The problem remains that such trust is notoriously difficult to
guarantee in the presence of systematic faults, e.g. non-traceable errors
caused by machine learning functions. One way to tackle this issue without
making rather specific assumptions about the perception process is plausibility
checking. Similar to the reasoning of human intuition, the final outcome of a
complex black-box procedure is verified against given expectations of an
object's behavior. In this article, we apply and evaluate collaborative,
sensor-generic plausibility checking as a mean to detect empirical perception
faults from their statistical fingerprints. Our real use case is
next-generation automated driving that uses a roadside sensor infrastructure
for perception augmentation, represented here by test scenarios at a German
highway and a city intersection. The plausibilization analysis is integrated
naturally in the object fusion process, and helps to diagnose known and
possibly yet unknown faults in distributed sensing systems.


  This article applies the principle of Occam's Razor to non-parametric model
building of statistical data, by finding a model with the minimal number of
bits, leading to an exceptionally effective regularization method for
probability density estimators. The idea comes from the fact that likelihood
maximization also minimizes the number of bits required to encode a dataset.
However, traditional methods overlook that the optimization of model parameters
may also inadvertently play the part in encoding data points. The article shows
how to extend the bit counting to the model parameters as well, providing the
first true measure of complexity for parametric models. Minimizing the total
bit requirement of a model of a dataset favors smaller derivatives, smoother
probability density function estimates and most importantly, a phase space with
fewer relevant parameters. In fact, it is able prune parameters and detect
features with small probability at the same time. It is also shown, how it can
be applied to any smooth, non-parametric probability density estimator.


  In this paper, we present and analyze methods for solving a system of linear
equations over idempotent semifields. The first method is based on the
pseudo-inverse of the system matrix. We then present a specific version of
Cramer's rule which is also related to the pseudo-inverse of the system matrix.
In these two methods, the constant vector plays an implicit role in solvability
of the system. Another method is called the normalization method in which both
the system matrix and the constant vector play explicit roles in the solution
process. Each of these methods yields the maximal solution if it exists.
Finally, we show the maximal solutions obtained from these methods and some
previous methods are all identical.


  We propose a learning-based, distributionally robust model predictive control
approach towards the design of adaptive cruise control (ACC) systems. We model
the preceding vehicle as an autonomous stochastic system, using a hybrid model
with continuous dynamics and discrete, Markovian inputs. We estimate the
(unknown) transition probabilities of this model empirically using observed
mode transitions and simultaneously determine sets of probability vectors
(ambiguity sets) around these estimates, that contain the true transition
probabilities with high confidence. We then solve a risk-averse optimal control
problem that assumes the worst-case distributions in these sets. We furthermore
derive a robust terminal constraint set and use it to establish recursive
feasibility of the resulting MPC scheme. We validate the theoretical results
and demonstrate desirable properties of the scheme through closed-loop
simulations.


  Mass spectrometry is used in a wide range of scientific disciplines including
proteomics, pharmaceutics, forensics, and fundamental physics and chemistry.
Given this ubiquity, there is a worldwide effort to improve the efficiency and
resolution of mass spectrometers. However, the performance of all techniques is
ultimately limited by the initial phase-space distribution of the molecules
being analyzed. Here, we dramatically reduce the width of this initial
phase-space distribution by sympathetically cooling the input molecules with
laser-cooled, co-trapped atomic ions, improving both the mass resolution and
detection efficiency of a time-of-flight mass spectrometer by over an order of
magnitude. Detailed molecular dynamics simulations verify the technique and aid
with evaluating its effectiveness. Our technique appears to be applicable to
other types of mass spectrometers.


  We study popular scalar extensions of the Standard Model, namely the singlet
extension, the 2-Higgs doublet model (2HDM) and its extension by a singlet
scalar. We focus on the contributions of the added scalars to the anomalous
magnetic moment of the muon, $(g-2)_\mu$ in the presence of CP-violation, and
the electric dipole moment of the electron ($e$EDM) in these models. In the
absence of CP-violation, CP-even and CP-odd scalars contribute with an opposite
sign to the anomalous magnetic moment of the muon and as a result these models
generally require very light scalars to explain the observed discrepancy in
$(g-2)_\mu$. We study the effect of CP-violation on the anomalous magnetic
moment of the muon and its compatibility with the $e$EDM constraints. We show
that given the current status of the global set of constraints applied on all
values of $\cot\beta$, in the CP-violating scalar extensions, there exist no
viable parameter space in agreement with both $a_\mu$ and $e$EDM bounds.


  Let $K\subset\mathbb{C}$ be a compact set in the plane whose logarithmic
capacity $c(K)$ is strictly positive. Let $\mathscr{P}_n(K)$ be the space of
monic polynomials of degree $n,$ \emph{all} of whose zeros lie in $K.$ For
$p\in \mathscr{P}_n(K),$ its filled \emph{unit leminscate} is defined by
$\Lambda_p = \{z: |p(z)| < 1\}.$ Let $\mathcal{C}(\Lambda_p) $ denote the
number of connected components of the open set $\Lambda_p,$ and define
$\mathscr{C}_n(K) = \max_{p\in \mathscr{P}_n(K)}\mathcal{C}(\Lambda_p).$ In
this paper we show that the quantity \[M(K) =
\limsup_{n\to\infty}\dfrac{\mathscr{C}_n(K)}{n},\] satisfies $M(K) < 1$ when
the logarithmic capacity $c(K) < 1,$ and $M(K) = 1$ when $c(K)\geq 1.$ In
particular, this answers a question of Erd\"os et. al. posed in $1958$. In
addition, we show that for nice enough compact sets whose capacity is strictly
bigger than $\frac{1}{2}$, the quantity $m(K) =
\liminf_{n\to\infty}\dfrac{\mathscr{C}_n(K)}{n} > 0.$


  Full-duplex (FD) transmission in a point-to-point (P2P) link, wherein
bidirectional traffic flows simultaneously share the same spectrum, has the
capability of doubling the link rate by completely removing self-interferences.
However, the rate gain of FD transmission in an interference-limited network is
not as clear as a P2P link due to the interferences induced by complex FD and
half-duplex (HD) transmission behaviors in the network. To thoroughly
investigate the achievable link rates of users and base stations (BSs) in a
heterogeneous network (HetNet) with decoupled user association, a general
approach is proposed to model a HetNet in which all BSs and users can perform
HD or FD transmission depending on their traffic patterns. We first
characterize the decoupled rate-optimal user association scheme and use it to
define the downlink and uplink rates in the HetNet. The tight lower bounds on
the link rates of the FD users and BSs are found in a neat form that
characterizes general channel fading, user and BS intensities and imperfect
self-interference cancellation. These bounds outline the rate regions of the FD
users that inspire proposing the opportunistic FD scheduling algorithms
maximizing the sum rate of each bidirectional traffic and stabilize each of the
queues in the HetNet.


  We revisit the Milky Way satellite problem using a semi-analytical model of
galaxy formation and compare the predicted luminosity function to recent result
from the SDSS. With cosmic photoionization, the luminosity function can be
brought into broad agreement with the data between $-15< M_{V} <-2$. This
improvement over previous semi-analytical model results (e.g., Benson et
al.2002) is from our adoption of improved models for galaxy merger history and
galaxy merging time-scales. The very faint satellites ($M_{v} > -5$) formed in
halos with virial temperature over $10^{4}K$ (mass around $10^{9} M_{\odot}$
before accretion), but their baryon content are strongly suppressed by
photoionization. We model the mass evolution of the subhalos, and compare the
predicted mass-to-light ratio with the data. We find that the measured total
mass inside the luminous radii of satellites are about 5% of their present
total dark matter mass.


  Minimization of the number of cluster heads in a wireless sensor network is a
very important problem to reduce channel contention and to improve the
efficiency of the algorithm when executed at the level of cluster-heads. In
this paper, we propose an efficient method based on genetic algorithms (GAs) to
solve a sensor network optimization problem. Long communication distances
between sensors and a sink in a sensor network can greatly drain the energy of
sensors and reduce the lifetime of a network. By clustering a sensor network
into a number of independent clusters using a GA, we can greatly minimize the
total communication distance, thus prolonging the network lifetime. Simulation
results show that our algorithm can quickly find a good solution.


  Superscaling of the quasielastic cross section in charged current
neutrino-nucleus reactions at energies of a few GeV is investigated within the
framework of the relativistic impulse approximation. Several approaches are
used to describe final state interactions and comparisons are made with the
plane wave approximation. Superscaling is very successful in all cases. The
scaling function obtained using a relativistic mean field for the final states
shows an asymmetric shape with a long tail extending towards positive values of
the scaling variable, in excellent agreement with the behavior presented by the
experimental scaling function.


  We study the problem of actuator and sensor placement in a linear advection
partial differential equation (PDE). The problem is motivated by its
application to actuator and sensor placement in building systems for the
control and detection of a scalar quantity such as temperature and
contaminants. We propose a gramian based approach to the problem of actuator
and sensor placement. The special structure of the advection PDE is exploited
to provide an explicit formula for the controllability and observability
gramian in the form of a multiplication operator. The explicit formula for the
gramian, as a function of actuator and sensor location, is used to provide test
criteria for the suitability of a given sensor and actuator location.
Furthermore, the solution obtained using gramian based criteria is interpreted
in terms of the flow of the advective vector field. In particular, the almost
everywhere stability property of the advective vector field is shown to play a
crucial role in deciding the location of actuators and sensors. Simulation
results are performed to support the main results of this paper.


  We propose MindPalace, a prototype of a versioned database for efficient
collaborative data management. MindPalace supports offline collaboration, where
users work independently without real-time correspondence. The core of
MindPalace is a critical step of offline collaboration: reconciling divergent
branches made by simultaneous data manipulation. We formalize the concept of
auto-mergeability, a condition under which branches may be reconciled without
human intervention, and propose an efficient framework for determining whether
two branches are auto-mergeable and identifying particular records for manual
reconciliation.


  The Alpha Centauri system is the primary target for planet search as it is
the closest star system composed of a solar twin Alpha Centauri A, a K-dwarf
Alpha Centauri B and an M-dwarf Proxima Centauri, which has a confirmed planet
in the temperate zone. Alpha Centauri A & B were monitored intensively with the
HARPS spectrograph for over 10 years, providing high-precision radial velocity
measurements. In this work we study the available data to better understand the
stellar activity and other contaminating signals. We highlight the importance
of telluric contamination and its impact on the radial velocity measurements.
Our suggested procedures lead to discarding about 5% of HARPS data, providing a
dataset with an RMS improved by a factor of 2. We compile and quantify the
behaviour of 345 spectral lines with a wide range of line shapes and
sensitivity to activity.


  Ultrasonic irradiation of liquids, such as water-alcohol solutions, results
in cavitation or the formation of small bubbles. Cavitation bubbles are
generated in real solutions without the use of optical traps making our system
as close to real conditions as possible. Under the action of the ultrasound,
bubbles can grow, oscillate, and eventually, they collapse or decompose. We
apply the mathematical method of separation of motions to interpret the
acoustic effect on the bubbles. While in most situations, the spherical shape
of a bubble is the most energetically profitable as it minimizes the surface
energy, when the acoustic frequency is in resonance with the natural frequency
of the bubble, shapes with the dihedral symmetry emerge. Some of these
resonance shapes turn unstable, so the bubble decomposes. It turns out that
bubbles in the solutions of different concentrations (with different surface
energies and densities) attain different evolution paths. While it is difficult
to obtain a deterministic description of how the solution concentration affects
bubble dynamics, it is possible to separate images with different
concentrations by applying the Artificial Neural Network (ANN) algorithm. An
ANN was trained to detect the concentration of alcohol in a water solution
based on the bubble images. This indicates that Artificial Intelligence (AI)
methods can complement deterministic analysis in non-equilibrium, near-unstable
situations.


  We consider a strongly interacting quasi-one dimensional Bose gas on a tight
ring trap subjected to a localized barrier potential. We explore the
possibility to form a macroscopic superposition of a rotating and a nonrotating
state under nonequilibrium conditions, achieved by a sudden quench of the
barrier velocity. Using an exact solution for the dynamical evolution in the
impenetrable-boson (Tonks-Girardeau) limit, we find an expression for the
many-body wavefunction corresponding to a superposition state. The
superposition is formed when the barrier velocity is tuned close to multiples
of integer or half-integer number of Coriolis flux quanta. As a consequence of
the strong interactions, we find that (i) the state of the system can be mapped
onto a macroscopic superposition of two Fermi spheres, rather than two
macroscopically occupied single-particle states as in a weakly interacting gas,
and (ii) the barrier velocity should be larger than the sound velocity to
better discriminate the two components of the superposition.


  We prove dispersive decay estimates for the one-dimensional Dirac operator
and use them to prove asymptotic stability of small gap solitons in the
nonlinear Dirac equations with quintic and higher-order nonlinear terms.


  High dynamic range (HDR) novel view synthesis (NVS) aims to create
photorealistic images from novel viewpoints using HDR imaging techniques. The
rendered HDR images capture a wider range of brightness levels containing more
details of the scene than normal low dynamic range (LDR) images. Existing HDR
NVS methods are mainly based on NeRF. They suffer from long training time and
slow inference speed. In this paper, we propose a new framework, High Dynamic
Range Gaussian Splatting (HDR-GS), which can efficiently render novel HDR views
and reconstruct LDR images with a user input exposure time. Specifically, we
design a Dual Dynamic Range (DDR) Gaussian point cloud model that uses
spherical harmonics to fit HDR color and employs an MLP-based tone-mapper to
render LDR color. The HDR and LDR colors are then fed into two Parallel
Differentiable Rasterization (PDR) processes to reconstruct HDR and LDR views.
To establish the data foundation for the research of 3D Gaussian
splatting-based methods in HDR NVS, we recalibrate the camera parameters and
compute the initial positions for Gaussian point clouds. Experiments
demonstrate that our HDR-GS surpasses the state-of-the-art NeRF-based method by
3.84 and 1.91 dB on LDR and HDR NVS while enjoying 1000x inference speed and
only requiring 6.3% training time. Code, models, and recalibrated data will be
publicly available at https://github.com/caiyuanhao1998/HDR-GS


  Bourgain(1993) proved that the periodic modified KdV equation (mKdV) is
locally well-posed in Sobolev spave H^s(T), s >= 1/2, by introducing new
weighted Sobolev spaces X^s,b, where the uniqueness holds conditionally, namely
in the intersection of C([0, T]; H^s) and X^s,b. In this paper, we establish
unconditional well-posedness of mKdV in H^s(T), s >= 1/2, i.e. we in addition
establish unconditional uniqueness in C([0, T]; H^s), s >= 1/2, of solutions to
mKdV. We prove this result via differentiation by parts. For the endpoint case
s = 1/2, we perform careful quinti- and septi-linear estimates after the second
differentiation by parts.


  We present new parsec-scale resolution data from a multi-phase study of the
ISM in the walls of Galactic supershells. HI synthesis images and CO survey
data reveal a wealth of substructure, including dense-tipped fingers and
extended molecular clouds embedded in shell walls. We briefly consider
formation scenarios for these features, and suggest that both the interaction
of an expanding shell with pre-existing dense clouds, as well as in-situ
formation of CNM and molecular gas, are likely to be relevant. Future work will
also examine the role of instabilities in structure formation and breakup, and
will investigate the presence of high-altitude gas associated with supershells
and chimneys.


  The Lambda Orionis Star Forming Region is dominated by the O8 III star
lambda^1 Ori. Among other structures, it includes a CO and a dust ring whose
diameter is about nine deg, the S264 HII region, a large number of IRAS
sources, the Barnard 30 and 35 dark clouds and a cluster associated with the
central star, the Lambda Ori cluster (Collinder 69). We derive the initial mass
function for this cluster (~5 Myr), covering several orders of magnitude in
mass (50 - 0.02 Msun).


  We present a new analysis of B_d,s -> KK modes within the SM, relating them
in a controlled way through SU(3)-flavour symmetry and QCD-improved
factorisation. We propose a set of sum rules for B_d,s -> K^0 \bar K^0
observables. We determine B_s -> KK branching ratios and CP-asymmetries as
functions of A_dir(B_d -> K^0 \bar K^0), with a good agreement with current
experimental measurements of CDF. Finally, we predict the amount of U-spin
breaking between B_d -> pi+ pi- and B_s -> K+K-.


  In this article, we employ multiscale physics-informed neural networks
(MscalePINNs) for the inverse retrieval of the effective permittivity and
homogenization of finite-size photonic media with stealthy hyperuniform (SHU)
disordered geometries. Specifically, we show that MscalePINNs are capable of
capturing the fast spatial variations of complex fields scattered by arrays of
dielectric nanocylinders arranged according to isotropic SHU point patterns,
thus enabling a systematic methodology to inverse retrieve their effective
dielectric profiles. Our approach extends the recently developed high-frequency
homogenization theory of hyperuniform media and retrieves more general
permittivity profiles for applications-relevant finite-size SHU systems,
unveiling unique features related to their isotropic nature. In particular, we
demonstrate the existence of a transparency region beyond the long-wavelength
approximation, enabling effective and isotropic homogenization even without
disorder-averaging, in contrast to the case of uncorrelated Poisson random
patterns. We believe that the multiscale network approach introduced here
enables the efficient inverse design of general effective media and finite-size
metamaterials with isotropic electromagnetic responses beyond the limitations
of traditional homogenization theories.


  Let $K$ be a number field, and $S$ a finite set of places in $K$ containing
all infinite places. We present an implementation for solving the $S$-unit
equation $x + y = 1$, $x,y \in\mathscr{O}_{K,S}^\times$ in the computer algebra
package SageMath. This paper outlines the mathematical basis for the
implementation. We discuss and reference the results of extensive computations,
including exponent bounds for solutions in many fields of small degree for
small sets $S$. As an application, we prove an asymptotic version of Fermat's
Last Theorem for totally real cubic number fields with bounded discriminant
where 2 is totally ramified. In addition, we use the implementation to find all
solutions to some cubic Ramanujan-Nagell equations.


  We give a short proof to the following tilting theorem by Happel, Reiten and
Smal{\o} via an explicit construction: given two abelian categories
$\mathcal{A}$ and $\mathcal{B}$ such that $\mathcal{B}$ is tilted from
$\mathcal{A}$, then $\mathcal{A}$ and $\mathcal{B}$ are derived equivalent.


  We present ferromagnetic neutron spin echo measurements and muon spin
relaxation ($\mu$SR) measurements of the spin dynamics in the ordered phase of
the metallic alloy INVAR Fe$_{65}$Ni$_{35}$. Both techniques display evidence
of slow spin dynamics in the ordered phase which could be associated with the
INVAR effect. Zero field (ZF) and longitudinal field $\mu$SR measurements
suggest the presence of two separate timescales of slow spin-dynamics in the
ordered phase; a fast component corresponding to that observed in our
neutron-spin-echo measurements and a second slow component which may be related
to the "cluster" spin-fluctuations or to the dynamics of disordered local
moments or both as suggested by previous neutron scattering measurements and
recent theoretical studies.


  We analyse a set of new pencil-beam galaxy redshift data in three small
regions around the South Galactic Pole (SGP) area. We investigate whether we
can find any evidence of the quasi-periodic peaks discovered by Broadhurst et
al. (1990) in the distribution of galaxies along the NGP-SGP directions. We use
both a power spectrum analysis and a cross-correlation with a sliding comb-like
window (the comb-template technique). Despite the data are less deep (~600
h^{-1} Mpc) and certainly not optimal for such an investigation, there is
evidence of the same preferential ~130 h^{-1} Mpc scale in two fields displaced
respectively 15 and 30 degrees West of the Broadhurst et al. original probe.
Taken alone, however, this peak would not be statistically distinguishable from
a noise fluctuation. Nevertheless, the statistical significance of the
detection raises to 99% when one considers the CONDITIONAL probability of
finding a peak around THE SAME scale measured by Broadhurst et al.


  Adapting the functioning of the collision warning systems to the specific
drivers' characteristics is of great benefit to drivers. For example, by
customizing collision warning algorithms we can minimize false alarms, thereby
reducing injuries and deaths in highway traffic accidents. In order to take the
behaviors of individual drivers into account, the system needs to have a
Real-Time estimation of the distribution of brake response times for an
individual driver. In this paper, we propose a method for doing this estimation
which is not computationally intensive and can take advantage of the
information contained in all data points.


  Electronic Health Records (EHR) have been heavily used in modern healthcare
systems for recording patients' admission information to hospitals. Many
data-driven approaches employ temporal features in EHR for predicting specific
diseases, readmission times, or diagnoses of patients. However, most existing
predictive models cannot fully utilize EHR data, due to an inherent lack of
labels in supervised training for some temporal events. Moreover, it is hard
for existing works to simultaneously provide generic and personalized
interpretability. To address these challenges, we first propose a hyperbolic
embedding method with information flow to pre-train medical code
representations in a hierarchical structure. We incorporate these pre-trained
representations into a graph neural network to detect disease complications,
and design a multi-level attention method to compute the contributions of
particular diseases and admissions, thus enhancing personalized
interpretability. We present a new hierarchy-enhanced historical prediction
proxy task in our self-supervised learning framework to fully utilize EHR data
and exploit medical domain knowledge. We conduct a comprehensive set of
experiments and case studies on widely used publicly available EHR datasets to
verify the effectiveness of our model. The results demonstrate our model's
strengths in both predictive tasks and interpretable abilities.