  In this paper, we propose a new method for the augmentation of numeric and
mixed datasets. The method generates additional data points by utilizing
cross-validation resampling and latent variable modeling. It is particularly
efficient for datasets with moderate to high degrees of collinearity, as it
directly utilizes this property for generation. The method is simple, fast, and
has very few parameters, which, as shown in the paper, do not require specific
tuning. It has been tested on several real datasets; here, we report detailed
results for two cases, prediction of protein in minced meat based on near
infrared spectra (fully numeric data with high degree of collinearity) and
discrimination of patients referred for coronary angiography (mixed data, with
both numeric and categorical variables, and moderate collinearity). In both
cases, artificial neural networks were employed for developing the regression
and the discrimination models. The results show a clear improvement in the
performance of the models; thus for the prediction of meat protein, fitting the
model to the augmented data resulted in a reduction in the root mean squared
error computed for the independent test set by 1.5 to 3 times.


  The apparent excess of $Z \rightarrow b \bar b$ events at LEP may be an
indication of new physics beyond the standard model. However, in either the
two-Higgs-doublet model or the minimal supersymmetric standard model, any
explanation would lead to an important new decay mode of the top quark and
suppresses the $t \rightarrow W b$ branching fraction, which goes against what
has been observed at the Tevatron. In the two-Higgs-doublet model, the
branching fraction of $Z \rightarrow b \bar b$ + a light boson which decays
predominantly into $b \bar b$ would be at least of order 10$^{-4}$.


  This note addresses the historical antecedents of the 1998 PageRank measure
of centrality. An identity relation links it to 1990-1991 models of Friedkin
and Johnsen.


  The electromagnetic response of graphene in a magnetic field is studied, with
particular emphasis on the quantum features of its ground state (vacuum). The
graphene vacuum, unlike in conventional quantum Hall systems, is a dielectric
medium and carries an appreciable amount of electric and magnetic
susceptibilities. The dielectric effect grows rapidly with increasing filling
factor nu in such a way that reflects the 'relativistic' Landau-level
characteristics of graphene as well as its valley and spin degeneracy. A close
look into the dielectric function also reveals that the Coulomb interaction is
efficiently screened on the scale of the magnetic length, leading to a
prominent reduction of the exciton spectra in graphene. In addition, an
effective gauge theory of graphene is constructed out of the response. It is
pointed out thereby that the electric susceptibility is generally expressed as
a ratio of the Hall conductance to the Landau gap.


  We study bound states of two fermions with opposite spins in an extended
Hubbard chain. The particles interact when located both on a site or on
adjacent sites. We find three different types of bound states. Type U is
predominantly formed of basis states with both fermions on the same site, while
two states of type V originate from both fermions occupying neighbouring sites.
Type U, and one of the states from type V, are symmetric with respect to spin
flips. The remaining one from type V is antisymmetric. V-states are
characterized by a diverging localization length below some critical wave
number. All bound states become compact for wave numbers at the edge of the
Brilloin zone.


  For a chaotic system pairs of initially close-by trajectories become
eventually fully uncorrelated on the attracting set. This process of
decorrelation may split into an initial exponential decrease, characterized by
the maximal Lyapunov exponent, and a subsequent diffusive process on the
chaotic attractor causing the final loss of predictability. The time scales of
both processes can be either of the same or of very different orders of
magnitude. In the latter case the two trajectories linger within a finite but
small distance (with respect to the overall extent of the attractor) for
exceedingly long times and therefore remain partially predictable.
  Tests for distinguishing chaos from laminar flow widely use the time
evolution of inter-orbital correlations as an indicator. Standard tests however
yield mostly ambiguous results when it comes to distinguish partially
predictable chaos and laminar flow, which are characterized respectively by
attractors of fractally broadened braids and limit cycles. For a resolution we
introduce a novel 0-1 indicator for chaos based on the cross-distance scaling
of pairs of initially close trajectories, showing that this test robustly
discriminates chaos, including partially predictable chaos, from laminar flow.
One can use furthermore the finite time cross-correlation of pairs of initially
close trajectories to distinguish, for a complete classification, also between
strong and partially predictable chaos. We are thus able to identify laminar
flow as well as strong and partially predictable chaos in a 0-1 manner solely
from the properties of pairs of trajectories.


  We theoretically investigate the interlayer magnetoresistance in
quasi-two-dimensional Dirac fermion systems, where the Fermi energy is at the
Dirac point. If there is an intermediate insulating layer that has an overlap
with the wave functions in the Dirac fermion layers, there appears a positive
magnetoresistance regime due to the intervalley tunneling. We show that the
interlayer magnetoresistance can be used to find whether Dirac fermions are
massive or not from the minimum in the interlayer magnetoresistance. As a
specific system, we consider \alphaI under high pressure. We also discuss that
one has to be careful in analyzing the crossover temperature from the positive
to negative magnetoresistance. A simple picture is applied to the crossover in
the zero temperature limit but it does not apply to the data at finite
temperatures. We show that the ratio of the Fermi velocity to the scattering
rate is evaluated from the zero temperature limit of the crossover temperature.


  Recent studies on network geometry, a way of describing network structures as
geometrical objects, are revolutionizing our way to understand dynamical
processes on networked systems. Here, we cope with the problem of epidemic
spreading, using the Susceptible-Infected-Susceptible (SIS) model, in
simplicial complexes. In particular, we analyze the dynamics of SIS in complex
networks characterized by pairwise interactions (links), and three-body
interactions (filled triangles, also known as 2-simplices). This higher-order
description of the epidemic spreading is analytically formulated using a
microscopic Markov chain approximation. The analysis of the fixed point
solutions of the model, reveal an interesting phase transition that becomes
abrupt with the infectivity parameter of the 2-simplices. Our results pave the
way for network theorists to advance in our physical understanding of epidemic
spreading in real scenarios where diseases are transmitted among groups as well
as among pairs, and to better understand the behaviour of dynamical processes
in simplicial complexes.


  We show that simple diffusive systems, such as the Lorentz gas and multibaker
maps are perfectly compatible with the laws of irreversible thermodynamics,
despite the fact that the moving particles, or their equivalents, in these
models do not interact with each other, and that the dynamics takes place in
low-dimensional phase spaces. The interaction of moving particles with
scatterers provides the dynamical mechanism responsible for an approach to
equilibrium, under appropriate conditions. This analysis provides a refutation
of the criticisms expressed recently by Cohen and Rondoni [Physica A 306 (2002)
117-128].


  We consider Langevin dynamics associated with a modified kinetic energy
vanishing for small momenta. This allows us to freeze slow particles, and hence
avoid the re-computation of inter-particle forces, which leads to computational
gains. On the other hand, the statistical error may increase since there are a
priori more correlations in time. The aim of this work is first to prove the
ergodicity of the modified Langevin dynamics (which fails to be hypoelliptic),
and next to analyze how the asymptotic variance on ergodic averages depends on
the parameters of the modified kinetic energy. Numerical results illustrate the
approach, both for low-dimensional systems where we resort to a Galerkin
approximation of the generator, and for more realistic systems using Monte
Carlo simulations.


  Short-lived radionuclides, such as 26Al and 60Fe, are tracers of star
formation. Therefore, their abundances can unravel the recent star formation
history of the host galaxy. In view of future gamma-ray surveys, we predict the
masses and fluxes of these two elements in the Large Magellanic Cloud (LMC)
using new chemical evolution models. Our best model reproduces the abundance
patterns of alpha/Fe ratios, the gas mass, the average metallicity, the present
time supernova and nova rates observed in LMC. We show three main results: i)
the best model for the LMC suggests a star formation rate very mild at the
beginning with a recent burst, and a Salpeter-like initial mass function. ii)
The predicted mass of 26Al is 0.33 M_Sun, 2/3 produced by massive stars and 1/3
by novae. iii) The predicted mass of 60Fe is 0.44 M_Sun, entirely produced by
massive stars. This result suggests a larger fraction of 60Fe, at variance with
the Milky Way. The explanation for this lies in the adopted initial mass
function, that for the LMC contains more massive stars than for the Milky Way.
These predictions can be useful for the COSI-SMEX mission planned for launch in
2027. The expected gamma-ray line fluxes for the 1.809 MeV line of 26Al and the
1.173 and 1.332 MeV lines of 60Fe are in the range of (0.2-2.7)x10^-6 ph cm^-2
s^-1 and (0.7-2.8)x10^-7 ph cm^-2 s^-1, respectively. This new instrument could
have the sensitivity to detect the upper end of the predicted 1.8 MeV flux
within its nominal two-year mission.


  The carbon content of protoplanetary disks is an important parameter to
characterize planets formed at different disk radii. There is some evidence
from far-infrared and submillimeter observations that gas in the outer disk is
depleted in carbon, with a corresponding enhancement of carbon-rich ices at the
disk midplane. Observations of the carbon content inside of the inner
sublimation rim could confirm how much carbon remains locked in kilometer size
bodies in the disk. Using near-infrared spectra, I self-consistently determine
the stellar, extinction, veiling, and accretion properties of the 26 stars in
my sample. From these values, I extract the inner disk excess of the target
stars and identify a series of C^0 recombination lines in 18 of these disks and
use the CHIANTI atomic line database with an optically thin slab model to
constrain the average n_e, T_e, and n_C for these lines in the five disks with
a complete set of lines. By comparing these values with other slab models of
the inner disk using the Cloudy photoionization code, I also constrain n_H and
the carbon abundance, X_C, and hence the amount of carbon `missing' from the
slab. The inner disks are extremely dense (n_H~10^{16} cm^{-3}), warm (T_e~4500
K), and moderately ionized (logX_e~3.3). Three of the five modeled disks show
robust carbon depletion <=42 relative to the solar value. I discuss multiple
ways in which the `missing' carbon could be locked out of the accreting gas.
Given the high-density inner disk gas, evidence for radial drift, and lack of
obvious gaps in these three systems, their carbon depletion is most consistent
with the `missing' carbon being sequestered in kilometer size bodies. For DR
Tau, nitrogen and silicon are also depleted by factors of 45 and 4,
respectively, suggesting that the kilometer size bodies into which the grains
are locked were formed beyond the N_2 snowline.


  Tri-bimaximal neutrino mixing may arise from see-saw models based on family
symmetry which is spontaneously broken by flavons with particular vacuum
alignments. However recent analyses hint that tri-bimaximal mixing may be
insensitive to the precise form of vacuum alignment. In this paper we derive
approximate analytic results which express the deviations from tri-bimaximal
neutrino mixing due to vacuum misalignment. We also relate vacuum misalignment
to departures from form dominance, corresponding to deviations from the real
orthogonal $R$ matrix, where such corrections are necessary to allow for
successful leptogenesis. The analytic results show that the corrections to
tri-bimaximal mixing and form dominance depend on the pattern of the vacuum
misalignment, with the two effects being uncorrelated.


  We present a tool-based approach for the runtime analysis of communicating
processes grounded on probabilistic binary session types. We synthesise a
monitor out of a probabilistic session type where each choice point is
decorated by a probability distribution. The monitor observes the execution of
a process, infers its probabilistic behaviour and issues warnings when the
observed behaviour deviates from the one specified by the probabilistic session
type.


  In this note we present that the patch counting entropy can be obtained as a
limit and investigate which sequences of compact sets are suitable to define
this quantity. We furthermore present a geometric definition of patch counting
entropy for Delone sets of infinite local complexity and that the patch
counting entropy of a Delone set equals the topological entropy of the
corresponding Delone dynamical system. We present our results in the context of
(non-compact) locally compact Abelian groups that contain Meyer sets.


  In a freely cooling granular material fluctuations in density and temperature
cause position dependent energy loss. Due to strong local dissipation, pressure
and energy drop rapidly and material moves from `hot' to `cold' regions,
leading to even stronger dissipation and thus causing the density instability.
The assumption of `molecular chaos' is valid only in the homogeneous cooling
regime. As soon as the density instability occurs, the impact parameter is not
longer uniformly distributed. The pair-correlation and the structure functions
show that the molecular chaos assumption --- together with reasonable excluded
volume modeling --- is important for short distances and irrelevant on large
length scales.
  In this study, the probability distribution of the collision frequency is
examined for pipe flow and for freely cooling granular materials as well.
Uncorrelated events lead to a Poisson distribution for the collision
frequencies. In contrast, the fingerprint of the cooperative phenomena
discussed here is a power-law decay of the probability for many collisions per
unit time.
  Keywords: discrete element method, event driven simulations, clustering
instability, arching, shock waves, power-law distribution, cooperative
phenomena.


  We computed the statistical entropy of nonextremal 4D and extremal 5D
Calabi-Yau black holes and found exact agreement with the Bekenstein-Hawking
entropy. The computation is based on the fact that the near-horizon geometry of
equivalent representations contains as a factor the
Ba\~nados-Teitelboim-Zanelli black hole and on subsequent use of Strominger's
proposal generalizing the statistical count of microstates of the BTZ black
hole due to Carlip.


  The physical aspects of partially coherent radiation interacting with
deterministic non-Hermitian periodic materials remain largely unexplored in the
statistical optics literature. Here, we consider the scattering of partially
coherent radiation by a deterministic periodic medium, symmetric under the
simultaneous transformations of parity inversion and time reversal, that is, a
parity-time (PT)-symmetric periodic medium. Taking into account light
fluctuations, one is able to describe the spectrum changes on propagation and
the influence of the coherence-driven angular divergence effect. The far-field
spectral density profile is found to depend crucially on the loss/gain
properties of the material, giving rise to unexpected and contrasting spectral
diffraction profiles when compared to the Hermitian ones.


  Let $\mu$ be a Radon measure on $R^d$, which may be non doubling. The only
condition that $\mu$ must satisfy is $\mu(B(x,r))\leq C r^n$, for all $x,r$ and
for some fixed $0<n\leq d$. Recently we introduced spaces of type $BMO(\mu)$
and $H^1(\mu)$ which proved to be useful to study the $L^p(\mu)$ boundedness of
Calder\'on-Zygmund operators without assuming doubling conditions. In this
paper a characterization of the new atomic space $H^1(\mu)$ in terms of a grand
maximal operator $M_\Phi$ is given. It is shown that $f$ belongs to $H^1(\mu)$
iff $f\in L^1(\mu)$, $\int f d\mu=0$ and $M_\Phi(f)\in L^1(\mu)$, as in the
usual doubling situation. The lack of any regularity condition on $\mu$, apart
from the size condition stated above, is one of the main difficulties that
appears when one tries to extend the classical arguments to the present
situation.


  Authenticated data storage on an untrusted platform is an important computing
paradigm for cloud applications ranging from big-data outsourcing, to
cryptocurrency and certificate transparency log. These modern applications
increasingly feature update-intensive workloads, whereas existing authenticated
data structures (ADSs) designed with in-place updates are inefficient to handle
such workloads. In this paper, we address this issue and propose a novel
authenticated log-structured merge tree (eLSM) based key-value store by
leveraging Intel SGX enclaves.
  We present a system design that runs the code of eLSM store inside enclave.
To circumvent the limited enclave memory (128 MB with the latest Intel CPUs),
we propose to place the memory buffer of the eLSM store outside the enclave and
protect the buffer using a new authenticated data structure by digesting
individual LSM-tree levels. We design protocols to support query authentication
in data integrity, completeness (under range queries), and freshness. The proof
in our protocol is made small by including only the Merkle proofs at selective
levels.
  We implement eLSM on top of Google LevelDB and Facebook RocksDB with minimal
code change and performance interference. We evaluate the performance of eLSM
under the YCSB workload benchmark and show a performance advantage of up to
4.5X speedup.


  Let $k$ be a non-archimedean complete field. We prove a substitute for the
reduced fiber theorem (of Bosch, L\"utkebohmert and Raynaud) that holds for
every morphism $Y\to X$ flat and with geometrically reduced fibers between
$k$-affinoid spaces in the sense of Berkovich, without assuming that $X$ and
$Y$ are strict, nor that the relative dimension of $Y$ over $X$ is constant. We
do not use the original reduced fiber theorem, nor the language or the
techniques of formal geometry. Our statement is formulated in terms of Temkin's
graded reduction; our proof rests on a finiteness result of Grauert and Remmert
and on Temkin's theory of (graded) reduction of germs of analytic spaces. It
will be used for describing the variation of the connected components of the
fiber of a quasi-smooth map in a forthcoming work on flattening in the
Berkovich setting.


  We demonstrate high-frequency (> 3 GHz), high quality factor radio frequency
(RF) resonators in unreleased thin film gallium nitride (GaN) on sapphire and
silicon carbide substrates by exploiting acoustic guided mode (Lamb wave)
resonances. The associated energy trapping, due to mass loading from the gold
electrodes, allows us to efficiently excite these resonances from a 50 $\Omega$
input. The higher phase velocity, combined with lower electrode damping,
enables high quality factors with moderate electrode pitch, and provides a
viable route towards high-frequency piezoelectric devices. The GaN platform,
with its ability to guide and localize high-frequency sound on the surface of a
chip with access to high-performance active devices, will serve as a key
building block for monolithically integrated RF front-ends.


  We introduce a globally-convergent algorithm for optimizing the
tree-reweighted (TRW) variational objective over the marginal polytope. The
algorithm is based on the conditional gradient method (Frank-Wolfe) and moves
pseudomarginals within the marginal polytope through repeated maximum a
posteriori (MAP) calls. This modular structure enables us to leverage black-box
MAP solvers (both exact and approximate) for variational inference, and obtains
more accurate results than tree-reweighted algorithms that optimize over the
local consistency relaxation. Theoretically, we bound the sub-optimality for
the proposed algorithm despite the TRW objective having unbounded gradients at
the boundary of the marginal polytope. Empirically, we demonstrate the
increased quality of results found by tightening the relaxation over the
marginal polytope as well as the spanning tree polytope on synthetic and
real-world instances.


  The EUV Imaging Spectrometer (EIS) instrument on board the Hinode satellite
has access to some of the best coronal density diagnostics and the high
sensitivity of the instrument now allows electron number density, N_e,
measurements to an unprecedented precision of up to +/-5 % in active regions.
This paper gives a thorough overview of data analysis issues for the best
diagnostics of Fe XII and Fe XIII and assesses the accuracy of the
measurements. Two density diagnostics each from Fe XII (186.88/195.12 and
196.64/195.12) and Fe XIII (196.54/202.04 and 203.82/202.04) are analysed in
two active region data-sets from 2007 May 3 and 6 that yield densities in the
range 8.5 < log N_e < 11.0. The densities are derived using v5.2 of the CHIANTI
atomic database. The Fe XII and Fe XIII diagnostics show broadly the same trend
in density across the active region, consistent with their similar temperatures
of formation. However the high precision of the EIS measurements demonstrates
significant discrepancies of up to 0.5 dex in derived log N_e values, with Fe
XII always giving higher densities than Fe XIII. The discrepancies may partly
be due to real physical differences between the emitting regions of the two
plasmas, but the dominant factor lies in the atomic models of the two ions. Two
specific problems are identified for Fe XII 196.64 and Fe XIII 203.82: the
former is found to be under-estimated in strength by the CHIANTI atomic model,
while the high density limit of the 203.82/202.04 is suggested to be inaccurate
in the CHIANTI atomic model. The small grating tilt of the EIS instrument is
found to be very significant when deriving densities from emission lines
separated by more than a few angstroms.


  Lead halide perovskites have recently been reported to demonstrate an
exceptionally high nonlinear (Kerr) refractive index n$_2$ of up to 10$^{-8}$
cm$^2$/W in CH$_3$NH$_3$PbBr$_3$. Other researchers however observe different,
substantially more conservative numbers. In order to resolve this disagreement
the nonlinear Kerr index of a bulk sample of lead halide perovskite was
measured directly by means of an interferometer. This approach has many
advantages as compared to the more standard z-scan technique. In particular
this method allows studying the induced changes to the refractive index in a
time-resolved manner, thus enabling to separate the different contributions to
$n_2$. The extracted n$_2$ values for CsPbBr$_3$ and MAPbBr$_3$ at $\lambda
\approx 1\mu m$ are $n_2=+2.1\times$ 10$^{-14}$ cm$^2$/W and $n_2=+6\times$
10$^{-15}$ cm$^2$/W respectively hence substantially lower than what has been
indicated in most of the previous reports implying the latter should be
regarded with a great care.


  Force fields developed with machine learning methods in tandem with quantum
mechanics are beginning to find merit, given their (i) low cost, (ii) accuracy,
and (iii) versatility. Recently, we proposed one such approach, wherein, the
vectorial force on an atom is computed directly from its environment. Here, we
discuss the multi-step workflow required for their construction, which begins
with generating diverse reference atomic environments and force data, choosing
a numerical representation for the atomic environments, down selecting a
representative training set, and lastly the learning method itself, for the
case of Al. The constructed force field is then validated by simulating complex
materials phenomena such as surface melting and stress-strain behavior - that
truly go beyond the realm of $ab\ initio$ methods both in length and time
scales. To make such force fields truly versatile an attempt to estimate the
uncertainty in force predictions is put forth, allowing one to identify areas
of poor performance and paving the way for their continual improvement.


  In this work We report on the extensive characterization of amorphous silicon
carbide (a-SiC) coatings prepared by physical deposition methods. We compare
the results obtained on two different sputtering systems (a standard RF
magnetron sputtering and a ion-beam assisted sputtering) to seize the impact of
two different setups on the repeatably of the results.
  After a thorough characterization of structural, morphological, and
compositional characteristics of the prepared samples, we focus on a detailed
study of the optical and mechanical losses in those materials. Mechanical
losses are further investigated from a microscopic point of view by comparing
our experimental results with molecular dynamic simulations of the amorphous
SiC structure: first we define a protocol to generate a numerical model of the
amorphous film, capturing the main features of the real system; then we
simulate its dynamical behaviour upon deformation in order to obtain its
mechanical response. Our results are discussed within the perspective
application of a-SiC as an optical material for high-precision optical
experiments and in particular in gravitational wave interferometry.


  The optical properties of Nd$_{0.7}$Sr$_{0.3}$MnO$_3$ thin films have been
studied from 5 meV to 25 meV and from 0.25 eV to 3 eV, at temperatures from 15
K to 300 K and magnetic fields up to 8.9 T. A large transfer of spectral weight
from high energy to low energy occurs as the temperature is decreased below 180
K, where the dc resistivity peaks, or as the magnetic field is increased. The
optical data are found to be consistent with models that include both the
double exchange interaction and the dynamic Jahn-Teller effect on the Mn$^{3+}$
e$_g$ levels.


  Artificial intelligence (AI) systems hold great promise to improve healthcare
over the next decades. Specifically, AI systems leveraging multiple data
sources and input modalities are poised to become a viable method to deliver
more accurate results and deployable pipelines across a wide range of
applications. In this work, we propose and evaluate a unified Holistic AI in
Medicine (HAIM) framework to facilitate the generation and testing of AI
systems that leverage multimodal inputs. Our approach uses generalizable data
pre-processing and machine learning modeling stages that can be readily adapted
for research and deployment in healthcare environments. We evaluate our HAIM
framework by training and characterizing 14,324 independent models based on
HAIM-MIMIC-MM, a multimodal clinical database (N=34,537 samples) containing
7,279 unique hospitalizations and 6,485 patients, spanning all possible input
combinations of 4 data modalities (i.e., tabular, time-series, text, and
images), 11 unique data sources and 12 predictive tasks. We show that this
framework can consistently and robustly produce models that outperform similar
single-source approaches across various healthcare demonstrations (by 6-33%),
including 10 distinct chest pathology diagnoses, along with length-of-stay and
48-hour mortality predictions. We also quantify the contribution of each
modality and data source using Shapley values, which demonstrates the
heterogeneity in data modality importance and the necessity of multimodal
inputs across different healthcare-relevant tasks. The generalizable properties
and flexibility of our Holistic AI in Medicine (HAIM) framework could offer a
promising pathway for future multimodal predictive systems in clinical and
operational healthcare settings.


  Context. Hydrogen ionized by young, high-mass stars in starburst galaxies
radiates radio recombination lines (RRLs), whose strength can be used as a
diagnostic of the ionization rate, conditions and gas dynamics in the
starburst, without problems of dust obscuration. However, the lines are weak
and only few extragalactic starburst systems have been detected.
  Aims. We aimed to increase the number of known starburst systems with
detectable RRLs for detailed studies.
  Methods. We searched for the RRLs H91alpha and H92alpha at 8 GHz in the
nearby Seyfert galaxy NGC 4945 using the ATCA with resolution of 3". This
yielded a detection from which we derived conditions in the starburst.
  Results. We detected RRLs from the nucleus of NGC 4945 with a peak strength
of 17.8 mJy, making it the strongest extragalactic RRL emitter known at this
frequency. The line emission from NGC 4945 can be matched by a model consisting
of a collection of 10 to 300 H II regions with temperatures of 5000 K,
densities of 10^3 cm^-3 to 10^4 cm^-3 and a total effective diameter of 2 pc to
100 pc. The Lyman continuum production rate required to maintain the ionization
requires 2000 to 10000 O5 stars to be produced in the starburst, inferring a
star formation rate of 2 Msun/yr to 8 Msun/yr. We resolved the rotation curve
within the central 70 pc region and this is well described by a set of rotating
rings that were coplanar and edge on with a simple flat rotation curve. The
rotation speed of 120 km/s within the central 1" (19 pc) radius infers an
enclosed mass of 3 x 10^7 Msun.
  Conclusion. We discovered RRLs from NGC 4945. It is the strongest known
extragalactic RRL emitter and is suited to high-quality spectroscopic study. We
resolved the dynamics of the ionized gas in the central 70 pc and derived
conditions and star formation rates in the ionized gas.


  A submanifold in space forms is isoparametric if the normal bundle is flat
and principal curvatures along any parallel normal fields are constant. We
study the mean curvature flow with initial data an isoparametric submanifold in
Euclidean space and sphere. We show that the mean curvature flow preserves the
isoparametric condition, develops singularities in finite time, and converges
in finite time to a smooth submanifold of lower dimension. We also give a
precise description of the collapsing.


  Nanocomposite metal oxide thin films exhibit assuring qualities in the field
of gas sensors because of the collective opportunities provided by the
heterointerface formation. In this work, we present the synthesis of nitrogen
doped mesoporous In$_2$O$_3$-ZnO nano-composite thin films by simple wet
chemical method using urea as the nitrogen precursor. SEM investigation
suggests formation of mesoporous nano-composite thin films, where the
uniformity of surface pore distribution depends on the relative proportion of
In$_2$O$_3$ and ZnO in the composites. HRTEM investigation suggest formation of
sharp interfaces between N-In$_2$O$_3$ and N-ZnO grains in the nano-composite
thin films. The nano-composite thin films have been tested for their ethanol
sensing performance over an extensive range of temperature, ethanol vapor
concentration and relative humidity. Nitrogen doped nano-composite thin film
with equal proportion of In$_2$O$_3$ and ZnO exhibits excellent ethanol sensing
performance at a reasonable operating temperature (~94 % at 200 {\deg}C for 50
ppm of ethanol), fast response time (~two seconds), stability over time,
enhanced resilience against humidity and selectivity to ethanol over various
other volatile organic compounds.


  Increased use of Wireless sensor Networks (WSNs) in variety of applications
has enabled the designers to create autonomous sensors, which can be deployed
randomly, without human supervision, for the purpose of sensing and
communicating valuable data. Many energy-efficient routing protocols are
designed for WSNs based on clustering structure. In this paper, we have
proposed iMODLEACH protocol which is an extension to the MODLEACH protocol.
Simulation results indicate that iMODLEACH outperforms MODLEACH in terms of
network life-time and packets transferred to base station. The mathematical
analysis helps to select such values of these parameters which can suit a
particular wireless sensor network application.


  As a recent successfully exfoliated non van der Waals layered material,
AgCrS2 has received a lot of attentions. Motivated by its structure related
magnetic and ferroelectric behavior, a theoretical study on its exfoliated
monolayer AgCr2S4 has been carried out in the present work. Based on density
functional theory, the ground state and magnetic order of monolayer AgCr2S4
have been determined. The centrosymmetry emerges upon two-dimensional
confinement and thus eliminates the bulk polarity. Moreover, two-dimensional
ferromagnetism appears in the CrS2 layer of AgCr2S4 and can persist up to room
temperature. The surface adsorption has also been taken into consideration,
which shows a nonmonotonic effect on the ionic conductivity through ion
displacement of the interlayer Ag, but has little impact on the layered
magnetic structure.


  This work presents an adaptive group testing framework for the range-based
high dimensional near neighbor search problem. The proposed method detects
high-similarity vectors from an extensive collection of high dimensional
vectors, where each vector represents an image descriptor. Our method
efficiently marks each item in the collection as neighbor or non-neighbor on
the basis of a cosine distance threshold without exhaustive search. Like other
methods in the domain of large scale retrieval, our approach exploits the
assumption that most of the items in the collection are unrelated to the query.
Unlike other methods, it does not assume a large difference between the cosine
similarity of the query vector with the least related neighbor and that with
the least unrelated non-neighbor. Following the procedure of binary splitting,
a multi-stage adaptive group testing algorithm, we split the set of items to be
searched into half at each step, and perform dot product tests on smaller and
smaller subsets, many of which we are able to prune away. We experimentally
show that our method achieves a speed-up over exhaustive search by a factor of
more than ten with an accuracy same as that of exhaustive search, on a variety
of large datasets. We present a theoretical analysis of the expected number of
distance computations per query and the probability that a pool with a certain
number of members will be pruned. In this way, our method exploits very useful
and practical distributional properties unlike other methods. In our method,
all required data structures are created purely offline. Moreover, our method
does not impose any strong assumptions on the number of true near neighbors, is
adaptible to streaming settings where new vectors are dynamically added to the
database, and does not require any parameter tuning.


  In this work we investigate the transition from kinks to compactons at high
temperatures. We deal with a family of models, described by a real scalar field
with standard kinematics, controlled by a single parameter, real and positive.
The family of models supports kinklike solutions, and the solutions tend to
become compact when the parameter increases to larger and larger values. We
study the one-loop corrections at finite temperature, to see how the thermal
effects add to the effective potential. The results suggest that the symmetry
is restored at very high temperatures.


  For every Banach space $Z$ with a shrinking unconditional basis satisfying
upper $p$-estimates for some $p > 1$, an isomorphically polyhedral Banach space
is constructed having an unconditional basis and admitting a quotient
isomorphic to $Z$.


  In a recent paper [16], the authors proposed a BGK model for relativistic gas
mixtures based on the Marle-type approximation, which satisfies the fundamental
kinetic properties: non-negativity of distribution functions, conservation
laws, H-theorem, and indifferentiability principle. In this paper, we are
concerned with the stationary problems to the relativistic BGK model for gas
mixtures in slab geometry. We establish the existence of a unique mild solution
with the fixed inflow boundary data when the collision frequencies for each
species are sufficiently small.


  We construct the moduli space of Enriques surfaces in positive characteristic
and eventually over the integers, and determine its local and global structure.
As an application, we show lifting of Enriques surfaces to characteristic zero.
The key observation is that the canonical double cover of an Enriques surface
is birational to the complete intersection of three quadrics in IP^5, even in
characteristic 2.


  It is shown that, even when the eigenmodes of an optical cavity are
wave-chaotic, the frequency splitting due to the rotation of the cavity occurs
and the frequency difference is proportional to the angular velocity although
the splitting eigenmodes are still wave-chaotic and do not correspond to any
unidirectionally-rotating waves.


  We present our new fabrication Process for Superconductor Electronics (PSE2)
that integrates two (2) layers of Josephson junctions in a fully planarized
multilayer process on 200-mm wafers. The two junction layers can be, e.g.,
conventional Superconductor-Insulator-Superconductor (SIS) Nb/Al/AlO_x/Nb
junctions with the same or different Josephson critical current densities, J_c.
The process also allows integration of high-J_c
Superconductor-Ferromagnet-Superconductor (SFS) or SFS'S JJs on the first
junction layer with Nb/Al/AlO_x/Nb trilayer junctions on the second junction
layer, or vice versa. In the present node, the SFS trilayer, Nb/Ni/Nb is placed
below the standard SIS trilayer and separated by one niobium wiring layer. The
main purpose of integrating the SFS and SIS junction layers is to provide
compact {\pi}-phase shifters in logic cells of superconductor digital circuits
and random access memories, and thereby increase the integration scale and
functional density of superconductor electronics. The current node of the
two-junction-layer process has six planarized niobium layers, two layers of
resistors, and 350-nm minimum feature size. The target Josephson critical
current densities for the SIS junctions are 100 {\mu}A/{\mu}m^2 and 200
{\mu}A/{\mu}m^2. We present the salient features of the new process,
fabrication details, and characterization results on two layers of Josephson
junctions integrated into one process, both for the conventional and
{\pi}-junctions.


  This work concerns the relation between the geometry of Lagrangian
Grassmannians and the CKP integrable hierarchy. The Lagrange map from the
Lagrangian Grassmannian of maximal isotropic (Lagrangian) subspaces of a finite
dimensional symplectic vector space $V\oplus V^*$ into the projectivization of
the exterior space $\Lambda V$ is defined by restricting the Pl\"ucker map on
the full Grassmannian to the Lagrangian sub-Grassmannian and composing it with
projection to the subspace of symmetric elements under dualization $V
\leftrightarrow V^*$. In terms of the affine coordinate matrix on the big cell,
this reduces to the principal minors map, whose image is cut out by the $2
\times 2 \times 2$ quartic {\em hyperdeterminantal} relations. To apply this to
the CKP hierarchy, the Lagrangian Grassmannian framework is extended to
infinite dimensions, with $V\oplus V^*$ replaced by a polarized Hilbert space $
{\mathcal H} ={\mathcal H}_+\oplus {\mathcal H}_-$, with symplectic form
$\omega$. The image of the Plucker map in the fermionic Fock space ${\mathcal
F}= \Lambda^{\infty/2}{\mathcal H}$ is identified and the infinite dimensional
Lagrangian map is defined. The linear constraints defining reduction to the CKP
hierarchy are expressed as a fermionic null condition and the infinite analogue
of the hyperdeterminantal relations is deduced. A multiparametric family of
such relations is shown to be satisfied by the evaluation of the
$\tau$-function at translates of a point in the space of odd flow variables
along the cubic lattices generated by power sums in the parameters.


  Using electron drift wave (eDW) as a paradigm model, we have investigated
analytically direct wave-wave interactions between a test DW and ambient
toroidal Alfv\'en eigenmodes (TAE) in toroidal plasmas, and their effects on
the stability of the eDW. The nonlinear effects enter via scatterings to
short-wavelength electron Landau damped kinetic Alfv\'en waves (KAWs).
Specifically, it is found that scatterings to upper-sideband KAW lead to
stimulated absorption of eDW. Scatterings to the lower-sideband KAW, on the
contrary, lead to its spontaneous emission. As a consequence, for typical
parameters and fluctuation intensity, nonlinear scatterings by TAE have
negligible net effects on the eDW stability; in contrast to the ``reverse"
process investigated in Ref. [Nuclear Fusion {\bf 62}, 094001 (2022)], where it
is shown that nonlinear scattering by ambient eDW may lead to significant
damping of TAE.


  We propose a scale-dependent cosmology in which the Robertson--Walker metric
and the Einstein equation are modified in such a way that $\Omega_0$, $H_0$ and
the age of the Universe all become scale-dependent. Its implications on the
observational cosmology and possible modifications of the standard Friedmann
cosmology are discussed. For example, the age of the Universe in this model is
longer than that of the standard model.


  The Thesaurus for the Social Sciences (TheSoz) is a Linked Dataset in SKOS
format, which serves as a crucial instrument for information retrieval based on
e.g. document indexing or search term recommendation. Thesauri and similar
controlled vocabularies build a linking bridge for other datasets from the
Linked Open Data cloud - even between different domains. The information and
knowledge, which is exposed by such links, can be processed by Semantic Web
applications. In this article the conversion process of the TheSoz to SKOS is
described including the analysis of the original dataset and its structure, the
mapping to adequate SKOS classes and properties, and the technical conversion.
Furthermore mappings to other datasets and the appliance of the TheSoz are
presented. Finally, limitations and modeling issues encountered during the
creation process are discussed.


  The nonconvex formulation of matrix completion problem has received
significant attention in recent years due to its affordable complexity compared
to the convex formulation. Gradient descent (GD) is the simplest yet efficient
baseline algorithm for solving nonconvex optimization problems. The success of
GD has been witnessed in many different problems in both theory and practice
when it is combined with random initialization. However, previous works on
matrix completion require either careful initialization or regularizers to
prove the convergence of GD. In this work, we study the rank-1 symmetric matrix
completion and prove that GD converges to the ground truth when small random
initialization is used. We show that in logarithmic amount of iterations, the
trajectory enters the region where local convergence occurs. We provide an
upper bound on the initialization size that is sufficient to guarantee the
convergence and show that a larger initialization can be used as more samples
are available. We observe that implicit regularization effect of GD plays a
critical role in the analysis, and for the entire trajectory, it prevents each
entry from becoming much larger than the others.


  There may exist extended configurations in the dark matter sector that are
analogues of structures in the visible sector. In this work, we explore
non-topological solitonic configurations, specifically Q-balls, and study when
they may form macroscopic astrophysical structures and what their distinct
characteristics might be. We study in some detail theoretical bounds on their
sizes and constraints on the underlying parameters, based on criteria for an
astrophysical Q-ball's existence, gravitational stability and viability of
solutions. Following this path, one is able to obtain novel limits on
astrophysical Q-ball sizes and their underlying parameters. We also explore the
gravitational lensing features of different astrophysical Q-ball profiles,
which are more general than the simple thin-wall limit. It is seen that the
magnification characteristics may be very distinct, depending on the actual
details of the solution, even for astrophysical Q-balls having the same size
and mass. Assuming that such astrophysical Q-balls may form a small component
of the dark matter in the universe, we place limits on this fraction from the
gravitational microlensing surveys EROS-2, OGLE-IV, HSC-Subaru and the proposed
future survey WFIRST. Exploring various astrophysical Q-ball profiles and
sizes, it is found that while for most intermediate masses that we consider,
the dark matter fraction comprising astrophysical Q-balls is at most
sub-percent, for other masses it may be significantly higher.


  Counting the frequencies of 3-, 4-, and 5-node undirected motifs (also know
as graphlets) is widely used for understanding complex networks such as social
and biology networks. However, it is a great challenge to compute these metrics
for a large graph due to the intensive computation. Despite recent efforts to
count triangles (i.e., 3-node undirected motif counting), little attention has
been given to developing scalable tools that can be used to characterize 4- and
5-node motifs. In this paper, we develop computational efficient methods to
sample and count 4- and 5- node undirected motifs. Our methods provide unbiased
estimators of motif frequencies, and we derive simple and exact formulas for
the variances of the estimators. Moreover, our methods are designed to fit
vertex centric programming models, so they can be easily applied to current
graph computing systems such as Pregel and GraphLab. We conduct experiments on
a variety of real-word datasets, and experimental results show that our methods
are several orders of magnitude faster than the state-of-the-art methods under
the same estimation errors.


  Classical trust region methods were designed to solve problems in which
function and gradient information are exact. This paper considers the case when
there are bounded errors (or noise) in the above computations and proposes a
simple modification of the trust region method to cope with these errors. The
new algorithm only requires information about the size of the errors in the
function evaluations and incurs no additional computational expense. It is
shown that, when applied to a smooth (but not necessarily convex) objective
function, the iterates of the algorithm visit a neighborhood of stationarity
infinitely often, and that the rest of the sequence cannot stray too far away,
as measured by function values. Numerical results illustrate how the classical
trust region algorithm may fail in the presence of noise, and how the proposed
algorithm ensures steady progress towards stationarity in these cases.


  Topological phononics offers numerous opportunities in manipulating elastic
waves that can propagate in solids without being backscattered. Due to the lack
of nanoscale imaging tools that aid the system design, however, acoustic
topological metamaterials have been mostly demonstrated in macroscale systems
operating at low (kilohertz to megahertz) frequencies. Here, we report the
realization of gigahertz topological valley Hall effect in
nanoelectromechanical AlN membranes. Propagation of elastic wave through
phononic crystals is directly visualized by microwave microscopy with
unprecedented sensitivity and spatial resolution. The valley Hall edge states,
protected by band topology, are vividly seen in both real- and momentum-space.
The robust valley-polarized transport is evident from the wave transmission
across local disorder and around sharp corners, as well as the power
distribution into multiple edge channels. Our work paves the way to exploit
topological physics in integrated acousto-electronic systems for classical and
quantum information processing in the microwave regime.


  Calibration of expensive computer models with high-dimensional output fields
can be approached via history matching. If the entire output field is matched,
with patterns or correlations between locations or time points represented,
calculating the distance metric between observational data and model output for
a single input setting requires a time intensive inversion of a
high-dimensional matrix. By using a low-dimensional basis representation rather
than emulating each output individually, we define a metric in the reduced
space that allows the implausibility for the field to be calculated
efficiently, with only small matrix inversions required, using projection that
is consistent with the variance specifications in the implausibility. We show
that projection using the $L_2$ norm can result in different conclusions, with
the ordering of points not maintained on the basis, with implications for both
history matching and probabilistic methods. We demonstrate the scalability of
our method through history matching of the Canadian atmosphere model, CanAM4,
comparing basis methods to emulation of each output individually, showing that
the basis approach can be more accurate, whilst also being more efficient.


  Suppose a small compact object (black hole or neutron star) of mass $m$
orbits a large black hole of mass $M \gg m$. This system emits gravitational
waves (GWs) that have a radiation-reaction effect on the particle's motion.
EMRIs (extreme--mass-ratio inspirals) of this type will be important GW sources
for LISA; LISA's data analysis will require highly accurate EMRI GW templates.
In this article I outline the "Capra" research program to try to model EMRIs
and calculate their GWs \textit{ab initio}, assuming only that $m \ll M$ and
that the Einstein equations hold. Here we treat the EMRI spacetime as a
perturbation of the large black hole's "background" (Schwarzschild or Kerr)
spacetime and use the methods of black-hole perturbation theory, expanding in
the small parameter $m/M$. The small body's motion can be described either as
the result of a radiation-reaction "self-force" acting in the background
spacetime or as geodesic motion in a perturbed spacetime. Several different
lines of reasoning lead to the (same) basic $\O(m/M)$ "MiSaTaQuWa" equations of
motion for the particle. Surprisingly, for a nonlinear field theory such as
general relativity, modelling the small body as a point particle works well.
The particle's own field is singular along the particle worldline so it's
difficult to formulate a meaningful "perturbation" theory or equations of
motion there. I discuss "mode-sum" and "puncture-function" regularization
schemes that resolve this difficulty and allow practical self-force
calculations, and I outline an important recent calculation of this type.
  Most Capra research to date has used 1st order perturbation theory. To obtain
the very high accuracies needed to fully exploit LISA's observations of the
strongest EMRIs, 2nd order perturbation theory will probably be needed.


  The measurement of the hole density in the ferromagnetic semiconductor
Ga$_{1-x}$Mn$_{x}$As is notoriously difficult using standard transport
techniques due to the dominance of the anomalous Hall effect. Here, we report
the first spectroscopic measurement of the hole density in four
Ga$_{1-x}$Mn$_{x}$As samples ($x=0, 0.038, 0.061, 0.083$) at room temperature
using Raman scattering intensity analysis of the coupled plasmon-LO-phonon mode
and the unscreened LO phonon. The unscreened LO phonon frequency linearly
decreases as the Mn concentration increases up to 8.3%. The hole density
determined from the Raman scattering shows a monotonic increase with increasing
$x$ for $x\leq0.083$, exhibiting a direct correlation to the observed $T_c$.
The optical technique reported here provides an unambiguous means of
determining the hole density in this important new class of ``spintronic''
semiconductor materials.


  An approach to the mechanical behaviour of textile composites at the scale of
their constituting fibres, using an implicit finite element simulation code, is
proposed in this chapter. The approach is based on efficient methods and
algorithms to detect and take into account contact-friction interactions
between elementary fibres. It allows one to model samples of woven textile
composites made of a few hundreds of fibres, with an elastic matrix. The
approach is employed first to determine the initial configuration of woven
fabric samples, before applying to them various loadings in order to identify
their mechanical properties under various solicitations


  In this work, we first characterize the hybrid execution patterns of GCNs on
Intel Xeon CPU. Guided by the characterization, we design a GCN accelerator,
HyGCN, using a hybrid architecture to efficiently perform GCNs. Specifically,
first, we build a new programming model to exploit the fine-grained parallelism
for our hardware design. Second, we propose a hardware design with two
efficient processing engines to alleviate the irregularity of Aggregation phase
and leverage the regularity of Combination phase. Besides, these engines can
exploit various parallelism and reuse highly reusable data efficiently. Third,
we optimize the overall system via inter-engine pipeline for inter-phase fusion
and priority-based off-chip memory access coordination to improve off-chip
bandwidth utilization. Compared to the state-of-the-art software framework
running on Intel Xeon CPU and NVIDIA V100 GPU, our work achieves on average
1509$\times$ speedup with 2500$\times$ energy reduction and average 6.5$\times$
speedup with 10$\times$ energy reduction, respectively.


  We describe, through the use of Rubin's theorem, the automorphism groups of
the Higman-Thompson groups $G_{n,r}$ as groups of specific homeomorphisms of
Cantor spaces $\mathfrak{C}_{n,r}$. This continues a thread of research begun
by Brin, and extended later by Brin and Guzm\'an: to characterise the
automorphism groups of the `Chameleon groups of Richard Thompson,' as Brin
referred to them in 1996. The work here completes the first stage of that
twenty-year-old program, containing (amongst other things) a characterisation
of the automorphism group of $V$, which was the `last chameleon.' The
homeomorphisms which arise fit naturally into the framework of Grigorchuk,
Nekrashevich, and Suschanskii's rational group $\mathscr{R}$: they are exactly
those homeomorphisms which are induced by bi-sychronizing transducers, which we
define in the paper. This result appears to offer insight into the nature of
Brin and Guzman's exotic automorphisms, while also uncovering connections with
the theory of reset words for automata (arising in the Road Colouring Problem)
and with the theory of automorphism groups of the full shift.


  In the present theoretical work we have considered impurities, either boron
or phosphorous, located at different substitutional sites in silicon quantum
dots (Si-QDs) with diameters around 1.5\,nm, embedded in a SiO2 matrix.
Formation energy calculations reveal that the most energetically-favored doping
sites are inside the QD and at the Si/SiO2 interface for P and B impurities,
respectively. Furthermore, electron and hole transport calculations show in all
the cases a strong reduction of the minimum voltage threshold, and a
corresponding increase of the total current in the low-voltage regime. At
higher voltage, our findings indicate a significant increase of transport only
for P-doped Si-QDs, while the electrical response of B-doped ones does not
stray from the undoped case. These findings are of support for the employment
of doped Si-QDs in a wide range of applications, such as Si-based photonics or
photovoltaic solar cells.


  We compute the Witten index of the Berenstein-Maldacena-Nastase matrix
quantum mechanics, which counts the number of ground states as well as the
difference between the numbers of bosonic and fermionic BPS states with nonzero
spins. The Witten index sets a lower bound on the entropy, which exhibits an
$N^2$ growth that predicts the existence of BPS black holes in M-theory,
asymptotic to the plane wave geometry. We also discuss a relation between the
Witten index in the infinite $N$ limit and the superconformal index of the
Aharony-Bergman-Jafferis-Maldacena theory.


  We present a one-parameter family $F_\lambda$ of transcendental entire
functions with zeros, whose Newton's method yields wandering domains,
coexisting with the basins of the roots of $F_\lambda$. Wandering domains for
Newton maps of zero-free functions have been built before by, e.g., Buff and
R\"uckert based on the lifting method. This procedure is suited to our Newton
maps as members of the class of projectable functions (or maps of the
cylinder), i.e. transcendental meromorphic functions $f(z)$ in the complex
plane that are semiconjugate, via the exponential, to some map $g(w)$, which
may have at most a countable number of essential singularities.
  In this paper we make a systematic study of the general relation (dynamical
and otherwise) between $f$ and $g$, and inspect the extension of the
logarithmic lifting method of periodic Fatou components to our context,
especially for those $g$ of finite-type. We apply these results to characterize
the entire functions with zeros whose Newton's method projects to some map $g$
which is defined at both $0$ and $\infty$. The family $F_\lambda$ is the
simplest in this class, and its parameter space shows open sets of
$\lambda$-values in which the Newton map exhibits wandering or Baker domains,
in both cases regions of initial conditions where Newton's root-finding method
fails.


  In this paper, we introduce a discrete variant of the meta-learning
framework. Meta-learning aims at exploiting prior experience and data to
improve performance on future tasks. By now, there exist numerous formulations
for meta-learning in the continuous domain. Notably, the Model-Agnostic
Meta-Learning (MAML) formulation views each task as a continuous optimization
problem and based on prior data learns a suitable initialization that can be
adapted to new, unseen tasks after a few simple gradient updates. Motivated by
this terminology, we propose a novel meta-learning framework in the discrete
domain where each task is equivalent to maximizing a set function under a
cardinality constraint. Our approach aims at using prior data, i.e., previously
visited tasks, to train a proper initial solution set that can be quickly
adapted to a new task at a relatively low computational cost. This approach
leads to (i) a personalized solution for each individual task, and (ii)
significantly reduced computational cost at test time compared to the case
where the solution is fully optimized once the new task is revealed. The
training procedure is performed by solving a challenging discrete optimization
problem for which we present deterministic and randomized algorithms. In the
case where the tasks are monotone and submodular, we show strong theoretical
guarantees for our proposed methods even though the training objective may not
be submodular. We also demonstrate the effectiveness of our framework on two
real-world problem instances where we observe that our methods lead to a
significant reduction in computational complexity in solving the new tasks
while incurring a small performance loss compared to when the tasks are fully
optimized.


  A giant peak in the temperature dependence of the screening current is
observed in the ac magnetic response of an ultra-pure YBa$_2$Cu$_3$O$_{6.993}$
crystal in a magnetic field. At H = 2.0 T ({\bf $H$}$||${\bf c}), the screening
current density $J_c(T)$ exhibits a 35-fold rise with 0.5 K increase in
temperature, indicating an abrupt $10^3$-fold collapse in the characteristic
volume of ordered regions in the vortex array. The peak-effect anomaly is most
pronounced for $H<$ 4.0 T, but detectable up to 7.0 T. The temperature
dependence of the equilibrium magnetization exhibits a small discontinuous jump
(for high fields) inside the peak-effect regime, suggesting that the underlying
phase transition is a weak first-order vortex-lattice melting transition.


  In this paper we propose a method to define the range of stability of fixed
points for a variety of discrete fractional systems of the order $0 < \alpha
<2$. The method is tested on various forms of fractional generalizations of the
standard and logistic maps. Based on our analysis we make a conjecture that
chaos is impossible in the corresponding continuous fractional systems.


  We consider the outward-propagating nonlinear concentric water waves within
the scope of the 2D Boussinesq system. The problem is axisymmetric, and we
derive the slow radius versions of the cylindrical Korteweg - de Vries (cKdV)
and extended cKdV (ecKdV) models. Numerical runs are initially performed using
the full axisymmetric Boussinesq system. At some distance away from the origin,
we use the numerical solution of the Boussinesq system as the "initial
condition" for the derived cKdV and ecKdV models. We then compare the evolution
of the waves as described by both reduced models and the direct numerical
simulations of the axisymmetric Boussinesq system. The main conclusion of the
paper is that the extended cKdV model provides a much more accurate description
of the waves and extends the range of validity of the weakly-nonlinear
modelling to the waves of moderate amplitude.


  Recent years have seen the advent of molecular simulation datasets that are
orders of magnitude larger and more diverse. These new datasets differ
substantially in four aspects of complexity: 1. Chemical diversity (number of
different elements), 2. system size (number of atoms per sample), 3. dataset
size (number of data samples), and 4. domain shift (similarity of the training
and test set). Despite these large differences, benchmarks on small and narrow
datasets remain the predominant method of demonstrating progress in graph
neural networks (GNNs) for molecular simulation, likely due to cheaper training
compute requirements. This raises the question -- does GNN progress on small
and narrow datasets translate to these more complex datasets? This work
investigates this question by first developing the GemNet-OC model based on the
large Open Catalyst 2020 (OC20) dataset. GemNet-OC outperforms the previous
state-of-the-art on OC20 by 16% while reducing training time by a factor of 10.
We then compare the impact of 18 model components and hyperparameter choices on
performance in multiple datasets. We find that the resulting model would be
drastically different depending on the dataset used for making model choices.
To isolate the source of this discrepancy we study six subsets of the OC20
dataset that individually test each of the above-mentioned four dataset
aspects. We find that results on the OC-2M subset correlate well with the full
OC20 dataset while being substantially cheaper to train on. Our findings
challenge the common practice of developing GNNs solely on small datasets, but
highlight ways of achieving fast development cycles and generalizable results
via moderately-sized, representative datasets such as OC-2M and efficient
models such as GemNet-OC. Our code and pretrained model weights are
open-sourced.


  Smart-meters are a key component of energy transition. The large amount of
data collected in near real-time allows grid operators to observe and simulate
network states. However, privacy-preserving rules forbid the use of such data
for any applications other than network operation and billing. Smart-meter
measurements must be anonymised to transmit these sensitive data to a third
party to perform network simulation and analysis. This work proposes two
methods for data anonymisation that enable the use of raw active power
measurements for network simulation and analysis. The first is based on an
allocation of an externally sourced load database. The second consists of
grouping smart-meter data with similar electric characteristics, then
performing a random permutation of the network load-bus assignment. A benchmark
of these two methods highlights that both provide similar results in
bus-voltage magnitude estimation concerning ground-truth voltage.


  Our aim is to detect and characterise long-period companions around main
sequence stars (spectral types late F to early M). We use the RV method to
search for exoplanets around stars. The RV variations are measured with HARPS
at the ESO 3.6 metre telescope. The true mass and inclination of our heavier
companions are provided by astrometry, for which we use proper motions from
Hipparcos and Gaia. Five Jupiter-mass exoplanets are reported to orbit
HIP54597, BD-210397 (x2), HD74698, and HD94771 with 8.9 yr, 5.2 yr, 17.4 yr,
9.4 yr, and 5.9 yr orbits, and to have minimum masses of $2.01 \pm 0.03$, $0.7
\pm 0.1$, $2.4^{+1.5}_{-0.2}$, $0.40 \pm 0.06$, and $0.53 \pm 0.03 M_J$
respectively. HD74698 also hosts a highly irradiated Neptune in a 15 day orbit
with a minimum mass of $0.07 \pm 0.01 M_J$. The mass of HIP54597 b can
maximally increase by 10% - 30%, the minimum mass of HD74698 c is likely equal
to its true mass, and BD-210397 c has a mass of $2.66^{+0.63}_{-0.32} M_J$.
HD62364 hosts a brown dwarf with a true mass of $18.77^{+0.66}_{-0.63} M_J$ in
an orbit of 14 yr. HD56380B, HD221638B, and HD33473C have minimum masses within
the brown dwarf limits, in orbits of 8.9 yr, 16.6 yr, and 50 yr respectively;
however, astrometric measurements reveal them to be stellar binaries, with
masses of $375.3^{+8.6}_{-8.4}$, $110.0^{+3.9}_{-3.7}$, and
$271.0^{+3.9}_{-3.8} M_J$. The orbits of the stellar binaries HD11938 and
HD61383 are incomplete. The preliminary result for HD61383 is a 0.190
$M_{\odot}$ binary in a 39 yr orbit. The secondary of the binary system HD11938
has a mass of 0.33 $M_{\odot}$ - which is confirmed by a secondary peak in the
CCF - and a preliminary period of 35 yr. The origin of the 3.0 yr RV signal of
HD3964 is uncertain as it shows entanglement with the magnetic cycle of the
star. We finally report one more star, HD11608, with a magnetic cycle that
mimics a planetary signal.


  3D Convolutional Neural Networks are gaining increasing attention from
researchers and practitioners and have found applications in many domains, such
as surveillance systems, autonomous vehicles, human monitoring systems, and
video retrieval. However, their widespread adoption is hindered by their high
computational and memory requirements, especially when resource-constrained
systems are targeted. This paper addresses the problem of mapping X3D, a
state-of-the-art model in Human Action Recognition that achieves accuracy of
95.5\% in the UCF101 benchmark, onto any FPGA device. The proposed toolflow
generates an optimised stream-based hardware system, taking into account the
available resources and off-chip memory characteristics of the FPGA device. The
generated designs push further the current performance-accuracy pareto front,
and enable for the first time the targeting of such complex model architectures
for the Human Action Recognition task.


  A numerical detection of the $\tau$-driven transition of galaxy spins is
presented, where $\tau$ is the degree of misalignment between the initial tidal
field and protogalaxy inertia tensors. Analyzing the data from the IllustrisTNG
300-1 simulations, we first measure the values of $\tau$ at the protogalactic
sites found by tracing the constituents of the galactic halos in the mass range
of $10.5\le \log \left[M_{h}/(h^{-1}M_{\odot})\right] \le 13$ back to the
initial stage, $z_{i}=127$. The probability density functions of $\tau$ are
shown to be well modeled by the $\Gamma$-distributions, whose shape and scale
parameters turn out to have universal values on a certain critical scale. Then,
we investigate how the strength and tendency of the galaxy spin alignments with
the principal axes of the local tidal fields depend on the initial condition,
$\tau$. It is found that on the scale lower than the critical one, the galaxy
spin transition occurs at two different thresholds from the major to
intermediate and from the intermediate to minor principal axes of the local
tidal fields, respectively. Noting that the $\tau$-dependent spin transition
supersedes in strength the previously found mass-dependent,
morphology-dependent, and radius-dependent counterparts, we suggest that $\tau$
should be the key driver of all types of the galaxy spin transition and that
the present galaxy spins are indeed excellent fossil records of the initial
condition.


  Filtration of real gases described by Peng-Robinson equations of state in
3-dimensional space is studied. Thermodynamic states are considered as either
Legendrian submanifolds in contact space, or Lagrangian submanifolds in
symplectic space. The correspondence between singularities of their projection
on the plane of intensives and phase transitions is shown, and coexistence
curves in various coordinates are constructed. A method of finding explicit
solutions of the Dirichlet boundary problem is provided and the case of a
number of sources is discussed in details. The domains corresponding to
different phases are shown.


  One of the challenges of quantum technologies is realising the quantum
advantage, predicted for ideal systems, in real applications, which have to
cope with decoherence and inefficiencies. In quantum metrology, sub-shot-noise
imaging (SSNI) and sensing methods can provide genuine quantum enhancement in
realistic situations. However, wide field SSNI schemes realized so far suffer a
trade-off between the resolution and the sensitivity gain over classical
counterpart: small pixels or integrating area, are necessary to achieve high
imaging resolution, but larger pixels allow a better detection efficiency of
quantum correlations, which means a larger quantum advantage. Here we show how
the SSNI protocol can be optimized to significantly improve the resolution
without giving up the quantum advantage in the sensitivity. We show a linear
resolution improvement (up to a factor 3) with respect to the simple protocol
used in previous demonstrations.


  This chapter summarizes our current understanding of the ionosphere of
Saturn. We give an overview of Saturn ionospheric science from the Voyager era
to the present, with a focus on the wealth of new data and discoveries enabled
by Cassini, including a massive increase in the number of electron density
altitude profiles. We discuss recent ground-based detection of the effect of
"ring rain" on Saturn's ionosphere, and present possible model interpretations
of the observations. Finally, we outline current model-data discrepancies and
indicate how future observations can help in advancing our understanding of the
various controlling physical and chemical processes.


  We report resonant inelastic X-ray scattering (RIXS) measurements on ordered
double perovskite samples containing Re$^{5+}$ and Ir$^{5+}$ with $5d^2$ and
$5d^4$ electronic configurations respectively. In particular, the observed RIXS
spectra of Ba$_2$YReO$_6$ and Sr$_2$MIrO$_6$ (M=Y, Gd) show sharp
intra-t$_{2g}$ transitions, which can be quantitatively understood using a
minimal `atomic' Hamiltonian incorporating spin-orbit coupling ($\lambda$) and
Hund's coupling ($J_H$). Our analysis yields $\lambda=0.38(2)$eV with
$J_H=0.26(2)$eV for Re$^{5+}$, and $\lambda=0.42(2)$eV with $J_H=0.25(4)$eV for
Ir$^{5+}$. Our results provide the first sharp estimates for the Hund's
coupling in $5d$ oxides, and suggest that it should be treated on equal footing
with spin-orbit interaction in multi-orbital $5d$ transition metal compounds.


  Let $2s$ points $y_i=-\pi\le y_{2s}<\ldots<y_1<\pi$ be given. Using these
points, we define the points $y_i$ for all integer indices $i$ by the equality
$y_i=y_{i+2s}+2\pi$. We shall write $f\in\bigtriangleup^{(1)}(Y)$ if $f$ is a
$2\pi$-periodic function and $f$ does not decrease on $[y_i, y_{i-1}]$ if $i$
is odd; and $f$ does not increase on $[y_i, y_{i-1}]$ if $i$ is even. We denote
$E_n^{(1)}(f;Y)$ the value of the best uniform comonotone approximation. In
this article the following Theorem -- the comonotone analogue of second
Jackson's Inequality -- is proved.
  Theorem. If $f\in\bigtriangleup^{(1)}(Y)\bigcap {\Bbb W}^r$, $r>2$, then
$$E_n^{(1)}(f;Y)\le \frac c{n^r}, $$ where $c=c(r,Y)=const$ depending only on
$r$ and $Y$, ${\Bbb W}^r$ Sobolev space.


  Cyber-physical control applications impose strict requirements on the
reliability and latency of the underlying communication system. Hence, they
have been mostly implemented using wired channels where the communication
service is highly predictable. Nevertheless, fulfilling such stringent demands
is envisioned with the fifth generation of mobile networks (5G). The
requirements of such applications are often defined on the application layer.
However, cyber-physical control applications can usually tolerate sparse packet
loss, and therefore it is not at all obvious what configurations and settings
these application level requirements impose on the underlying wireless network.
In this paper, we apply the fundamental metrics from reliability literature to
wireless communications and derive a mapping function between application level
requirements and network level parameters for those metrics under deterministic
arrivals. Our mapping function enables network designers to realize the
end-to-end performance (as the target application observes it). It provides
insights to the network controller to either enable more reliability
enhancement features (e.g., repetition), if the metrics are below requirements,
or to enable features increasing network utilization, otherwise. We evaluate
our theoretical results by realistic and detailed simulations of a factory
automation scenario. Our simulation results confirm the viability of the
theoretical framework under various burst error tolerance and load conditions.


  Conversational Emotion Recognition (CER) aims to predict the emotion
expressed by an utterance (referred to as an ``event'') during a conversation.
Existing graph-based methods mainly focus on event interactions to comprehend
the conversational context, while overlooking the direct influence of the
speaker's emotional state on the events. In addition, real-time modeling of the
conversation is crucial for real-world applications but is rarely considered.
Toward this end, we propose a novel graph-based approach, namely Event-State
Interactions infused Heterogeneous Graph Neural Network (ESIHGNN), which
incorporates the speaker's emotional state and constructs a heterogeneous
event-state interaction graph to model the conversation. Specifically, a
heterogeneous directed acyclic graph neural network is employed to dynamically
update and enhance the representations of events and emotional states at each
turn, thereby improving conversational coherence and consistency. Furthermore,
to further improve the performance of CER, we enrich the graph's edges with
external knowledge. Experimental results on four publicly available CER
datasets show the superiority of our approach and the effectiveness of the
introduced heterogeneous event-state interaction graph.


  Polarized forward-backward asymmetries in the (B_s -> l^+ l^- gamma) decay
are calculated using the most general, model independent form of the effective
Hamiltonian, including all possible forms of interactions. The dependencies of
the asymmetries on new Wilson coefficients are investigated. The detectability
of the asymmetries at LHC is discussed.


  Analytical/quasi-analytical solutions are proposed for a steady,
compressible, two-phase flow in mechanical equilibrium in a rectilinear duct
subjected to heating followed by cooling. The flow is driven by the pressure
ratio between a variable outlet pressure and an upstream tank. A critical
pressure ratio distinguishes subsonic and supersonic outlet regimes: the
article proposes a methodology to determine the full flow behaviour, as a
function of pressure ratio and heat-flux distribution. Going forward, these
analytical reference solutions will help validate numerical codes for more
complex industrial applications. Specific results are studied for a mixture of
liquid water and water vapour.


  We prove that under very mild conditions for any interpolation formula $f(x)
= \sum_{\lambda\in \Lambda} f(\lambda)a_\lambda(x) + \sum_{\mu\in M}
\hat{f}(\mu)b_{\mu}(x)$ we have a lower bound for the counting functions
$n_\Lambda(R_1) + n_{M}(R_2) \ge 4R_1R_2 - C\log^{2+\varepsilon}(4R_1R_2)$
which very closely matches interpolation formulas discovered by Radchenko and
Viazovska and by Bondarenko, Radchenko and Seip.


  Starting from Generating functional for Green Function (GF), constracted from
Lagrangian action in Duffin-Kemmer-Petiau (DKP) theory (L-approach) we strictly
prove that the physical matrix elements of S-matrix in DKP and
Klein-Gordon-Fock (KGF) theories coincide in cases of interaction spin O
particles with external and quantized Maxwell and Yang-Mills fields and in case
of external gravitational field (without or with torsion). For the proof we use
reduction formulas of Lehmann Symanzik, Zimmermann (LSZ). We prove that many
photons and Yang-Mills particles GF coincide in the both theories, too.


  We define the (Hadamard) product $s\odot t := (s_\alpha\cdot
t_\alpha)_{\alpha\in\mathbb{N}_0^n}$ of two sequences $s =
(s_\alpha)_{\alpha\in\mathbb{N}_0^n}$ and $t =
(t_\alpha)_{\alpha\in\mathbb{N}_0^n}$. If $s$ is represented by a measure $\mu$
and $t$ by a measure $\nu$, i.e., both are moment sequences, then we give an
explicit measure $\mu\odot\nu$ that represents $s\odot t$, i.e., $s\odot t$ is
a moment sequence. We pick a result from Moment-sequence transforms (J. Eur.
Math. Soc. 24 (2022), 3109--3160) and show how the multiplication $s\odot t$ of
moment sequences simplifies the proof and also gives deeper insight by
providing the representing measure $\mu\odot\nu$.


  We find one-shot bounds for concentration of maximally coherent states in the
so called assisted scenario. In this setting, Bob is restricted to performing
incoherent operations on his quantum system, however he is assisted by Alice,
who holds a purification of Bob's state and can send classical data to him. We
further show that in the asymptotic limit our one-shot bounds recover the
previously computed rate of asymptotic assisted concentration.


  Collisionless shocks are fundamental processes that are ubiquitous in space
plasma physics throughout the Heliosphere and most astrophysical environments.
Earth's bow shock and interplanetary shocks at 1 AU offer the most readily
accessible opportunities to advance our understanding of the nature of
collisionless shocks via fully-instrumented, in situ observations. One major
outstanding question pertains to the energy budget of collisionless shocks,
particularly how exactly collisionless shocks convert incident kinetic bulk
flow energy into thermalization (heating), suprathermal particle acceleration,
and a variety of plasma waves, including nonlinear structures. Furthermore, it
remains unknown how those energy conversion processes change for different
shock orientations (e.g., quasi-parallel vs. quasi-perpendicular) and driving
conditions (upstream Alfv\'enic and fast Mach numbers, plasma beta, etc.).
Required to address these questions are multipoint observations enabling direct
measurement of the necessary plasmas, energetic particles, and electric and
magnetic fields and waves, all simultaneously from upstream, downstream, and at
the shock transition layer with observatory separations at ion to
magnetohydrodynamic (MHD) scales. Such a configuration of spacecraft with
specifically-designed instruments has never been available, and this white
paper describes a conceptual mission design -- MAKOS -- to address these
outstanding questions and advance our knowledge of the nature of collisionless
shocks.


  We propose a new, flexible approach for dynamically maintaining successful
mutation rates in evolutionary algorithms using $k$-bit flip mutations. The
algorithm adds successful mutation rates to an archive of promising rates that
are favored in subsequent steps. Rates expire when their number of unsuccessful
trials has exceeded a threshold, while rates currently not present in the
archive can enter it in two ways: (i) via user-defined minimum selection
probabilities for rates combined with a successful step or (ii) via a
stagnation detection mechanism increasing the value for a promising rate after
the current bit-flip neighborhood has been explored with high probability. For
the minimum selection probabilities, we suggest different options, including
heavy-tailed distributions.
  We conduct rigorous runtime analysis of the flexible evolutionary algorithm
on the OneMax and Jump functions, on general unimodal functions, on minimum
spanning trees, and on a class of hurdle-like functions with varying hurdle
width that benefit particularly from the archive of promising mutation rates.
In all cases, the runtime bounds are close to or even outperform the best known
results for both stagnation detection and heavy-tailed mutations.


  Somatic mutations in cancer can be viewed as a mixture distribution of
several mutational signatures, which can be inferred using non-negative matrix
factorization (NMF). Mutational signatures have previously been parametrized
using either simple mono-nucleotide interaction models or general
tri-nucleotide interaction models. We describe a flexible and novel framework
for identifying biologically plausible parametrizations of mutational
signatures, and in particular for estimating di-nucleotide interaction models.
The estimation procedure is based on the expectation--maximization (EM)
algorithm and regression in the log-linear quasi--Poisson model. We show that
di-nucleotide interaction signatures are statistically stable and sufficiently
complex to fit the mutational patterns. Di-nucleotide interaction signatures
often strike the right balance between appropriately fitting the data and
avoiding over-fitting. They provide a better fit to data and are biologically
more plausible than mono-nucleotide interaction signatures, and the
parametrization is more stable than the parameter-rich tri-nucleotide
interaction signatures. We illustrate our framework on three data sets of
somatic mutation counts from cancer patients.


  We introduce SketchGNN, a convolutional graph neural network for semantic
segmentation and labeling of freehand vector sketches. We treat an input
stroke-based sketch as a graph, with nodes representing the sampled points
along input strokes and edges encoding the stroke structure information. To
predict the per-node labels, our SketchGNN uses graph convolution and a
static-dynamic branching network architecture to extract the features at three
levels, i.e., point-level, stroke-level, and sketch-level. SketchGNN
significantly improves the accuracy of the state-of-the-art methods for
semantic sketch segmentation (by 11.2% in the pixel-based metric and 18.2% in
the component-based metric over a large-scale challenging SPG dataset) and has
magnitudes fewer parameters than both image-based and sequence-based methods.


  A path in an edge-colored graph is said to be rainbow if no color repeats on
it. An edge-colored graph is said to be rainbow $k$-connected if every pair of
vertices is connected by $k$ internally disjoint rainbow paths. The rainbow
$k$-connection number $\mathrm{rc}_k(G)$ is the minimum number of colors $\ell$
such that there exists a coloring with $\ell$ colors that makes $G$ rainbow
$k$-connected. Let $f(k,t)$ be the minimum integer such that every $t$-partite
graph with part sizes at least $f(k,t)$ has $\mathrm{rc}_k(G) \le 4$ if $t=2$
and $\mathrm{rc}_k(G) \le 3$ if $t \ge 3$. Answering a question of Fujita, Liu
and Magnant, we show that
  \[
  f(k,t) = \left\lceil \frac{2k}{t-1} \right\rceil
  \]
  for all $k\geq 2$, $t\geq 2$. We also give some conditions for which
$\mathrm{rc}_k(G) \le 3$ if $t=2$ and $\mathrm{rc}_k(G) \le 2$ if $t \ge 3$.


  We construct and investigate the pulsar luminosity function using the new
catalogue which includes data for 1315 radio pulsars. The luminosity functions
are constructed for 400 and 1400 MHz separately, and they are compared. Also,
the luminosity functions excluding the binary millisecond pulsars and other
pulsars with low magnetic fields are constructed. The 1400 MHz luminosities as
a function of characteristic age and as a function of magnetic field for radio
pulsars, anomalous X-ray pulsars and dim radio quiet neutron stars are
presented and the implications of the pulsar luminosity function on these new
kind of neutron stars are discussed.


  We analyze the nuclear stellar dynamics of the SB0 galaxy NGC 1023, utilizing
observational data both from the Space Telescope Imaging Spectrograph aboard
the Hubble Space Telescope and from the ground. The stellar kinematics measured
from these long-slit spectra show rapid rotation (V = 70 km/s at a distance of
0.1 arcsec = 4.9 pc from the nucleus) and increasing velocity dispersion toward
the nucleus (where sigma = 295 +/- 30 km/s). We model the observed stellar
kinematics assuming an axisymmetric mass distribution with both two and three
integrals of motion. Both modeling techniques point to the presence of a
central dark compact mass (which presumably is a supermassive black hole) with
confidence > 99%. The isotropic two-integral models yield a best-fitting black
hole mass of (6.0 +/- 1.4) x 10^7 M_sun and mass-to-light ratio (M/L_V) of 5.38
+/- 0.08, and the goodness-of-fit (chi^2) is insensitive to reasonable values
for the galaxy's inclination. The three-integral models, which
non-parametrically fit the observed line-of-sight velocity distribution as a
function of position in the galaxy, suggest a black hole mass of (3.9 +/- 0.4)
x 10^7 M_sun and M/L_V of 5.56 +/- 0.02 (internal errors), and the edge-on
models are vastly superior fits over models at other inclinations. The internal
dynamics in NGC 1023 as suggested by our best-fit three-integral model shows
that the velocity distribution function at the nucleus is tangentially
anisotropic, suggesting the presence of a nuclear stellar disk. The nuclear
line of sight velocity distribution has enhanced wings at velocities >= 600
km/s from systemic, suggesting that perhaps we have detected a group of stars
very close to the central dark mass.


  This study first reviews fuzzy random Portfolio selection theory and
describes the concept of portfolio optimization model as a useful instrument
for helping finance practitioners and researchers. Second, this paper
specifically aims at applying possibility-based models for transforming the
fuzzy random variables to the linear programming. The harmony search algorithm
approaches to resolve the portfolio selection problem with the objective of
return maximization is applied. We provide a numerical example to illustrate
the proposed model. The results show that the evolutionary method of this paper
with harmony search algorithm, can consistently handle the practical portfolio
selection problem.


  Adaptive therapy is a dynamic cancer treatment protocol that updates (or
"adapts") treatment decisions in anticipation of evolving tumor dynamics. This
broad term encompasses many possible dynamic treatment protocols of
patient-specific dose modulation or dose timing. Adaptive therapy maintains
high levels of tumor burden to benefit from the competitive suppression of
treatment-sensitive subpopulations on treatment-resistant subpopulations. This
evolution-based approach to cancer treatment has been integrated into several
ongoing or planned clinical trials, including treatment of metastatic castrate
resistant prostate cancer, ovarian cancer, and BRAF-mutant melanoma. In the
previous few decades, experimental and clinical investigation of adaptive
therapy has progressed synergistically with mathematical and computational
modeling. In this work, we discuss 11 open questions in cancer adaptive therapy
mathematical modeling. The questions are split into three sections: 1) the
necessary components of mathematical models of adaptive therapy 2) design and
validation of dosing protocols, and 3) challenges and opportunities in clinical
translation.


  This paper concerns a question that frequently occurs in various
applications: Is any diffusive coupling of stable linear systems, also stable?
Although it has been known for a long time that this is not the case, we shall
identify a reasonably diverse class of systems for which it is true.


  Animating an avatar that reflects a user's action in the VR world enables
natural interactions with the virtual environment. It has the potential to
allow remote users to communicate and collaborate in a way as if they met in
person. However, a typical VR system provides only a very sparse set of up to
three positional sensors, including a head-mounted display (HMD) and optionally
two hand-held controllers, making the estimation of the user's full-body
movement a difficult problem. In this work, we present a data-driven
physics-based method for predicting the realistic full-body movement of the
user according to the transformations of these VR trackers and simulating an
avatar character to mimic such user actions in the virtual world in real-time.
We train our system using reinforcement learning with carefully designed
pretraining processes to ensure the success of the training and the quality of
the simulation. We demonstrate the effectiveness of the method with an
extensive set of examples.


  Crop yield forecasting depends on many interactive factors, including crop
genotype, weather, soil, and management practices. This study analyzes the
performance of machine learning and deep learning methods for winter wheat
yield prediction using an extensive dataset of weather, soil, and crop
phenology variables in 271 counties across Germany from 1999 to 2019. We
proposed a Convolutional Neural Network (CNN) model, which uses a 1-dimensional
convolution operation to capture the time dependencies of environmental
variables. We used eight supervised machine learning models as baselines and
evaluated their predictive performance using RMSE, MAE, and correlation
coefficient metrics to benchmark the yield prediction results. Our findings
suggested that nonlinear models such as the proposed CNN, Deep Neural Network
(DNN), and XGBoost were more effective in understanding the relationship
between the crop yield and input data compared to the linear models. Our
proposed CNN model outperformed all other baseline models used for winter wheat
yield prediction (7 to 14% lower RMSE, 3 to 15% lower MAE, and 4 to 50% higher
correlation coefficient than the best performing baseline across test data). We
aggregated soil moisture and meteorological features at the weekly resolution
to address the seasonality of the data. We also moved beyond prediction and
interpreted the outputs of our proposed CNN model using SHAP and force plots
which provided key insights in explaining the yield prediction results
(importance of variables by time). We found DUL, wind speed at week ten, and
radiation amount at week seven as the most critical features in winter wheat
yield prediction.


  We generalize the wavelet transform modulus maxima (WTMM) method to
multifractal analysis of 3D random fields. This method is calibrated on
synthetic 3D monofractal fractional Brownian fields and on 3D multifractal
singular cascade measures as well as their random function counterpart obtained
by fractional integration. Then we apply the 3D WTMM method to the dissipation
field issue from 3D isotropic turbulence simulations. We comment on the need to
revisiting previous box-counting analysis which have failed to estimate
correctly the corresponding multifractal spectra because of their intrinsic
inability to master non-conservative singular cascade measures.


  We study the behaviour of the monopole at finite temperature in the
(2+1)-dimensional lattice gauge theory dual to the percolation model; by
exploiting the correspondences to statistical systems, we possess powerful
tools to evaluate the monopole mass both above and below the critical
temperature with high-precision Monte Carlo simulations.


  The present study introduces the notions of statistical convergence of order
$\alpha$ and strong $p-$ Ces\`{a}ro summability of order $\alpha$ in partial
metric spaces. Also, we examine the inclusion relations between these concepts.
In addition, we introduce the notion of $\lambda -$% statistical convergence of
order $\alpha$ in partial metric spaces while providing relations linked to
these sequence spaces.


  Balancing a matrix is a preprocessing step while solving the nonsymmetric
eigenvalue problem. Balancing a matrix reduces the norm of the matrix and
hopefully this will improve the accuracy of the computation. Experiments have
shown that balancing can improve the accuracy of the computed eigenval- ues.
However, there exists examples where balancing increases the eigenvalue
condition number (potential loss in accuracy), deteriorates eigenvector
accuracy, and deteriorates the backward error of the eigenvalue decomposition.
In this paper we propose a change to the stopping criteria of the LAPACK
balancing al- gorithm, GEBAL. The new stopping criteria is better at
determining when a matrix is nearly balanced. Our experiments show that the new
algorithm is able to maintain good backward error, while improving the
eigenvalue accuracy when possible. We present stability analysis, numerical
experiments, and a case study to demonstrate the benefit of the new stopping
criteria.


  The finite size effects on nucleon masses are calculated in relativistic
chiral perturbation theory. Results are compared with two-flavor lattice
results.


  I review recent developments in cosmology and astrophysics relevant to
particle physics, focussing on the following questions: What's new in 1992?
What have we learned since the last ICHEP meeting in 1990? and What are the
prospects for the future? AMong the topics explicitly discussed are: COBE,
Large Scale Structure, and Dark Matter; Bib Bang Nucleosynthesis; the Solar
Neutrino Problem; and High Energy Gamma Ray PHysics.


  We review some of our recent work on the conformal geometry corresponding to
the triangulated surfaces used in 2-dimensional simplicial quantum gravity. In
particular, we discuss the regularized Liouville action associated with random
Regge triangulations and its connection with Hodge-Deligne theory.


  We study the equilibrium and nonequilibrium properties of Boolean decision
problems with competing interactions on scale-free networks in an external bias
(magnetic field). Previous studies at zero field have shown a remarkable
equilibrium stability of Boolean variables (Ising spins) with competing
interactions (spin glasses) on scale-free networks. When the exponent that
describes the power-law decay of the connectivity of the network is strictly
larger than 3, the system undergoes a spin-glass transition. However, when the
exponent is equal to or less than 3, the glass phase is stable for all
temperatures. First, we perform finite-temperature Monte Carlo simulations in a
field to test the robustness of the spin-glass phase and show that the system
has a spin-glass phase in a field, i.e., exhibits a de Almeida-Thouless line.
Furthermore, we study avalanche distributions when the system is driven by a
field at zero temperature to test if the system displays self-organized
criticality. Numerical results suggest that avalanches (damage) can spread
across the whole system with nonzero probability when the decay exponent of the
interaction degree is less than or equal to 2, i.e., that Boolean decision
problems on scale-free networks with competing interactions can be fragile when
not in thermal equilibrium.


  In this paper, we follow a data-centric philosophy and propose a novel motion
annotation method based on the inherent representativeness of motion data in a
given dataset. Specifically, we propose a Representation-based
Representativeness Ranking R3 method that ranks all motion data in a given
dataset according to their representativeness in a learned motion
representation space. We further propose a novel dual-level motion constrastive
learning method to learn the motion representation space in a more informative
way. Thanks to its high efficiency, our method is particularly responsive to
frequent requirements change and enables agile development of motion annotation
models. Experimental results on the HDM05 dataset against state-of-the-art
methods demonstrate the superiority of our method.


  We consider different large ${\cal N}$ limits of the one-dimensional
Chern-Simons action $i\int dt~ \Tr (\del_0 +A_0)$ where $A_0$ is an ${\cal
N}\times{\cal N}$ antihermitian matrix. The Hilbert space on which $A_0$ acts
as a linear transformation is taken as the quantization of a $2k$-dimensional
phase space ${\cal M}$ with different gauge field backgrounds. For slowly
varying fields, the large ${\cal N}$ limit of the one-dimensional CS action is
equal to the $(2k+1)$-dimensional CS theory on ${\cal M}\times {\bf R}$.
Different large ${\cal N}$ limits are parametrized by the gauge fields and the
dimension $2k$. The result is related to the bulk action for quantum Hall
droplets in higher dimensions. Since the isometries of ${\cal M}$ are gauged,
this has implications for gravity on fuzzy spaces. This is also briefly
discussed.


  In this paper, we re-investigate the resolution of Toeplitz systems $T u =g$,
from a new point of view, by correlating the solution of such problems with
syzygies of polynomials or moving lines. We show an explicit connection between
the generators of a Toeplitz matrix and the generators of the corresponding
module of syzygies. We show that this module is generated by two elements of
degree $n$ and the solution of $T u=g$ can be reinterpreted as the remainder of
an explicit vector depending on $g$, by these two generators.