  We investigate the attractor mechanism in Kaluza-Klein black hole solution in
the presence of higher derivative terms. In particular, we discuss the
attractor behavior of static black holes by using the effective potential
approach as well as entropy function formalism. We consider different higher
derivative terms with a general coupling to moduli field. For the $R^2$ theory,
we use effective potential approach, looking for solutions which are analytic
near the horizon and show that they exist and enjoy the attractor behavior. The
attractor point is determined by extremization of the modified effective
potential at the horizon. We study the effect of the general higher derivative
corrections of $R^n$ terms. Using the entropy function we define the modified
effective potential and we find the conditions to have the attractor solution.
In particular for a single charged Kaluza-Klein black hole solution we show
that higher derivative correction dresses the naked singularity for an
appropriate coupling, and we can find the attractor solution.


  The emergence of new wearable technologies such as action cameras and
smart-glasses has increased the interest of computer vision scientists in the
First Person perspective. Nowadays, this field is attracting attention and
investments of companies aiming to develop commercial devices with First Person
Vision recording capabilities. Due to this interest, an increasing demand of
methods to process these videos, possibly in real-time, is expected. Current
approaches present a particular combinations of different image features and
quantitative methods to accomplish specific objectives like object detection,
activity recognition, user machine interaction and so on. This paper summarizes
the evolution of the state of the art in First Person Vision video analysis
between 1997 and 2014, highlighting, among others, most commonly used features,
methods, challenges and opportunities within the field.


  In this paper, we study the peak-aware energy scheduling problem using the
competitive framework with machine learning prediction. With the uncertainty of
energy demand as the fundamental challenge, the goal is to schedule the energy
output of local generation units such that the electricity bill is minimized.
While this problem has been tackled using classic competitive design with
worst-case guarantee, the goal of this paper is to develop learning-assisted
competitive algorithms to improve the performance in a provable manner. We
develop two deterministic and randomized algorithms that are provably robust
against the poor performance of learning prediction, however, achieve the
optimal performance as the error of prediction goes to zero. Extensive
experiments using real data traces verify our theoretical observations and show
15.13% improved performance against pure online algorithms.


  We report on the magnetic merging of ultracold atomic gases of $^{85}$Rb and
$^{87}$Rb by the controlled overlap of two initially spatially-separated
magnetic traps. We present a detailed analysis of the combined magnetic field
potential as the two traps are brought together which predicts a clear optimum
trajectory for the merging. We verify this prediction experimentally using
$^{85}$Rb and find that the final atom number in the merged trap is maximised
with minimal heating by following the predicted optimum trajectory. Using the
magnetic merging approach allows us to create variable ratio isotopic Rb
mixtures with a single laser cooling setup by simply storing one isotope in a
magnetic trap before jumping the laser frequencies to the transitions necessary
to laser cool the second isotope. This offers a simple and cost effective way
to achieve suitable starting conditions for sympathetic cooling of $^{85}$Rb by
$^{87}$Rb towards quantum degeneracy.


  This White Paper outlines the importance of addressing the fundamental
science theme <<How are charged particles energized in space plasmas>> through
a future ESA mission. The White Paper presents five compelling science
questions related to particle energization by shocks, reconnection,waves and
turbulence, jets and their combinations. Answering these questions requires
resolving scale coupling, nonlinearity and nonstationarity, which cannot be
done with existing multi-point observations. In situ measurements from a
multi-point, multi-scale L-class plasma observatory consisting of at least 7
spacecraft covering fluid, ion and electron scales are needed. The plasma
observatory will enable a paradigm shift in our comprehension of particle
energization and space plasma physics in general, with very important impact on
solar and astrophysical plasmas. It will be the next logical step following
Cluster, THEMIS and MMS for the very large and active European space plasmas
community. Being one of the cornerstone missions of the future ESA Voyage
2035-2050 science program, it would further strengthen the European scientific
and technical leadership in this important field.


  O(100 GeV) physics beyond the standard model (BSM) could be overlooked
provided that it is hidden in the untouched Higgs sector or jets. The top quark
forward-backward asymmetry measurements and di-jet bump, which is observed in
the associated production with charged lepton plus missing energy (supposed
arising from W decay), may indicate the existence of a new color-octet
axial-vector Z_C with a mass about 145 GeV. Here Z_C only decays into two jets.
In this paper we investigated the possibility to discover Z_C pair via
analyzing the four jets as the final states, which are heavily polluted by huge
QCD background. Our simulation showed that, however, both Tevatron and LHC have
the excellent chance to discover Z_C through analyzing the four jets events in
the current accumulated data.


  We present the first detailed X-ray study and simultaneous optical
observations of the active fast rotating star FR Cnc. The X-ray spectra are
found to be explained by two-temperature plasma model with temperature of cool
and hot components of 0.34 and 1.1 keV, respectively. The X-ray light curve in
the 0.5 -- 2.0 keV energy band is found to be rotationally modulated with the
degree of rotational modulation of ~17%. We have also found that the X-ray
light curve is anti-correlated with the optical light and colour curves in the
sense that maximum X-ray light corresponds to minimum optical light and cooler
region on the surface of FR Cnc. The X-ray luminosity of FR Cnc is found to be
almost consistent in the last 30 years with an average value of 4.85x10^{29}
erg/s in the 0.5 -- 2.0 keV energy band.


  With the adoption of machine learning into routine clinical practice comes
the need for Explainable AI methods tailored to medical applications. Shapley
values have sparked wide interest for locally explaining models. Here, we
demonstrate their interpretation strongly depends on both the summary statistic
and the estimator for it, which in turn define what we identify as an 'anchor
point'. We show that the convention of using a mean anchor point may generate
misleading interpretations for survival analysis and introduce median-SHAP, a
method for explaining black-box models predicting individual survival times.


  This is an expository article aiming to introduce the reader to the
underlying mathematics and geometry of quantum error correction. Information
stored on quantum particles is subject to noise and interference from the
environment. Quantum error-correcting codes allow the negation of these effects
in order to successfully restore the original quantum information. We briefly
describe the necessary quantum mechanical background to be able to understand
how quantum error-correction works. We go on to construct quantum codes:
firstly qubit stabilizer codes, then qubit non-stabilizer codes, and finally
codes with a higher local dimension. We will delve into the geometry of these
codes. This allows one to deduce the parameters of the code efficiently, deduce
the inequivalence between codes that have the same parameters, and presents a
useful tool in deducing the feasibility of certain parameters. We also include
sections on quantum maximum distance separable codes and the quantum
MacWilliams identities.


  The optical and near-infrared (OIR) polarization of starlight is typically
understood to arise from the dichroic extinction of that light by dust grains
whose axes are aligned with respect to a local magnetic-field. The size
distribution of the aligned-grain population can be constrained by measurements
of the wavelength dependence of the polarization. The leading physical model
for producing the alignment is radiative alignment-torques (RAT), which
predicts that the most efficiently aligned grains are those with sizes larger
than the wavelengths of light composing the local radiation field. Therefore,
for a given grain-size distribution, the wavelength at which the polarization
reaches a maximum ($\lambda_\mathrm{max}$) should correlate with the
characteristic reddening along the line of sight between the dust grains and
the illumination source. A correlation between $\lambda_\mathrm{max}$ and
reddening has been previously established for extinctions up to $A_V\approx4$
mag. We extend the study of this relationship to a larger sample of stars in
the Taurus cloud complex, including extinctions $A_V>10$ mag. We confirm the
earlier results for $A_V<4$ mag, but find that the $\lambda_\mathrm{max}$ vs.
$A_V$ relationship bifurcates above $A_V\approx4$ mag, with part of the sample
continuing the previously observed relationship and the remaining part
exhibiting a significantly steeper rise. We propose that the data exhibiting
the steep rise represent lines-of-sight towards high density "clumps," where
grain coagulation has taken place. We present RAT-based modeling supporting
these hypotheses. These results indicate that multi-band OIR polarimetry is a
powerful tool for tracing grain growth in molecular clouds, independent of
uncertainties in the dust temperature and emissivity.


  In his previous papers (Math. Res. Letters 7 (2000), 123--13; Progress in
Math. 195 (2001), 473--490; Math. Res. Letters 8 (2001), 429--435; Moscow Math.
J. 2 (2002), issue 2, 403-431; Proc. Amer. Math. Soc. 131 (2003), no. 1,
95--102) the author introduced certain explicit constructions of hyperelliptic
jacobians without nontrivial endomorphisms. In the present paper we discuss
when these jacobians are mutually non-isogenous.
  In addition, a special case ($n=m=3$) of our Theorem 1.2 provides the
following criterion for elliptic curves $C_f: y^2=f(x)$ and $C_h: y^2=h(x)$ to
be non-isogenous. (Here $f(x)$ and $h(x)$ are cubic polynomials with
coefficients in a field $K$ of characteristic zero.) Suppose that $f(x)$ and
$h(x)$ are irreducible over $K$, their Galois groups over $K$ coincide with the
full symmetric group $S_3$, and their splitting fields are linearly disjoint
over $K$. Then the elliptic curves $C_f$ and $C_h$ are non-isogenous over an
algebraic closure of $K$.


  Effective management of cooling tower systems requires thorough disinfection.
While traditional chemical water treatment methods are currently the most
prominent strategy, they are costly and yield limited results when relied upon
as the sole approach. Cross-flow microsand filtration systems offer a promising
alternative with the added benefit of potentially increasing evaporative
cooling efficiency, thus saving energy. A comprehensive data-driven analysis
over two cooling seasons evaluated the energetic performance of a system
equipped with and without an operating filter. For similar environmental
conditions, the coefficient of performance was on average 18% higher and was
higher 63% of the time when the filter was operating, indicating superior heat
transfer efficiency and significant energy savings. It was also 41% higher
during periods of high cooling demand. Consequently, the filter and the system
work more efficiently at high wet-bulb temperature and thermal load. Machine
learning modeling suggested that operating the filter year-round could save
between 5% and 13% of the energy bill, primarily during the cooling season.
Continuous filter operation is essential as it mitigates biofouling,
underscoring its long-term significance, even during periods of lower thermal
loads. Integrating filtration systems into cooling tower management therefore
fosters sustainable practices by decreasing energy consumption and biofouling.


  Magnetic levitation based on the flux pinning nature of type II
superconductors has the merit of self-stability, making it appealing for
applications such as high speed bearings, maglev trains, space generators, etc.
However, such levitation systems physically rely on the superconductor
pre-capturing magnetic flux (i.e. field cooling process) before establishing
the levitation state which is nonadjustable afterwards. Moreover, practical
type II superconductors in the levitation system inevitably suffer from various
sources of energy losses, leading to continuous levitation force decay. These
intrinsic drawbacks make superconducting maglev inflexible and impractical for
long term operation. Here we propose and demonstrate a new form of
superconducting maglev which is tunable and with self-stability. The maglev
system uses a closed-loop type II superconducting coil to lock flux of a
magnet, establishing self-stable levitation between the two objects. A flux
pump is used to modulate the total magnetic flux of the coil without breaking
its superconductivity, thus flexibly tuning levitation force and height
meanwhile maintaining self-stability. For the first time, we experimentally
demonstrate a self-stable type II superconducting maglev system which is able
to: counteract long term levitation force decay, adjust levitation force and
equilibrium position, and establish levitation under zero field cooling
condition. These breakthroughs may bridge the gap between demonstrations and
practical applications of type II superconducting maglevs.


  Until recently, multiple synthetic data sets were always released to
analysts, to allow valid inferences to be obtained. However, under certain
conditions - including when saturated count models are used to synthesize
categorical data - single imputation ($m=1$) is sufficient. Nevertheless,
increasing $m$ causes utility to improve, but at the expense of higher risk, an
example of the risk-utility trade-off. The question, therefore, is: which value
of $m$ is optimal with respect to the risk-utility trade-off? Moreover, the
paper considers two ways of analysing categorical data sets: as they have a
contingency table representation, multiple categorical data sets can be
averaged before being analysed, as opposed to the usual way of averaging
post-analysis. This paper also introduces a pair of metrics, $\tau_3(k,d)$ and
$\tau_4(k,d)$, that are suited for assessing disclosure risk in multiple
categorical synthetic data sets. Finally, the synthesis methods are
demonstrated empirically.


  This paper addresses the escalating challenges posed by the ever-increasing
data volume, velocity, and the demand for low-latency applications, driven by
the proliferation of smart devices and Internet of Things (IoT) applications.
To mitigate service delay and enhance Quality of Service (QoS), we introduce a
hybrid optimization of Particle Swarm (PSO) and Chemical Reaction (CRO) to
improve service delay in FogPlan, an offline framework that prioritizes QoS and
enables dynamic fog service deployment. The method optimizes fog service
allocation based on incoming traffic to each fog node, formulating it as an
Integer Non-Linear Programming (INLP) problem, considering various service
attributes and costs. Our proposed algorithm aims to minimize service delay and
QoS degradation. The evaluation using real MAWI Working Group traffic data
demonstrates a substantial 29.34% reduction in service delay, a 66.02% decrease
in service costs, and a noteworthy 50.15% reduction in delay violations
compared to the FogPlan framework.


  Measurements of the double-differential $\pi^{\pm}$ production cross-section
in the range of momentum $100 \MeVc \leq p < 800 \MeVc$ and angle $0.35 \rad
\leq \theta < 2.15 \rad$ in proton--beryllium, proton--aluminium and
proton--lead collisions are presented. The data were taken with the HARP
detector in the T9 beam line of the CERN PS. The pions were produced by proton
beams in a momentum range from 3 \GeVc to 12.9 \GeVc hitting a target with a
thickness of 5% of a nuclear interaction length. The tracking and
identification of the produced particles was performed using a small-radius
cylindrical time projection chamber (TPC) placed inside a solenoidal magnet.
Incident particles were identified by an elaborate system of beam detectors.
Results are obtained for the double-differential cross-sections at six incident
proton beam momenta (3 \GeVc, 5 \GeVc, 8 \GeVc, 8.9 \GeVc (Be only), 12 \GeVc
and 12.9 \GeVc (Al only)) and compared to previously available data.


  We present an updated analysis of meson-baryon sigma terms in the
perturbative chiral quark model, which is based on effective chiral Lagrangian.
The new feature concerns the inclusion of excited states in the quark
propagator. Its influence on meson loops is shown to lead in particular for the
pion-nucleon sigma term to an enhancement relevant for the current evaluation
of this quantity. We also determine various flavor combinations of the scalar
nucleon form factors and their respective low-momentum transfer limits.


  The radial velocity method for detecting extra-solar planets relies on
measuring the star's wobble around the system's center of mass. Since this is
an indirect method, we may ask if there are other dynamical effects that can
mimic such wobble. In recent articles\cite{1,2,3}, we modeled the effect of a
nearby binary system on a star's radial velocity. We showed that, if we are
unaware of this nearby binary, for instance because one component is unresolved
or both components are faint stars, the binary's effect may mimic a planet.
Here, we review this work, explaining in which circumstances the binary's
effect may mimic a planet and we discuss what can be done in practice in order
to distinguish between these two scenarios (planet or nearby binary).


  A major challenge in autonomous driving is designing control architectures
that guarantee safety in all relevant driving scenarios. Given a safe desired
reference trajectory for the vehicle, a trajectory following controller has to
ensure that the trajectory is followed with a maximally allowed deviation, even
in the presence of external disturbances, such as wind gusts or inclined roads.
In this paper, a method for computing upper bounds for linear, time invariant
single-input-multiple-output systems with state feedback controllers and
additive bounded disturbances is proposed. The bounds for the offset between
states and the reference are derived analytically based on worst case
disturbance sequences. For systems with two states the bounds are strict, while
for higher order systems they are conservative. The method is applied to obtain
position bounds for lateral trajectory following controllers, and to analyze
how different choices of feedback parameters affect safety margins.


  We exploit the idea of [Fen22] which proposes to build an efficient signature
scheme based on a zero-knowledge proof of knowledge of a solution of a MinRank
instance. The scheme uses the MPCitH paradigm, which is an efficient way to
build ZK proofs. We combine this idea with another idea, the hypercube
technique introduced in [AMGH+22], which leads to more efficient MPCitH-based
scheme. This new approach is more efficient than classical MPCitH, as it allows
to reduce the number of party computation. This gives us a first scheme called
MIRA-Additive. We then present an other scheme, based on low-threshold secret
sharings, called MIRA-Threshold, which is a faster scheme, at the price of
larger signatures. The construction of MPCitH using threshold secret sharing is
detailed in [FR22]. These two constructions allows us to be faster than
classical MPCitH, with a size of signature around 5.6kB with MIRA-Additive, and
8.3kB with MIRA-Threshold. We detail here the constructions and optimizations
of the schemes, as well as their security proofs.


  We report four new 22 GHz water masers found in a Green Bank Telescope search
toward 37 nearby objects. Our goal was to find new maser galaxies, AGN
disk-masers, and objects where both hydroxyl and water maser species coexist.
We observed 37 sources within 250 Mpc that were selected by high X-ray
luminosity (L_X > 10^40 W) and high absorbing column density (N_H ~ 10^22
cm^-2). Sources also included dual or triple AGN and interacting systems. We
further included objects detected in hydroxyl (OH). The selection consisted of
16 new sources, 13 previous non-detections to follow up with a factor ten
higher sensitivity, 10 OH masers and one deep OH absorber, of which 37 were
observed. Water megamasers were detected towards the Sy 2 galaxy 2MFGC 13581 (a
disk-maser), towards the 6 GHz OH absorber NGC 4261 (3C 270 with a twin-jet and
dusty torus; broad water maser emission), and towards the two 1.6 GHz OH maser
sources IRAS 17526+3253 and IRAS 20550+1656 (possible star formation water
masers). We set upper limits on 33 non-detections. The search increased the
number of known "dual-species" objects containing both OH and H2O masers to
eight. The detection rate was 25 % in OH galaxies and 11 % overall. Combined
with other searches, a total of 95 objects have now been searched for both OH
and H2O masers. We found the overall dual-species detection rate (8 in 95) to
be of the order of the joint probability of both species independently
occurring in the same object (1 % lower bound). However, this needs to be
verified by a more detailed analysis of selection criteria. Lastly, we see a
lack of H2O kilomasers in OH megamaser objects already noted by Tarchi et al.
(2011). This may be due to sensitivity bias rather than astrophysical reasons.


  We report the results of a systematic survey for 95 GHz class I methanol
masers towards a new sample of 192 massive young stellar object (MYSO)
candidates associated with ongoing outflows (known as extended green objects or
EGOs) identified from the Spitzer GLIMPSE survey. The observations were made
with the Australia Telescope National Facility (ATNF) Mopra 22-m radio
telescope and resulted in the detection of 105 new 95 GHz class I methanol
masers. For 92 of the sources our observations provide the first identification
of a class I maser transition associated with these objects (i.e. they are new
class I methanol maser sources). Our survey proves that there is indeed a high
detection rate (55%) of class I methanol masers towards EGOs. Comparison of the
GLIMPSE point sources associated with EGOs with and without class I methanol
maser detections shows they have similar mid-IR colors, with the majority
meeting the color selection criteria -0.6<[5.8]-[8.0]<1.4 and
0.5<[3.6]-[4.5]<4.0. Investigations of the IRAC and MIPS 24 um colors and the
associated millimeter dust clump properties (mass and density) of the EGOs for
the sub-samples based on which class of methanol masers they are associated
with suggests that the stellar mass range associated with class I methanol
masers extends to lower masses than for class II methanol masers, or
alternatively class I methanol masers may be associated with more than one
evolutionary phase during the formation of a high-mass star.


  Monte Carlo algorithms such as the Wang-Landau algorithm and similar
`entropic' methods are able to accurately sample the density of states of model
systems and thereby give access to thermal equilibrium properties at any
temperature. Thermal equilibrium is however not achievable at low temperatures
in glassy systems. Such systems are characterized by a multitude of metastable
configurations, pictorially referred to as `valleys' of an energy landscape.
Geometrical properties of the landscape, e.g. the local density of states
describing the distribution in energy of the states belonging to a single
valley, are key to understand the dynamical properties of such systems. In this
paper we combine the lid algorithm, a tool for landscape exploration previously
applied to a range of models, with the Wang-Swendsen algorithm. To test this
improved exploration tool, we consider a paradigmatic complex system, the
Edwards-Andersom model in two and three spatial dimension. We find a striking
difference between the energy dependence of the local density of states in the
two cases: nearly flat in the first case, and nearly exponential in the second.
The lid dependence of the data is analyzed to estimate the form of the global
density of states.


  In this paper we have shown without assuming the four color theorem of planar
graphs that every (bridgeless) cubic planar graph has a three-edge-coloring.
This is an old-conjecture due to Tait in the squeal of efforts in settling the
four-color conjecture at the end of the 19th century. We have also shown the
applicability of our method to another well-known three edge-coloring
conjecture on cubic graphs. Namely Tutte's conjecture that "every 2-connected
cubic graph with no Petersen minor is 3-edge colorable". Hence the conclusion
of this paper implies another non-computer proof of the four color theorem by
using spiral-chains in different context.


  For a bounded domain $D$ and a real number $p>0$, we denote by $A^p(D)$ the
space of $L^p$ integrable holomorphic functions on $D$, equipped with the
$L^p$- pseudonorm. We prove that two bounded hyperconvex domains $D_1\subset
\mc^n$ and $D_2\subset \mc^m$ are biholomorphic (in particular $n=m$) if there
is a linear isometry between $A^p(D_1)$ and $A^p(D_2)$ for some $0<p<2$. The
same result holds for $p>2, p\neq 2,4,\cdots$, provided that the $p$-Bergman
kernels on $D_1$ and $D_2$ are exhaustive. With this as a motivation, we show
that, for all $p>0$, the $p$-Bergman kernel on a strongly pseudoconvex domain
with $\mathcal C^2$ boundary or a simply connected homogeneous regular domain
is exhaustive. These results shows that spaces of pluricanonical sections of
complex manifolds equipped with canonical pseudonorms are important invariants
of complex manifolds. The second part of the present work devotes to studying
variations of these invariants. We show that the direct image sheaf of the
twisted relative $m$-pluricanonical bundle associated to a holomorphic family
of Stein manifolds or compact K\"ahler manifolds is positively curved, with
respect to the canonical singular Finsler metric.


  We study a model of interacting vortices in a type II superconductor. In the
weak coupling limit, we constructed a mean-field theory which allows us to
accurately calculate the vortex density distribution inside a confining
potential. In the strong coupling limit, the correlations between the particles
become important and the mean-field theory fails. Contrary to recent
suggestions, this does not imply failure of the Boltzmann-Gibbs statistical
mechanics, as we clearly demonstrate by comparing the results of Molecular
Dynamics and Monte Carlo simulations.


  This paper presents a new way to construct single-valued many-body
wavefunctions of identical particles with intermediate exchange phases between
Fermi and Bose statistics. It is demonstrated that the exchange phase is not a
representation character but the \textit{word metric} of the permutation group,
beyond the anyon phase from the braiding group in two dimensions. By
constructing this type of wavefunction from the direct product of
single-particle states, it is shown that a finite \textit{capacity q} -- the
maximally allowed particle occupation of each quantum state, naturally arises.
The relation between the permutation phase and capacity is given, interpolating
between fermions and bosons in the sense of both exchange phase and occupation
number. This offers a quantum mechanics foundation for \textit{Gentile
statistics} and new directions to explore intermediate statistics and anyons.


  Visual lifelogging consists of acquiring images that capture the daily
experiences of the user by wearing a camera over a long period of time. The
pictures taken offer considerable potential for knowledge mining concerning how
people live their lives, hence, they open up new opportunities for many
potential applications in fields including healthcare, security, leisure and
the quantified self. However, automatically building a story from a huge
collection of unstructured egocentric data presents major challenges. This
paper provides a thorough review of advances made so far in egocentric data
analysis, and in view of the current state of the art, indicates new lines of
research to move us towards storytelling from visual lifelogging.


  We define a moduli space of translation structures on the open topological
disk with a basepoint and endow it with a locally-compact metrizable topology.
We call this the immersive topology, because it is defined using the concept of
immersions: continuous maps between subsets of translation surfaces that
respect the basepoints and the translation structures. Immersions induce a
partial ordering on the moduli space, and we prove the ordering is nearly a
complete lattice in the sense of order theory: The space is only missing a
minimal element. Subsequent articles will uncover more structure and develop a
topology on the space of all translation structures.


  For an oriented irreducible 3-manifold M with non-empty toroidal boundary, we
describe how sutured Floer homology ($SFH$) can be used to determine all
fibered classes in $H^1(M)$. Furthermore, we show that the $SFH$ of a balanced
sutured manifold $(M,\gamma)$ detects which classes in $H^1(M)$ admit a taut
depth one foliation such that the only compact leaves are the components of
$R(\gamma)$. The latter had been proved earlier by the first author under the
extra assumption that $H_2(M)=0$. The main technical result is that we can
obtain an extremal $\text{Spin}^c$-structure $\mathfrak{s}$ (i.e., one that is
in a `corner' of the support of $SFH$) via a nice and taut sutured manifold
decomposition even when $H_2(M) \neq 0$, assuming the corresponding group
$SFH(M,\gamma,\mathfrak{s})$ has non-trivial Euler characteristic.


  Recently, there has been renewed interest in studying the asymptotic
properties of the number theoretic partition function $p(n)$. Ramanujan, Hardy,
and Rademacher provided detailed asymptotic analyses for $p(n)$. Presently,
attention has shifted towards Poincar\'e-type asymptotic expansions,
characterised by their simplicity albeit reduced accuracy compared to the
earlier works of Ramanujan, Hardy, and Rademacher. This paper aims to establish
computable error bounds for one such simplified expansion. The bounds presented
herein are sharper, and their derivation is considerably simpler compared to
those found in recent literature.


  We present a novel numerical method to the time-harmonic inverse medium
scattering problem of recovering the refractive index from near-field scattered
data. The approach consists of two stages, one pruning step of detecting the
scatterer support, and one resolution enhancing step with mixed regularization.
The first step is strictly direct and of sampling type, and faithfully detects
the scatterer support. The second step is an innovative application of
nonsmooth mixed regularization, and it accurately resolves the scatterer sizes
as well as intensities. The model is efficiently solved by a semi-smooth
Newton-type method. Numerical results for two- and three-dimensional examples
indicate that the approach is accurate, computationally efficient, and robust
with respect to data noise.


  The evolution of clothing styles and their migration across the world is
intriguing, yet difficult to describe quantitatively. We propose to discover
and quantify fashion influences from catalog and social media photos. We
explore fashion influence along two channels: geolocation and fashion brands.
We introduce an approach that detects which of these entities influence which
other entities in terms of propagating their styles. We then leverage the
discovered influence patterns to inform a novel forecasting model that predicts
the future popularity of any given style within any given city or brand. To
demonstrate our idea, we leverage public large-scale datasets of 7.7M Instagram
photos from 44 major world cities (where styles are worn with variable
frequency) as well as 41K Amazon product photos (where styles are purchased
with variable frequency). Our model learns directly from the image data how
styles move between locations and how certain brands affect each other's
designs in a predictable way. The discovered influence relationships reveal how
both cities and brands exert and receive fashion influence for an array of
visual styles inferred from the images. Furthermore, the proposed forecasting
model achieves state-of-the-art results for challenging style forecasting
tasks. Our results indicate the advantage of grounding visual style evolution
both spatially and temporally, and for the first time, they quantify the
propagation of inter-brand and inter-city influences.


  We study the consequences due to time varying $G$ and $\Lambda$ in
scalar-tensor theories of gravity for cosmology, inspired by the modifications
introduced by the Renormalization Group (RG) equations in the Quantum Einstein
Gravity. We assume a power-law scale factor in presence contemporarily of both
the scalar field and the matter components of the cosmic fluid, and analyze a
special case and its generalization, also showing the possibility of a phantom
cosmology. In both such situations we find a negative kinetic term for the
scalar field $Q$ and, possibly, an equation-of-state parameter $w_Q<-1$. A
violation of dominant energy condition (DEC) for $Q$ is also possible in both
of them; but, while in the first special case the $Q$-energy density then
remains positive, in the second one we find it negative.


  Web archiving is the process of collecting portions of the Web to ensure that
the information is preserved for future exploitation. However, despite the
increasing number of web archives worldwide, the absence of efficient and
meaningful exploration methods still remains a major hurdle in the way of
turning them into a usable and useful information source. In this paper, we
focus on this problem and propose an RDF/S model and a distributed framework
for building semantic profiles ("layers") that describe semantic information
about the contents of web archives. A semantic layer allows describing metadata
information about the archived documents, annotating them with useful semantic
information (like entities, concepts and events), and publishing all this data
on the Web as Linked Data. Such structured repositories offer advanced query
and integration capabilities, and make web archives directly exploitable by
other systems and tools. To demonstrate their query capabilities, we build and
query semantic layers for three different types of web archives. An
experimental evaluation showed that a semantic layer can answer information
needs that existing keyword-based systems are not able to sufficiently satisfy.


  During the noisy intermediate-scale quantum (NISQ) era, it is important to
optimize the quantum circuits in circuit depth and gate count, especially
entanglement gates, including the CNOT gate. Among all the unitary operators,
diagonal unitary matrices form a special class that plays a crucial role in
many quantum algorithms/subroutines. Based on a natural gate set {CNOT, Rz},
quantum circuits for general diagonal unitary matrices were discussed in
several previous works, and an optimal synthesis algorithm was proposed in
terms of circuit depth. In this paper, we are interested in the implementation
of diagonal unitary matrices with reflection symmetry, which has promising
applications, including the realization of real-time evolution for first
quantized Hamiltonians by quantum circuits. Owing to such a symmetric property,
we show that the quantum circuit in the existing work can be further simplified
and propose a constructive algorithm that optimizes the entanglement gate
count. Compared to the previous synthesis methods for general diagonal unitary
matrices, the quantum circuit by our proposed algorithm achieves nearly half
the reduction in both the gate count and circuit depth.


  A graph H is k-common if the number of monochromatic copies of H in a
k-edge-coloring of K_n is asymptotically minimized by a random coloring. For
every k, we construct a connected non-bipartite k-common graph. This resolves a
problem raised by Jagger, Stovicek and Thomason [Combinatorica 16 (1996),
123-141]. We also show that a graph H is k-common for every k if and only if H
is Sidorenko and that H is locally k-common for every k if and only if H is
locally Sidorenko.


  Subset weighted-Tempered Gibbs Sampler (wTGS) has been recently introduced by
Jankowiak to reduce the computation complexity per MCMC iteration in
high-dimensional applications where the exact calculation of the posterior
inclusion probabilities (PIP) is not essential. However, the Rao-Backwellized
estimator associated with this sampler has a high variance as the ratio between
the signal dimension and the number of conditional PIP estimations is large. In
this paper, we design a new subset weighted-Tempered Gibbs Sampler (wTGS) where
the expected number of computations of conditional PIPs per MCMC iteration can
be much smaller than the signal dimension. Different from the subset wTGS and
wTGS, our sampler has a variable complexity per MCMC iteration. We provide an
upper bound on the variance of an associated Rao-Blackwellized estimator for
this sampler at a finite number of iterations, $T$, and show that the variance
is $O\big(\big(\frac{P}{S}\big)^2 \frac{\log T}{T}\big)$ for a given dataset
where $S$ is the expected number of conditional PIP computations per MCMC
iteration. Experiments show that our Rao-Blackwellized estimator can have a
smaller variance than its counterpart associated with the subset wTGS.


  Let $\Sigma$ be a compact orientable surface of finite type with at least one
boundary component. Let $f \in \textup{Mod}(\Sigma)$ be a pseudo Anosov mapping
class. We prove a conjecture of McMullen by showing that there exists a finite
cover $\widetilde{\Sigma} \to \Sigma$ and a lift $\widetilde{f}$ of $f$ such
that $\wt{f}_*: H_1(\wt{\Sigma}; \mathbb{Z}) \to H_1(\wt{\Sigma}; \mathbb{Z})$
has an eigenvalue off the unit circle.


  We present a decision support system for flood early warning and disaster
management. It includes the models for data-driven meteorological predictions,
for simulation of atmospheric pressure, wind, long sea waves and seiches; a
module for optimization of flood barrier gates operation; models for stability
assessment of levees and embankments, for simulation of city inundation
dynamics and citizens evacuation scenarios. The novelty of this paper is a
coupled distributed simulation of surface and subsurface flows that can predict
inundation of low-lying inland zones far from the submerged waterfront areas,
as observed in St. Petersburg city during the floods. All the models are
wrapped as software services in the CLAVIRE platform for urgent computing,
which provides workflow management and resource orchestration.


  Marginally bound systems of two types of branes are considered, such as the
prototypical case of Dp+4 branes and Dp branes. As the transverse separation
between the two types of branes goes to zero, different behaviour occurs in the
supergravity solutions depending on p; no-hair theorems result for p<=1 only.
Within the framework of the AdS/CFT correspondence, these supergravity no-hair
results are understood as dual manifestations of the Coleman-Mermin-Wagner
theorem. Furthermore, the rates of delocalization for p<=1 are matched in a
scaling analysis. Talk given at ``Strings '99''; based on hep-th/9903213 with
D. Marolf.


  In this paper, we propose an image re-sampling compression method by learning
virtual codec network (VCN) to resolve the non-differentiable problem of
quantization function for image compression. Here, the image re-sampling not
only refers to image full-resolution re-sampling but also low-resolution
re-sampling. We generalize this method for standard-compliant image compression
(SCIC) framework and deep neural networks based compression (DNNC) framework.
Specifically, an input image is measured by re-sampling network (RSN) network
to get re-sampled vectors. Then, these vectors are directly quantized in the
feature space in SCIC, or discrete cosine transform coefficients of these
vectors are quantized to further improve coding efficiency in DNNC. At the
encoder, the quantized vectors or coefficients are losslessly compressed by
arithmetic coding. At the receiver, the decoded vectors are utilized to restore
input image by image decoder network (IDN). In order to train RSN network and
IDN network together in an end-to-end fashion, our VCN network intimates
projection from the re-sampled vectors to the IDN-decoded image. As a result,
gradients from IDN network to RSN network can be approximated by VCN network's
gradient. Because dimension reduction can be further achieved by quantization
in some dimensional space after image re-sampling within auto-encoder
architecture, we can well initialize our networks from pre-trained auto-encoder
networks. Through extensive experiments and analysis, it is verified that the
proposed method has more effectiveness and versatility than many
state-of-the-art approaches.


  In this paper we develop a numerical scheme based on quadratures to
approximate solutions of integro-differential equations involving convolution
kernels, $\nu$, of diffusive type. In particular, we assume $\nu$ is symmetric
and exponentially decaying at infinity. We consider problems posed in bounded
domains and in $\R$. In the case of bounded domains with nonlocal Dirichlet
boundary conditions, we show the convergence of the scheme for kernels that
have positive tails, but that can take on negative values. When the equations
are posed on all of $\R$, we show that our scheme converges for nonnegative
kernels. Since nonlocal Neumann boundary conditions lead to an equivalent
formulation as in the unbounded case, we show that these last results also
apply to the Neumann problem.


  We discuss the need for a confluence of localization, sensing and
communications if RISs are to be deployed in a self-adaptive manner in the
dynamically evolving rich-scattering settings that are typical for 6G
deployment scenarios such as factories. We establish that in such problems the
rich-scattering wireless channels are subject to a highly nonlinear
deterministic double-parametrization through both the RIS and uncontrolled
moving objects. Therefore, acquiring full context-awareness through
localization and sensing is a prerequisite for RIS-empowered communications.
Yet, the byproducts of this daunting communications overhead can feed many
appliances that require context awareness, such that overhead concerns may
vanish. We illustrate the essential steps for operating a self-adaptive RIS
under rich scattering based on a prototypical case study. We discover that
self-adaptive RISs outperform context-ignorant RISs only below a certain noise
threshold that depends, among other factors, on how strongly uncontrolled
perturbers impact the wireless channel. We also discuss ensuing future research
directions that will determine the conditions under which RISs may serve as
technological enabler of 6G networks.


  SOM is a type of unsupervised learning where the goal is to discover some
underlying structure of the data. In this paper, a new extraction method based
on the main idea of Concurrent Self-Organizing Maps (CSOM), representing a
winner-takes-all collection of small SOM networks is proposed. Each SOM of the
system is trained individually to provide best results for one class only. The
experiments confirm that the proposed features based CSOM is capable to
represent image content better than extracted features based on a single big
SOM and these proposed features improve the final decision of the CAD.
Experiments held on Mammographic Image Analysis Society (MIAS) dataset.


  We consider a multiple depot, multiple vehicle routing problem with fuel
constraints. We are given a set of targets, a set of depots and a set of
homogeneous vehicles, one for each depot. The depots are also allowed to act as
refueling stations. The vehicles are allowed to refuel at any depot, and our
objective is to determine a route for each vehicle with a minimum total cost
such that each target is visited at least once by some vehicle, and the
vehicles never run out fuel as it traverses its route. We refer this problem as
Multiple Depot, Fuel-Constrained, Multiple Vehicle Routing Problem (FCMVRP).
This paper presents four new mixed integer linear programming formulations to
compute an optimal solution for the problem. Extensive computational results
for a large set of instances are also presented.


  We present recent DO results based on approximately 1 fb$^{-1}$ of $p
\bar{p}$ collisions at $\sqrt{s} = 1.96$ TeV recorded at the Fermilab Tevatron.
Preliminary results on a search for the flavor changing neutral current process
$D^+\to \pi^+\mu^+\mu^-$, a measurement of the CP violation parameter in $B$
mixing, $\epsilon_B$, and a two sided limit on the $B_s$ oscillation frequency
$\Delta m_s$ are presented. The limits on $\epsilon_B$ and ${\cal B}(D^+\to
\pi^+\mu^+\mu^-)$ are the world's best limits. The two sided bound on $\Delta
m_s$ is the first direct indication by a single experiment that $\Delta m_s$ is
bounded from above.


  We introduce a method that uses low-rank approximations of cross-correlation
matrices in mixed continuous and categorical Gaussian Process models. This new
method -- called Low-Rank Correlation (LRC) -- offers the ability to flexibly
adapt the number of parameters to the problem at hand by choosing an
appropriate rank of the approximation. Furthermore, we present a systematic
approach of defining test functions that can be used for assessing the accuracy
of models or optimization methods that are concerned with both continuous and
categorical inputs. We compare LRC to existing approaches of modeling the
cross-correlation matrix. It turns out that the new approach performs well in
terms of estimation of cross-correlations and response surface prediction.
Therefore, LRC is a flexible and useful addition to existing methods,
especially for increasing numbers of combinations of levels of the categorical
inputs.


  Let E/F be a quadratic number (resp. p-adic) field extension, and F' an odd
degree cyclic field extension of F. We establish a base-change functorial
lifting of automorphic (resp. admissible) representations from the unitary
group U(3,E/F) associated with E/F to the unitary group U(3,F'E/F'). As a
consequence, we classify the invariant packets of U(3,F'E/F'), namely those
which contain (irreducible) automorphic (resp. admissible) representations
which are invariant under the action of the Galois group Gal(F'E/E). To do this
we use the trace formula technique, and well-known results on the base-change
lifting from U(3,E/F) to GL(3,E) and on the base-change lifting for the general
linear groups. We also determine the invariance of individual representations,
using Howe correspondence. This work is the first study of base change into an
algebraic group whose packets are not all singletons, and which does not
satisfy the "strong multiplicity one theorem." Novel phenomena are encountered:
e.g. there are invariant packets where not every irreducible automorphic (resp.
admissible) member is Galois-invariant. We also obtain local twisted character
identities with respect to this base-change lifting.


  We overview our recent results on the nonlinear localized modes in
two-dimensional (2D) photonic crystals and photonic-crystal waveguides.
Employing the technique based on the Green function, we describe the existence
domains for nonlinear guided modes in photonic crystal waveguides and study
their unique properties including bistability. We also show that low-amplitude
nonlinear modes near the band edge of a reduced-symmetry 2D square-lattice
photonic crystals, which are usually unstable, can be stabilized due to
effective long-range linear and nonlinear interactions.


  We propose a new approach for high-contrast imaging at the diffraction limit
using segmented telescopes in a modest observation bandwidth. This concept,
named "spectroscopic fourth-order coronagraphy", is based on a fourth-order
coronagraph with a focal-plane mask that modulates the complex amplitude of the
Airy disk along one direction. While coronagraphs applying the complex
amplitude mask can achieve the theoretical limit performance for any arbitrary
pupils, the focal plane mask severely limits the bandwidth. Here, focusing on
the fact that the focal-plane mask modulates the complex amplitude along one
direction, we noticed that the mask can be optimized for each spectral element
generated by a spectrograph. We combine the fourth-order coronagraph with two
spectrographs to produce a stellar spectrum on the focal plane and reconstruct
a white pupil on the Lyot stop. Based on the wavefront analysis of an optical
design applying an Offner-type imaging spectrograph, we found that the
achievable contrast of this concept is 10^{-10} at 1.2 - 1.5 times the
diffraction limit over the wavelength range of 650 - 750 nm for the entrance
pupil of the LUVOIR telescope. Thus, this coronagraph concept could bring new
habitable planet candidates not only around G- and K-type stars beyond 20 - 30
pc but also around very nearby M-type stars. This approach potentially promotes
the characterization of the atmospheres of nearby terrestrial planets with
future on- and off-axis segmented large telescopes.


  Using the clathrate hydrates trapping theory, we discuss the enrichments in
volatiles in the atmosphere of Jupiter measured by the \textit{Galileo} probe
in the framework of new extended core-accretion planet formation models
including migration and disk evolution. We construct a self-consistent model in
which the volatile content of planetesimals accreted during the formation of
Jupiter is calculated from the thermodynamical evolution of the disk. Assuming
CO2:CO:CH4 = 30:10:1 (ratios compatible with ISM measurements), we show that we
can explain the enrichments in volatiles in a way compatible with the recent
constraints set from internal structure modeling on the total amount of heavy
elements present in the planet.


  There exist well-developed frameworks for causal modelling, but these require
rather a lot of human domain expertise to define causal variables and perform
interventions. In order to enable autonomous agents to learn abstract causal
models through interactive experience, the existing theoretical foundations
need to be extended and clarified. Existing frameworks give no guidance
regarding variable choice / representation, and more importantly, give no
indication as to which behaviour policies or physical transformations of state
space shall count as interventions. The framework sketched in this paper
describes actions as transformations of state space, for instance induced by an
agent running a policy. This makes it possible to describe in a uniform way
both transformations of the micro-state space and abstract models thereof, and
say when the latter is veridical / grounded / natural. We then introduce
(causal) variables, define a mechanism as an invariant predictor, and say when
an action can be viewed as a ``surgical intervention'', thus bringing the
objective of causal representation \& intervention skill learning into clearer
focus.


  Disorder-induced magnetoresistance has been reported in a range of solid
metals and semiconductors, however, the underlying physical mechanism is still
under debate because it is difficult to experimentally control. Liquid metals,
due to lack of long-range order, offers an ideal model system where many forms
of disorder can be deactivated by freezing the liquid. Here we report
non-saturating magnetoresistance discovered in the liquid state of three
metals: Ga, Ga-In-Sn and Bi-Pb-Sn-In alloys. The giant magnetoresistance
appears above the respective melting points and has a maximum of 2500% at 14
Tesla. The reduced diamagnetism in the liquid state implies that a short-mean
free path of the electron, induced by the spatial distribution of the liquid
structure, is a key factor. A potential technological merit of this
liquidtronic magnetoresistance is that it naturally operates at higher
temperatures.


  We present a direct bijection between reduced pipe dreams and reduced
bumpless pipe dreams by interpreting reduced compatible sequences on bumpless
pipe dreams and show that this bijection preserves Monk's formula, establishing
its canonical nature.


  PAX (Physics Analysis Expert) is a novel, C++ based toolkit designed to
assist teams in particle physics data analysis issues. The core of PAX are
event interpretation containers, holding relevant information about and
possible interpretations of a physics event. Providing this new level of
abstraction beyond the results of the detector reconstruction programs, PAX
facilitates the buildup and use of modern analysis factories. Class structure
and user command syntax of PAX are set up to support expert teams as well as
newcomers in preparing for the challenges expected to arise in the data
analysis at future hadron colliders.


  We propose a new theoretical framework that exploits convolution kernels to
transform a Volterra path-dependent (non-Markovian) stochastic process into a
standard (Markovian) diffusion process. This transformation is achieved by
embedding a Markovian "memory process" within the dynamics of the non-Markovian
process. We discuss existence and path-wise regularity of solutions for the
stochastic Volterra equations introduced and we provide a financial application
to volatility modeling. We also propose a numerical scheme for simulating the
processes. The numerical scheme exhibits a strong convergence rate of 1/2,
which is independent of the roughness parameter of the volatility process. This
is a significant improvement compared to Euler schemes used in similar models.
  We propose a new theoretical framework that exploits convolution kernels to
transform a Volterra path-dependent (non-Markovian) stochastic process into a
standard (Markovian) diffusion process. This transformation is achieved by
embedding a Markovian "memory process" (the goldfish) within the dynamics of
the non-Markovian process (the elephant). Most notably, it is also possible to
go back, i.e., the transformation is reversible. We discuss existence and
path-wise regularity of solutions for the stochastic Volterra equations
introduced and we propose a numerical scheme for simulating the processes,
which exhibits a remarkable convergence rate of $1/2$. In particular, in the
fractional kernel case, the strong convergence rate is independent of the
roughness parameter, which is a positive novelty in contrast with what happens
in the available Euler schemes in the literature in rough volatility models.


  In this paper, we present a microscopic agent-based pedestrian behavior model
Intend-Wait-Cross. The model is comprised of rules representing behaviors of
pedestrians as a series of decisions that depend on their individual
characteristics (e.g. demographics, walking speed, law obedience) and
environmental conditions (e.g. traffic flow, road structure). The model's main
focus is on generating realistic crossing decision-model, which incorporates an
improved formulation of time-to-collision (TTC) computation accounting for
context, vehicle dynamics, and perceptual noise.
  Our model generates a diverse population of agents acting in a highly
configurable environment. All model components, including individual
characteristics of pedestrians, types of decisions they make, and environmental
factors, are motivated by studies on pedestrian traffic behavior. Model
parameters are calibrated using a combination of naturalistic driving data and
estimates from the literature to maximize the realism of the simulated
behaviors. A number of experiments validate various aspects of the model, such
as pedestrian crossing patterns, and individual characteristics of pedestrians.


  We consider a generalized version in continuous time of the parking problem
of Knuth. Files arrive following a Poisson point process and are stored on a
hardware identified with the real line. We specify the distribution of the
space of unoccupied locations at a fixed time and give its asymptotics when the
hardware is becoming full.


  Sharp Moser-Trudinger type inequalities and their extremal functions play an
important role in studying nonlinear PDEs and geometry. We establish a new
sharp Moser-Trudinger type inequality in the upper half space in two dimensions
and prove the existence of extremal functions for a sharp Moser-Trudinger type
inequality under dynamic changes in the unit ball.


  We measure the scaling properties of the probability distribution of the
smoothed density field in $N$-body simulations of expanding universes with
scale-free initial power-spectra, with particular attention to the predictions
of the stable clustering hypothesis. We concentrate our analysis on the ratios
$S_Q(\ell)\equiv {\bar \xi}_Q/{\bar \xi}_2^{Q-1}$, $Q \leq 5$, where ${\bar
\xi}_Q$ is the averaged $Q$-body correlation function over a cell of radius
$\ell$. The behavior of the higher order correlations is studied through that
of the void probability distribution function.
  As functions of ${\bar \xi}_2$, the quantities $S_Q$, $3 \leq Q \leq 5$,
exhibit two plateaus separated by a smooth transition around ${\bar \xi}_2 \sim
1$. In the weakly nonlinear regime, ${\bar \xi}_2 \la 1$, the results are in
reasonable agreement with the predictions of perturbation theory. In the
nonlinear regime, ${\bar \xi}_2 > 1$, the function $S_Q({\bar \xi}_2)$ is
larger than in the weakly nonlinear regime, and increasingly so with $-n$. It
is well-fitted by the expression $S_Q= ({\bar \xi}_2/100)^{0.045(Q-2)}\
{\widetilde S}_Q$ for all $n$. This weak dependence on scale proves {\em a
small, but significant departure from the stable clustering predictions} at
least for $n=0$ and $n=+1$. The analysis of $P_0$ confirms that the expected
scale-invariance of the functions $S_Q$ is not exactly attained in the part of
the nonlinear regime we probe, except possibly for $n=-2$ and marginally for
$n=-1$. In these two cases, our measurements are not accurate enough to be
discriminant.


  Let (g,b,J_+,J_-) be the bihermitian structure corresponding to a generalized
Kaehler structure. We find natural integrability conditions under which db=0.


  The baseline design and implementation of the DataFlow system, to be
documented in the ATLAS DAQ/HLT Technical Design Report in summer 2003, will be
presented. Empahsis will be placed on the system performance and scalability
based on the results from prototyping studies which have maximised the use of
commercially available hardware.


  The electroweak baryogenesis depends on the profile of the CP-violating
bubble wall created at the first order phase transition. We attempt to
determine it by solving the coupled equations of motion for the moduli and
phases of the two Higgs doublets at the transition temperature. A variety of
CP-violating bubble walls are classified by boundary conditions. We point out
that a sufficiently small explicit CP violation gives nonperturbative effects
to yield the baryon asymmetry of the universe.


  With low redshift probes reaching unprecedented precision, uncertainty of the
CMB optical depth is expected to be the limiting factor for future cosmological
neutrino mass constraints. In this paper, we discuss to what extent
combinations of CMB lensing and galaxy surveys measurements at low redshifts
$z\sim 0.5-5$ will be able to make competitive neutrino mass measurements
without relying on any optical depth constraints. We find that the combination
of LSST galaxies and CMB-S4 lensing should be able to achieve constraints on
the neutrino mass sum of 25meV without optical depth information, an
independent measurement that is competitive with or slightly better than the
constraint of 30meV possible with CMB-S4 and present-day optical depth
measurements. These constraints originate both in structure growth probed by
cross-correlation tomography over a wide redshift range as well as, most
importantly, the shape of the galaxy power spectrum measured over a large
volume. We caution that possible complications such as higher-order biasing and
systematic errors in the analysis of high redshift galaxy clustering are only
briefly discussed and may be non-negligible. Nevertheless, our results show
that new kinds of high-precision neutrino mass measurements at and beyond the
present-day optical depth limit may be possible.


  We consider so called $2$-stage stochastic integer programs (IPs) and their
generalized form of multi-stage stochastic IPs. A $2$-stage stochastic IP is an
integer program of the form $\max \{ c^T x \mid Ax = b, l \leq x \leq u, x \in
\mathbb{Z}^{nt + s} \}$ where the constraint matrix $A \in \mathbb{Z}^{r \times
s}$ consists roughly of $n$ repetition of a block matrix $A$ on the vertical
line and $n$ repetitions of a matrix $B \in \mathbb{Z}^{r \times t}$ on the
diagonal. In this paper we improve upon an algorithmic result by Hemmecke and
Schultz form 2003 to solve $2$-stage stochastic IPs. The algorithm is based on
the Graver augmentation framework where our main contribution is to give an
explicit doubly exponential bound on the size of the augmenting steps. The
previous bound for the size of the augmenting steps relied on non-constructive
finiteness arguments from commutative algebra and therefore only an implicit
bound was known that depends on parameters $r,s,t$ and $\Delta$, where $\Delta$
is the largest entry of the constraint matrix. Our new improved bound however
is obtained by a novel theorem which argues about the intersection of paths in
a vector space. As a result of our new bound we obtain an algorithm to solve
$2$-stage stochastic IPs in time $poly(n,t) \cdot f(r,s,\Delta)$, where $f$ is
a doubly exponential function. To complement our result, we also prove a doubly
exponential lower bound for the size of the augmenting steps.


  We study the dynamics of an intriguing crossover from a chaotic to a power
law state as a function of strain rate within the context of a recently
introduced model which reproduces the crossover. While the chaotic regime has a
small set of positive Lyapunov exponents, interestingly, the scaling regime has
a power law distribution of null exponents which also exhibits a power law. The
slow manifold analysis of the model shows that while a large proportion of
dislocations are pinned in the chaotic regime, most of them are pushed to the
threshold of unpinning in the scaling regime, thus providing insight into the
mechanism of crossover.


  The distance of a binary operation from being associative can be "measured"
by its associative spectrum, an appropriate sequence of positive integers.
Particular instances and general properties of associative spectra are studied.


  Post-pandemic world has thrown up several challenges, such as, high
inflation, low growth, high debt, collapse of economies, political instability,
job losses, lowering of income in addition to damages caused natural disasters,
more convincing attributed to climate change, apart from existing inequalities.
Efforts are being made to mitigate these challenges at various levels. To the
best of the knowledge of the author, most of the prior researches have focussed
on specific scenarios, use cases, inter-relationships between couple of sectors
and more so on optimal policies, such as, impact of carbon tax on individuals,
interaction between taxes and welfare, etc. However, not much effort have been
made to understand the actual impact on individual agents due to diverse policy
changes and how agents cope with changing economic dynamics. This paper
considers progressive deteriorating conditions of increase in expense,
degrading environmental utility, increase in taxation, decrease in welfare and
lowering of income with recourse to inherited properties, credits and return on
investments, and tries to understand how the agents cope with the changing
situations using an agent based model with matrices related to savings,
credits, assets. Results indicate that collapse of agents' economic conditions
can be quite fast, sudden and drastic for all income groups in most cases.


  MoTe2 is a Weyl semimetal, which exhibits unique non-saturating
magnetoresistance and strongly reinforced superconductivity under pressure.
Here, we demonstrate that a novel mesoscopic superconductivity at ambient
pressure arises on the surface of MoTe2 with a critical temperature up to 5 K
significantly exceeding the bulk Tc=0.1K. We measured the derivatives of I-V
curves for metallic hetero-contacts of MoTe2 with Ag or Cu, homo-contacts of
MoTe2 as well as "soft" point contacts (PCs). Large number of these
hetero-contacts exhibit a dV/dI dependence, which is characteristic for Andreev
reflection. It allows to determine the superconducting gap \Delta. The average
gap values are 2\Delta=1.30+/-0.15 meV with a 2\Delta/kBTc ratio of 3.7+/-0.4,
which slightly exceeds the standard BCS value of 3.52. Furthermore, the
temperature dependence of the gap follows a BCS-like behavior, which points to
a nodeless superconducting order parameter with some strong-coupling
renormalization. Remarkably, the observation of a "gapless-like" single minimum
in the dV/dI of "soft" PCs may indicate a topological superconducting state of
the MoTe2 surface as these contacts probe mainly the interface and avoid
additional pressure effect. Therefore, MoTe2 might be a suitable material to
study new forms of topological superconductivity.


  Transformer models are powerful sequence-to-sequence architectures that are
capable of directly mapping speech inputs to transcriptions or translations.
However, the mechanism for modeling positions in this model was tailored for
text modeling, and thus is less ideal for acoustic inputs. In this work, we
adapt the relative position encoding scheme to the Speech Transformer, where
the key addition is relative distance between input states in the
self-attention network. As a result, the network can better adapt to the
variable distributions present in speech data. Our experiments show that our
resulting model achieves the best recognition result on the Switchboard
benchmark in the non-augmentation condition, and the best published result in
the MuST-C speech translation benchmark. We also show that this model is able
to better utilize synthetic data than the Transformer, and adapts better to
variable sentence segmentation quality for speech translation.


  This review reports the user experience of symptom checkers, aiming to
characterize users studied in the existing literature, identify the aspects of
user experience of symptom checkers that have been studied, and offer design
suggestions. Our literature search resulted in 31 publications. We found that
(1) most symptom checker users are relatively young; (2) eight relevant aspects
of user experience have been explored, including motivation, trust,
acceptability, satisfaction, accuracy, usability, safety or security, and
functionality; (3) future symptom checkers should improve their accuracy,
safety, and usability.


  Recent surveys of multiplicity fluctuations, transverse momentum
fluctuations, and two-particle azimuthal correlations are presented for several
collision systems as a function of centrality and transverse momentum. Both
multiplicity and transverse momentum fluctuations exhibit a power law scaling
as a function of the number of participants that is independent of the
collision system. Although these observations are consistent with critical
behavior, the critical exponent $\eta$ measured using azimuthal correlations is
seen to be independent of centrality and collision system. Also observed in the
azimuthal correlations is a displaced away side peak in central Au+Au
collisions when the pairs are restricted to low transverse momentum.


  The topological properties of a multiband topological superconductor in two
dimensions are studied, when the latter is obtained by introducing electron
pairing in an otherwise topological insulator. The type of pairing, doping and
Fermi surface topology play an essential role. Considering the Andreev
reflection problem, we use a previously developed quantum waveguide theory for
multiorbital systems and find that, when the Fermi surface has several pockets,
this theory retrieves the correct number of Majorana fermion states as
predicted by the topological index. By varying band structure parameters, the
Fermi surface topology of the normal phase can be made to change, whereby the
number of Majorana modes also varies. We calculate the effect of such
transitions on the Andreev differential conductance.


  Pentaquarks, namely baryons made by 4 quarks and one antiquark have been
predicted and searched for since several decades without success. Theoretical
and experimental advances in the last 2 years led to the observation of a
number of pentaquark candidates. We review the experimental evidence for
pentaquarks as well as their non-observations by some experiments, and discuss
to which extend these sometimes contradicting informations may lead to a
consistent picture.


  General relativity may be an effective theory with higher curvature
correction terms in the action. Inclusion of these terms leads to exciting new
possibilities, e.g., gravitational and electromagnetic perturbations following
different geodesics; leading to a time delay. Such a time delay was observed
between the gravitational wave event GW170817 and its electromagnetic
counterpart GRB 170817A. We describe how this effect can be used to constrain
the coupling of the higher curvature term. Our method is sufficiently general
and applicable to any higher curvature theory.


  The objective of this work is to propose a new algorithm to fit a sphere on a
noisy 3D point cloud distributed around a complete or a truncated sphere. More
precisely, we introduce a projected Robbins-Monro algorithm and its averaged
version for estimating the center and the radius of the sphere. We give
asymptotic results such as the almost sure convergence of these algorithms as
well as the asymptotic normality of the averaged algorithm. Furthermore, some
non-asymptotic results will be given, such as the rates of convergence in
quadratic mean. Some numerical experiments show the efficiency of the proposed
algorithm on simulated data for small to moderate sample sizes.


  We report a detailed magnetotransport study on single crystals of PrBi. The
presence of $f$-electrons in this material raises the prospect of realizing a
strongly correlated version of topological semimetals. PrBi shows a magnetic
field induced metal insulator transition below $T \sim 20$ K and a very large
magnetoresistance ($\approx 4.4 \times 10^4~$) at low temperatures ($T= 2$ K).
We have also probed the Fermi surface topology by de Haas van Alphen (dHvA) and
Shubnikov de Haas (SdH) quantum oscillation measurements complimented with
density functional theory (DFT) calculations of the band structure and the
Fermi surface. Angle dependence of the SdH oscillations have been carried out
to probe the possible signature of surface Dirac fermions. We find three
frequencies corresponding to one electron ($\alpha$) and two hole ($\beta$ and
$\gamma$) pockets in experiments, consistent with DFT calculations. The angular
dependence of these frequencies is not consistent with a two dimensional Fermi
surface suggesting that the transport is dominated by bulk bands. Although the
transport properties of this material originate from the bulk bands, the high
mobility and small effective mass are comparable to other compounds in this
series proposed as topologically nontrivial.


  Using Gauge transformation method, we generate rogue waves for the two
component Bose Einstein Condensates (BECs) governed by the symmetric coupled
Gross-Pitaevskii (GP) equations and study their dynamics. We also suggest a
mechanism to tame the rogue waves either by manipulating the scattering length
through Feshbach resonance or the trapping frequency, a new phenomenon not
witnessed in the domain of BEC, we believe that these results may have wider
ramifications in the management of rogons.


  Product attribute extraction is an growing field in e-commerce business, with
several applications including product ranking, product recommendation, future
assortment planning and improving online shopping customer experiences.
Understanding the customer needs is critical part of online business,
specifically fashion products. Retailers uses assortment planning to determine
the mix of products to offer in each store and channel, stay responsive to
market dynamics and to manage inventory and catalogs. The goal is to offer the
right styles, in the right sizes and colors, through the right channels. When
shoppers find products that meet their needs and desires, they are more likely
to return for future purchases, fostering customer loyalty. Product attributes
are a key factor in assortment planning. In this paper we present PAE, a
product attribute extraction algorithm for future trend reports consisting text
and images in PDF format. Most existing methods focus on attribute extraction
from titles or product descriptions or utilize visual information from existing
product images. Compared to the prior works, our work focuses on attribute
extraction from PDF files where upcoming fashion trends are explained. This
work proposes a more comprehensive framework that fully utilizes the different
modalities for attribute extraction and help retailers to plan the assortment
in advance. Our contributions are three-fold: (a) We develop PAE, an efficient
framework to extract attributes from unstructured data (text and images); (b)
We provide catalog matching methodology based on BERT representations to
discover the existing attributes using upcoming attribute values; (c) We
conduct extensive experiments with several baselines and show that PAE is an
effective, flexible and on par or superior (avg 92.5% F1-Score) framework to
existing state-of-the-art for attribute value extraction task.


  The article provides an analytical solution of the Navier-Stokes equations
for the case of the steady flow of an incompressible fluid between two
uniformly co-rotating disks. The solution is derived from the asymptotical
evolution of unknown components of velocity and pressure in a radial
direction--in contrast to the Briter-Pohlhausen analytical solution, which is
supported by simplified Navier-Stokes equations. The obtained infinite system
of ordinary differential equations forms recurrent relations from which unknown
functions can be calculated successively. The first and second approximations
of solution are solved analytically and the third and fourth approximations of
solutions are solved numerically. The numerical example demonstrates agreements
with results obtained by other authors using different methods.


  Cell-free massive MIMO is a variant of multiuser MIMO and massive MIMO, in
which the total number of antennas $LM$ is distributed among the $L$ remote
radio units (RUs) in the system, enabling macrodiversity and joint processing.
Due to pilot contamination and system scalability, each RU can only serve a
limited number of users. Obtaining the optimal number of users simultaneously
served on one resource block (RB) by the $L$ RUs regarding the sum spectral
efficiency (SE) is not a simple challenge though, as many of the system
parameters are intertwined. For example, the dimension $\tau_p$ of orthogonal
Demodulation Reference Signal (DMRS) pilots limits the number of users that an
RU can serve. Thus, depending on $\tau_p$, the optimal user load yielding the
maximum sum SE will vary. Another key parameter is the users' uplink transmit
power $P^{\rm ue}_{\rm tx}$, where a trade-off between users in outage,
interference and energy inefficiency exists. We study the effect of multiple
parameters in cell-free massive MIMO on the sum SE and user outage, as well as
the performance of different levels of RU antenna distribution. We provide
extensive numerical investigations to illuminate the behavior of the system SE
with respect to the various parameters, including the effect of the system
load, i.e., the number of active users to be served on any RB. The results show
that in general a system with many RUs and few RU antennas yields the largest
sum SE, where the benefits of distributed antennas reduce in very dense
networks.


  The same physical reality in Two Higgs doublet model (2HDM) can be described
by different Lagrangians. We call this property the reparametrization
invariance (in space of Lagrangians) and study corresponding symmetry group and
its subgroup describing rephasing invariance. Next we consider the
$Z_2$-symmetry of the Lagrangian, which prevents a
$\phi_1\leftrightarrow\phi_2$ transitions, and the different levels of its
violation, soft and hard. We argue that the 2HDM with a soft breaking of
$Z_2$-symmetry is a natural model in the description of EWSB. We also consider
vacuum structure of the 2HDM. We find very simple condition for a CP violation
in the Higgs sector. In the Model II for Yukawa interactions we obtain the set
of relations among the couplings to gauge bosons and to fermions which allows
one to analyze different physical situations (including CP violation) in terms
of these very couplings, instead of the parameters of Lagrangian. We discuss
possible interaction of Higgs fields of SM or 2HDM with inflatory Higgs field
describing exponential expansion of Universe after Big Bang and possible
variation in the scenario of beginning of Time.


  Nanofabricated quantum bits permit large-scale integration but usually suffer
from short coherence times due to interactions with their solid-state
environment. The outstanding challenge is to engineer the environment so that
it minimally affects the qubit, but still allows qubit control and scalability.
Here we demonstrate a long-lived single-electron spin qubit in a Si/SiGe
quantum dot with all-electrical two-axis control. The spin is driven by
resonant microwave electric fields in a transverse magnetic field gradient from
a local micromagnet, and the spin state is read out in single-shot mode.
Electron spin resonance occurs at two closely spaced frequencies, which we
attribute to two valley states. Thanks to the weak hyperfine coupling in
silicon, Ramsey and Hahn echo decay timescales of 1us and 40us, respectively,
are observed. This is almost two orders of magnitude longer than the intrinsic
timescales in III-V quantum dots, while gate operation times are comparable to
those achieved in GaAs. This places the single-qubit rotations in the
fault-tolerant regime and strongly raises the prospects of quantum information
processing based on quantum dots.


  Port scanning refers to the systematic exploration of networked computing
systems. The goal of port scanning is to identify active services and
associated information. Although this technique is often employed by malicious
actors to locate vulnerable systems within a network, port scanning is also a
legitimate method employed by IT professionals to troubleshoot network issues
and maintain system security. In the case of the latter, cybersecurity
practitioners use port scanning catalog exposed systems, identify potential
misconfigurations, or test controls that may be running on a system. Existing
literature has thoroughly established a taxonomy for port scanning. The
taxonomy maps the types of scans as well as techniques. In fact, there are
several tools mentioned repeatedly in the literature. Those are Nmap, Zmap, and
masscan. Further, the presence of multiple tools signals that how a port
scanner interacts with target systems impacts the output of the tool. In other
words, the various tools may not behave identically or produce identical
output. Yet, no work has been done to quantify the efficacy for these popular
tools in a uniform, rigorous manner. Accordingly, we used a comparative
experimental protocol to measure the accuracy, false positive, false negative,
and efficiency of Nmap, Zmap, and masscan. The results show no difference
between port scanners in general performance. However, the results revealed a
statistically significant difference in efficiency. This information can be
used to guide the selection of port scanning tools based on specific needs and
requirements. As well, for researchers, the outcomes may also suggest areas for
future work in the development novel port scanning tools.


The paper gives the main lines of a general theory for physical measurements.


  We report the observation of enhanced spin dependent shot noise in magnetic
tunnel barriers, suggesting transport through localized states within the
barrier. This is supported by the existence of negative magnetoresistance and
structure in the differential conductance curves. A simple model of tunneling
through two interacting localized states with spin dependent tunneling rates is
used to explain our observations.


  Estimating time-varying graphical models are of paramount importance in
various social, financial, biological, and engineering systems, since the
evolution of such networks can be utilized for example to spot trends, detect
anomalies, predict vulnerability, and evaluate the impact of interventions.
Existing methods require extensive tuning of parameters that control the graph
sparsity and temporal smoothness. Furthermore, these methods are
computationally burdensome with time complexity $O(NP^3)$ for $P$ variables and
$N$ time points. As a remedy, we propose a low-complexity tuning-free Bayesian
approach, named BASS. Specifically, we impose temporally-dependent
spike-and-slab priors on the graphs such that they are sparse and varying
smoothly across time. A variational inference algorithm is then derived to
learn the graph structures from the data automatically. Owning to the
pseudo-likelihood and the mean-field approximation, the time complexity of BASS
is only $O(NP^2)$. Additionally, by identifying the frequency-domain
resemblance to the time-varying graphical models, we show that BASS can be
extended to learning frequency-varying inverse spectral density matrices, and
yields graphical models for multivariate stationary time series. Numerical
results on both synthetic and real data show that that BASS can better recover
the underlying true graphs, while being more efficient than the existing
methods, especially for high-dimensional cases.


  Braverman and Finkelberg have recently proposed a conjectural analogue of the
geometric Satake isomorphism for untwisted affine Kac-Moody groups. As part of
their model, they conjecture that (at dominant weights) Lusztig's q-analog of
weight multiplicity is equal to the Poincare series of the principal nilpotent
filtration of the weight space, as occurs in the finite-dimensional case. We
show that the conjectured equality holds for all affine Kac-Moody algebras if
the principal nilpotent filtration is replaced by the principal Heisenberg
filtration. The main body of the proof is a Lie algebra cohomology vanishing
result. We also give an example to show that the Poincare series of the
principal nilpotent filtration is not always equal to the q-analog of weight
multiplicity. Finally, we give some partial results for indefinite Kac-Moody
algebras.


  This paper introduces a method of navigation in a large family of tilings of
the hyperbolic plane and looks at the question of possible applications in the
light of the few ones which were already obtained.


  Multiple design iterations are inevitable in nanometer Integrated Circuit
(IC) design flow until desired printability and performance metrics are
achieved. This starts with placement optimization aimed at improving
routability, wirelength, congestion and timing in the design. Contrarily, no
such practice exists on a floorplanned layout, during the early stage of the
design flow. Recently, STAIRoute \cite{karb2} aimed to address that by
identifying the shortest routing path of a net through a set of routing regions
in the floorplan in multiple metal layers. Since the blocks in hierarchical
ASIC/SoC designs do not use all the permissible routing layers for the internal
routing corresponding to standard cell connectivity, the proposed STAIRoute
framework is not an effective for early global routability assessment. This
leads to improper utilization of routing area, specifically in higher routing
layers with fewer routing blockages, as the lack of placement of standard cells
does not facilitates any routing of their interconnections.
  This paper presents a generalized model for early global routability
assessment, HGR, by utilizing the free regions over the blocks beyond certain
metal layers. The proposed (hybrid) routing model comprises of (a) the junction
graph model in STAIRoute routing through the block boundary regions in lower
routing layers, and (ii) the grid graph model for routing in higher layers over
the free regions of the blocks.
  Experiment with the latest floorplanning benchmarks exhibit an average
reduction of $4\%$, $54\%$ and $70\%$ in netlength, via count, and congestion
respectively when HGR is used over STAIRoute. Further, we conducted another
experiment on an industrial design flow targeted for $45nm$ process, and the
results are encouraging with $~3$X runtime boost when early global routing is
used in conjunction with the existing physical design flow.


  Let $(X,d,m)$ be a geodesic metric measure space. Consider a geodesic
$\mu_{t}$ in the $L^{2}$-Wasserstein space. Then as $s$ goes to $t$ the support
of $\mu_{s}$ and the support of $\mu_{t}$ have to overlap, provided an upper
bound on the densities holds. We give a more precise formulation of this
self-intersection property. We consider for each $t$ the set of times for which
a geodesic belongs to the support of $\mu_{t}$ and we prove that $t$ is a point
of Lebesgue density 1 for this set, in the integral sense. Our result applies
to spaces satisfying $\mathsf{CD}(K,\infty)$. The non branching property is not
needed.


  Stein variational gradient descent (SVGD) refers to a class of methods for
Bayesian inference based on interacting particle systems. In this paper, we
consider the originally proposed deterministic dynamics as well as a stochastic
variant, each of which represent one of the two main paradigms in Bayesian
computational statistics: variational inference and Markov chain Monte Carlo.
As it turns out, these are tightly linked through a correspondence between
gradient flow structures and large-deviation principles rooted in statistical
physics. To expose this relationship, we develop the cotangent space
construction for the Stein geometry, prove its basic properties, and determine
the large-deviation functional governing the many-particle limit for the
empirical measure. Moreover, we identify the Stein-Fisher information (or
kernelised Stein discrepancy) as its leading order contribution in the
long-time and many-particle regime in the sense of $\Gamma$-convergence,
shedding some light on the finite-particle properties of SVGD. Finally, we
establish a comparison principle between the Stein-Fisher information and
RKHS-norms that might be of independent interest.


  New low-order $H(\textrm{div})$-conforming finite elements for symmetric
tensors are constructed in arbitrary dimension. The space of shape functions is
defined by enriching the symmetric quadratic polynomial space with the
$(d+1)$-order normal-normal face bubble space. The reduced counterpart has only
$d(d+1)^2$ degrees of freedom. Basis functions are explicitly given in terms of
barycentric coordinates. Low-order conforming finite element elasticity
complexes starting from the Bell element, are developed in two dimensions.
These finite elements for symmetric tensors are applied to devise robust mixed
finite element methods for the linear elasticity problem, which possess the
uniform error estimates with respect to the Lam\'{e} coefficient $\lambda$, and
superconvergence for the displacement. Numerical results are provided to verify
the theoretical convergence rates.


  A class of vector coherent states is derived with multiple of matrices as
vectors in a Hilbert space, where the Hilbert space is taken to be the tensor
product of several other Hilbert spaces. As examples vector coherent states
with multiple of quaternions and octonions are given. The resulting generalized
oscillator algebra is briefly discussed. Further, vector coherent states for a
tensored Hamiltonian system are obtained by the same method. As particular
cases, coherent states are obtained for tensored Jaynes-Cummings type
Hamiltonians and for a two-level two-mode generalization of the Jaynes-Cummings
model.


  A new shape-based geometric method (SBGM) is proposed for generation of
multi-impulse transfer trajectories between arbitrary coplanar oblique orbits
via a heuristic algorithm. The key advantage of the proposed SBGM includes a
significant reduction in the number of design variables for an N-impulse
orbital maneuver leading to a lower computational effort and energy
requirement. The SBGM generates a smooth transfer trajectory by joining a
number of confocal elliptic arcs such that the intersections share common
tangent directions. It is proven that the well-known classic Hohmann transfer
and its bi-elliptic counterpart between circular orbits are special cases of
the proposed SBGM. The performance and efficiency of the proposed approach is
evaluated via computer simulations whose results are compared with those of
optimal Lambert maneuver and traditional methods. The results demonstrate a
good compatibility and superiority of the proposed SBGM in terms of required
energy effort and computational efficiency.


  Modeling the evolution of progenitors of gravitational-wave merger events in
binary stars faces two major uncertainties: the common-envelope phase and
supernova kicks. These two processes are critical for the final orbital
configuration of double compact-object systems with neutron stars and black
holes. Predictive one-dimensional models of common-envelope interaction are
lacking and multidimensional simulations are challenged by the vast range of
relevant spatial and temporal scales. Here, we present three-dimensional
hydrodynamic simulations of the common-envelope interaction of an initially
$10\,M_{\odot}$ red supergiant primary star with a black-hole and a
neutron-star companion. We show that the high-mass regime is accessible to full
ab-initio simulations. Nearly complete envelope ejection is reached assuming
that all recombination energy still available at the end of our simulation
continues to help unbinding the envelope. In contrast to previous assumptions,
we find that the dynamical plunge-in of both companions terminates at orbital
separations too wide for gravitational waves to merge the systems in a Hubble
time. We discuss the further evolution of the system based on analytical
estimates. A subsequent mass-transfer episode from the remaining $3\,M_{\odot}$
core of the supergiant to the compact companion does not shrink the orbit
sufficiently either. A neutron-star--neutron-star and neutron-star--black-hole
merger is still expected for a fraction of the systems if the supernova kick
aligns favorably with the orbital motion. For double neutron star
(neutron-star--black-hole) systems we estimate mergers in about $9 \%$ ($1 \%$)
of cases while about $77 \%$ ($94 \%$) of binaries are disrupted, i.e.,
supernova kicks actually enable gravitational-wave mergers in our cases;
however, we expect a reduction in predicted gravitational-wave merger events.
(abbr.)


  Solidification of alloys in a thermal gradient usually involves the
generation of flows by thermal or thermosolutal convection. We experimentally
study their effects on the dynamics of a solidification interface by inducing a
controlled sweeping flow in a directional solidification device. Flow is
induced in the sample from an external thermosiphon. Downstream inclination of
microstructures and downstream sidebranch development are observed. However,
the major outcome is the evidence of large scale travelling waves on the
solidification interface. They are induced by the coupling between
solidification and flow and yield repetitive striations of the solid phase. Two
waves are observed and characterized.


  We present a general framework for automatic continuity results for groups of
isometries of metric spaces. In particular, we prove automatic continuity
property for the group of isometries of the Urysohn space and the Urysohn
sphere, i.e. that any homomorphism from either of these groups into a separable
group is continuous. As a consequence, we get that the group of isometries of
the Urysohn space has unique Polish group topology and the group of isometries
of the Urysohn sphere has unique separable group topology. Moreover, as an
application of our framework we obtain new proofs of the automatic continuity
property for the group $\mathrm{Aut}([0,1],\lambda)$, due to Ben Yaacov,
Berenstein and Melleray and for the unitary group of the infinite-dimensional
separable Hilbert space, due to Tsankov. The results and proofs are stated in
the language of model theory for metric structures.


  Given two C*-algebras A and B, abstract A-B bimodules that can be
isometrically represented as operator bimodules are characterised in terms of
their norm. Various properties of such bimodules are given. Their theory is
very similar to those of classical normed spaces.


  Let $E$ be a directed graph, $\mathbb K$ be a field, and $\mathbb F$ be the
free group on the edges of $E$. In this work, we use the isomorphism between
Leavitt path algebras and partial skew group rings to endow $L_{\mathbb K}(E)$
with an $\mathbb F$-gradation and study some algebraic properties of this
gradation. More precisely, we show that graded cleanness, graded
unit-regularity, and strong gradeness of $L_{\mathbb K}(E)$ are all equivalent.


  The information detection of complex systems from data is currently
undergoing a revolution, driven by the emergence of big data and machine
learning methodology. Discovering governing equations and quantifying dynamical
properties of complex systems are among central challenges. In this work, we
devise a nonparametric approach to learn the relative entropy rate from
observations of stochastic differential equations with different drift
functions.The estimator corresponding to the relative entropy rate then is
presented via the Gaussian process kernel theory. Meanwhile, this approach
enables to extract the governing equations. We illustrate our approach in
several examples. Numerical experiments show the proposed approach performs
well for rational drift functions, not only polynomial drift functions.


  The aim of this paper is to elucidate the implications of quantum computing
in present cryptography and to introduce the reader to basic post-quantum
algorithms. In particular the reader can delve into the following subjects:
present cryptographic schemes (symmetric and asymmetric), differences between
quantum and classical computing, challenges in quantum computing, quantum
algorithms (Shor's and Grover's), public key encryption schemes affected,
symmetric schemes affected, the impact on hash functions, and post quantum
cryptography. Specifically, the section of Post-Quantum Cryptography deals with
different quantum key distribution methods and mathematicalbased solutions,
such as the BB84 protocol, lattice-based cryptography, multivariate-based
cryptography, hash-based signatures and code-based cryptography.


  Audio event localization and detection (SELD) have been commonly tackled
using multitask models. Such a model usually consists of a multi-label event
classification branch with sigmoid cross-entropy loss for event activity
detection and a regression branch with mean squared error loss for
direction-of-arrival estimation. In this work, we propose a multitask
regression model, in which both (multi-label) event detection and localization
are formulated as regression problems and use the mean squared error loss
homogeneously for model training. We show that the common combination of
heterogeneous loss functions causes the network to underfit the data whereas
the homogeneous mean squared error loss leads to better convergence and
performance. Experiments on the development and validation sets of the DCASE
2020 SELD task demonstrate that the proposed system also outperforms the DCASE
2020 SELD baseline across all the detection and localization metrics, reducing
the overall SELD error (the combined metric) by approximately 10% absolute.


  In democratic countries such as India, voting is a fundamental right given to
citizens of their countries. Citizens need to physically present and cast their
vote in ballot-paper-based voting systems. Most of the citizens fail to fulfill
this constraint and have stayed away from their fundamental duty.
Electronic-voting systems are often considered one efficient alternative in
such situations. Blockchain Technology is an emerging technology that can
provide a real solution as it is characterized by immutable, transparent,
anonymous, and decentralized properties. This paper presents a security
requirement analysis for e-voting systems and evaluates blockchain technology
against these requirements.


  Recent observations of the high-mass X-ray binary Cygnus X-1 have shown that
both the companion star (41 solar masses) and the black hole (21 solar masses)
are more massive than previously estimated. Furthermore, the black hole appears
to be nearly maximally spinning. Here we present a possible formation channel
for the Cygnus X-1 system that matches the observed system properties. In this
formation channel, we find that the orbital parameters of Cygnus X-1, combined
with the observed metallicity of the companion, imply a significant reduction
in mass loss through winds relative to commonly used prescriptions for stripped
stars.


The status of VHE Astronomy is reviewed


  We rewrite the action for $QCD_2$ in the light cone gauge only in terms of a
bilocal mesonic field. In this formalism the $1/N$ expansion can be done in a
straightforward way by a saddle point technique that determines the master
field to be identified with the vacuum expectation value of the bilocal field.
Finally we show that the equation of motion for the fluctuations around the
master field is identical with the 't Hooft meson equation.